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A molecular genetic survey of immune response genes and biodiversity of industrial and non-industrial… Izadi Shavakand, Fariba 2011

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A molecular genetic survey of immune response genes and biodiversity of industrial and non-industrial chickens by Fariba Izadi Shavakand M.Sc. University of Mazandaran, College of Agriculture, 2000 B.Sc. Ferdowsi University of Mashhad, College of Agriculture, 1996  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Animal Science) The University of British Columbia (Vancouver) August 2011 ©Fariba Izadi Shavakand 2011  Abstract The current practices in industrial poultry breeding developed specialized production lines from very few breeds, resulting in reducting in genetic diversity. “Free run/free range” production systems (non-industrial) are a more recent trend in the poultry sector. Non-industrial chicken populations may differ genetically and have more diversity in disease resistance genes than industrial populations. To test this hypothesis, six chicken populations from non-industrial source; Silkies (SK), Taiwanese Cross (TC), Shiqi (SQ), Yellow Shiqi (YSQ), Yellow Wai-Chow (YW), and Agassiz Cross (AC), and industrial populations; Lohmann White (LW) and Lohmann Brown (LB), were sampled and three related experiments were carried out. First, I used 18 microsatellite markers to study genetic diversity within and among the chicken populations. The industrial population LB and the experimental cross AC which shared some common ancestors, were closely related. Non-industrial chickens SK and TC, with Chinese breed ancestry, were related to SQ, YSQ and YW. LW with White Leghorn ancestry, was not related to the nonindustrial populations. Except for YSQ and SQ, STRUCTURE clustered these chicken populations to the genetically distinct groups. Secondly, I used microsatellite marker (LEI0258), situated within the Major Histocompatibility Complex (MHC) region, to reflect the genetic variability of the adaptive immune system. Results indicated that the industrial chicken populations may have less genetic variability in the MHC compared to non-industrial populations but industrial populations may have higher frequency of certain alleles that were part of their selection history against specific pathogens. Finally, I used SNP markers to examine the genetic variation in candidate genes associated with the innate immune system (ChB6, Casp-1, IAP-1, TGF-β3, BMP-7, TLR4, MD-2, IFN-γ, iNOS, IL-2, Mx1, and TVB). There was no difference between industrial and non-industrial populations in genetic variability in this immune system. The results provided partial support for the hypothesis that industrial populations may have higher resistance to specific diseases, while nonindustrial populations may have higher general disease resistance.  ii  In conclusion, the results of this thesis research provided information on the genetic diversity of these chicken populations that can be used in decision making on conservation and in developing breeding stocks for free run/free range production.  iii  Preface In this thesis, I designed and conducted the research, analysed the data, and produced the figures, tables and draft thesis; however, this research would not have been possible without the contribution of other collaborators. I wrote each chapter and they were subsequently edited with supervision of my supervisor, Dr. Kim Cheng. All committee members provided valuable input and comments on each chapter and on the final version of my thesis. I entered corrections and was responsible for the final version of my thesis. My research approved by the UBC Animal Care Committee. The ACC approval certificate number was A05-1737. I was actively involved in the preparation of manuscript which has been accepted for publication in Poultry Science under the guidance of Dr. Kim Cheng and I am grateful for valuable advice from Dr. Janet Fulton at Hy-Line International company for her great scientific guidance. Dr. Carol Ritland at Genetic Data Centre (GDC, Forestry, UBC) helped in laboratory technical training and data collection. [ F. Izadi, C. Ritland, J. E. Fulton, and K. M. Cheng (2011) Genetic diversity of the Major Histocompatibilty Complex region in commercial and non-commercial source chicken flocks using the LEI0258 microsatellite marker. Poultry Science, in press (PS11-01721.R1). ] Two more manuscripts are in preparation and will be submitted for publication based on my thesis objectives. I wrote the manuscripts with the guidance of Dr. Kim Cheng and will be edited by all co-authors. One paper will be on molecular genetic assessment of variability of candidate genes associated with the immune system of commercial and non-commercial source chicken flocks. Dr. Kim Cheng, my supervisor, Dr. Susan Lamont and Michael Kaiser from Iowa State University will be co-authors. The other paper will be based on characterization of the population structure and genetic diversity of commercial and non-commercial source chicken flocks based on microsatellite markers and Dr. Kim Cheng and Dr. Fred Silversides will be co-authors.  iv  Table of contents Abstract.............................................................................................................................. ii Preface............................................................................................................................... iv Table of contents ............................................................................................................... v List of tables..................................................................................................................... vii List of figures.................................................................................................................. viii List of abbreviations ........................................................................................................ ix Acknowledgements .......................................................................................................... xi Chapter 1  Introduction................................................................................................. 1  1.1 Thesis objectives ................................................................................................................. 3 1.2 Justification for my thesis research ................................................................................... 5 1.3 Overview of the thesis ......................................................................................................... 6  Chapter 2  Review of literature .................................................................................... 7  2.1 The history of poultry breeding and state of the poultry industry in North America .. 8 2.2 Maintaining the genetic diversity in chickens................................................................. 11 2.3 Free run and free range production systems .................................................................. 13 2.4 Steps in conservation......................................................................................................... 14 2.4.1 Inventory and evaluation........................................................................................... 14 2.4.1.1 Tools for evaluation............................................................................................. 15 2.4.1.1.1 Phenotypic: production data and performance ...................................... 15 2.4.1.1.2 Pedigrees ..................................................................................................... 16 2.4.1.1.3 Molecular markers..................................................................................... 16 2.4.1.1.3.1 Allozyme markers ............................................................................... 17 2.4.1.1.3.2 Restriction Fragment Length Polymorphism (RFLP)..................... 17 2.4.1.1.3.3 Random Amplified Polymorphism DNA (RAPD) ........................... 18 2.4.1.1.3.4 Amplified Fragment Length Polymorphism (AFLP) ...................... 19 2.4.1.1.3.5 Microsatellite markers........................................................................ 20 2.4.1.1.3.6 Single Nucleotide Polymorphism (SNP)............................................ 20 2.4.1.1.3.6.1 Development and application of high-density SNP arrays .... 25 2.4.1.1.4 Statistical tools for molecular marker analysis ....................................... 27 2.4.2 Choice .......................................................................................................................... 29 2.4.3. Preservation ............................................................................................................... 29 2.4.3.1 In situ conservation ............................................................................................. 30 2.4.3.2 Ex situ conservation ............................................................................................ 30 2.5 Phyogenetic and population genetic analysis .................................................................. 32 2.5.1 Measures of genetic distances.................................................................................... 32 2.5.1.1 Fixation index ...................................................................................................... 33 2.5.1.2 Nei's genetic distance .......................................................................................... 34 2.5.1.3 Reynolds, Weir, and Cockerham’s genetic distance ........................................ 34 2.5.1.4 Dc, Da and Das genetic distances....................................................................... 35 2.5.1.5 Delta-mu-square distance (ðµ)².......................................................................... 36 2.5.2 Population dendograms ............................................................................................ 36 2.6 Disease resistance in chickens .......................................................................................... 37 2.6.1 Major Histocampatibility Complex (MHC) - the adaptive immune system......... 38  v  2.6.2 Candidate genes involved in disease resistance – the innate immune system....... 42 2.6.2.1 Avian Leukosis Viruses (ALV) receptors.......................................................... 44 2.6.2.2 Mx1 gene .............................................................................................................. 46  Chapter 3  Material and methods............................................................................... 47  3.1 Experimental birds............................................................................................................ 48 3.2 Tissue collection and extraction of genomic DNA .......................................................... 50 3.3 Molecular markers ............................................................................................................ 51 3.3.1 Microsatellite markers............................................................................................... 51 3.3.2 MHC microsatellite marker (LEI0258) genotyping................................................ 53 3.3.3 SNPs............................................................................................................................. 53 3.3.3.1 SNP genotyping of the cytokine genes ............................................................... 54 3.3.3.2 SNP genotyping of the Mx1 gene ....................................................................... 54 3.3.3.3 SNP genotyping of the TVB gene....................................................................... 55 3.3.3.4 PCR- RFLP genotyping...................................................................................... 56 3.4 Statistical analysis ............................................................................................................. 58 3.4.1 Hardy-Weinberg Equilibrium (HWE) test .............................................................. 58 3.4.2 Genetic diversity within and between populations.................................................. 58 3.4.2.1 Genetic diversity using 18 Microsatellite markers........................................... 58 3.4.2.2 Genetic variations in the MHC region (using the LEI0258 Microsatellite marker) ............................................................................................................................ 59 3.4.2.3 Genetic variations in genes related to disease resistance................................. 59 3.4.3 Phylogenetic cladogram (using 18 Microsatellite markers) ................................... 60 3.4.4 Cluster analysis (using 18 Microsatellite markers) ................................................. 60  Chapter 4  Results........................................................................................................ 62  4.1 Genetic diversity within and between populations......................................................... 63 4.1.1 Genetic variation within populations ....................................................................... 63 4.1.2 Genetic variation in the MHC region ....................................................................... 65 4.1.3 Genetic variations in genes associated with the innate immune system................ 67 4.2 Genetic differentiation, population structure and genetic relationship among populations............................................................................................................................... 71 4.2.1 Population structure .................................................................................................. 74 4.2.2 Genetic relationships among populations ................................................................ 76  Chapter 5  Discussion .................................................................................................. 77  5.1 Genetic diversity ................................................................................................................ 78 5.1.1 The study of genetic variability using microsatellite loci........................................ 78 5.1.2 The study of genetic relationship and population structure using clustering analysis ................................................................................................................................. 79 5.1.3 Within-population genetic variability in the eight chicken populations ............... 80 5.1.4 Genetic relationship and population structure of the eight chicken populations. 82 5.2 Genetic variation for adaptive immune system .............................................................. 85 5.2.1 LEI0258 alleles as molecular markers...................................................................... 85 5.2.2 Genetic variation in the MHC region ....................................................................... 87 5.3 Genetic variation in genes associated with the innate immune system ........................ 90 5.4 Conclusions ........................................................................................................................ 94 5.5 Future research ................................................................................................................. 97  References........................................................................................................................ 99  vi  List of tables Table 3.1 Characterization of 18 chicken microsatellite markers and PCR primer sequences selected for the present study................................................................... 52 Table 3.2 Primer sets for polymorphism identification of 13 candidate genes. ............... 57 Table 4.1 Mean number of alleles (MNA), mean number of alleles per polymorphic locus (AP), expected (He) and observed (Ho) heterozygosity and inbreeding (FIS) of the eight chicken populations using 18 microsatellite markers. ..................................... 64 Table 4.2 Allele frequencies of LEI0258 in chicken populations. ................................... 66 Table 4.3 Observed and expected heterozygosity using LEI0258.................................... 66 Table 4.4 Deviation from Hardy-Weinberg Equilibrium (Fisher’s exact test)................. 67 Table 4.5 The observed and expected heterozygosity in the five chicken populations using 13 SNPs. .......................................................................................................... 70 Table 4.6 Frequency of haplotype-defined TVB genotypes in chicken populations........ 71 Table 4.7 The observed and estimated heterozygosity in each SNP in five chicken populations................................................................................................................ 71 Table 4.8 Number of alleles per locus (A), polymorphic information content (PIC), number of private alleles per locus (Npa), expected (He) and observed (Ho) heterozygosities and Wright’s fixation indices of the 18 microsatellite markers. .... 72 Table 4.9 Estimates of FST (above diagonal) and Chord genetic distances (Dc) (below diagonal) between pairs of eight chicken populations.............................................. 73 Table 4.10 Molecular coancestry estimates between and within (in diagonal) eight chicken populations. ................................................................................................. 73  vii  List of figures Fig. 2.1 The chicken Major Histocompatibility Complex map (revised from Miller et al., 2004; Fulton et al., 2006) showing the location of marker LEI0258. Cosmid cluster 1 (Kaufman et al., 1999) sequenced genes are indicated.......................................... 42 Fig. 2.2 Genomic organization of Mx1 gene in chicken (Derived from Li et al., 2007).. 46 Fig. 4.1 Allelic frequencies of the 13 SNPs in five chicken populations. ........................ 68 Fig. 4.2 Observed genotypic frequencies of the 13 SNPs in five chicken populations. ... 69 Fig. 4.3 STRUCTURE clustring of eight chicken populations. ....................................... 74 Fig. 4.4 ∆K (a measure of the rate of change in the STRUCTURE likelihood function) values as a funtion of K, the number of putative populations. ................................ 75 Fig. 4.5 Unrooted neigbor-joining cladograms obtained from Chord genetic distnace among eight chicken populations.............................................................................. 76  viii  List of abbreviations AC  Agassiz Cross  AFLP  Amplified Fragment Length Polymorphism  ALV  Avian Leukosis Viruses  DCE  Chord Genetic Distance  DNA  Deoxyribonucleic Acid  FAO  Food and Agricultural Organization  GWAS  Genome-Wide Association Studies  HPAI  Highly Pathogenic Avian Influenza  HWE  Hardy-Weinberg equilibrium  IAM  Infinite Allele Model  LB  Lohmann Brown  LD  Linkage Disequilibrium  LPS  Bacterial Lipopolysaccharide  LW  Lohmann White  MAF  Minor Allele Frequencies  MAS  Marker-Assisted Selection  MCMC  Markov Chain Monte Carlo  MD  Marek’s Disease  MHC  Major Histocampatibility Complex  MNA  Mean Number of Alleles  NJ  Neighbor-Joining  PCR  Polymerase Chain Reaction  PIC  Polymorphism Information Content  RAPD  Random Amplified Polymorphism DNA  RFLP  Restriction Fragment Length Polymorphism  SK  Silkies  SMM  Stepwise Mutation Model  SNP  Single Nucleotide Polymorphism  SSCP  Single-Strand Conformation Polymorphism  ix  SS-PCR  Sequence-Specific Polymerase Chain Reaction  STS  Sequence Tagged Sites  SQ  Shiqi  TC  Taiwanese Cross  TPM  Two Phase Model  TVB  Tumor Virus B  QTL  Quantitative Trait Loci  UN  United Nations  YW  Yellow Wai-Chau  YSQ  Yellow Shiqi  x  Acknowledgements I would like to take this opportunity to thank many people who help me to throughout my journey in the PhD program at UBC. I am grateful to my supervisor Dr. Kimberly Cheng whose expertise, understanding and enthusiasm added considerably to my graduate experience. I would like to thank my committee members Dr. Fred Silversides and Dr. Yousry El-Kessaby for directing me throughout my research. During my stay at Iowa State University, I received valuable guidance from Dr. Susan Lamont and Michael Kaiser. I would like to thanks them for help and also for the generous permission to use their facilities. I’m grateful for valuable advice from Dr. Janet Fulton at Hy-Line International company for her great scientific knowledge and her guidance during my research. Both Dr. Lamont and Dr. Fulton acted as external members of my thesis supervisory committee. I would not be able to carry out this research without cooperation of the BC Specialty Poultry Producers’ Association in sampling their members populations. The BC Ministry of Agriculture and Lands provided funding support (administered by the The Specialty Birds Research Committee) for my PhD program. I would like to thank Dr. Carol Ritland at Genetic Data Centre (GDC, Forestry, UBC) for her laboratory technical training and assistance. I would like to thank GDC lab technicians, Allyson Miscampbell and Heather Yueh, for their technical assistance and laboratory management. Dr. Frederick Leung from University of Hong Kong provided DNA samples from three Chinese chicken populations and enabled us to expand the genetic data set. I would like to thank to all of my fellow graduate students for their friendship and advice specially Avian Research center members. Also I would like to thank all my good friends in Iran and Vancouver for their friendship and support. Ultimately, I would like to express my deepest gratitude to my family for their amazing and unlimited support from the very first days of my school up to the very last days of my PhD program. xi  Chapter 1  Introduction  1  Natural and artificial selection has differentiated chicken populations during 8,000 years of domestication (Romanov and Weigend, 2001). The current practices in the intensive poultry industry tend to concentrate on specialized production lines selected from a few breeds, resulting in a reduction in effective population sizes and hence genetic diversity (Besbes et al., 2007). Intensive poultry husbandry has resulted in environmental and welfare problems such as poultry waste pollution and animal health. “Free run/free range” production systems are a more recent development in chicken management, and are still a small sector of the industry. From available information, industrial breeding companies seem not to have devoted much effort to developing stocks that specialize and perform well as free run/range chickens. Some free run/free range producers are attempting to develop their own breeding stock but most of them lack knowledge of genetic resources. Chickens from non-industrial sources, which are not used for industrial purposes, are characterized by medium or low performance and are often maintained in small populations. They may have more genetic diversity as compared with present industrial breeds and may be better choices in free run/free range” production systems. Genetic erosion of these local breeds may lead to the loss of valuable genetic variability. Therefore, conservation of local chicken breeds as a genetic resource is important to fill unanticipated breeding demands in the future (Weigend et al., 1995). In this thesis, I define industrial populations as products of three or four way crosses used for industrial purposes produced by the multi-national poultry breeding companies. I hypothesized that non-industrial populations used for free range and organic productions may be genetically more heterogeneous than industrial populations. I used different molecular markers to assess the genetic diversity within and among the studied chicken populations. It can be assumed that local breeds contain genes and alleles adapted to the local environment and particular breeding goals and they may have more genetic variation in disease resistance. They may also have had less exposure to antibiotics and greater exposure to these disease vectors and may have developed disease resistance. It is therefore worthwhile to examine the allelic variation of genes related to disease resistance in these populations in comparison to industrial populations. For this purpose, I used  2  molecular markers to assess the genetic variability in genes associated with the innate and adaptive immune system in chicken populations.  1.1 Thesis objectives For this thesis research, the first objective was to examine the genetic variation in several populations of chickens that have been used for free run/free range production and the second objective was to examine the genetic relatedness of these chickens to industrial populations. Free run/free range chickens are exposed to parasites and disease vectors more readily than chickens that are reared in confined indoor cages. Disease resistance is therefore an important factor to consider when developing breeding stocks (Fulton, 2004). Genetic diversity in disease resistance genes may be an important factor to consider when developing breeding stocks. In terms of biodiversity conservation, a thorough understanding of the extent and nature of genetic diversity among and within breeds and populations is required in order to evaluate to their potential contribution to agricultural production in the future. The overall objective of my research was to obtain information that will facilitate breeding stock development and may identify unique and valuable genetic resources. To fulfill the thesis objectives I used the following approaches: 1. Examination of genetic diversity, population structure and evaluation relatedness of free run/free range and industrial chickens using 18 microsatellite markers. Microsatellite markers are tandem stretches of very short DNA sequences that frequently occur as randomly and widely dispersed elements in an animal’s genome. A high degree of polymorphism in these markers makes them useful for assessing genetic diversity and genetic relationships among chicken populations (Hillel et al., 2003). The results will also help in determining the breeding history of these breeds. The information acquired from population and breed genetic relationships estimated by microsatellite analysis may be useful as an initial guide in defining objectives for designing future investigations of genetic variation and developing conservation strategies (Romanov and Weigend, 2001).  3  2. Examination of genetic diversity of the MHC region (part of the adaptive immune system) in the chicken populations. The chicken Major Histocompatibility Complex (MHC) has an important role in the immune system and is commonly defined by serological reactions between red blood cells, as well as antibodies specific to the MHC Class 1 (BF) antigens and the highly polymorphic MHC class IV (BG) antigens (Meyer et al., 2003). The microsatellite marker LEI0258 has been found to be physically located within the MHC on chromosome 16 (McConnell et al., 1999). I used this marker as a genetic indicator for MHC haplotypes due to its close physical location to genes of the MHC to examine the genetic variability of disease resistance in the populations under study. Stocks with the greater observed genetic diversity in the MHC region could serve as a genetic resource in conservation and breeding programs. In populations with lower diversity of MHC genes, introgression (e.g. crossbreeding) may be used efficiently in increasing the diversity of MHC haplotypes in industrial chickens. Nevertheless, the results could provide a basis for the inclusion of MHC markers among candidate genes for investigation of the genetic architecture of possible diversity of disease resistance in chickens. 3. Examination of genetic diversity in genes associated with the innate immune system using SNP markers Single nucleotide polymorphisms (SNPs) have the potential to become the genetic marker of choice in studies of conservation of populations because their abundance, stability, and distribution would enable them to assess variability across the genome. Many candidate genes have been associated with the chicken’s response to disease and inflammation. In this study, I hypothesize that non-industrial populations used for free range and organic productions may be genetically more heterogeneous than industrial populations. I used SNP markers to assess the variability of those genes.  4  1.2 Justification for my thesis research Free run/free range management is a recent development and is still considered a small sector of the industry. Industrial breeding companies have just started to put effort into developing stocks that specialize and perform well under this management method, but at present specialized stocks are not yet available to free run/free range producers. Some free run/free range producers are attempting to develop their own breeding stock but most of them lack the necessary skills and resources. My thesis research is therefore timely. For this research, I was able to access the genetic material maintained at the Agassiz Research Centre (Dr. Fred Silversides), the Kadoorie Farm and Botanic Garden of the University of Hong Kong (Dr. Fred Leung), and the South China Agriculture University (Dr. Fred Leung). I was also able to sample the populations managed by members of the BC Specialty Poultry Producers’ Association. These genetic materials comprised the chicken populations for this thesis research. In this research, I used microsatellite marker LEI0258 to examine the genetic variability of the adaptive immune system because I have access to the expertise of Dr Janet Fulton, a geneticist with HyLine International Inc. Dr Fulton has carried out extensive research with the LEI0258 marker. I used SNP markers to assess the variability of genes associated with the innate immune system because I was able to carry out this part of my research in Dr. Susan Lamont’s laboratory at Iowa State University. Dr. Lamont is the world authority on poultry immunogenetics and has gained the honour of “Distinguished Professor” at Iowa State University. I was very fortunate to be allowed to carry out this part of my thesis research in her laboratory. The results from my thesis study should be useful in supporting decisions on conservation and further use of the populations in crossbreeding programs designed to create genetic stocks with improved adaptability and productivity in free run/free range production systems. By the end of the study, I will have information of the genetic diversity in the selected populations of chickens to facilitate decision making for conservation and for developing breeding stocks of free run/free range production. I will  5  also have information on genetic disease resistance which can be used to develop strategies for protection against infectious disease outbreaks in these chickens.  1.3 Overview of the thesis This PhD thesis is written in the "traditional” format and includes five chapters. Chapter one is a general introduction to the topics researched and objectives in this thesis. In the following chapter, I present a review of literature of the topics related to the thesis objectives and the general background of this information. In the third chapter, I present the material and methods used in this research. In the fourth chapter I present the results of the different research studies. In Chapter five, I discuss all the questions and objectives of this thesis, draw conclusions and discuss possible future works. I wrote each chapter and they were subsequently edited by my thesis supervisor Dr. Kim Cheng. I entered corrections and was responsible for the final version of each chapter. All committee members provided valuable input and comments on each chapter. I designed and conducted the research, analysed the data, and produced the figures, and tables; however, this research would not have been possible without the contribution of other collaborators.  6  Chapter 2  Review of literature  7  In this chapter, I will review literature in the following specific areas related to this study to gather information needed to carry out my thesis research: 1. History of poultry domestication and breeding 2. State of poultry breeding industry in North America (North America includes USA, Canada, and Mexico). 3. Importance of conservation genetics and how chicken populations are maintained 4. Free run and free range production system 5. Main conservation steps including inventory, evaluation, choice and preservation. 6. Phyogenetic and population genetic analysis 7. Disease resistance in chicken and different immune responses in chickens I will also develop in greater detail the discussion about my thesis objectives as presented in Chapter 1 including theoretical perspectives and existing studies in terms of scope and context.  2.1 The history of poultry breeding and state of the poultry industry in North America Poultry production is the most industrialized farming system compared to the other livestock and is getting widespread in many developing countries while playing an important role in the small holder farming system in these countries. Poultry meat and eggs are considered to be one of the most important protein sources in the world, and poultry is predicted to become the most highly consumed meat in the next 10-20 years (Cheng, 2003). FAO (2007) predicted that poultry production would have a growth of 2.5 and 3.4 percent per annum up to 2030 in developed and developing countries, respectively. Current worldwide consumer demand is more than 61 metric tons of meat and more than 55 million metric tons of eggs. To meet this demand, the industrial broiler and layer markets produce more than 40 billion birds annually (Muir et al., 2008). Besides significantly contributing to the nutrition of humans, chickens and other poultry support many activities such as entertainment (e.g. cock-fighting) and therapy (e.g. the use of silkies for medicinal purposes; the use of the chicken egg to produce vaccine), and  8  serve as models for biomedical research. The chicken (Gallus gallus) is the first bird and first farm animal whose genome was sequenced. Since genetic information has been highly conserved during avian species evolution, the chicken sequence information can be used as a model genome for the existing 9600 avian species, as well as in evolution, agriculture and biomedical researches (Burnside et al., 2005). The chicken was domesticated nearly 8,000 years ago from the Jungle Fowl (Gallus gallus) in Southeast Asia. Beginning in the 1930’s, industrial breeders and producers developed poultry breeds for meat or egg production from long term selection programs including complex population structures, pedigree breeding and intensive record keeping (USDA-APHS, 1973; Hunton, 2006). In the 1950’s and 1960’s, various poultry breeding programs started considering egg production as mainly an intensive cage operation. The period from 1960 to about 1980, was considered to be the “golden age” of poultry genetics in both academic and industrial sections. By the 1980’s many of the institutions and the breeding industry began to consolidate (Hunton, 2006). In the last century, breeding companies have decreased in number, increased in size, and dominated the market with their products, such as chicken layers and broilers and turkeys (Hoffmann, 2009). Subsequently, the genetic base in industrial layer and broiler stocks has been restricted to a few strains or breeds used in the breeding programs in Europe and North America (Hoffmann, 2009). By 2001, there were nine major breeding companies holding breeding stocks for egg-type chickens and their products (less than 20) and eight breeding companies in broilers. Currently, nearly all nine major layer breeding companies had been acquired by only two holding companies – Hendrix Genetics and Erich Wesjohann (Besbes et al., 2007). In broilers, eight breeding companies in 2000 have consolidated and used to be owned by four companies – Aviagen, Cobb, Hubbard and Hybro (Besbes et al., 2007) and in 2008, Hybro was bought by Cobb (Hunton, 2009). Eric Wesjohann, based in Germany, owns Aviagen (Ross, Arbor-Acres, Lohmann-Indian River meat chicken brands, plus BUT and Nicholas Turkeys), Lohmann, HyLine and H&N International layers. Hendrix Genetics, based in the Netherlands, owns Institut de Sélection Animale (ISA), Shaver, Babcock, Warren, Bovans, Hisex and Dekalb brands of  9  layer, the Hybro and Pilch broiler brands and Hybrid and Orlopp turkeys (Besbes et al., 2007). Most of the commercial white-egg layer stocks may be related to the Mount Hope farm strain of Single Comb White Leghorns (Delany, 2003). The brown-egg layer lines were derived from several dual-purpose breeds (e.g., Barred Plymouth Rock, White Plymouth Rock, New Hampshire) (Delany, 2003). Breeding programs in broilers have been focused on generating specialized lines with distinct breeding goals in each line due to the negative genetic correlation between production (growth) and reproduction (egg number) (Fairfull and Gowe, 1990). Genetic diversity has been maintained in industrial broiler (meat-type) breeders by keeping separate male and female lines each derived from various crosses of breeds such as Cornish and White Rock (Hunton, 1990); even though both White Rock and Leghorn breeds are from Jungle Fowl, they are genetically distant from one another (Khatib et al., 1993). There are four generations between the purebreeding lines and the final broilers for the market. Breeding companies fully control great-grandparent stocks which are kept as pure lines, and this generation is used to produce grandparent stocks. Parent stocks are crossbreds of different pure lines and are used to produce market broilers. Most of the market broilers are therefore three-way or four-way crosses. In 1946, there were approximately 300 Canadian breeders of industrial chickens, and each research institution maintained a breeding program (Silversides et al., 2008). In 1981, Crawford (1984) found two Canadian primary breeders (Shaver and Hybrid), 13 middle level breeders of chickens, turkeys, and waterfowl, and 79 lines of chickens kept in 11 institutions. A survey by Agriculture and Agri-Food Canada (AAFC) in 2005, found 33 lines of chickens representing 23 different populations kept in five Canadian institutions (Silversides et al., 2008). Today, there are no primary breeders and few or no middle level breeders based in Canada. According to Rare Breeds Canada, a national conservation organization, 37 poultry breeds are at risk of extinction in Canada.  10  There are increasing concerns about retaining genetic diversity in the breeding structure and within-line selection by the few breeding companies (Delany, 2003). Industrial breeding programs using intense selection have restricted today’s world market to a few specialized industrial lines kept in very small populations with a great genetic uniformity of traits. Usage of only these few specialized industrial lines is considered to be the first tier of genetic diversity reduction (Notter, 1999). Reduced competition will limit the potential for innovative research and development and there is a danger that genetically uniform populations will not have the potential to confront new diseases (Sheldon, 2000, Hoffmann, 2009). The poultry industry mainly has taken advantage of heterosis to produce the parent stocks of the industrial birds from grandparents that are kept as inbred lines. In industrial stocks, it is important to keep genetic diversity for the basis of longterm selection gain and the raw material for the application of new genetic tools (e.g. gene transfer) that will facilitate production and research.  2.2 Maintaining the genetic diversity in chickens Poultry biodiversity encompasses the genetic variants within and among all poultry species including chickens, turkeys, quails, ducks, geese, and pheasants distributed around the world that have evolved as the result of domestication, selection and breeding. Chickens, among poultry species, probably show the highest rate of variation of population types. Chicken populations can be categorized into i) wild populations, ii) indigenous and local breeds (unselected but domesticated), iii) selected breeds for morphological traits mostly by fanciers, iv) selected lines for quantitative traits such as industrial layers and broilers, and v) experimental research lines (Pisenti et al., 1999; Weigend and Romanov, 2001). The Food and Agriculture Organization of the United Nations has focused on the loss and endangered status of animal genetic resources at the global level (FAO, 2000; 2007). In the 1990’s, the FAO provided country surveys to describe the existent resources. These collected data were incorporated into the FAO Global Data Bank for Farm Animal Genetic Resources and were used to compile the World Watch List for Domestic Animal  11  Diversity (WWL-DAD). An increasing loss of genetic diversity has been observed for all agriculturally used species (Frankham, 1994) and poultry genetic resources are considered one of the most endangered (Romanov et al., 1996; Hoffmann, 2009). Of the 938 avian breeds of the five species (chicken, duck, goose, turkey and muscovy duck), about 50% of breeds have been classified as being at risk of loss (Weigend and Romanov, 2002). There are mainly two reasons why this sector needs a specific program to determine the state of its genetic resources, industrial companies do not publicize the size and nature of their operation and resources, and fancy breeders do not always apply a universal system for individual identification to keep private registers (Tixier-Boichard et al., 2009). Due to management decisions at the academic, industrial and local levels, most genetic stocks are at risk of being lost. In the past decades, for example more than 200 mutant, inbred, and selected avian genetic stocks used as avian research stocks have disappeared and more than one-third of the remaining stocks are at risk of elimination in the future (Pisenti et al., 1999). There are numerous reasons for elimination of poultry stocks, including reallocation of budgets at many universities and the loss of poultry science departments (Fulton and Delany, 2003). There is a need to conserve the genetic variation of existing populations in order for them to be able to evolve in response to future environmental changes and to maintain population fitness against inbreeding depression (Reed and Frankham, 2003). In developing countries, locally adapted populations are often maintained in small populations and the loss of these genetic resources is still happening by replacement with the modern industrial stocks (Tadelle et al., 2000; Safalaoh, 2001; Besbes et al., 2007). Most of these indigenous breeds are not well characterized, and monitoring them as poultry genetic resources will be difficult. Although it is said that indigenous or local chickens have become adapted to harsh environments and they may contain genes and alleles adapted to a local environment (Romanov et al., 1996), there is some variability among populations (Tixier-Boichard et al., 2009). Documentation is lacking on local chicken disease resistance and adaptation mechanisms of these chicken populations  12  (Anderson, 2009). The Fayoumi breed (Tixier-Boichard et al., 1998) has been selected in Egypt and shows resistance to Marek's disease (Tixier-Boichard et al., 1998) and coccidiosis (Pinard-van der Laan et al., 1998) and can not be considered to be a local chicken (Tixier-Bichard et al., 2009). This breed also has shown better resistance to disease when vaccinated and fed anti-coccidial additives after 20 years, which makes it a unique breed from the viewpoint of disease resistance. There are different genetic principles that can be used in long-term conservation and management of genetic resources, the most important ones being population heterozygosity. By integrating population genetics and molecular genetics, conservation genetics can assess variation within and among populations. It is important to maintain allelic diversity across the species (Notter, 1999). A combination of information based on phenotypic data, historical record and molecular genetic variation should be used in decision-making (Delany, 2003). Conservation approaches should be applied to all individuals, breed organizations, local communities and industrial hatcheries.  2.3 Free run and free range production systems When chickens are raised indoor on the floor, they are referred to as free run chickens. If the birds are allowed outdoor access, they are referred to as free range. Traditionally, broilers are raised on the floor indoors and therefore free run broilers are not considered an alternative production method. As with free-run housing, free-range systems are not required to provide resources such as nest boxes, perches, or substrate for dust-bathing. “Organic” chickens may be raised in floor or cages but are given certified organic feed which should not be a genetically modified (GM) product and should have never been treated with chemical fertilizers or chemical pesticides. Organic chickens in the USA are raised with preventive health management which includes vaccination but are never given hormones, antibiotics, or chemical pesticides (Fanatico et al., 2009). In addition to economic sustainability, current production goals for farm animals should include animal health, environmental sustainability, product quality (and consumer 13  health), and animal welfare. Intensive poultry husbandry has produced environmental problems and has increased consumer awareness and animal welfare movements in developed countries (Anderson, 2009). Free run/free range and organic chicken may satisfy some of these goals. Because of this, free run/free range and organic production methods are increasing consumer acceptance of poultry not only in Europe but also in North America. As a result, The European Union has agreed to ban the use of conventional battery cages by 2012 (Livestock Welfare Insights, 2000). A survey done by the Whole Foods Market Inc. (2004) showed that the reasons consumers prefer to buy organic products is because they think organics are environmentally friendly, locally produced, healthier, higher quality, and better tasting. In the USA, from 1997 to 2005, organic poultry production increased from less than 1 million birds to more than 13 million birds.  2.4 Steps in conservation Conservation has four main steps including inventory, evaluation, choice and preservation.  2.4.1 Inventory and evaluation A thorough understanding of the extent and nature of genetic biodiversity is an essential prerequisite for the management and utilization of genetic resources (FAO, 2007). All national programs for management of animal genetic resources for food and agriculture start with an inventory. The first step is to identify breed associations, breeding companies and research laboratories. All animal keepers and breeding organizations should be involved; this will help to increase awareness about the conservation program and the value of breeds in question in order to prevent the loss of relevant data especially in local communities (Tixier-Boichard et al., 2008, Tixier-Boichard et al., 2009). Inventory and characterization efforts start by assessing genetically distinct populations  14  or ‘breeds’, the number of animals per population and number of farms that keep these resources (Tixier-Boichard et al., 2009). The next step is to evaluate populations using different methods to calculate value or potential value of the line, such as the genetic distance to other lines or the vulnerability of the line to extinction (Ollivier and Foulley, 2009). Characterization can be done based on many criteria such as assessment of the current state of the population such as population size and structure, number of breeding males and females, overall numbers, trends in population size, historical development of the breed (crossbreeding, selection), geographical distribution and phenotypic attributes (physical features, performance levels and any unique features) and reproductive efficiency (Tixier-Boichard et al., 2009). Inventory and evaluation are connected so that endangered populations can be identified and the genetic material to be conserved can be selected. This may be important for evaluating the consequences of selection for economic traits on biodiversity. Information on these steps may be available for standardized breeds and experimental lines, but in industrial lines it is generally confidential and it is difficult to record for village chickens (Tixier-Boichard et al., 2009).  2.4.1.1 Tools for evaluation 2.4.1.1.1 Phenotypic: production data and performance Morphological attributes, biomedical indices, production levels, historical development of the breed (crossbreeding, selection, with other breeds), specific adaptation or any unique feature can be used as tools for evaluation. However, morphological markers are often affected by environmental conditions and are often recessive in nature so their usage may not be consistent through the time (Vendramin and Hansen, 2005). The assessment of phenotypic measurements should include both the mean and the variation associated with the mean. Because the variation is the basic tool for conservation purposes at present and in the future, the assessment of phenotypic measurements should be based on a large portion of the population by direct recording or exploiting 15  information from published data (Tixier-Boichard et al., 2008). In chickens, almost all phenotypic evaluations include color, comb type, and skin color as visible markers. For example Dana et al. (2010) found pea comb to be the dominant comb type and yellow the dominant skin colour in all regions of Ethiopia. In another study by Vij et al. (2006) found that Punjab Brown chickens, which are multi-purpose, had plumage colour that is mostly brown with a solid pattern but was sometimes spotted or striped. These phenotypic characteristics can be used as valuable markers when considering conservation programs.  2.4.1.1.2 Pedigrees Another tool to evaluate the genetic variability of a population is the average inbreeding coefficient of a population based on pedigree information. Conserving genetic variation requires that inbreeding be minimized. Molecular markers also can be used to calculate inbreeding coefficients especially in monitoring endangered populations (e.g. Kim et al., 2007). Complete five-generation pedigree data is needed to control past breeding strategies in conservation breeding programs (Baumung and Sölkner, 2003).  2.4.1.1.3 Molecular markers Molecular markers include genomic and mitochondrial DNA (mtDNA) loci. Molecular markers are powerful tools for initial evaluation in assessing breeds regarding in-situ and ex-situ conservation methods. Molecular markers offer a direct assessment of genetic diversity without considering environmental effects, and can quantify population relatedness and detect introgression. Molecular markers can be used to evaluate genetic variability, either within or among individuals, families, and populations. Development of many fundamental DNA technologies such as Southern-blot hybridization, sequencing, and PCR in the last 3 decades has helped in increasing the application of molecular markers (Weigend et al., 2004a).  16  Molecular markers’ characteristics are their polymorphism, their ubiquity over the genome and their possibility for automated identification. Molecular markers differ in many aspects such as abundance, level of polymorphism, reproducibility, technical requirements and costs. Factors such as the objective of study, the efficiency in terms of cost and time, and the presumed level of polymorphism are main factors used to decide which marker will be used (Vendramin and Hansen, 2005). Research has shown that molecular markers and amount of genetic variation in quantitative (polygentic) traits are often connected but there is a debate about the degree of this connection and how it can be generalized (Reed and Frankham, 2001).  2.4.1.1.3.1 Allozyme markers Allozymes are considered to be the first known molecular markers. Allozymes are allelic variants of an enzyme encoded by different alleles at the same locus. Unlike isozymes which are multiple molecular forms of an enzyme that catalyze the same reaction encoded by genes located at different loci. Use of allozyme markers is considered to be a classical assay in population diversity studies (Zhang et al., 2002a). One advantage of these markers is that they are usually co-dominant in nature. Furthermore, these markers can be easily transferred and used in all different species. One disadvantage of allozyme markers is that they are invariant in many species (Vendramin and Hansen, 2005). Zhang et al. (2002b) used allozymes to study relationships of chicken populations and compared these with microsatellite (see Section 1.4.1.1.3.5) and RAPD (see Section 1.4.1.1.3.3) markers. They examined nine allozymes of which seven were polymorphic. Average heterozygosity per locus was low, in the range of 0 to 0.5091. Allozymes show a lower degree of polymorphism than other markers especially in closely related populations.  2.4.1.1.3.2 Restriction Fragment Length Polymorphism (RFLP) With this technique, DNA polymorphism can easily be recognized by using restriction endonucleases which cleave DNA molecules at specific sites producing DNA fragments 17  of varying length. In 1974, Grodzicker et al. first described RFLP as a new class of genetic polymorphism. Variation in the DNA sample that alters the length of fragments produced by restriction enzymes (RE) may result from base substitutions, additions, deletions, or sequence rearrangements within RE recognition sequences. The RFLP technique has been utilized in genetic fingerprinting (Hillel et al., 1989) and paternity testing and is mostly suited for studies at the intraspecific level or among closely related taxa. One of the advantages of RFLP analysis is that it is a highly robust analytical method when genome and sequence information is not available. Most RFLP markers are codominant and RFLP markers are exceedingly numerous. Genomic RFLPs have Mendelian inheritance and are detectable in all tissues and of all ages, which enables early detection. Disadvantages of RFLP analysis are that it provides a low quantity of information and is a difficult and time-consuming method to use and develop. As well, the start up cost of RFLP analysis is moderate to high and the recurring cost is high. The RFLP analysis requires relatively large amounts of tissue (often requiring destructive sampling). The more recently developed PCR-RFLP is an alternative method which takes advantage of PCR techniques to enable more samples to be analyzed in a shorter time with very small amounts of DNA. Kumar et al. (2007), using 10 genotyped SNP markers (see Section 1.4.1.1.3.6) of the Myostatin gene (GDF-8) by PCR-RFLP, examined the genetic relationships among the indigenous chicken populations of India. The results showed that Punjab Brown and Nicobari populations have the most recent common ancestry with Red Jungle Fowl.  2.4.1.1.3.3 Random Amplified Polymorphism DNA (RAPD) The RAPD technique was invented as a genetic marker in 1990 (Williams et al., 1990). RAPD uses arbitrary short primers (8-12 bp) which match to very similar sequences and amplify several loci. In this method 10-20 fragments are produced with a maximum size  18  of 2 kb. Polymorphism between genotypes is due to either nucleotide base change or an insertion or deletion within the amplified fragment. Often, with one single reaction, many loci can be identified. This assay is very simple, inexpensive and rapid. No prior knowledge of the DNA sequence is required. One of the disadvantages is that this method is not easily replicated by different laboratories because of the low stringency of the PCR procedure. Also, it is a dominant marker and it is not possible to distinguish whether a DNA segment is amplified from a locus that is heterozygous (1 copy) or homozygous (2 copies) because diploid homozygotes make a band and monozygous null alleles do not. Sharma et al. (2001) used RAPD markers to detect polymorphisms among five breeds of chicken. Using twelve random primers, 96 fragments were amplified. Higher within population genetic similarity was estimated in industrial breeds in comparison to the Kadaknath native breed and this breed showed the highest similarity with Rhodes Island Reds.  2.4.1.1.3.4 Amplified Fragment Length Polymorphism (AFLP) Zabeau and Vos originally described the AFLP technique in 1993 (Zabeau and Vos, 1993). The application of this marker is in estimating genetic diversity in populations, parentage analysis, plant and animal breeding, forensic genotyping, DNA fingerprinting and quantitative trait loci (QTL) mapping. In AFLP, the presence or absence of restriction fragments is used for restriction fragment length polymorphism. Optimal restriction digestion is fundamental for correct AFLP patterns analysis. The choice of either different restriction enzymes or corresponding adaptor and primer combinations as well as the complexity of the genomic DNA can affect the number of AFLP polymorphisms (variation) detected (Ripabelli and McLauchlin, 2004). The major advantage of the AFLP technique is the large number of bands that the method generates and therefore potentially the greater number of polymorphisms. Amplification patterns are more reproducible than with RAPD and RFLP markers because of the high stringency of the PCR procedure. No prior knowledge of the DNA sequence is required.  19  Intensive labour and challenging technical work are some of disadvantages. The AFLPs are dominant, so multilocus markers are scored as present or absent and this characteristic reduces AFLP reliability. Another disadvantage is artifactual amplification (or amplification failure) of a fragment which can be minimized by the high stringency (high annealing temperature) permitted by the long AFLP primers. It is crucial to ensure complete digestion by using high-quality DNA and an excess of restriction enzyme. Specialized equipment is necessary to increase efficiency of this method and for automated sequencers, fragment analysis software has been developed (Vendramin and Hansen, 2005).  2.4.1.1.3.5 Microsatellite markers Microsatellites are highly polymorphic loci widely dispersed throughout animal genomes and consist of randomly repeated motifs or simple sequence repeats of mono-, di-, tri-, tetra-, or penta-nucleotide units (Tautz, 1989). The variability at microsatellite loci is due to differences in the number of repeat units recognized as a major source of genetic variation (Weber and May, 1989). Microsatellites are useful for unveiling genetic diversity, individual identification, gene mapping, paternity analysis, and the assessment of relatedness, phylogenetic studies, and as a means to measure inbreeding and differences among populations. Microsatellites have a very rapid rate of evolution, making them particularly useful in working out the relationships among very closely related species. Microsatellite markers also provide tools for study of linkages with quantitative trait loci (QTL) (Zhou and Lamont, 1999). Microsatellites have not been used successful in reconstructing phylogenies because of some restrictions to divergence caused by range constraints, irregularities and asymmetries in the mutation process, and the degradation of microsatellites over time. They also are inappropriate for the study of deep phylogeny because their high mutation rates lead to a large amount of homoplasy over a relatively short period of time. Microsatellite markers are present in both coding and noncoding regions so that selection and environmental pressure do not influence their expression directly when they are 20  neutral. Although microsatellites are usually considered to be evolutionarily neutral markers, there is some evidence that some microsatellites have functional roles shown by critical tests in various biological events in several organisms. Clustering of tetranucleotide repeats in the centromeric region of humans, tomatoes, and the sex chromosomes of snakes, have been demonstrated to have specific functions (Li et al., 2002; Selkoe and Toonen, 2006). Moreover, they are codominant markers, so that all alleles can be scored and the results are reproducible. Since they are usually less than 100 bp long and embedded in DNA with unique sequences, they can be amplified in vitro using PCR methodology. Another advantage is that the gel runs can be multiplexed, that is, PCR products of different markers can be run on the same gel, thus saving time, labour and money. Microsatellite markers have little crossing over except where the repeat size is large, and flanking regions change slowly. All these advantages make microsatellites the preferred genetic markers for estimating genetic variation (Baumung et al., 2004; Smith and Wayne, 1996). Mutation rate and process in microsatellites have been evaluated using a variety of in vivo and in vitro studies. Mutation rate has been measured directly through pedigree analysis and indirectly using linkage data in cell lines. The observed mutation rates range from 103  to 10-6 and have been inferred through artificial constructs in yeast (Henderson and  Petes 1992) and in human pedigrees (Weber and Wong, 1993). Microsatellite loci are highly unstable, having some of the highest mutation rates observed at molecular loci. Repeat motif type, length or contiguity affect the mutation rate and level of allelic variation. Higher mutation rates are observed in longer loci (repeat size) but the increase in rate is not linear. Tetranucleotide repeats compared to dinucleotide repeats have been shown to have higher mutation rates and sequences with a high AT content mutate faster than those with a high GC content (Scribner and Pearce, 2000). There are two proposed mechanisms which can explain microsatellite polymorphism. The first is unequal crossing over between DNA molecules in meiosis (Jeffreys et al., 1994). The second mechanism is DNA slippage during DNA replication, which has been shown to be the main mechanism of microsatellite mutations (Levinson and Gutman,  21  1987; Schlotterer and Tautz, 1992). Different theoretical models have been considered for the evolution of microsatellites. One mutation model is called a stepwise mutation model (SMM); a stepwise change in the number of repeats (Ohta and Kimura, 1973). Another mutation model is the two phase model (TPM), which is like the SMM, but allows for mutations of a larger magnitude to occur. The SMM can likely explain in microsatellites with three to five bp repeats while microsatellites with one to two bp repeats are more likely explained by the TPM. In microsatellites with irregular or composite repeat structure, there is a lower likelihood that they were caused by single step mutational events. The infinite allele model (IAM) (Kimura and Crows, 1964) has been evoked to account for such microsatellites. IAM can creates another allele involving any number of tandem repeats and always results in a new allele state not previously existed in population. Microsatellites with more IAM-like evolution (i.e. composite repeats) that show the lowest levels of homoplasy are most suitable for population studies (Estoup et al., 1995b). In the SMM mutation model homoplasy exists that underestimates the amount of genetic variation and genetic distance among populations (see Section 2.5.1). The evolution of microsatellites is a complex mutational process involving at least two different mechanisms. Two drawbacks must also be noted in the interpretation of microsatellite marker amplification patterns: stutter bands and null alleles. Stutter bands cause (i) uncertainty in the estimation of the allele size and (ii) the possibility of mistaking a heterozygote for a homozygote if the two bands are so close on the gel that the bands produced by the two alleles overlap. This problem can be easily overcome by running an internal standard (an allele of known size), plus a molecular size marker, along with the test samples. One further problem is the presence of null alleles. These alleles are not amplified due to base substitutions or insertion/ deletions within the priming site and therefore a heterozygote carrying one null allele can not be scored, leading to underestimates of heterozygosity. This problem has no direct resolution, because there is no direct way to turn these (partially) dominant loci into codominant alleles to produce consistent effects (Smith and Wayne, 1996). This problem can be overcome by designing a new primer, which does  22  not include the site of indel or base substitution. This can be very time consuming and may not always be possible due to base composition of the flanking sequences. There are several examples of genetic diversity studies in chickens employing microsatellite markers (Takahashi et al., 1998; Vanhala et al., 1998; Romanov and Weigend, 2001; Sharma et al., 2001; Pirany et al., 2007). For example, Hillel et al. (2003) studied biodiversity within and between 52 populations in a wide range of chicken types. They used twenty-two di-nucleotide microsatellite markers to genotype DNA pools of 50 birds from each population. The results showed polymorphisms in all 22 markers in at least 69% of the populations. The mean number of alleles was 9.6 across populations and 3.5 within populations, and average gene diversity was 0.47. As expected, the unselected populations showed more polymorphism than selected breeds. The distribution of population specific (private) alleles and the amount of genetic variation shared among populations supported the breeding history of the populations and the hypothesis that the red jungle fowl is the main progenitor of the domesticated chicken.  2.4.1.1.3.6 Single Nucleotide Polymorphism (SNP) The single nucleotide polymorphism (SNP) is a new and very promising molecular marker, which offers opportunities to assess the genetic diversity in plant and farm animal species. In the chicken, 3.1 million unique, high quality sequence variants have been identified; these are primarily SNPs (Wang et al., 2005). SNPs are particularly interesting as markers because many known genetic diseases are in fact caused by single base mutations. The SNPs exist at defined positions within genomes (sequence tagged sites [STS]) and can be used for gene mapping, defining population structure, and performing functional studies. SNPs may unravel the fine structure of the genome and identify chromosomal segments showing selection signature. The SNPs are simply DNA point mutations - single base-pair changes distributed across the genome and are bi-, tri-, or tetra-allelic polymorphisms. Although any of the four  23  different possibilities of nucleotide substitution is possible, SNPs are usually bi-allelic and other forms are rare. The SNPs may fall within the coding sequences of genes (coding SNPs or cSNPs), the noncoding regions of genes (ncSNPs), in regulatory regions (rSNPs) or in the intergenic regions (between genes) (intronic SNPs). Any SNPs on the exon of a gene that may have an impact on the function of the encoded protein are called candidate SNPs. The SNPs within a coding sequence will not necessarily change the amino acid sequence of the protein that is produced due to degeneracy of the genetic code. A SNP which induces the same polypeptide sequence is termed a synonymous SNP (sometimes called a silent mutation or sSNP) and when a different polypeptide sequence is produced the SNP is non-synonymous or anonymous (Li and Grauer, 1991). Some advantages of SNPs over other types of genetic markers include: high level of polymorphism; distribution throughout the genome; presence within coding regions; introns and regions that flank genes; simple and unambiguous assay techniques; stable Mendelian inheritance; and low levels of spontaneous mutation (Brown, 1999). In comparison with the highly polymorphic microsatellite markers, SNPs are less informative due to their biallelic nature. Because of their abundance, even spacing, and stability across the genome, SNPs have significant advantages over RFLP and microsatellite markers as a basis for high-resolution whole genome allelotyping (this technique is used to identify the paternal and maternal alleles of a given gene based on polymorphisms) with accurate copy number measurements. One example of the application of SNPs in population genetics in chickens is a study done by Twito et al. (2007). They used 25 SNPs in different genes and chromosomes to examine genetic relationships of 20 chicken populations using the STRUCTURE software program. They compared their results with an analysis using microsatellites and concluded that microsatellites provide a higher clustering success due to a higher polymorphic nature. Nevertheless, SNPs provide broader genome coverage and reliable estimates of genetic relatedness due to their large number in the genome and are considered to be an efficient and cost-effective genetic tool. For phylogenetic studies of  24  closely related populations, use of a large number of SNPs is more important than genotyping a large number of individuals per population (Twito et al., 2007).  2.4.1.1.3.6.1 Development and application of high-density SNP arrays Locus discovery and genotyping are two principle steps in the use of SNP markers. There are different methods to identify SNPs such as single-strand conformation polymorphism (SSCP), PCR-RFLP technique, heteroduplex analysis, and sequencing of DNA. The SSCP and heteroduplex analyses are relatively time-consuming and cumbersome procedures. The cost of old sequencing methods like Sanger has dropped dramatically over the past decade, but this cost is still too high in practice for many research projects (Brockman et al., 2008). Several new methods of sequencing called next-generation sequencing methods have been developed to yield large amounts of DNA sequence at a lower cost (Brockman et al., 2008). Two methods of pairwise sequence comparison from databases and deep resequencing can be used in SNP discovery. Appropriate software such as PolyBayes and Polyphred should be used to distinguish accurate identification of genetic variants (occurring at a low frequency) from sequencing errors. Most SNP detection methods can only detect one or the other allele as defined within the assay. There are some methods that can detect all 4 possible SNPs, but these are generally more expensive and not commonly used. There are many SNP genotyping techniques that have been developed during the last decade to scan discovered SNPs at a low to high-throughput scale. The SNP assay techniques can be categorized by their reaction principle, assay format and detection method. The core of genotyping methods is allele specific detection or allele discrimination, by which the main mechanisms are: allele-specific oligonucleotide (ASO) hybridization (Conner et al., 1983), allele-specific oligonucleotide ligation, allele-specific enzymatic cleavage (Lyamichev et al., 1999) or allele-specific nucleotide incorporation (Chen, 2003). Two different assay formats, solid-phase-mediated and homogeneous, are used in SNP genotyping. The basis of homogeneous SNP genotyping assays is to use ASO as hybridization probes or as PCR primers which are monitored in real time during  25  PCR, and no purification and separation steps are needed. In solid-phase assays, enzymeassisted genotyping, using a DNA ligase or a DNA polymerase provides more specific allele distinction than ASO hybridization (Mir and Southern, 1999). These methods have been multiplexed (a number of different genetic loci simultaneously analyzed per experiment), automated and adapted to various detection strategies used in current highthroughput SNP-genotyping methods. A high-density SNP array is a platform using large numbers of single nucleotide polymorphism (SNP) markers to get a high-resolution of the whole genome allelotyping with accurate copy number measurements (Wong et al., 2004). In domestic animals, creation of high-density SNP arrays has been facilitated by recent advances in developing high-throughput sequencing, whole-genome sequencing technologies and highthroughput genotyping platforms. With the proper coverage and density over the wholegenome, high density SNP arrays can be used in theoretical and practical studies of animal breeding and genetics in farm animals such as genomic selection, genome-wide association, and study of whole-genome linkage disequilibrium (LD) patterns (Fan et al., 2010). There are still some challenges facing these applications, such as a lack of even coverage throughout the genome, incorporation of linkage disequilibrium (LD), confusion of sequencing errors with the true SNPs, the need for more advanced bioinformatic tools and efficient analytical methods (Fan et al., 2010) . The two biggest and most competitive SNP chip genotyping platforms are Illumina's BeadArray based on single-base extension or allele-specific primer extension (http://www.illumina.com) and Affymetrix's GeneChip based on molecular inversion probe hybridization (www.affymetrix.com). Both have the capability of performing highthroughput genotyping for large scale samples. Several BeadChips have been developed for domestic animals using Illumina's iSelect Infinium technology. Muir et al. (2008) used high-throughput genotyping to evaluate the existing genetic diversity in 12 non-industrial and 7 industrial poultry breeds. They investigated the genetic diversity by genotyping 2551 informative SNPs on 2580 individuals including  26  1,440 industrial birds. They found considerably less allelic diversity in industrial poultry breeds compared with non-industrial breeds. Some SNP-chips are now being utilised in genome-wide selection in both layer and broiler lines to increase effectiveness of breeding programs by shortening the generation interval, providing more accurate estimates of breeding values, and to select for specific traits. There are still some challenges in wide application of SNP-chips such as costs of typing all candidates in each generation of selection, complex genotyping and the correlation between phenotypic parameters and markers. Recently, high throughput SNP assays like the new 60k Illumina SNP BeadChip, are increasing being used. The SNPs used are those known to be segregating at high to medium minor allele frequencies (MAF) and distributed across the genome in the two major types of industrial chicken (broilers and layers) (Groenen et al., 2011). This 60k SNP chip contains a large percentage of the 54,293 SNPs (>94 %) which have been shown to be segregating in different chicken populations and that can reliably be genotyped (Groenen et al., 2011). This moderate density SNP chip in chicken can be used for whole genome association studies and genomic selection can be a more efficient breeding tool.  2.4.1.1.4 Statistical tools for molecular marker analysis Several software packages have been developed and are available for the analysis of data obtained using molecular markers. There are about 307 phylogeny packages, and 40 free servers (free to the public) at http://evolution.gs.washington.edu/phylip/software.html, such as ARLEQUIN (Excoffier et al., 2005; http://lgb.unige.ch/arlequin/), GENEPOP (Raymond and Rousset, 1995; http://genepop.curtin.edu.au/), and POPGEN (Yeh et al., 1999; http://www.ualberta.ca/~fyeh/). Both ARLEQUIN and GENEPOP compute population indices, F-statistics and genetic distances between populations; exact test of HWE, and LD. The ARLEQUIN program is the only one that can be used for recessive alleles when there is an estimation of haplotype frequencies.  27  Some software uses Bayesian analysis of genetic population structure such as BAPS (Corander et al., 2003; http://www.abo.fi/fak/mnf/mate/jc/software/baps.html) and STRUCTURE (Pritchard et al., 2000; http://pritch.bsd.uchicago.edu/structure.html). Bayesian analysis facilitates the incorporation of some prior knowledge on the parameters of interest, often leading to improved estimation. Bayesian or maximumlikelihood estimations based on Markov chain Monte Carlo (MCMC) methods often require several consecutive runs to be performed to assure the chains have converged and parameter space has been correctly explored. The STRUCTURE program estimates the allele frequencies in K clusters from multilocus genotypes which may be derived from admixture between the clusters. The STRUCTURE program does not require the membership of the clusters to be specified at the start of the analysis. These two Bayesian based programs assign individuals to genetic clusters by either considering them as immigrants (mixture analysis) or as descendents from immigrants (admixture analysis). The only assumption used in the BAPS and STRUCTURE programs is HWE within clusters. The BAPS program starts first by determining the optimal number of virtual populations or ‘clusters’, and then it allocates individuals to these clusters, whereas, the STRUCTURE program performs this allocation sequentially for different numbers of clusters, and then flags the number of clusters with the highest likelihood, which might not always be optimal (Excoffier and Heckel, 2006). The MICROSAT program (http://hpgl.stanford.edu/projects/microsat) is especially designed for microsatellite analysis and it can calculate delta mu (see Section 2.5.1.5) and the proportion of shared alleles, bothof which have proven useful for distance measurements for microsatellite analysis. Also this software package contains other frequently used genetic distance measurements such as Nei’s distance (Nei, 1972). The PHYLIP software package has the same measurements and can also be used for converting distance data into trees (http://evolution.genetics.washington.edu/phylip.html). These programs have many overlapping functions but they have their own unique features. In general choosing a suitable program depends on the amount of data and the research question (see also: Excoffier and Heckel, 2006 ; Labate, 2000).  28  2.4.2 Choice The UN through the FAO is involved in the organization of a conservation system for animals, but unlike the plant situation, there is no coordinated international program for active preservation of animal genetic resources. Choice of breeds for conservation must include cultural reasons, specific characteristics such as possession of desirable attributes in terms of productivity and/or adaptation, and threat of extinction (Barker, 1994). Effective preservation programs need to be developed to answer these questions and to prevent more genetic resources loss and to maintain existing genetic stocks (Delany, 2003). One of the criteria that can be used in choice in conservation programs is disease resistance (see section 2.6).  2.4.3. Preservation After inventory, evaluation, and choice, conservation methods such as in situ and ex situ methods can be considered to preserve animal genetic resources. In preservation of inbred lines, unique strains, breeds or varieties, keeping genetic materials of both females and males is necessary. These genetic materials should be kept in more than one location and there is a need to establish some organization to prioritize and coordinate the conservation of genetic materials worldwide. One of the main problems in the conservation of poultry resources is deciding who is going to pay for, and to own the genetic materials. Individual researchers and breeder companies are two main sources of genetic resources. Federal and provincial governments in Canada (often through the university system) have kept lines and even collections of specific breeds but their management decisions and changes in financial resources can result in instant loss of genetic resources. Based on the priority and political allocation of resources, the status of implementation of conservation activities in the different countries can vary widely.  29  2.4.3.1 In situ conservation In situ conservation means the maintenance of live animal populations. These animals can be kept within their adaptive environment or as close to it as is practically possible, to develop and adapt to changing environmental pressures. Live animals can be kept outside of their origin area, for example in experimental farms, farm parks, within protected areas or in zoos. The most cost-effective approach to in situ conservation is to maintain locally adapted breeds within industrial or subsistence production systems. Locally adapted breeds have developed specific traits such as hardiness, fitness, longevity, low feed requirements, resistance to disease and relatively high reproductive performance. Moreover, lower yields from locally adapted breeds can be compensated for by higher lifetime production, as well as from lower total maintenance costs. To preserve these breeds, development of niche marketing schemes emphasizing the breed’s specific traits can be successful. The ability of locally adapted breeds to perform in low-input stressful production systems provides the basis for sustainable agriculture. This is true especially in many regions of the world where there is routine exposure to environmental stressors such as disease and extreme climatic variation (Henry, 2006; Wollny, 2003; Gandini and Oldenbroek, 1999). In developing countries where food supplies is short and people are poor, there is a need to support the small scale farmers in order to improve the productivity of such breeds and to develop niche markets wherever relevant (FAO, 2008).  2.4.3.2 Ex situ conservation the ex situ method is to conserve and store the genetic material in genebanks or by cryoconservation. Cryoconservation provides a long-term insurance to conserve genetic diversity for future needs compared to in situ conservation. Cryoconservation technology is advancing (Song and Silversides, 2007; Liu et al., 2010). However, in cryoconservation, as the genetic make up of a breed is frozen, it neither permits adaptation to changing environmental conditions nor provides other gains achieved by in situ methods such as the full range of socio-economic, ecological or cultural benefits. Another disadvantage of cryoconservation is time and cost in breed restoration but it is considered to be a complementary conservation approach. In the majority of ex situ  30  banks, the most common genetic materials are semen and embryos. There are also programs that include the storage of semen, oocytes, tissue, DNA and preferably embryos. The technology to use DNA and cloning to re-develop breeds, is still new and costs are high. Key elements of the operation of ex situ conservation banks is the establishment of protocols for the collection of genetic material, health, and quarantine requirements, evaluation of biological value of stored material, access to stored resources and replenishment procedures (Henry, 2006; Wollny, 2003; Gandini and Oldenbroek, 1999). Because of anatomical and physiological differences between poultry and mammals, preservation technologies have not been developed for poultry gametes and embryos to the same degree as for other domestic livestock species (Long, 2006). Chicken sperm cryopreservation has been applied in poultry breeding and cryopreserved chicken sperm retain their prefreezing fertility (Day and Stacy, 2007) and it requires a higher vitality of frozen-thawed semen as compared to mammalian spermatozoa (Hammerstedt and Graham, 1992). Fertility and variability of frozen/thawed semen is not satisfactory for commercial application but may be adequate for genetic conservation. Etches et al. (1997) developed a cryopreservation method of blastodermal cells from newly laid, unincubated eggs. Their genetic information can be transferred from donor to recipient embryos. New developments in cryopreservation and transplantation of testes and ovaries provide a simple approach for the conservation of chicken germplasm and may provide a universal protocol for the conservation of avian germplasm of all species and lines (Silversides et al., 2008). In Canada, Silversides et al. (2008) describe freezing gonads from 24 populations of chickens representing 18 distinct lines (including samples from AAFC, University of Alberta, University of Saskatchewan, and the Nova Scotia Agricultural College).  31  2.5 Phyogenetic and population genetic analysis 2.5.1 Measures of genetic distances Genetic distance is a measure of the dissimilarity of genetic material between different species or individuals of the same species. Genetic distance between different populations can be assessed by different methods using different molecular markers. Genetic distance between two populations gives a relative estimate of the time that has passed since two populations existed as a single, panmictic population. Small estimations of distance may indicate population substructure or may be because the populations have only been separated for a short period of time. By increasing time that two populations are separated, the difference in allele frequencies should also increase until each population is completely fixed for separate alleles, increasing the estimation of the genetic distance between them. Two main factors of mutation and genetic drift lead to differentiation in the allele frequencies at selectively neutral loci. Genetic distance results can provide the information for criteria such as studying the level of genetic variation, studying the history of populations, and for conservation purposes. (Ruane, 1999). Sometimes it is difficult to decide which genetic distance should be used (Laval et al., 2002). Most classical distance measures are based on IAM or on multidimensional geometric considerations without reference to a particular evolutionary model. Mutational models are used in statistical analyses of genetic variation but none of modeles matches microsatellite mutation. Wright's F-statistic, based on the IAM, underestimates population variation, and Slatkin's R-statistic, based on the SMM, overestimates population variation. Genetic distances which increase linearly with time and are low in their coefficient of variance should be used if a distance is used to estimate relative times of divergence. The combination of linearity and variance determines the performance if a distance is used to reconstruct phylogenetic relationships (Pollock and Goldstein, 1995)  32  2.5.1.1 Fixation index The fixation index was developed by Wright (1965, also called F-statistics), and provides a view of the variance structure of populations and is widely used in population and evolutionary genetics. As such, F-statistics describe the amount of inbreeding-like effects within subpopulations, among subpopulations, and within the entire population showing overall comparison of the degree to which populations are structured. The inbreeding coefficient, FIS, is a measure of the nonrandom association of alleles within an individual. The FIT is the overall inbreeding coefficient (F) of an individual relative to the total population. The FIS is the fixation coefficient of an individual within a subpopulation and it is what is known as the inbreeding coefficient (f), which is conventionally defined as the probability that two alleles in an individual are identical by descent (autozygous). It is calculated in a single population as FIS = 1 - (Ho / He) where Ho is the observed heterozygosity and He is the expected heterozygosity calculated based on the assumption of random mating in the population. The value of FIS ranges between -1 and +1. Negative FIS values indicate heterozygote excess (outbreeding) and positive values indicate heterozygote deficiency (inbreeding) compared with HWE expectations (Weir and Cockerham, 1984; Kalinowski, 2002). The FST is the fixation coefficient of a subpopulation within the total population and its value ranges from zero, when random mating occurs and there is no genetic divergence within the populations, to one, when all the subpopulations are fixed for different alleles (extreme subdivision). The FST is most useful for examining the overall genetic divergence among subpopulations. The Pairwise FST between populations assess pairwise distances between populations and can be biased when variation within subpopulations is high (Weir and Cockerham, 1984; Kalinowski, 2002).  33  2.5.1.2 Nei's genetic distance Nei’s genetic distance (DN) (Nei, 1972, 1987) assumes that mutation and genetic drift are the main reasons for breed divergence from a common ancestor with a stable effective population size. This measure remains one of the most commonly used genetic distances. This measure assumes the infinite allele mutation model (IAM) with a same rate of neutral mutation at all loci. This genetic distance measure is good for very closely related populations but it is less accurate when a bottleneck occurs. This genetic distance measure is also sensitive to fluctuations in effective population size and it increases linearly with time under the IAM, making this distance both non-linear and inaccurate for microsatellite loci (Goldstein et al., 1995a, Takezaki and Nei 1996). Nei’s genetic distance (DN) is defined as: DN = - ln I I = Σxiyi / (Σxi2 Σyi2) 0.5 Where xi and yi are the frequencies of the ith allele at the jth locus in populations x and y, respectively.  2.5.1.3 Reynolds, Weir, and Cockerham’s genetic distance Reynolds, Weir, and Cockerham’s genetic distance (1983) assumes that breed divergence from a common ancestor is due to genetic drift only within a constant population size. It is appropriate in closly related populations. Reynolds' distance and its neglect of the importance of mutation, may work better when population size is small and there is a high potential for drift. This distance is defined as: DR = -ln (1- θ) This distance is based on the coancestry coefficient, θ which is degree of relationship by descent between two individuals.  34  2.5.1.4 Dc, Da and Das genetic distances Three related distances which are not based on the stepwise mutation model (SMM) or any other evolutionary model, are Cavalli-Sforza and Edwards' (1967) chord distance, Dc, Nei et al.'s (1983) distance Da, and Stephens et al.'s (1992) allele sharing distance, Das. These distances are better to reconstruct phylogenies in closely-related populations than SMM-based distances. This genetic distance measure is sensitive to fluctuations in effective population size and bottlenecks. Their accuracy depends on the degree of overlap between the allele-frequency distributions of two populations. The degree of allele sharing may be affected by sampling, especially when the sample size is small. These distances can reflect divergence in time in closely-related populations because they do not increase linearly with time and become extremely flat as time becomes large (Takezaki and Nei 1996). Cavalli-Sforza chord genetic distance (Cavalli-Sforza and Edwards, 1967) assumes that breed divergence from a common ancestor is due to genetic drift only within a constant population size. The Da distance of Nei (Nei et al., 1983) is a modification of Cavalli-Sforza chord genetic distance. Bootstrapping over loci is needed to assess reliability and even the between locus variance of these measures is large. In such cases bootstrapping over individuals may provide useful information. These three distances can be defined as: D = c {1-[Sum (Xi Yi)]^a}^b where Xi and Yi are the frequencies of the ith allele in populations X and Y respectively, and a, b, and c are constants. For Das, a, b, and c are all equal to one. For Da, a=0.5, while b and c are equal to one, and for Dc, a and b equal 0.5, and c equals 2 Sqrt(2)/Pi.  35  2.5.1.5 Delta-mu-square distance (ðµ)² Delta-mu-square distance (ðµ)² (Goldstein et al., 1995a) is defined as the squared differences in mean allele size, and its expected value increases linearly with time under the unconstrained stepwise mutation model (SMM). Computer simulations showed independence of this distance from population size only when the standardisation achieved by averaging scores within populations (Takezaki and Nei, 1996) but between close related populations has lower accuracy than non linear distances such as CavalliSforza chord genetic distance. Its high variance, partly due to its dependence on the variation within populations is the main difficulty with this distance (Laval et al., 2002). Delta-mu-square distance (ðµ)² is defined as: (ðµ)² = (mx – my )2 where mx and my are the means of allele sizes in population x and y, respectively.  2.5.2 Population dendograms Population dendograms are used based on genetic similarity of populations to assess their relationships. Genetic distances, in matrix form, can then be used as input for phylogenetic tree-building routines such as the UPGMA (Unweighted Pair Group Method with Arithmetic Mean), and neighbor-joining approaches which are distance methods. The preferred tree is the one that minimizes the total distance among taxa. The UPGMA starts by finding the two populations with the smallest distance in the matrix between them. Then, it then joins the two populations together at an internal node by half of this distance. The process is repeated on the new matrix until the matrix consists of a single entry to construct the clusters. The UPGMA assumes that all taxa are equally distant from a root and it is an additive tree. Neighbor-joining (NJ) is the most commonly used algorithm in constructing dendograms and is useful for data sets with lineages evolving at different rates. In Neighbor-joining, the branch lengths for sister taxa can be different, and thus can provide additional information on relationships between 36  populations but it gives only one possible tree which depends on the model of evolution used. NJ can be used as a starting point for a model-based analysis such as Maximum Likelihood (Hall, 2007). Parsimony, Maximum Likelihood (ML) and Bayesian analysis are considered to be character-based methods. Parsimony looks for the tree with the minimum number of changes. The ML looks for the tree that, under some models of evolution, maximizes the likelihood of observing the data. Known likelihood of the resulting tree is an advantage of this method but it is slower than NJ and Parsimony. Bayesian analysis is a recent version of Maximum Likelihood and it seeks the tree with greatest likelihoods given data instead of maximizing the likelihood of the data. It produces a set of trees of roughly equal likelihoods and reliability of the tree can be evaluated without bootstrapping which is impractical with ML (Hall, 2007).  2.6 Disease resistance in chickens Avian infectious diseases are costly for the poultry industry and they increase management costs, production losses, and human health concerns. The outbreak of avian influenza in Hong Kong in 1997 was contained by the slaughter of 1.5 million chickens (Chan, 2002). In Thailand, the highly pathogenic avian influenza (HPAI) outbreak in 2003/2004 resulted in destroying of around 30 million birds (Ministry of Agriculture and Cooperatives, 2005). The Canadian Food Inspection Agency (CFIA) ordered approximately 17 million chickens, turkeys, ducks, geese, and speciality birds were killed to control the avian influenza (AI) found in British Columbia in 2004 (Pasick et al., 2007). In the US, poultry is the largest agricultural commodity. Presently, antibiotics and vaccines are major tools used to combat avian infectious disease but a major consumer concern is that it may cause the creation of antibiotic-resistant bacteria and the possibility of antibiotic residues in food (White et al., 2001). In addition, if the virulence of pathogens increases, then the development of new vaccines is required (Witter, 1998).  37  The immune response in chickens is complex, controlled by many genetic factors and influenced by environmental conditions. Genetic resistance provides a reliable, economical, and environmentally sound strategy for disease control (Cheng, 2003). Disease resistance is a typical quantitative trait, controlled by quantitative trait loci (QTL). Genetic selection for disease resistance may increase immune responses, and reduce the cost of vaccination, the need for antibiotic treatment, and drug residues in food products (Lamont, 1998; Kramer et al., 2003). Lantier et al., (2002) recommended direct selection of resistant populations in domestic species as a promising strategy against a number of infectious diseases. Due to its low to medium heritability, the quantitative nature of this trait, and the difficulties associated with reliable measurements, improvement of the immune response by direct phenotypic selection is difficult (Gibson and Bishop, 2005). The facilities and expertise needed to evaluate the response of birds to pathogenic challenges are quite limited in availability. In addition, procedures are expensive and labour intensive and not possible for pedigreed industry chickens. Using molecular tools for the discovery and the use of resistance genes in poultry is a sustainable method to ensure the health of poultry production used as our food (Fulton, 2004). Both candidate gene and QTL mapping strategies have been used for discovery of markers suitable for marker-assisted selection (MAS). The detection of linkage between DNA markers and QTL associated with immune response is preferred in selection of individuals according to their genotype based on MAS. Candidate gene and QTL mapping strategies have their limitations and genome-wide association studies (GWAS) and genomic selection using high-density SNP array seem to be the promising approach for the improvement of economically important traits (Fan et al., 2010).  2.6.1 Major Histocampatibility Complex (MHC) - the adaptive immune system The best-characterized genetic control of disease resistance and immune response in the chicken is the Major Histocompatibility Complex (MHC). The MHC is a large genomic region or gene family found in most vertebrates containing many genes (Meyer et al.,  38  2003). The MHC region has also attracted the attention of many evolutionary biologists due to the high level of allelic diversity found within many of its genes and has been used in the study of natural selection at the molecular level (Hedrick, 1994). The extreme polymorphism in this region should be an important consideration in the conservation programs of captive populations and endangered species (Hughes, 1991a; Sommer, 2005). The MHC in the chicken was first discovered as a blood group locus and is termed the B complex (Briles et al., 1950) and later identified as the MHC (Schierman and Nordskog, 1961). At least 30 B alleles or B haplotypes have been identified with certainty within rather narrowly based genetic stocks of inbred and partially inbred lines and the MHC haplotype nomenclature was standardized initially using serologic reagents (Briles et al., 1982). In some instances, B haplotypes are selectively combined in industrial layer stocks to provide disease resistance. Several studies have revealed the association between the chicken MHC and immune response (Heller et al., 1991; Parmentier et al., 2004; JuulMadsen et al., 2002), disease resistance (Lamont, 1989; Bacon, 1987b; Wakenell et al., 1996), productivity (Lamont et al., 1987a) and other important economic traits in chickens (Lamont, 1989). The MHC contains numerous genes encoding antigen processing and presenting molecules, as well as other immunologically related genes, which play a critical role in the regulation of the immune response. The MHC contains 19 tightly linked genes (Kaufman et al., 1999) including three major groups of genes known as B-F (similar to mammalian Class I genes), B-G (Class IV) and B-L (similar to mammalian Class II genes), which control cell-surface antigens. The BG and BF genes are in linkage disequilibrium with each other and with BL (MHC Class II genes). The B-G is unique in poultry and is a large family of polymorphic Ig superfamily (IgSF) genes. The B-G locus is expressed on erythrocytes, which enables convenient typing of blood groups. Also on the MHC-bearing chromosome 16, but genetically unlinked to the MHC, is the Rfp-Y region (Miller et al., 1994a). The MHC Class I and Class II antigens are involved in T cell repertoire selection and in the presentation of antigenic peptides to the immune  39  system (Klein, 1986). Therefore they can be used as molecular markers in selection for disease resistance. The associations of different MHC haplotypes with the chicken’s response to vaccination and disease challenge have been known for decades (Briles et al., 1983; Bacon and Witter, 1995). The best-known association of the MHC with resistance to disease is that of Marek’s disease (MD), a lymphoproliferative disease (T cell cancer) caused by a member of the herpesvirus family. The MHC involvement in resistance to MD has been mapped to the B-F/B-L region (Briles et al., 1983; Bacon, 1987b). The haplotypes B15, B13, and B19 are associated with susceptibility to MD, whereas the B21 haplotype conveys MD resistance in many different genetic backgrounds (Briles et al., 1983; Bacon et al., 2001). Kaufman (2000) showed that haplotype B21 confers resistance, B2, B6, and B14 moderate resistance, and B19 confers susceptibility to MD. Yonash et al. (2000) examined the association between RFLP (Restriction Fragment Length Polymorphism) data and genetic variation in immune response, using haplotype, single-band, and multiband analysis. They examined candidate genes for the association between DNA markers and antibody response. A cross between lines divergently selected for high or low antibody response to Escherichia coli vaccination was used to generate backcross (BC1) and F2 families. They found an association of antibody response to several antigens (E. coli, SRBC (sheep red blood cell), NDV(Newcastle disease)) using RFLP analysis of the highly polymorphic MHC Class IV (B-G) region. Several studies have shown differences in MHC allelic frequencies between selected and control populations. Emara et al. (2002) investigated genetic diversity at the MHC and non-MHC loci in three industrial broiler chicken pure lines using Southern hybridizations. They found four MHC Class II and eight Class IV genotypes with different frequencies in broiler lines. The observed heterozygosities at MHC loci (59 to 67%) suggests high polymorphism in the broiler lines. Iglesias et al. (2003) analyzed 51 DNA samples from free-range broiler chickens developed in Argentina. They looked for diversity of MHC B and Rƒp-Y genes using restriction fragment pattern (rfp) and SSCP typing methods. They found at least 28 B-F, 29 B-Lb and 44 different Rƒp-Y genotypes. This variability found  40  in broiler chickens could be used to increase immunological fitness and disease resistance. Two microsatellite markers, LEI0258 (McConnell et al., 1999) and MCW0371 (Buitenhuis et al., 2003), located 10,560 bp downstream of LEI0258, both located on chromosome 16, can be used as genetic indicators for MHC haplotype due to their close physical location to genes of the MHC (Fig. 2.1). The microsatellite marker LEI0258 is known to be physically located within the MHC, between the BG and BF regions. It is important to verify whether the LEI0258 variants are associated with these B haplotypes, since they could provide markers that are detectable with an easier, and less expensive, laboratory method than sequencing (Fulton et al., 2006). Fulton et al. (2006) identified 26 distinctive allele sizes using LEI0258, which by the addition of information from another nearby marker (MCW0371) or by small indels or SNP differences between the alleles, could be associated to different MHC haplotypes. The association between LEI0258 alleles and serologically defined MHC haplotypes was very consistent for the same haplotype from different populations. Lima-Rosa et al. (2005) tested the variability of the LEI0258 microsatellite in 149 chickens from two different sources (one non-industrial, the other industrial). They identified fifty-three genotypes using 15 alleles found in their study. They found strong genetic disequilibrium between MHC B-F haplotypes using this system and also using sequencing and cloning techniques and a direct correspondence between a given LEI0258 allele and a specific B-F haplotype at 67% of the cases. Therefore, this microsatellite can provide an inexpensive alternative for MHC genotyping.  41  Fig. 2.1 The chicken Major Histocompatibility Complex map (modified from Kaufman et al., 1999 with updated information from Delany et al., 2009 and Solinhac et al., 2010) showing the location of marker LEI0258. Cosmid cluster 1 sequenced genes are indicated.  2.6.2 Candidate genes involved in disease resistance – the innate immune system Many candidate genes such as cytokine genes, CD encoding genes, T cell receptor genes, NRAMP1, growth hormone, and the immunoglobulin genes have been identified that have shown their roles in disease resistance or can be used in disease resistance (Lamont, 1998). The candidate gene approach has been used to identify genes of interest controlling a particular trait, such as resistance to disease by thoughtful and through review of existing information on the mechanisms of the health traits and reported gene associations. Once a candidate gene is identified and proven, markers near or within the gene sequence can be developed for marker-assisted selection (MAS) to improve immunity (Gibson and Bishop, 2005). By this approach, only a single gene (marker or 42  candidate) is being directly tested to see if it can explain some or all of the variation observed in the disease trait measure. The disadvantage of this strategy includes the need to identify polymorphism in the gene or in the marker closely linked to the gene. In F2 populations or other resource populations with significant LD (linkage disequilibrium), the candidate gene is acting more like a genetic marker. This strategy has been successfully applied to the poultry immune response and disease resistance in several laboratories. It was used to identify specific genes and genomic regions, for example, associated with bacterial burden after challenge with pathogenic Salmonella, with response to commercial Salmonella vaccines (Kramer et al., 2003; Lamont et al., 2002), and with antibody response kinetics in the adult hen (Zhou and Lamont, 2003b). A single nucleotide polymorphism (SNP) in the NRAMP1 gene in line C together with a marker linked to lipopolysaccharide (LPS) showed association for 33% of the differential in resistance early in infection between the parental lines. These data suggest that NRAMP1 and LPS control disease resistance to Salmonella in chickens (Hu et al., 1997). Genes such as PSAP and the MHC complex (Lamont et al., 2002; Liu and Lamont 2001), PSAP, and IAP1 (Lamont et al., 2002) have been associated with Salmonella enteritidis response. Kramer et al. (2003) investigated 12 candidate genes that are involved in the pathogenesis of Salmonella infection using five different unrelated populations of meattype chickens (outbred broiler and Dutch Landrace lines). Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) assays were used to genotype all animals for each gene. Nine of 12 genes showed a significant association (SLC11A1, IAP1, PSAP, CASP1, iNOS, IL2, IGL, TGFβ2 and TGFβ4) with SE load in the caecum content. From the liver, five genes (SLC11A1, CASP1, IL2, IGL, and TGFβ4) and from spleen, only one gene (TGFβ2) showed a significant association with Salmonella enteritidis load. The results confirmed that disease resistance to Salmonella enteritidis is a polygenic trait.  43  In other studies, fragments of each gene were sequenced from the founder lines of the resource population to identify genomic sequence variation. The SNPs were identified in 3 genes (TLR4, CD28 and MD-2) (Malek et al., 2004) and iNOS, TRAIL, TGFβ2, TGFβ3, and IgL (Malek and Lamont, 2003) that were associated with Salmonella enteritidis resistance in the chicken. These SNPs could potentially be used in markerassisted selection (MAS) to enhance the immune response to Salmonella. Mitochondrial phosphoenolpyruvate carboxykinase (PEPCK-M) was found to be a candidate gene containing variants affecting Marek’s disease susceptibility. Li et al. (1998) found seven alleles of MspI RLPF of PEPCK-M in different strains of egg-type chickens (White Leghorn) of different origins. Liu et al. (2001) reported that GH1 was associated with resistance to MDV protein SORF2 in industrial White Leghorn chickens with the MHC B*2/B*15 genotype. Ye et al. (2006) examined associations of twelve immune-related genes with general mortality and other performance traits in three elite industrial broiler chicken lines raised in high and low hygiene environments by PCRRFLP. Ten of the 12 genes had associations with at least one trait. The disadvantage of MAS is that most marker/trait associations varied by genetic lines or with the environment. These results indicate that variation in candidate genes with important broiler traits can be identified in multiple environments, and offer a potential for the implementation of marker-assisted selection (MAS) for traits expressed in the environment in which the industrial broiler needs to perform. The immune-related genes may have pleiotropic effects and their expression is affected by environment and genetic background.  2.6.2.1 Avian Leukosis Viruses (ALV) receptors Avian Leukosis Viruses (ALV's) are considered to be the major grouping within both the avian tumor-causing oncornaviruses (Bagust, 1993) and lymphoproliferative disease of turkeys (Biggs, 1997). Avian Leukosis Viruses that naturally infect chickens cause virusinduced tumor disease and are divided into six subgroups, designated A, B, C, D, E and J based on receptor usage, host range, and infection interference patterns (Crittenden et al.,  44  1967; Vogt, 1970; Crittenden and Motta, 1975; Weiss, 1981, 1993; Crittenden, 1991; Payne et al., 1991; Barnard and Young, 2003). Avian Leukosis Virus subgroups have also been isolated from other species, including from pheasants (subgroups F and G), Hungarian partridge (H) and Japanese quail (subgroup I). Payne et al. (1991) discovered subgroup J, which is considered to be the most recently isolated ALV subgroup. The subgroups ALVA to ALVD and ALVJ are exogenous viruses while ALVE is an endogenous virus. An endogenous virus initiate the cell entry phase of infection through an interaction between surface units on the viral subgroup-specific glycoprotein envelope and subgroup-specific surface receptors on host cells. Tumor Virus B (TVB) gene has been mapped to chicken Chr22 (Smith and Cheng, 1998). The TVB locus is complex because a set of three different alleles, TVB*S1, TVB*S3 and TVB*R. TVB*S1 at this locus encodes receptors for ALVB, ALVD and ALVE to support the viral entry. The TVB*S3 allele encodes a receptor that permits viral entry for both subgroups ALVB and ALVD, but not for ALVE. The TVB*R allele encodes a defective incomplete receptor, due to a premature stop codon within its DNA sequence; the TVB*R encoded receptor permits no viral entry to any of the ALV subgroups (Crittenden and Motta, 1975; Weiss, 1993; Barnard and Young, 2003). Zhang et al. (2005), developed a PCR-RFLP assay using these two published TVB single nucleotide polymorphisms (Brojatsch et al., 1996; Adkins et al., 2001; Klucking et al., 2002) to distinguish among three TVB alleles, TVB*S1, TVB*S3 and TVB*R types, and to identify the six possible TVB genotypes consisting of the three allelic haplotypes in defined laboratory strains of chickens. Chickens from parents heterozygous for different TVB alleles were challenged with Rous sarcoma viruses of subgroup ALVB and ALVE to induce wing-web tumors. Tumor incidences were evaluated between chickens of the genotypes determined with this newly developed PCR-RFLP assay. Importantly, chickens typed with this assay as TVB*S3/*S3 were resistant to infection by ALVE only, and those typed as TVB*R/*R were resistant to both ALVE and ALVB. Furthermore, a vast majority of chickens with the susceptible TVB*S1/– genotypes developed a tumor.  45  2.6.2.2 Mx1 gene Avian influenza has drawn attention and is also a major concern to the poultry industry. The Mx gene exists in a wide variety of species ranging from yeast to most animals. The Mx1 gene confers resistance, in vivo, to influenza and influenza-like viruses in the mouse (reviewed in Haller, 1981) and human (Pavlovic et al., 1995). In the chicken, the Mx1 gene is a single gene (Schumacher et al., 1994) with multiple alleles (Ko et al., 2002) and certain alleles have more antiviral function. The Mx1 gene has been associated with commercial traits such as egg and meat production, as well as immune functions in chickens. Livant et al. (2007) found significant associations between the SNP determining antiviral activity located on Mx1 exon 13 and several traits of economic interest. In exon 13 of the chicken Mx1 gene (Fig. 2.2), one non-synonymous single nucleotide polymorphism (SNP) causes a single amino acid substitution from Serine (Ser) to Asparagine (Asn) at position 631 (Ser631Asn). This SNP has been shown to cause resistance to activities of the avian influenza virus in vitro (Ko et al. , 2002; Li et al., 2007). Li et al. (2006) showed skewed allele frequencies with a much higher frequency of the favourable allele A in native breeds than in highly selected lines. Balkissoon et al. (2007), compared contemporary meat-type (broiler) birds to egg-laying strains and found a higher frequency of the susceptibility allele in broiler type chickens.  Fig. 2.2 Genomic organization of Mx1 gene in chicken (Derived from Li et al., 2007).  46  Chapter 3  Material and methods  47  In this chapter, I will describe the material and methods used to carry out my research to test the hypotheses associated with the objectives of this thesis: 1. Description of chicken populations used in this study 2. Data collection and sampling 2. Different molecular markers used as tools to achieve thesis objectives 3. Different tests and softwares used to analyse and process the data  3.1 Experimental birds Two chicken populations from industrial sources were used : 1. Lohmann Brown Classic (LB) – This is a brown egg layer marketed world wide by Lohmann Tierzucht GmbH and has been developed from crosses of several dual-purpose breeds in addition to the White Leghorn (Flock, 2009; American Poultry Association 2001). They have a good temperament, are easily managed and are adaptable to all types of production systems. While the bird was selectively bred as a cage layer, it is frequently used for free range production in many regions of the world (eg. Araujo et al., 2008; Yakubu et al., 2007; Eigaard et al., 2003). 2. Lohmann White ISL (LW) – This is a White Leghorn white egg cage layer marketed by Lohmann Tierzucht GmbH (American Poultry Association, 2001). This bird is primarily used for cage layer production. These birds are highly productive with a high quality and shell stability. Due to replacing conventional cages by alternative management systems, these laying hens showed well adapted to alternative management like free range system or organic farming (Flock, 2008). An experimental population of brown egg layers resulting from a cross: Agassiz Cross (AC) – This is a cross (F1) between a pure Barred Plymouth Rock line and a pure Rhode Island Red line. The two parental lines are resource lines maintained by Agriculture and Agri-Food Canada (AAFC) at the Agassiz Research Centre, Agassiz, BC (Silversides, 2010). These two pure lines were acquired from Dr. Donald Shaver, the former CEO of Shaver Poultry Breeding Farms Ltd. The two parent lines have very good  48  egg production records but have not been utilized for industrial crosses (Shaver, D., pers comm.). In the cross, males show the barred feather pattern while females are dark brown in plumage colour, and these sex-linked feather color patterns are helpful in distinguishing the two sexes. The AC cross has been suggested to have good potential to be developed as a free range layer because of its good egg production and hybrid vigor (Shaver D., pers comm.) and was produced specifically for evaluation. The two non-industrial populations: Non-industrial Free run/free range chickens have not been under the same type or intensity of selection as the industrial breeds and are also likely to have been exposed to more parasite and disease vectors than the industrial breeds that are bred and reared indoors in well controlled environments. 1. Silkies (SK) - This local population has been maintained as separate male and female lines with different origins. Selection has been done to improve the performance of the lines. The selected traits are: weight, growth rate, breast meat ratio to body weight, rose comb and five toes in the male line, and egg production, longevity of production, hatchability, feather bonnet, and five toes in the female line. The lines are crossed to produce the market birds (Donaldson R., pers. comm.). The population has been closed to immigration of new genetic material since the lines were established in 1998. The silkies are marketed as a soup bird as their meat is dry, lean and unmarbled and is said to have medicinal properties. Their market has grown steadily in BC and Silkies are widely used as specialty birds by the Asian community (Planning for profit, 1998). DNA samples were collected from the F1 cross market birds. 2. Taiwanese Cross (TC) – The population was initially established with a few Taiwan native chickens (Huang and Lee, 1990) imported to British Columbia. To avoid inbreeding, the population was crossed with white industrial broiler type roosters before expansion. The population has since been selected to mimic the original native chicken type and maintained as a stable breed. The bird is a brown/black feathered, black legged  49  meat type chicken used for the organic and free range market and has a niche market in BC. Three non-industrial Chinese chicken populations which are believed to be ancestral to many of the local free range/organic populations imported from Southeast Asia (Cheng K., pers. comm.). 1. Yellow Wai-Chau (YW) – This is a resource population maintained by the Kadoorie Experimental Farm, University of Hong Kong. The breed was established in the 1950s by crossing the local Wai-Chau chickens with industrial broiler type chickens brought back to China from California by expatriates. The population has since been closed and maintained as a stable breed. It is commonly used in the South China provinces and is well-known for its meat flavour and yellow fat (Xi, 2000). 2. Shiqi (SQ) – This is a resource population maintained by the Department of Animal Science, South China Agricultural University (Quangzhou, China). The Shiqi breed is widely used in south-eastern China for meat production. It is well-known for its hardiness and disease resistance (Chan et al., 2005). As flavour is one of the main characteristics in chicken meat for consumers, Zhiqun (2000) studied the effect of different age and sex on these chickens’ flavour component and found that certain aldehydes increase when chickens grow, which is an important component for good flavour. 3. Yellow Shiqi (YSQ) – This is a Shiqi cross developed by the South China Agricultural University. The breed was established by crossing the Shiqi with another local breed, the Qingyuan. The population has been maintained as a stable breed since the late 1980’s.  3.2 Tissue collection and extraction of genomic DNA I randomly sampled 30 individuals from each of the LW, LB, AC, SK, and TC populations. Genomic DNA was extracted from either blood (LW, LB, and TC), trunk blood (AC) or breast muscles (SK) by using Proteinase K digestion and phenol  50  chloroform extraction. A salting-out method, modified for chickens (Miller et al., 1988), was used for the extraction of DNA from blood samples. The DNA was then dissolved in TE buffer (TE is a mix of Tris, a common pH buffer, and EDTA, a molecule chelating cations like Mg2+) and DNA quality and quantity were assessed by spectrophotometer and gel electrophoresis. Genomic DNA samples (20 individuals from each population) of the Chinese populations (SQ, YSQ, and YW) were provided by Dr Frederick Leung (University of Hong Kong).  3.3 Molecular markers 3.3.1 Microsatellite markers Eighteen highly polymorphic microsatellite markers, widely distributed over the genome covering 13 different chromosomes (out of the 39 known chicken chromosomes) were studied (Table 3.1). Multiple markers on the same chromosome, are well-spaced and they are genetically independent because no evidence for linkage was obtained using ARLEQUIN version 3.1 (Excoffier et al., 2005). These markers have been widely used in previous biodiversity studies in chickens (Granevitze et al., 2007; Hillel et al., 2007). Nine of the microsatellite loci chosen (ADL0268, LEI0094, MCW0014, MCW0183, MCW0067,  MCW0123,  MCW0330,  MCW0111,  MCW0037)  were  initially  recommended by the FAO/ISAG MoDAD Advisory Group (2004, http://dad.fao.org) and three more (MCW0069, MCW0034, ADL0278) were included in the updated microsatellite  list  for  biodiversity  studies  in  chickens  (http://aviandiv.tzv.fal.de/primer_table.html). M13 universal sequence tail was added to the 5’-end of either forward or reverse primers (Li-Cor).  51  Table 3.1 Characterization of 18 chicken microsatellite markers and PCR primer sequences selected for the present study Marker (GenBank Accession)  Chr.  MCW0111 (L48909.1)  1  ADL0268 (G01688) ADL0185 (G01607) MCW0034 (L43674) ADL0146 (G01571)  1 2 2 2  MCW0004 L40038  3  MCW0037 (L43676)  3  LEI0094 X83246  4  MCW0029 (L43634.1)  5  ADL0298 (G01714 )  5  MCW0014 ( L40040 )  6  MCW0183 (G31974)  7  ADL 278 (G01698 )  8  MCW0067 (G31945) ADL210 (G01630 )  10  Position CM1 118 288 103 233 403 155 317 153 128 198 50 86 94 59 54  Repeat  (AC)8 (GT)12 (CA)16 (CA )24 (TG)17  Primers GCTCCATGTGAAGTGGTTTA ATGTCCACTTGTCAATGATG  Allele size (PCR product length) 96-120 102-116  CTCCACCCCTCTCAGAACTA CAACTTCCCATCTACCTACT CATGGCAGCTGACTCCAGAT AGCGTTACCTGTTCGTTTGC  128-150 223-245  TGCACGCACTTACATACTTAGAGA TGTCCTTCCAATTACATTCATGGG GACCTGCATTGTCAGTGACC TGCTTCCTACCCATTCTCCT  150-166  (CA )28  GGATTACAGCACCTGAAGCCACTA AAACCAGCCATGGGTGCAGATTGG  149-199  (CA)8  ACCGGTGCCATCAATTACCTATTA GAAAGCTCACATGACACTGCGAAA  154-160  (AC)16  GATCTCACCAGTATGAGCTGC TCTCACACTGTAACACAGTGC  253-285  (CA)29  CATGCAATTCAGGACCGTGCA GTGGACACCCATTTGTACCCTATG  149-194  (CA)14  CAAGGCTGGGATTGATGAAA TGGCGTGTGGGTTTACAAAA  (CA)18 Compound2 (TG)18 (GT)11  120  AAAATATTGGCTCTAGGAACTGTC ACCGGAAATGAAGGTAAGACTAGC  164-188  ATCCCAGTGTCGAGTATCCGA TGAGATTTACTGGAGCCTGCC CCAGCAGTCTACCTTCCTAT TGTCATCCAAGAACAGTGTG  290-311 114-126  GCACTACTGTGTGCTGCAGTTT GAGATGTAGTTGCCACATTCCGAC  ACAGGAGGATAGTCACACAT GCCAAAAAGATGAATGAGTA 45 CCACTAGAAAAGAACATCCTC MCW0123 14 (CA)10 GGCTGATGTAAGAAGGGATGA (L43645 ) 41 TGGACCTCATCAGTCTGACAG MCW0330 17 Compound AATGTTCTCATAGAGTTCCTGC (G32085) 47 GCACTCGAGAAAACTTCCTGCG MCW0069 26 (CA)11 (L43684 ) ATTGCTTCAGCAAGCATGGGAGGA 1 CM: Centimorgan; See Chicken Genome Resources at NCBI Web site 11  (AC)15  178-184 124-147 94 260-290 145-185  (http://www.ncbi.nlm.nih.gov/projects/genome/guide/chicken/) and ArkDB database Web site by the Roslin Bioinformatics Group (http://www.thearkdb.org/) for details. 2 Microsatellites with variable tandem repeats  52  The sequence fragments were amplified by PCR using previously designed primers fluorescently labelled with IRD700 and IRD800, obtained from the published literature for each of the microsatellite markers. The PCR reactions were performed in a total volume of 10 μl containing 10 ng chicken genomic DNA, 1 μl of 10x PCR buffer (final concentration: 10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3; Applied Biosystems, Foster city, CA, USA), 5 pmol of each forward and reverse primer, 200 μM of each dNTP, 0.3 - 0.5 pmol fluorescently labeled (IRD700 or 800) M13 sequence primer (Li-Cor Biosciences, Lincoln, NE, USA), and 1 U of AmpliTaq Gold Taq DNA polymerase (Applied Biosystems, Foster city, CA, USA). The amplification was carried out in a thermocycler (PTC-100, MJ research, Inc., Watertown, MA, USA) using the following conditions: an initial denaturation step at 94 °C for 3 min followed by 35 cycles of 94 °C for 30 sec, annealing at 50-55 °C for 45 sec, 72 °C for 1 min and a final extension of 10 min at 72 °C. The cycling conditions were optimized for each marker. Using a LI-COR semi- automated DNA analyzer (LICOR Biotechnology Division) PCR products were visualized on 8% polyacrylamide gels. Electropherogram processing and allele-size scoring were performed with SAGAGT genotyping software (http://www.licor.com/bio/genomics/SAGA/BioSoft1.jsp).  3.3.2 MHC microsatellite marker (LEI0258) genotyping The  PCR  primers  (McConnell  CACGCAGCAGAACTTGGTAAGG  et -3’  al.,  1999) and  Forward Reverse  5’5’-  AGCTGTGCTCAGTCCTCAGTGC - 3’ (GenBank Z83781) were used for amplification of LEI0258 which is a microsatellite with tetranucleotide repeats. M13 universal sequence tail was added to the 5’-end of either forward or reverse primers (Li-Cor). The PCR reactions were performed in a total volume of 10 μl containing 10 ng chicken genomic DNA, 1 μl of 10x PCR buffer (final concentration: 10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3; Applied Biosystems, Foster city, CA, USA), 5 pmol of each forward and reverse primers, 200 μM of each dNTP, 0.5 pmol fluorescently labeled  53  (IRD800) M13 sequence primer (Li-Cor Biosciences, Lincoln, NE, USA), and 1 U of AmpliTaq Gold Taq DNA polymerase (Applied Biosystems, Foster city, CA, USA). The amplification was carried out in a thermocycler (PTC-100, MJ Research, Inc., Watertown, MA, USA) using the following conditions: an initial denaturation step at 94 °C for 1 min followed by 35 cycles of 92 °C for 45s, annealing at 57 °C for 45 sec, 72 °C for 45 sec and a final extension of 1.5 hr at 72 °C. The cycling conditions were optimized for this marker. Using a LI-COR semi- automated DNA analyzer (LICOR Biotechnology Division) PCR products were visualized on 8% polyacrylamide gels. Electropherogram processing and allele-size scoring were performed with SAGAGT genotyping software (http://www.licor.com/bio/genomics/SAGA/BioSoft1.jsp).  Alleles  from  known  haplotypes of inbred lines, with the majority being derived from the White Leghorn (WL), were run on each gel as reference for more accurate scoring.  3.3.3 SNPs 3.3.3.1 SNP genotyping of the cytokine genes For ChB6, Casp-1, IAP-1, TGF- β3, BMP-7, TLR4, MD-2, IFN- γ, iNOS, and IL-2, ten pairs of primers that were designed in previous studies were used for genotyping (detail and references in Table 3.2). The PCR reactions were performed in a total volume of 25 μl containing 25 ng of chicken genomic DNA, 2.5 μl of 10x buffer (final concentration: 10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3; Applied Biosystems, Foster city, CA, USA), 5 pmol of each forward and reverse primers, 200 μM of each dNTP, and 1 U of AmpliTaq Gold Taq DNA polymerase (Applied Biosystems, Foster city, CA, USA). The cycling conditions were: initially denaturation at 94 °C for 3 min, followed by 35 cycles at 94 °C for 1 min, optimum annealing temperature and optimum annealing time (Table 3.2), 72 °C for 1 min, and final extension step of 10 min at 72 °C.  3.3.3.2 SNP genotyping of the Mx1 gene Forward and reverse primers 5'- GAATAGCAACTCCATACCGTG -3' from intron 1213 sequence and 5'- GTATTAAAGGTTGCTGCTAATG -3' from the 3’-untranslated 54  region were used to amplify the 450 bp MX1 exon 13 product. These primers were designed by Livant et al. (2007) based on the chicken MX1 gene sequence in Ensembl (ENSGALG00000016142). The PCR amplification was carried out in a total volume of 10 μl containing of 25 ng chicken genomic, 1 μL of 10x buffer (final concentration: 10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3; Applied Biosystems, Foster city, CA, USA), 5 pmol of each forward and reverse primers, 200 μM of each dNTP, and 1 U of AmpliTaq Gold Taq DNA polymerase (Applied Biosystems, Foster city, CA, USA). The reaction conditions consisted of an initial denaturation step held at 94 °C for 5 min, followed by 35 cycles of 94 °C for 1 min, 60 °C for 30 sec and 72 °C for 1 min, and a final 10-minute extension at 72 °C. The PCR products were subsequently subjected to separate endonuclease digestion using Hpy8I restriction enzyme.  3.3.3.3 SNP genotyping of the TVB gene Two SNPs at positions 172(C/T) and 184 (T/A) based on GenBank accession numbers AF1617I3, AFI61712, and AF507016.l, differentiate the allelic transcripts for TVB*S1, TVB*S3 and TVB*R (Zhang et al., 2005). Two pairs of PCR primers based on 797 base pairs (bp) of the TVB DNA sequence were used to amplify two different PCR products, TVB303 and TVB172. The TVB303 primers (forward: 5'-ACC CCT TCT TGC AGG CAC CTA TGA: reverse, 5'-GGA TGC TGT GCT GCG TGG AGA) flanked a 303 bp segment of the TVB genomic DNA. This PCR product named TVB303 encompasses the SNPs at positions 172 and 184. The second primer pair (forward, 5'-GGT AAG GCA GTC ACA AGC ATC ACT C; reverse: 5'-TAC TCG TCT TTC TTA CAT GGG AGG CTC T) was designed to flank the TVB 172 SNP and it generated a 202 bp PCR product designated as TVB202. The underlined base close to the 3' end of the TVB202 reverse primer indicates a designed single A/T substitution. This substitution was made during primer synthesis. If the reverse primer was synthesized exactly as the TVB gene sequence i.e. TAC TCG TCT TTC TTA CAT GGG AGG CAC T (the third from the last base is an A), when this base  55  was synthesized as a T, an Xba I restriction site was formed. This allowed one to distinguish the TVB*R allele from the TVB*S1 and TVB*S3 alleles. The PCR amplification was performed in in a total volume of 10 μl containing of 25 ng chicken genomic, 1 μL of 10x buffer (10 mM Tris-HCl, 1.5 mM MgCl2, 50 mM KCl, pH 8.3; Applied Biosystems, Foster city, CA, USA), 5 pmol of each forward and reverse primers, 200 μM of each dNTP, and 0.5 of U AmpliTaq Gold Taq DNA polymerase (Applied Biosystems, Foster city, CA, USA). This mixture was immediately subjected to the cycling conditions of 94 °C for 3 min for initial denaturation, then 30 cycles of 1 min at 94 °C, 1 min at 60 °C for TVB202 and 56 °C for TVB303 as annealing temperatures and 1 min at 72 °C, and a final extension cycle for 5 min at 72 °C. The PCR products TVB303 and TVB202 were subsequently subjected to separate endonuclease digestion using Nla III and Xba I restriction enzymes respectively.  3.3.3.4 PCR- RFLP genotyping To confirm the SNP polymorphism of each gene, two pooled genomic DNA samples from 15 individuals randomly selected within each of the lines were sequenced using an ABI 377 sequencer. Sequencher software (Gene Codes Corporation) was used to align the sequences for polymorphism identification. The restriction enzyme sites for genes were based on previous work (Table 3.2), and were confirmed after sequencing using a sequence analysis web server (http://mbcr.bcm.tmc.edu/). The PCR products were digested based on restriction enzyme condition, for 4 hours to overnight at 37 or 65 °C in a 10 μL reaction volume, including 5 μL of PCR product and 2 or 3 units of restriction enzyme. The buffer and other conditions for restriction enzyme digestion followed manufacturer’s recommendations. The digested  fragments  were  separated  by  electrophoresis through 2 - 4 % agarose gel depending on the size of the DNA fragments in the digest. Ethidium bromide staining was used for DNA visualization. Data were then scored visually to differentiate genotypes.  56  Table 3.2 Primer sets, restriction enzymes and annealing temeperatures for polymorphism detection of 13 candidate genes. Gene ChB6  GenBank Access # X92865  CASP1  AF031351  IAP1  AF008592  TGF- β3  X60091  BMP7  AF223970  TLR4  AY064697  IFN- γ  Y079221  iNOS  AF537190  IL-2  AJ224516  MD-2  BI066409.1  TVB303  AF507016.1  TVB202  AF507016.1  Mx1  SNP location -89 bp of exon 3 (C/A) , Chr. 1 * -667 bp of exon 1 (T/C), Chr. 19 Ala 157 (C/T), Chr. 1 -171 bp of exon 5 (C/A), Chr. 5 -425 bp of 5’ flank. Region (A/G), Chr. 20 -3954 bp of intron (G/C), Chr. 17 -1729 bp of promoter (A/G), Chr. 1 -173 bp of intron (T/C), Chr. 19 -425 bp of 5’ flank. Region (C/T), Chr. 4 -102 bp of exon 1 (G/A), Chr. 2 184 bp (A/T), 172 bp (C/T), Chr. 22 172 bp (C/T), Chr. 22  Primer Sequences Forward and Reversed 5’-GCTTCCCCAATGGAACTG-3’ 5’-GAGCACAATGGGCCTAGTC-3’ 5’-CCATGCTTGGGCTCTCAGTG-3’ 5’-GGTCCCGCAGATCCCAGTG-3’ 5’-TCACCATCTCTACGTTCCAT-3’ 5’-CATTGAAACTTGGTTGGTCT-3’ 5’-CGGCCTGGAAATCAGCATAC -3’ 5’-GAAGCAGTAGTTGGTATCCAG-3’ 5-CGGGAATGTCTGGAAGAAGAAA-3’ 5’-AGAGGGGGGAATGCTGAAAT-3’ 5’- CCTGGACTTGGACCTCAG-3’ 5’- GGACTGAAAGCTGCACATC-3’ 5’-GTAAGGAACTTCAGCCATTG-3’ 5’-GACGAATGAACTTCATCTGCC-3’ 5’-CCAATAAAAGTAGAAGCGA -3’ 5’-CTCTTCCAGGACCTCCA-3’ 5’-TGCTTTTAACCGTCTTTG-3’ 5’-GATGCTCCATAAGCTGTAGT-3’ 5’-GTAACAACAAAGGCAGAA-3’ 5’-AGAAAAATCCACTGACTCC-3’ 5'-ACCCCTTCTTGCAGGCACCTATGA-3’ 5'-GGATGCTGTGCTGCGTGGAGA-3’ 5'-GGTAAGGCAGTCACAAGCATCACTC-3’ 5'-TACTCGTCTTTCTTACATGGGAGGCTCT-3’ 5’-GAATAGCAACTCCATACCGTG -3’ 5’-GTATTAAAGGTTGCTGCTAATG-3’  Restrict. Enzyme  Allele  size (bp)  Temp. °C  Reference  Pvu II  215  55/1min  Zhou & Lamont (2003b)  Hsp92 II  1070  60/1min  Bgl I  394  62/1min  Bsr I  1078  56/1min  HaeIII  1216  61/1min  156  55/30s  670  58/1min  Sau96I Tsp509 I Alu I Mnl I  479  50/1min  Liu & Lamont (2003); Malek & Lamont (2003) Lamont et al. (2002); Liu & Lamont (2003) Li et al. (2002) Ye et al. (2006) Malek et al. (2004) Zhou et al. (2001a) Malek & Lamont (2003)  659  59/1min  Zhou et al. (2001a)  252  48.5/30s  Malek et al. (2004)  Nla III  303  60/1min  Zhang et al. (2005)  Xba I  202  56/1min  Zhang et al. (2005)  Ase I  ENSGALG0 Exon 13 (A/G, Chr. Hpy8I 450 60/30s 0000016142 16 * This non-synonymous substitution is a predicted to change the amino acid Glutamine to Lysine (Zhou and Lamont, 2003b)  Livant et al. (2007)  57  3.4 Statistical analysis 3.4.1 Hardy-Weinberg Equilibrium (HWE) test The test of the Hardy-Weinberg equilibrium (HWE) at each locus-population combination was implemented by a test analogous to Fisher’s exact test based on Markov Chain Monte Carlo simulation (Markov chain length: 1,000,000; dememorization steps: 100,000) (Guo and Thompson, 1992) using ARLEQUIN version 3.1 (Excoffier et al., 2005).  3.4.2 Genetic diversity within and between populations 3.4.2.1 Genetic diversity using 18 Microsatellite markers The GDA software (http://hydrodictyon.eeb.uconn.edu/people/-plewis/software-.php) was used to characterize population genetic indices such as allele frequencies, mean number of alleles per locus and per population (A), mean number of alleles per polymorphic locus (Ap), number of private alleles per locus (Npa), observed heterozygosity (Ho) and unbiased expected heterozygosity (He; Nei 1987) of each population across all microsatellite loci and for each locus across populations. Polymorphism information content (PIC) was calculated using MolKin software (Gutiérrez et al., 2005) and is a general measure of how polymorphic and useful a marker is (Botstein et al. 1980). Wright’s fixation indices (Weir and Cockerham, 1984) including FIT (fixation coefficient of an individual within the total population), FST (fixation coefficient of a subpopulation within the total population), and FIS (fixation coefficient of an individual within a subpopulation) for each locus, and the level of significance for pair-wise FST values between populations were calculated using the variance-based method of Weir and Cockerham using GENEPOP version 4.0.10 (Raymond and Rousset, 1995; 2008;  58  http://genepop.curtin.edu.au/). Gene flow between breeds was measured using molecular coancestry between two individuals which is is the probability that two randomly sampled alleles from the same locus in two individuals are identical by state (Caballero and Toro, 2002). Molecular coancestry calculated using MolKin software (Gutiérrez et al., 2005) can be used to assess genetic diversity within and between populations (Eding and Meuwissen 2001). We used POPGENE (Yeh et al., 1999) to do the Ewens-Watterson neutrality test (Manly, 1985) which uses Stewart’s algorithm to test the markers for selective neutrality. One thousand iterations were employed for generation of simulated distributions of the F-statistic under null hypothesis. A locus is considered neutral when the observed homozygosity falls within the 95% CI obtained under the null hypothesis of neutral evolution.  3.4.2.2 Genetic variations in the MHC region using the LEI0258 Microsatellite marker The alleles were identified by their sizes. Allele frequencies, the observed (Na), effective (Ne) and private (Npa) allele numbers, expected and observed heterozygosity (He and Ho) of each population were estimated by GenALEx6 (Peakall and Smouse, 2006).  3.4.2.3 Genetic variations in genes related to disease resistance GenALEx6 (Peakall and Smouse, 2006) was used to estimate several statistics related to genetic variation: Ho – observed frequency of heterozygotes. Using the calculated allele frequencies (Pi) of allele i among n number of alleles in the analyzed population, the gene diversity (He), namely the expected frequency of heterozygosity, was calculated as: He = 1 - ∑ni P2i. The average He and Ho values were calculated for each locus across populations and for each population across loci. GENEPOP  version  4.0.10  (Raymond  and  Rousset,  1995;  2008;  http://genepop.curtin.edu.au/) was also used to test for genotypic and allelic  59  differentiation, for each population pair across all loci and for each locus across all populations (exact G test).  3.4.3 Phylogenetic cladogram using 18 Microsatellite markers Chord genetic distance (Dc) (Cavalli-Sforza and Edwards, 1967) was calculated based on allele frequencies using the computer software package PHYLP version 3.69 (Felsenstein, 1993-2002; http://evolution.genetics.washington.edu/phylip.html). Unrooted cladograms and a consensus tree were constructed using the neighbor -joining method (Saitou and Nei, 1987) with the Neighbor program from the PHYLIP package. Bootstrapping of 1,000 replicates over loci was performed in order to test the robustness of tree topology to obtain a consensus cladogram that was depicted using SPLITSTREE4 software (Hudson and Bryant 2006). The bootstrapping technique applied in phylogeny reconstruction is different from other statistical methods in that it is not the statistic that is being estimated, but rather a phylogenetic pattern, to determine how well each branching point is supported by the data (Lanyon, 1987).  3.4.4 Cluster analysis (using 18 Microsatellite markers) I used the model-based clustering method implemented by the STRUCTURE program to cluster individuals from multilocus genotypes (Pritchard et al., 2000). The analysis involved an admixture model with correlated allele frequencies as suggested by several authors (Pritchard et al., 2000; Muchadeyi et al., 2007; Bodzsar et al., 2009; Li et al., 2009). The model assumes K populations (or clusters) each modelled by its own set of allele frequencies at each locus without any information regarding ancestry. I used 20,000 iterations of burn-in followed by 50,000 MCMC iterations for each of K clusters ranging between 2 and 8, where K was the number of assumed clusters to be examined. The most likely value for K was estimated by comparing the log-likelihood of each K-value. The program also calculates the membership of each individual in each cluster (Q). I calculated ∆K as another approach to choosing K presented by Evanno et  60  al. (2005); ∆K is a measure of the second order rate of change in the likelihood of K corresponding to the most pronounced genetic subdivision present in the data. The run with the highest posterior probability P (X | K) was used to construct the dendrogram, and was visualized by the Distruct program (Rosenberg, 2004).  61  Chapter 4  Results  62  In this chapter, the results of my thesis research will be presented in the following order: 1. Ewens-Watterson test and test for deviation from Hardy-Weinberg equilibrium 2. Population genetic indices including allele frequencies, number of alleles and heterozygosity as reflected by 18 microsatellite markers 3. Genetic variation of the Major Histocompatibility Complex (MHC) region of the eight chicken populations examined. 4. Genetic variation (as reflected by SNP markers) in the 10 candidate genes associated with disease and inflammation response, antiviral Mx1 exon 13 and tumor virus B (TVB) haplotype coding receptors for avian leukosis viruses. 5. Genetic differentiation, population structure and genetic relationship among populations.  4.1 Genetic diversity within and between populations 4.1.1 Genetic variation within populations A total of 102 alleles were observed across 210 birds in all eight populations using the 18 microsatellite loci examined. The average number of alleles per locus (MNA) ranged from 2.6 in the SK population to 3.64 in the YW population (Table 4.1). The average number of alleles per locus over all populations was 3.1. The MCW0029 locus showed the highest number of alleles per locus (14) with the highest PIC value of 0.773 while MCW0067 and MCW0037 showed the lowest (2) and the mean number of alleles per locus was 5.67. The ADL0210 locus showed the lowest PIC value of 0.065 (Table 4.8). Among all populations, there were a total of 22 private alleles at 12 microsatellite marker loci. The SK population had 2 private alleles at 2 different loci, whereas the TC had 5 private alleles at 5 loci. Population LB had 5 private alleles at 3 different loci, whereas the LW had 1 private allele at 1 locus. Chinese populations, YW, SQ and YSQ had 2, 2 and 3 private alleles respectively at 2 loci. Finally, the Agassiz Cross had 2 private alleles at 2 loci. A total of 7 private alleles had frequencies higher than 10%.  63  The average observed heterozygosity varied from a maximum of 0.52 in the SQ to a minimum of 0.32 in the SK population and the mean estimates of observed and expected heterozygosity in all eight populations were 0.418 and 0.569, respectively (Table 4.1). The observed heterozygosity per locus ranged from 0.626 for locus MCW0069 to 0.034 for locus ADL0210 and the mean observed and expected heterozygosity over loci were 0.394 and 0.583, respectively (Table 4.8). The number of loci deviating from Hardy-Weinberg equilibrium in each of the studied populations is shown in Table 4.1. In some locus/breed combinations the test for deviation from HWE could not be done because of monomorphic loci (number showed in parenthesis).  None of the populations showed deviation from HWE in MCW0029,  MCW0004, MCW0067 and MCW0069. The results of the Ewens-Watterson test provide no evidence for the presence of diversifying or balancing selection in any of the examined microsatellite loci. Table 4.1 Mean number of alleles (MNA), mean number of alleles per polymorphic locus (AP), expected (He) and observed (Ho) heterozygosity and inbreeding (FIS) of the eight chicken populations using 18 microsatellite markers. Population Silkies (SK) Taiwanese Cross (TC) Lohmann Brown (LB) Lohmann White (LW) Agassiz Cross (AC) Yellow Wai-Chau (YW) Shiqi (SQ) Yellow Shiqi (YSQ) Mean  N  MNA  30 30 30 30 30 20 20 20 22.91  2.6 3.5 3 2.78 3.11 3.64 3.27 2.87 3.1  Ap 3 3.65 3 2.78 3.38 2.88 3.27 3.64 3.2  Ho 0.325 0.433 0.369 0.333 0.460 0.489 0.526 0.408 0.418  He  FIS  0.399 0.512 0.387 0.368 0.493 0.569 0.591 0.448 0.569  0.189 0.160 0.051 0.098 0.068 0.142 0.093 0.122 0.118  HWE deficiency* 5(3) 7(1) 6 6 7(2) 3(2) 1(3) 2(7)  N = Sample size; *; Number of loci deviating (p<0.05) from Hardy-Weinberg equilibrium; Number in parentheses shows the monomorphic loci in each population. SK: Silkies, TC: Taiwanese Cross, LB: Lohmann Brown, LW: Lohmann White, AC: Agassiz Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi.  64  4.1.2 Genetic variation in the MHC region In the eight chicken populations (N = 210) under study, 22 different sized alleles (182552 bp) were identified for the LEI0258 marker. The Chinese chicken populations (TC, YW, SQ, YSQ) and the AC harboured more alleles for this locus than the two industrial populations (LW, LB) (Table 4.2). The SK populations harboured the same number of alleles as the two industrial populations. There were four alleles that were unique to each one of the Chinese chicken populations (TC: 239; YW: 437; SQ: 456; YSQ: 324). Five alleles were found only in the Chinese chickens (182, 216, 321, 487 and 513). Of these, 3 (182, 487, and 513) were unique to the Shiqi (SQ and YSQ) chickens. The LW, being the only Leghorn type chickens studied, harboured 2 unique alleles (420 and 474). None of the alleles were common to all eight populations. Alleles 249 and 357 were shared by 7 populations whereas allele 193 was shared by 6 populations. The different combinations of 22 alleles created 66 genotypes (eight homozygous and 58 heterozygous) in the eight chicken populations. The TC chickens had the highest number of observed genotypes (20). The observed and expected heterozygosity in the populations are summarized in Table 4.3. The range of observed heterozygosity was from 0.73 to 1 and expected heterozygosity was lower than observed heterozygosity in all eight populations. Using Fisher’s exact test, deviation from HWE in this locus was observed in LW, AC, YW and YSQ populations and there was a significant difference (P<0.05) between observed and expected heterozygosity within these populations.  65  Table 4.2 Allele frequencies of LEI0258 in chicken populations. pop Allele SK TC LB LW AC YW SQ YSQ 2 182 0.000 0.000 0.000 0.000 0.000 0.000 0.075 0.025 6 193 0.483 0.083 0.033 0.000 0.017 0.200 0.000 0.025 4 205 0.000 0.017 0.000 0.017 0.000 0.000 0.075 0.025 3 216 0.000 0.000 0.000 0.000 0.000 0.150 0.075 0.200 1 239 0.000 0.183 0.000 0.000 0.000 0.000 0.000 0.000 2 247 0.033 0.000 0.017 0.000 0.000 0.000 0.000 0.000 7 249 0.033 0.050 0.317 0.000 0.117 0.150 0.050 0.050 4 261 0.000 0.000 0.017 0.383 0.000 0.000 0.050 0.125 4 295 0.017 0.000 0.000 0.000 0.017 0.000 0.150 0.200 5 307 0.283 0.100 0.467 0.000 0.350 0.200 0.000 0.000 3 321 0.000 0.250 0.000 0.000 0.000 0.000 0.375 0.175 1 324 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.050 7 357 0.150 0.000 0.150 0.283 0.100 0.050 0.050 0.025 2 381 0.000 0.133 0.000 0.000 0.017 0.000 0.000 0.000 1 420 0.000 0.000 0.000 0.083 0.000 0.000 0.000 0.000 1 437 0.000 0.000 0.000 0.000 0.000 0.200 0.000 0.000 3 443 0.000 0.017 0.000 0.000 0.383 0.050 0.000 0.000 1 456 0.000 0.000 0.000 0.000 0.000 0.000 0.025 0.000 1 474 0.000 0.000 0.000 0.017 0.000 0.000 0.000 0.000 2 487 0.000 0.000 0.000 0.000 0.000 0.000 0.050 0.075 2 513 0.000 0.000 0.000 0.000 0.000 0.000 0.025 0.025 2 552 0.000 0.167 0.000 0.217 0.000 0.000 0.000 0.000 Na 6 9 6 6 7 7 11 12 Na = number of alleles. Pop = number of populations sharing the allele. SK: Silkies, TC: Taiwanese Cross, LB: Lohmann Brown, LW: Lohmann White, AC: Agassiz Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi  Table 4.3 Observed and expected heterozygosity using LEI0258. Populations Silkies (SK) Taiwanese Cross (TC) Lohmann Brown (LB) Lohmann White (LW) Agassiz Cross (AC) Yellow Wai-Chau (YW) Shiqi (SQ) Yellow Shiqi (YSQ)  N 30 30 30 30 30 20 20 20  Ne 2.951 6.186 2.922 3.550 3.403 5.882 5.229 7.143  Npa 0 1 0 2 0 1 1 1  Ho 0.733 0.867 0.800 1.000 1.000 1.000 0.850 0.950  He 0.672 0.852 0.669 0.730 0.718 0.851 0.829 0.882  P-Value 0.07317 0.90707 0.21432 0.00004 0.00000 0.00001 0.74797 0.00516  N = Sample size; Ne = Number of effective alleles; Npa= Number of private alleles; Ho = Observed heterozygosity; He = Expected heterozygosity. LW: Lohmann White, LB: Lohmann Brown, AC: Agassiz Cross, SK: Silkies, TC: Taiwanese Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi  66  4.1.3 Genetic variations in genes associated with the innate immune system Pooled sequence results were aligned and analysis confirmed the selected SNPs based on previously reported sites. Thirteen SNPs were genotyped by PCR-RFLP in the five chicken populations (not tested in YSQ, YW and SQ Chinese populations). The allelic frequencies are shown in Fig. 4.1. The observed genotypic frequencies are shown in Fig. 4.2. The average heterozygosity of these SNPs in the five populations is presented in Table 4.5. Table 4.4 Deviation from Hardy-Weinberg equilibrium (Fisher’s exact test). SNP/Population ChB6 iNOS TLR4 TGFβ3 INFγ MD-2 CASP1 IAP IL-2 BMP7 TVB303 TVB202 Mx1  SK Ns Ns *** Ns Ns Ns M *** *** Ns Ns Ns Ns  TC ns * ns ns ns ns ns * *** ns *** **** ***  LB * ns ns ns ns ns ns *** m ns ns m ns  LW ns ns ns ns ns ns ns *** *** * m m ns  AC ns ** ns ns ** ns ns *** ** ** ns ns ns  m = monomorphic; ns = non-significant, * P<0.05, ** P<0.01, *** P<0.001 SK: Silkies, TC: Taiwanese Cross, LB: Lohmann Brown, LW: Lohmann White, AC: Agassiz Cross,  Using GENEPOP, the allelic differentiation (except MD2, IAP and TVB303 loci) was shown to be significant in other loci across all populations. Genotypic differentiation was significant for each locus across all populations. Allelic differentiation for all pairs of populations across all loci was significant. Genotypic differentiation was significant for each population pair in all loci except TLR4, MD2 and TVB303 (p<0.05). The deviation from HWE of each individual SNP across the five chicken populations is shown in Table 4.4. The LB had the least deviation from HWE. As to individual SNPs, both IAP and IL2 exhibited significant deviation from HWE in all chicken populations.  67  SNP Variations in Silkies SNP Variations in Taiwanese Cross  1  0.8  0.6  Allelic Frequency  Allelic Frequency  1  0.8  0.4 0.2  0.6  0.4  0.2  0 Ch  B6  iN -C  OS  TL -C  R4  M -C  D-  2-  CA G  SP  IA  P-  C  -2  -C  BM  P7  B3  03  -T  B2  M 02  x1  -C  0  -A  13 SNPs  SNP variation in Lohmann Brown  SNP Variations in Lohmann White  1  1  0.8  Allelic Frequency  Allelic Frequency  -A x1 C M 220 B T TV 330 B TV -G P7 M B -C -2 IL C PIA C 1SP A C -G -2 D  -G  TV  M  C  TV  C 4R TL C SO iN C 6hB C  1-  IL  0.6 0.4 0.2  0.8  0.6  0.4  0.2  0  0  -A x1 C M 220 B TV -T 03 B3 TV -G P7 M B -C -2 IL C PIA C 1SP A C -G -2 D M  -G  C 4R TL C SO iN C 6hB  C  B  -A x1 C M 220 B T TV 330  TV  C 1-  C 2-  P7 M  C  -G  SP  P-  A  IL  B  IA  C  C  C 4-  -2 D M  R  C 6-  SO  hB  TL  C  iN  13 SNPs  13 SNPs  SNP Variations in Agassiz Cross 1  Allelic Frequency  0.8  0.6  0.4  0.2  0  -A x1 C M 220 B T TV 330 B TV G P7 M B -C -2 IL C PIA C 1SP A C -G -2 D M  C 4R TL C SO iN C 6hB C  Fig. 4.1 Allelic frequencies of the 13 SNPs in five chicken populations.  68  Observed Genotypic Frequencies in Silkies  Observed Genotypic Frequencies in Taiwanese Cross  100%  100%  80%  80%  60%  Homo2 Het Homo1  40%  60%  Homo2 Het Homo1  40%  20%  20%  0% 0%  -2 IL  x1 M 02 B2 TV 3 0 B3 TV P7 BM  -2  P IA P1 AS  D M  C  4  6  S  R TL  O  hB  iN  C  P7  03 B3  P1  x1 M 02 B2  TV  TV  -2  P  BM  IL  IA  2 D-  4  S  AS  M  C  O  6 hB  R TL  C  iN  SNPs  SNPs  Observed Genotypic Frequencies in Lohmann White  Observed Genotypic Frequencies in Lohmann Brown 100% 100%  80% 80%  60%  60%  Homo2 Het Homo1  40%  Homo2 Het Homo1  40%  20%  20%  0%  0%  M  B2  02  03  P7  B3  x1  TV  TV  -2  P1  -2  P  BM  IL  IA  D  4  AS  M  C  R  6  S O  hB  TL  C  iN  -2 IL  x1 M 02 B2 TV 3 0 B3 TV P7 BM  P IA 1 P AS C -2 D M  4  S  R TL  O  6 hB  iN  C  SNPs  SNPs  Observed Genotypic Frequencies in Agassiz Cross 100%  80%  60%  Homo2 Het Homo1  40%  20%  0% -2 IL  -2 D  x1 M 02 B2 TV 3 0 B3 TV P7 BM  P IA P1 AS  C  M  4 R TL S  6 hB  O iN  C  SNPs  Fig. 4.2 Observed genotypic frequencies of the 13 SNPs in five chicken populations. Among the five chicken populations examined, SK had the lowest heterozygosity. The SK was monomorphic for CHB6, CASP1 and TVB202 SNPs. As well, MD2 SNP-G was also almost fixed in this population and the MD2-A allele frequency was very low in all 69  populations and absent in the SK (Fig 4.2). No heterozygote was observed for IL2. On the other hand, all 30 SK birds sampled were heterozygous for IAP. The LW population showed the highest frequency of the TLR4-C allele and SK had the lowest frequency. The LW had the highest frequency of the TLR4-C allele and SK had the lowest frequency (Fig. 4.1). At the other end of the scale, all five populations showed a moderate observed heterozygosity and the AC showed the highest observed heterozygosity (Table 4.5). The A allele is positive for anti-viral activities (resistant to viral infection) and the G allele is negative. Ranges of frequencies of the favorable allele A were 0.28 to 0.9 in AC and LW populations, respectively (Fig 4.1). Except for TC, the chicken populations maintained considerable heterozygosity for this SNP. Table 4.5 The observed and expected heterozygosity in the five chicken populations using 13 SNPs. Population Silkies (SK) Taiwanese Cross (TC) Lohmann Brown (LB) Lohmann White (LW) Agassiz Cross (AC) Average  Obs. Heterozygosity 0.28 0.28 0.35 0.37 0.41 0.33  Exp. Heterozygosity 0.27 0.31 0.31 0.34 0.32 0.31  Two SNPs (TVB202 and TVB303) were examined in TVB. The two SNPs combined to form the TVB genotype (Table 4.6). In all five chicken populations, there was low genetic variation in the two SNPs. Except for the LW, the C allele in TVB202 and the T allele in TVB303 were either fixed or close to being fixed in LB, TC, AC, and SK. As a result, except for the LW, there was a high frequency of the S1S1 genotype, which is the susceptible genotype (Table 4.6). The S1S1 was the only genotype observed in TC. The LW had a high frequency of the RR genotype which is the resistant genotype. The RR genotype was observed only in the LW population.  70  Table 4.6 Frequency of haplotype-defined TVB genotypes in chicken populations. Population Silkies (SK) Taiwanese Cross (TC) Lohmann Brown (LB) Lohmann White (LW) Agassiz Cross (AC)  S1S1 24 30 28 14 27  S1S3 5 0 1 5 2  TVB genotypes S1R S3S3 S3R 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0  RR 0 0 0 10 0  S1S1, S1S3, S1R, S3S3, S3R, RR are haplotype-defined TVB genotypes based on electrophoresis patterns of both NlaIII digested TVB303 and XbaI digested TVB202.  The observed and expected heterozygosity of SNPs in the five chicken populations are shown in Table 4.7. The TVB SNPs and IL2 had a very low level of observed heterozygosity. Table 4.7 The observed and estimated heterozygosity in each SNP in five chicken populations. SNPs INOS TLR4 TGFB3 IFNG MD2 Casp1 IAP IL2 BMP7 CHB6 TVB303 TVB202 MX1 Mean  4.2  Genetic  Obs. Heterozygosity 0.31 0.37 0.4 0.47 0.27 0.15 0.89 0 0.48 0.41 0.087 0.014 0.51 0.327  differentiation,  Exp. Heterozygosity 0.29 0.47 0.37 0.42 0.25 0.14 0.49 0.17 0.42 0.35 0.1 0.1 0.4 0.294  population  structure  and  genetic  relationship among populations Genetic differentiation was examined by fixation indices FIT, FST, and FIS for each locus. The results of F-statistic analysis for 18 microsatellite markers in the eight chicken populations are presented in Table 4.8. The fixation coefficients of subpopulations (FIS) 71  within the total population (FIT), measured as the FST value, for the 18 loci varied from 0.044 (ADL210) to 0.510 (ADL0298). All loci contributed significantly to this differentiation. The average FIT, FST, and FIS in all loci were 0.11, 0.27 and 0.35 respectively. The global deficit of heterozygotes across populations (FIT) amounted to 0.349 and was partly due to the genetic differentiation among breeds (FST = 0.269) and, to a larger extent, to a significant homozygote excess or inbreeding within breeds (FIS = 0.110). All populations showed a positive FIS. According to pair-wise FST genetic distance estimates the closest populations were YSQ and SQ (0.033) while the most differentiated were LW and YW (0.45) (Table 4.9). There was a significant (P < 0.001) differentiation between all pairwise FST values of populations. Table 4.8 Number of alleles per locus (A), polymorphic information content (PIC), number of private alleles per locus (Npa), expected (He) and observed (Ho) heterozygosities and Wright’s fixation indices of the 18 microsatellite markers. Locus  A  MCW0330 MCW0029 MCW0183 MCW0123 MCW0014 MCW0185 ADL0278 MCW0004 MCW0037 MCW0067 MCW0069 LEI0094 MCW0111 ADL0298 ADL0268 ADL0146 ADL0210 MCW0034 All loci  8 14 5 8 6 9 4 5 2 2 6 5 4 7 4 5 3 5 102  Private alleles (Npa) 3 4 0 1 2 3 0 1 0 0 1 1 0 3 0 0 2 1 22  Ho 0.463 0.53 0.575 0.439 0.192 0.355 0.431 0.541 0.134 0.386 0.626 0.47 0.14 0.285 0.381 0.556 0.034 0.561 0.394  He 0.636 0.802 0.61 0.657 0.531 0.789 0.596 0.709 0.44 0.5 0.709 0.586 0.247 0.659 0.594 0.719 0.067 0.632 0.583  PIC 0.576 0.773 0.573 0.606 0.418 0.763 0.522 0.656 0.343 0.374 0.658 0.548 0.235 0.618 0.542 0.663 0.065 0.561 0.527  HWE* Deficiency 2 0 1 5 1 (2) 5 3 0 6 0 0 1(1) 2(3) 2(3) 3(2) 2(1) (6) 2(3)  FIS 0.069 0.113 - 0.051 0.178 0.351 0.229 0.155 - 0.026 0.596 0.029 - 0.111 0.044 0.279 0.194 0.256 0.127 0.471 - 0.169 0.110  F Statistics FST FIT 0.242 0.282 0.116 0.210 0.479 0.454 0.163 0.283 0.272 0.228 0.231 0.185 0.238 0.510 0.162 0.133 0.044 0.282 0.269  FIS and FIT = measures of the deviation from Hardy-Weinberg proportions within subpopulations and in the total population, respectively; FST = a measure of genetic differentiation over subpopulations. * HWE deficiency = Number of population deviating form Hardy-Weinberg equilibrium (p<0.05) number in parentheses shows the monomorphic loci.  72  0.295 0.363 0.072 0.351 0.662 0.579 0.293 0.264 0.706 0.251 0.146 0.221 0.457 0.505 0.377 0.244 0.494 0.161 0.349  Table 4.9 Estimates of FST (above diagonal) and Chord genetic distances (Dc) (below diagonal) between pairs of eight chicken populations. Population SK TC LB LW AC YW SQ YSQ  SK 0.1939 0.2508 0.3673 0.2296 0.2696 0.3121 0.2612  TC LB 0.2610 0.354 0.2412 0.1708 0.1862 0.2305 0.1290 0.1021 0.2155 0.2159 0.1998 0.1931 0.1523 0.1975  LW 0.4201 0.2472 0.389  AC 0.2441 0.1373 0.2767 0.1788  YW SQ 0.3084 0.3205 0.2416 0.1833 0.3277 0.2086 0.4494 0.3183 0.1654 0.1702 0.0837 0.3250 0.1811 0.2384 0.2539 0.1531 0.1937 0.2334 0.1442 0.2161 0.0534  YSQ 0.2846 0.1264 0.2357 0.2804 0.0971 0.2868 0.0327  SK: Silkies, TC: Taiwanese Cross, LB: Lohmann Brown, LW: Lohmann White, AC: Agassiz Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi.  The Chord genetic distance (Dc) ranged from 0.0534 (between YSQ and YSQ) to 0.3673 (between SK and LW) and Dc between the LW and YW populations was high (0.3250) (Table 4.10). Table 4.10 shows the estimates of molecular coancestry between and within the analyzed populations. The within breed molecular coancestry varied from 0.4411 in YSQ to 0.6326 in LW and the highest estimates observed were between population pairs LB-AC, SQ-YSQ and LW-SQ. The lowest value of molecular coancestry was between the LW and YW populations (0.25). Table 4.10 Molecular coancestry estimates between and within (in diagonal) eight chicken populations. Population SK TC LB LW AC YW SQ YSQ  SK 0.5983 0.3642 0.3817 0.3165 0.3983 0.3625 0.3072 0.3520  TC  LB  LW  AC  YW  SQ  YSQ  0.4784 0.3903 0.3857 0.3996 0.3462 0.3175 0.3623  0.6209 0.3759 0.4455 0.3858 0.4072 0.3956  0.6326 0.3928 0.2448 0.2967 0.3128  0.5080 0.4067 0.5686 0.3677 0.3175 0.4616 0.3545 0.2849 0.4270 0.4411  SK: Silkies, TC: Taiwanese Cross, LB: Lohmann Brown, LW: Lohmann White, AC: Agassiz Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi.  73  4.2.1 Population structure The clustering algorithm implemented in the Structure package (Pritchard et al., 2000) was used to analyze population structure of chicken populations. The results of STRUCTURE analysis are given in Figure 4.3. According to both posterior probability P (X | K) values resulting from STRUCTURE and ∆K using the Structure Harvester program, seven was the most appropriate number of clusters to model the data. Structure Harvester uses a membership coefficients matrix (Q-matrix) to align multiple replicate analyses of several independent runs for each K value in the estimation of the value of K or clustering reliability. Based on Evanno et al. (2005), the largest value of ∆K and deep subdivision was at K = 7 (Fig. 4.4).  K=2  K=3  K=4  K=5  K=6  K=7  SK  TC  LB  LW  AC  YW  SQ  YSQ  Fig. 4.3 STRUCTURE clustering of eight chicken populations. LW: Lohmann White, LB: Lohmann Brown, AC: Agassiz Cross, SK: Silkies, TC: Taiwanese Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi 74  The results of the STRUCTURE clustering are displayed in Fig. 4.3. The SK, TC, and LW populations clustered together and AC, LB, YW, SQ and YSQ clustered each as a separate group at the lowest K-value (K = 2). At K = 3, the Silkies and Lohmann White (SK and LW) formed separate clusters and the rest of populations clustered all together separately. At K = 4 LB split to form another cluster in comparison with the K= 3 case. At K = 5, TC and YW populations clustered together as did AC, SQ and YSQ; SK, LB and LW populations clustered each separately. At K = 6 the two Chinese populations (SQ and YSQ) and Agassiz Cross split off as individual groups. At K = 7, all populations appeared as discrete populations, except that the two Chinese populations (SQ and YSQ) formed a separate cluster and showed they are indistinguishable. The K = 7 and K = 8 showed identical solutions, indicating that a maximum of seven clusters describe the data. When increasing the number of K, populations tended to form their own distinct cluster until reaching the most appropriate number (K = 7). Above K = 7, the similarity coefficients dropped dramatically.  Fig. 4.4 ∆K (a measure of the rate of change in the STRUCTURE likelihood function) values as a function of K, the number of putative populations. 75  4.2.2 Genetic relationships among populations Neighbor-joining tree of Chord genetic distance (Dc) detected five clusters as shown in Figure 4.5. The Chinese populations YSQ, and SQ together, and YW were grouped closely in two clusters. The LB, AC and TC and the SK grouped as two separate clusters. The LW showed that it is genetically distant from other populations and grouped as a separate cluster. Bootstrapping of 1,000 replicates over loci resulted in a range from the lowest value of 281 in grouping AC and LB populations to the highest value of 967 in grouping SQ and YSQ populations which shows the highest reliability of identifying the branching point from the data.  Fig. 4.5 Unrooted neighbor-joining cladograms obtained form Chord genetic distance among eight chicken populations. LW: Lohmann White, LB: Lohmann Brown, AC: Agassiz Cross, SK: Silkies, TC: Taiwanese Cross, YW: Yellow Wai-Chau, SQ: Shiqi, YSQ: Yellow Shiqi. The number at each node represents the percentage of the bootstrap value from 1000 replications.  76  Chapter 5  Discussion  77  In this chapter, I will discuss the significant new findings of my thesis research and their implications to conservation of chicken populations and development of breeding stocks. I will first discuss the pros and cons of the tools I have selected to collect and process the data and justify their use. I will then discuss these results: 1. Genetic relationship and population structure of the chicken populations in this study 2. Genetic variation in adaptive immune system of the study populations 3. Genetic variation in innate immune system of the study populations. Finally, I will discuss how information obtained from these studies combined to enable me to achieve the objectives of this research. In the conclusion part of this chapter, I will discuss new questions generated by this thesis research, why these are important questions, and what kind of future research approach one can take to deal with these questions.  5.1 Genetic diversity 5.1.1 The study of genetic variability using microsatellite loci When one uses microsatellites for population studies, one has to maximize their efficiency to obtain unbiased information. The assumption that the microsatellite loci used in population analyses are selectively neutral is essential (Murray, 1996). I conducted the Ewens-Watterson test (Manly, 1985) to find no evidence for the presence of diversifying or balancing selection in all the 18 microsatellite loci that I was using. This has provided some assurance that the neutrality assumption has been met. In order to obtain a wide coverage of the genome and the independent assortment of the alleles at different loci, I have selected microsatellites that were distributed over the different chromosomes. I have also examined the polymorphism information content (PIC, Botstein et al., 1980) and determined that, with the exception of  MCW0111 and  ADL0210, the microsatellites that I was using can be classified as “reasonably informative”. Null allele are most often present in microsatellite loci when mutations occurred in one or both primer binding sites, preventing effective amplification of the microsatellite allele. Without any pedigree information, the evidence for the existence or 78  non-existence of null alleles is difficult to obtain. Null alleles are a common cause of apparent deviations from Hardy-Weinberg equilibrium at microsatellite loci (Pemberton et al., 1995). Null alleles can also cause higher than expected FIS values for that microsatellite (Murray, 1996). However, neither of these parameters is a good indicator of the presence of null alleles. Barker (1994) suggested that in order to reduce the standard error in the estimation of genetic distances, the number of alleles per locus over all the populations under study must be greater than 4. In my study, most of the microsatellite markers showed a high degree of polymorphism and, with the exception of  MCW0037, MCW0067 and  ADL0210, the number of alleles per locus was more than 4 (Table 4.8.). These results indicated that  the  micorsatellite markers I have selected may be appropriate for  estimating genetic distances and relationships in the chicken populations.  5.1.2 The study of genetic relationship and population structure using clustering analysis Two different cluster genetic analyses were used to provide tools for identification of history and population relationships. First I used Chord genetic distance to develop an unrooted neighbor-joining cladogram. Chord genetic distance was selected because the objective of my study is more focused on the correct distribution of the topology, rather than revealing evolutionary times (Takezaki and Nei, 1996). The evolution of microsatellites is a complex mutational process involving different mechanisms (e.g. SMM, IAM, or TPM). Most methods to analyse genetic distance assumed one or another evolutionary model of microsatellites. Chord genetic distance is not based on the SMM or any other microsatellite evolutionary model. Thus, Chord genetic distance values may be more appropriate for identifying populations that are more valuable for the preservation of genetic variation. Bootstrapping over loci is needed to assess reliability even if the between locus variance of these measures is large.  79  Secondly, I used the STRUCTURE program for clustering analysis. The STRUCTURE program uses individual genotypic data and it provides a more appropriate characterization of population structure than does a cladogram based on genetic distance matrices which compress all information about two populations into a single number (Pritchard et al., 2000; Rosenberg et al., 2002). Based on Evanno et al., (2005), the ∆K value, a quantity based on the second order rate of change with respect to K (number of clusters) of the likelihood function, was used to detect the real number of groups. The STRUCTURE algorithm clusters individuals in the sampled population into a number of clusters (K) based only on multilocus genotypic data and independent of locality information. It therefore minimizes deviations from Hardy–Weinberg and linkage equilibrium. When genetic differentiation between populations is well defined, the Bayesian clustering method implemented in STRUCTURE is considered to correctly infer assignment of individuals (Pritchard et al., 2000). STRUCTURE has been commonly used to define chicken breeds by their locations, phenotypes or culture of origins (Li et al., 2009; Bodzsar et al., 2009; Muchadeyi et al., 2007).  5.1.3 Within-population genetic variability in the eight chicken populations The mean observed (Ho) and expected heterozygosity (He) over all eight populations was determined to be 0.418 and 0.569, respectively, which shows a moderate level of genetic variation in these populations. The lower Ho than He in each population may indicate the presence of overdominant selection or the occurrence of outbreeding (Murray, 1996). However, it may also be affected by the combined effects of modest sample sizes, populations which are crosses, and a large number of alleles of the microsatellites (Murray, 1996). In addition, a lower Ho than He may indicate the presence of null alleles. Because deviations from HWE were found in population/locus combinations in my study, I can rely more on Ho instead of He to indicate genetic variability within each population. Among the eight chicken populations I studied, Ho ranged from 0.325 to 0.526. This range was similar to that reported in studies on chicken populations reared in other areas  80  of the world. Chen et al. (2004) reported Ho from 0.35 to 0.59 with some Chinese chicken populations. The range of Ho reported by Vanhala et al. (1998) varied from 0.29 to 0.67 in eight chicken populations and the range was from 0.31 to 0.61 in seven indigenous chicken populations reported by Marle-Köster and Nel (2000). The Ho estimated by Osman et al. (2006) on local Japanese chickens and two industrial breeds ranged from 0.21 to 0.67. The kind of marker used, population dynamics and sampling error associated with the breeding program can affect heterozygosity levels. Genetic diversity in poultry has been evolved during domestication, development of breeds and selection for production traits. Among the eight chicken populations that I studied, the highest Ho was observed in the SQ (0.526) and the lowest Ho was observed in the LW and SK at 0.333 and 0.325, respectively. Theoretically, industrial chickens such as the LW and LB, which are F1 of three-way or four-way crosses, should show higher heterozygosity than the non-industrial populations, which are either two-way crosses or stable breeds. However, LW was one of two populations with the lowest Ho, and LB has also low level of heterozygosity. This low genetic variation may be reflected in the LW’s breeding history. The parent and grand-parent lines that were crossed to form the LW are all White Leghorn lines, and all industrial White Leghorn lines can be traced back to a single origin, the Mount Hope strain (Delany 2003). Similarly, although the SK is a F1 cross of two inbred lines, both inbred lines can be traced back to the few Silkies that were imported to North America. The low Ho in both LW and SK can be attributed to the narrow genetic base of the founder population (Crawford, 1990). The low Ho in SK may also be attributed to small population size (Alizadeh, V., pers. Comm.). The LB is derived from several dual-purpose breeds in addition to the White Leghorn and may harbour more genetic variation (Flock, 2009). Never-the-less, Ho of LB was just a little higher than LW and SK. Within populations, the number of alleles at each locus can also be considered to be a good indicator of genetic variability (Nevo, 1978). In this study, the MNA in each population varied from 2.6 to 3.64. This result was comparable to many reported studies with different chicken populations and different sets of microsatellite markers (Marle-  81  Köster and Nel, 2000; Osman et al., 2006; Tadano et al. 2007; Bodzsar et al., 2009). The LW, SK and LB also had very low MNA values compared with the other five populations. The hypothesis was that industrial chicken populations have less genetic variation than non-industrial chicken populations. The results indicated that except for the SK, the two industrial populations had the lowest genetic variability as indicated by Ho and MNA. Since I can explain the reason for the low genetic variation in SK (which seems to be an exception to other free range populations), the hypothesis can be supported by the results of the present study. Hillel et al. (2003) also found less diversity in the selected layer breeds that they have studied, and more polymorphism in their unselected populations. Moreover, they found lower genetic diversity among chicken breeds than in other domesticated species. This loss of genetic diversity in the industrial chicken gene pool may be the result of intensive selection and development of high-performing specialized chickens. Decreasing the number of breeding companies along with reducing the number of active breeding stocks within the industrial gene pool of chickens has also reduced genetic variation (Pisenti et al., 2001). These practices have led to the replacement of local chicken populations by industrial lines in developing countries. On the other hand, loss of genetic diversity will unavoidably lead to inbreeding especially in small populations. Frankham (1994) showed empirically using many species and model systems that small population size leads to inbreeding, loss of genetic diversity and increased risk of extinction. It is important to reconsider management strategies in these populations in terms of maintaining genetic variation within these populations toward ensuring that variation exists for future breeding programs.  5.1.4 Genetic relationship and population structure of the eight chicken populations Collected information from microsatellite markers on different genomic regions can be used to model the divergence of populations. The FIS values of populations indicated a deficiency of heterozygotes and represent a degree of non-random mating (deviation  82  from Hardy-Weinberg equilibrium) and the presence of inbreeding. The average inbreeding coefficient (FIS) of these eight populations across loci was 0.118 and the highest was observed in the SK population (0.189), possibly due to the small effective population size of the founder population. The global deficit of heterozygotes across populations was 0.11 and the mean fixation index (FST) was 0.269, suggesting a high degree of population differentiation. However, Vanhala et al. (1998) reported a mean FST value of 0.303 from eight Finnish chicken lines using nine microsatellite markers. Tadano et al. (2007) reported that the mean FST value in native Japanese long-tailed breeds was 0.383. Chicken populations in my study were genetically subdivided to a lower extent than chicken breeds in these other studies mentioned. Never-the-less, pairwise FST values between the populations were in agreement with the history of the populations and how they are structured. Furthermore, a FST of 0.15 or above for each microsatellite can be considered to be significant differentiation among populations (Frankham et al., 2002). The FST values for all the microsatellite loci except for the MCW0185 locus (0.116) were higher than 0.15. Based on this result, 26.9% of the total genetic variation is caused by breed differences (among populations), presumably reflecting their divergent origins, whereas the remaining 74.1% is due to differences among individuals within breeds (within populations). On the other hand, Wright's F-statistics assume an IAM evolutionary model for microsatellite markers, and tend to underestimate population variation. The higher level of genetic divergence can be attributed to the long term selective breeding of some of the populations resulting in inbreeding, with little or no gene flow between these populations as evidenced by the results of STRUCTURE analysis. This notion is also supported by the high molecular coancestry estimates within populations (diagonal values in Table 4.10). Wright's F-statistic analysis showed that chicken populations included in this study clearly diverged from each other. The STRUCTURE analysis (Fig. 4.3 and 4.4) showed a deep clustering of populations at K = 7. The Taiwanese cross, Agassiz Cross and all three Chinese populations stay together up to a less pronounced differentiation in the STRUCTURE analysis, but they appear as clearly distinguishable subpopulations at higher levels except for two YSQ and SQ populations. The clustering obtained by STRUCTURE reflects the situation that the  83  YSQ breed was established by crossing the SQ with another local breed, as they grouped together in one cluster. This finding agrees with the low FST value between these two populations (SQ and YSQ) and confirmed that they are closest populations of the ones studied. The very low differentiation among the populations shows the high variability within populations (Hedrick, 1999). In this study, STRUCTURE clustered these chicken populations to the most genetically distinct groups with low admixture between groups. Chord genetic distance (Dc) shows the relationships between these populations and how these populations have developed from several different ancestral populations. Three Chinese breeds (YW, SQ and YSQ), having the same origin, made up a major branch. The LW, being a White Leghorn, formed a separate group as was expected as it is the only White Leghorn type layer. The TC and SK grouped together and as a group close to the three Chinese branch and this grouping was expected as both originated from China but were established in BC. The AC and LB populations were grouped together as was expected because these populations have similar genetic background (same common ancestors) (Hunton, 1990). The bootstrap neighbor-joining cladogram was less capable of grouping the populations and may correspond to genetic exchange among chicken populations. In this study, bootstrapping measured the overall dendogram stability, with the most reliable relationship demonstrated between the grouping of SQ and YSQ populations. My low bootstrapping values showed a phylogenetic pattern (branching points) rather than an accurate and reliable measurement of relationships of populations. The results from assignment of these populations based on the distance model were in agreement with the observed FST values. Molecular coancestry between LW and YW populations was low as they also had the highest observed FST value, suggesting that these two populations had originated from different ancestral populations. High observed molecular coancestry could be due to either a very large effective population size or relatively strong and continuous gene flow between populations. High molecular coancestry between SQ and YSQ shows their very low genetic distance and the presence of admixture between them, and these were confirmed with the STRUCTURE analysis and history of the populations.  84  5.2 Genetic variation for adaptive immune system 5.2.1 LEI0258 alleles as molecular markers The associations of different MHC haplotypes or alleles with the chicken’s response to vaccination and disease challenge has been known for decades (Briles et al., 1983; Bacon and Witter, 1995). Several studies have shown a very strong association between the MHC genes and disease resistance and susceptibility to numerous pathogens including Marek’s disease (Hansen et al., 1967; Briles et al., 1977; Wakenell et al., 1996; Bacon et al., 2001), Rous sarcoma tumor virus (Bacon et al., 1981; Taylor, 2004), avian leukosis virus (Yoo and Sheldon, 1992), fowl cholera (Lamont et al., 1987a), coccidiosis (Lilleho et al., 1989; Caron et al., 1997), salmonella (Cotter et al., 1998; Liu et al., 2002), avian influenza virus (Boonyanuwat et al., 2006), northern fowl mites (Owen et al., 2008) and bacteria such as Staphylococcus aureus (Joiner et al., 2005). The best known association of the MHC with resistance to disease is that of Marek’s disease (MD), a lymphoproliferative disease (T cell cancer) caused by a member of the herpes virus family. The haplotypes B15, B13, and B19 are associated with susceptibility to MD, whereas B21 haplotype has shown strong associations with Marek's resistance (MD) (Hansen et al., 1967, Briles et al., 1983; Bacon et al., 2001) and also resistance in many different genetic backgrounds. In some instances, B haplotypes are selectively combined in industrial layer stocks to provide disease resistance. Development of serological reagents to determine MHC haplotype is a time-consuming technique and, in outbred populations that may have more novel combinations of alleles, the high level of cross reactivity can yield inaccurate haplotype identification (Fulton et al., 1995; Kroemer et al., 1990). Other molecular methods used to identify MHC haplotypes in chicken populations such as two-dimensional gel electrophoresis (2-D gels) (Miller et al., 1994a), RFLP (Li et al., 1997; Nishibor et al., 2000; Emara et al., 2002; Iglesias et al., 2003), sequencing (Miller et al., 2004) single-strand conformation polymorphism (Goto et al., 2002), and sequence-specific polymerase chain reaction (SS-  85  PCR) (Livant et al., 2001; Livant and Ewald, 2005) are not always practical for large numbers of samples. However, LEI0258 variants can be used as markers to detect most of MHC haplotypes as an easier (and cheaper) laboratory method than other methods. The LEI0258 locus can also be useful for the initial development of serological reagents and also to identify MHC haplotypes in outbred populations of chickens. The association of some but not all LEI0258 alleles with serologically defined haplotypes was consistent in different chicken populations (Fulton et al., 2006). Therefore, additional information from another nearby marker (MCW0371) or from small indels or SNP differences between the alleles can be used to define their association with the different MHC haplotypes (Fulton et al., 2006). Furthermore, the same allele size may not always be associated with the same serologically defined haplotypes in different populations (LimaRosa et al., 2005; Fulton et al., 2006). Lima-Rosa et al. (2005) evaluated the B-F haplotype variability in the blue-egg Caipira breed through sequencing and cloning techniques and there was a direct correspondence between a given LEI0258 allele and a specific B-F haplotype in 67% of the cases. Consistent LEI0258 allele sizes from serologically well-defined haplotype and from samples from diverse populations can be used as an alternative method to genotype B haplotypes in chicken populations. Allele specific (PCR-SSP; Polymerase Chain Reaction with Sequence Specific Primers) selective typing can be performed when more than one B haplotype is associated with a LEI0258 allele and this method can be cheaper than sequencing. The development of rapid tests for each kind of DNA polymorphisms such as microsatellites, SNP, or indels in other regions or in the actual genes of interest could help in identifying all kinds of haplotypes. The LEI0258 is a highly polymorphic microsatellite locus physically located within the MHC region, which is an important part of the adaptive immune system of chickens and harbors many immune-related genes. The variations in the MHC region shown by LEI0258 have been studied in both laboratory and industrial populations (Fulton et al., 2006). Little is known about MHC variation of non-industrial populations, particularly those commonly used for free run/free range and organic populations (except for  86  Lwelamira et al., 2008, Lima-Rosa et al., 2005, and Schou et al., 2006). Two related parameters, heterozygosity and the number of alleles harboured by the population, have been used to indicate genetic variability.  5.2.2 Genetic variation in the MHC region Industrial production populations are usually products of three or four way crosses and theoretically speaking, the two industrial populations should have the most heterozygosity. The AC is a cross of two breeds and the SK is a cross of two lines of the same breed and based on how much the two breeds or lines at the parental level are genetically different, the observed diversity in these crosses will differ but should be lower than the two industrial populations. The TC, YW, SQ, and YSQ are maintained as “stable” breeds and theoretically should have the lowest heterozygosity. In this study, such a trend was not observed. The He of the LEI0258 marker indicated that LW, LB, and SK had very low heterozygosity compared with the other populations, while the Ho reflected that the heterozygosity of the MHC region was very high in all the populations except the SK. In terms of number of alleles harboured by a population, the results have indicated that the two industrial populations (LW and LB) and one of the non-industrial populations (SK) harboured the least number of LEI0258 alleles (6) and the two Shiqi populations (SQ and YSQ), which have the reputation of being hardy and resistant to disease vectors, harboured the most (11 and 12 respectively). When judged by this parameter, the two industrial populations showed the least amount of genetic variation in the MHC region among the populations that I have studied. My study has uncovered several alleles from the Chinese chicken populations (216, 239, 324, 437, and 456) that were not found in the industrial layer populations examined by Fulton et al. (2006). Of these, alleles 324 and 456 were also found in Vietnamese chicken populations examined by Schou et al. (2006). It should be noted that one population that harbours both 324 and 456, the Ri, has a Chinese origin (Schou et al., 2006). None of the five alleles from Chinese chicken populations was found in the Tanzanian chicken populations (Lwelamira et al., 2008) or the Brazilian chicken ecotypes (Lima-Rosa et al.,  87  2005). I can therefore conclude that I have identified three new alleles (216, 239, and 437) that have not been reported before. Allele 307 was found to be positively correlated with body weight traits (Lwelamira et al., 2008). In this study, Allele 307 was absent in LW and its frequency was from high to intermediate (0.467 – 0.1) in LB, AC, SK, TC and YW populations. Except for SK, all of these populations have been crossed with industrial meat type chickens in the past. Allele 307 was absent in SQ and YSQ, which had not been crossed with broiler type chickens. The MHC haplotype B21 (identified with LEI0258 allele 357) is strongly associated with resistance to Marek’s disease (Hansen et al., 1967, Briles et al., 1983; Bacon et al., 2001) in white egg layers. The 357 allele has also been found in brown egg layers that are not serologically identified as B21 (Fulton et al., 2006). In this study, LW, LB and SK had the highest allelic frequency for allele 357 (0.28 0.15, and 0.15 respectively) and the Chinese chicken populations (YW, SQ, YSQ, and TC) had the lowest (0.00 – 0.05). This allele was one of the most common, found in seven of the eight populations in this study. The frequency of this allele was very low in the Tanzanian chickens (Lwelamira et al., 2008) and not found in the Vietnamese and Brazilian chicken populations studied (LimaRosa et al., 2005; Schou et al., 2006). Fulton et al. (2006) found an LEI0258 allele of 261 in all B2 and B15 haplotypes obtained from seven different sources. The LW has a frequency of 0.38 for this allele, and it may be due to selection of this B haplotype in industrial layer stocks in the past to provide disease resistance. Additional information is needed to distinguish between these different haplotypes. Lwelamira et al. (2008) reported that allele 205 was positively correlated with high antibody response to Newcastle vaccination while allele 307 was negatively correlated with the same trait. In this study, the allelic frequency of 205 was low in LW, TC, SQ, and YSQ and the allele was absent in LB, AC, SK, and YW. Fulton et al. (2006) found that two serologically distinct B haplotypes, B17 and B13, shared the same LEI0258 allele as 205 but sequence analysis revealed a single SNP difference between these two LEI0258 alleles.  88  Fulton et al. (2006) identified 26 LEI0258 alleles, ranging from 182 to 552 bp in North American and European layer type chickens. In two Tanzanian chicken ecotypes, 22 and 23 alleles were identified (Lwelamira et al., 2008), 19 alleles were identified in local chickens of Vietnam (Schou et al., 2006), and 15 alleles were found in local Caipira chickens of Brazil (Lima-Rosa et al., 2005). Many of these alleles were shared among different chicken populations. This is an indication that the MHC region may have significantly more heterozygosity than the rest of the genome (Takahashi et al., 1998; Zhou and Lamont, 1999; Wimmers et al., 1999,2000; Marle-Köster and Nel, 2000), It will be difficult to compare my results with those previously published on free range and local populations (e.g. Lwelamira et al., 2008, Schou et al., 2006) because the populations others examined were different (originated from widely separated populations), and the questions they were asking were different (they did not have industrial lines as a control). Moreover, their methodology may also be different. The high level of MHC polymorphism has been attributed to balancing selection, which favours high levels of allelic diversity within individuals (Hughes and Yeager, 1998; Penn et al., 2002). A hitchhiking effect due to selection could be another reason for higher number of LEI0258 alleles because of its joint location in the MHC region. This effect means that selection generates diversity not only in the alleles under its influence, but also in adjoining regions. Observed differences in allele distribution and degree of heterozygosity is because of other possible factors influencing this difference in distribution, such as founder or sampling effects, sexual selection (unequal contribution of genetically different males), or even some adaptive advantage of given alleles. Considering the two parameters I have examined, I can generalize that industrial populations in this study seem to have less genetic variability in the MHC region than the non-industrial populations. The LB, LW, and AC were developed under more protected (against diseases) and controlled environments and some had been under intensive selection for many generations, whereas the YSQ, YW, SQ, and TC lines were developed under more open and exposed (to disease vectors) environments (Pinard-van der Laan, 2002). The less intensively selected varieties used for free range meat production may  89  have more variability in the MHC region than the intensively selected industrial egg layer varieties. In a practical sense, the industrial populations, being three or four way crosses, may have maximized their heterozygosity to partially compensate for the lack of genetic variability in the MHC. My study also indicated that different populations, regardless of industrial or non-industrial origin, may carry certain LEI0258 alleles in significantly higher frequencies than in other populations as well as carrying unique alleles.  5.3 Genetic variation in genes associated with the innate immune system Exploiting genetic variation in disease resistance among livestock hosts is important in controlling infectious disease in livestock (Gibson and Bishop, 2005), and evaluating variability using molecular markers is the first step in characterizing the various populations for compiling an inventory list. After examining the genetic variability of the MHC region, which is part of the adaptive immune system, I examined the variability of some candidate genes associated with the innate immune system using SNP markers. The SNPs are a good tool for evaluation studies because of their frequent diversity estimated in poultry species to range from 1:48 to 1:1632 bp (Soller et al., 2006). Muir et al. (2008) assessed genetic biodiversity in industrial broilers and layers using a 3K chicken SNP array. Their results showed a loss of up to 70% in genetic diversity in industrial lines, and confirm the importance of conservation of all potential chicken genetic resources. The inventory of variability of candidate genes that are associated with the innate immune system will provide additional information for choosing the appropriate conservation method. It also allows me to compare the genetic variability of the innate immune system with that of the adaptive immune system in the same chicken populations. Although high-throughput SNP genotyping is common currently, it was not feasible when I started this study. I examined variability of 13 candidate genes that are associated with the innate immune system. The genes selected for this study were based on their function and the availability of SNP markers and were studied previously (Ye et al., 90  2006; Malek et al., 2004; Li et al., 2003; Liu and Lamont, 2003; Malek and Lamont, 2003; Kramer et al., 2003; Zhou and Lamont, 2003 a, b; Lamont et al., 2002; Li et al., 2002; Zhou et al., 2001a). I hypothesized that non-industrial populations used for free range and organic production may be genetically more heterogeneous than industrial populations. In this study, I did not find that industrial populations had less variation than non-industrial populations in the genes associated with the innate immune system. There was little or no difference in heterozygosity in the populations of non-industrial origin (SK, TC and AC) compared with the industrial populations (LB and LW). Observed heterozygosity (Ho) in the SK and TC populations had the lowest value while the AC had the highest. The low Ho value found in SK and the TC may be explained by a small founder population size. The SK is a cross of two lines of the same breed and the observed diversity in these crosses depends on how much the two breeds or lines at the parental level are genetically different. The AC is F1 of a cross of two breeds and the industrial production populations (LW and LB) are usually products of three or four way crosses of distinct parent lines. Being the F1 of crosses may have allowed them to optimize their Ho but low variation in these populations may correspond to the somewhat narrow genetic range of founder breeds. Nie et al. (2004) studied diversity of one SNP located in a polymorphic site of intron 4 of the Ghrelin gene, using a PCR-RFLP procedure in 10 chicken populations. Their results showed that the allelic frequencies differ among these breeds; however, no significant difference was observed between imported breeds and Chinese native ones, or between egg layers and meat type chickens. Malek and Lamont (2003) found a mild association (P<0.21) between iNOS SNP-T and antibody level to Salmonella enteritidis vaccine in broiler breeder and Fayoumi chickens. In these study populations, this SNP was carried in extremely low frequencies (< 5%) in SK and LW, and in moderately low frequencies (30% - 35%) in the other 3 populations. The TGF- β3 SNP-C has been strongly associated (P<0.04) with ceacum bacterial load and mildly associated (P<0.17) with antibody level to Salmonella enteritidis vaccine in the same chicken populations (Malek and Lamont, 2003). The SNP-C occurs in low  91  frequencies in SK and TC (36% and 7%, respectively) and moderately high frequencies (56% - 80%) in LB, LW, and AC chickens. The SNP-C allele of TLR4 has only a mild (P< 0.14) association with spleen bacteria load and no association with antibody level to Salmonella enteritidis vaccine. The SNP-C is carried with an intermediate frequency (70% - 38%) in these populations. The SNP-A allele in MD2 has been strongly associated (P<0.04) with spleen and cecum bacterial (Salmonella) load, and mildly associated (P< 0.10) with ceacum bacterial load (Malek et al., 2004). The SNP-A was carried at a very low frequency in all of these study populations. Different Mx alleles have been reported to confer resistance or susceptibility to influenza virus replication. It is expected that SNP-G coding for the amino acid serine at position 631 in the Mx1 gene would be more susceptible to Influenza A, while populations with SNP-A are more resistant to viral infection (Ko et al., 2002,2004; Li et al., 2007). Li et al. (2006) showed skewed allele frequencies with a much higher frequency of the favorable SNP-A in native breeds than in highly selected lines. Livant et al. (2007) also found significant associations between this SNP determining antiviral activity and several traits of economic interest. Balkissoon et al. (2007) sequenced full-length complementary DNA (c-DNA) in a range of chicken lines and ancestral breed stocks. They found a low frequency of the resistance allele (SNP-A) in most industrial broiler lines compared to industrial and ancestral White Leghorn layer stocks, which were either fixed with the SNP-A allele or carried the allele in very high frequencies (above 93%). Their findings seem to support Livant et al’s (2007) suggestion that there may be a linkage of the Mx gene with egg production traits. In this study, LW, a White Leghorn type layer had the highest allelic frequency for SNP-A at 90% but LB, an industrial brown egg layer, only carried the allele with a frequency of 64%. These data indicated that the very high SNP-A frequency may be peculiar to White Leghorn chickens and it may not have strong association with egg production traits. I also found that industrial populations, especially the LW, were carrying alleles that make them more resistant to Influenza A and Avian Leukosis Virus infection. I have thus provided partial support for the hypothesis that industrial populations may have higher resistance to specific diseases that were part of their selection history.  92  I used a PCR-RFLP assay developed by Zhang et al. (2005) to evaluate the diversity of the six possible TVB genotypes in these chicken populations. The two SNPs, TVB202 and TVB303, are both found in the TVB receptor gene which has three alleles that mediate or block the entry of different sub-groups of the Avian Leukosis Virus (Crittenden and Motta, 1975; Weiss, 1993; Barnard and Young, 2003). Different combinations of the two SNP alleles marked the different TVB genotypes (see Table 4.6). The allele S1 encodes a cellular receptor mediating infection of subgroups B, D, and E, whereas the allele R encodes a dysfunctional receptor that does not permit infection by any of the sub-groups, B, D, and E (Zhang et al., 2005). Chicken populations with a high frequency of the RR genotype may therefore be most resistant to ALV (types B, D, and E) infection and those with the S1S1 genotype the least. In my study, the RR genotype was only found in LW, with an allelic frequency of 42%. The R allele was also found at a low frequency in LB, AC, and SK. On the other hand, The S1 (susceptible) allele was fixed in TC. Zhang et al. (2005) also reported that the S1 allele was fixed in 14 of the 36 broiler lines that they sampled, and existed at a high frequency in the rest of the broiler lines, which is what I found in the TC population which is a meat type chicken. In their egg-layer populations, the S1 allele also existed at a high frequency and the R allele had a moderate to low frequency. There was homozygosity or near homozygosity of TVB303 and TVB202 in most of the chicken populations in this study. Furthermore, this may be an indication that the TVB receptor gene has been under intense selection pressure in these chicken populations. This genotyping method can be uses as tool with relative ease and high accuracy to determine all of the six possible TVB genotypes in research and industry. The ChB6 SNP is non-synonymous as a C → A substitution at base 470 causes an amino acid change from Glutamine to Lysine (Zhou and Lamont, 2003b). Whether this change in the amino acid has resulted in a change in the gene function is not known. Zhou and Lamont (2003b) found, in an association study using two inbred chicken lines and their crosses, that chickens with homozygous SNP-A had a significantly higher primary antibody response to B. abortus than chickens carrying the SNP-C. In this study’s  93  populations, SNP-A was almost fixed in SK and was carried at a high frequency (77%) in LW. The SNP-A was carried at moderate to low frequencies in the other populations. It would be worthwhile to carry out association studies with these populations to find out whether the same association occurs. The results from the SNP study indicated that there was no clear pattern in the frequencies of these SNPs to delineate industrial populations from populations of nonindustrial sources. Furthermore, industrial populations (e.g. LW) carried more favorable alleles of some candidate genes than non-industrial populations. Combining the results from the two studies of genetic variation in the adaptive and innate immune systems led me to formulate the hypothesis that while non-industrial populations may have higher general disease resistance than industrial populations, industrial populations may have higher resistance to specific diseases that were part of their selection history. However, association and challenge studies will have to be conducted to test this hypothesis.  5.4 Conclusions Poultry production used to be from local backyard populations and there was a great diversity of genetic variation among the various local populations because they were exposed to a diversity of selection factors. Many breeds were developed during this time. With the development of the poultry industry came a drastic reduction of breeds and intense breeding and selection (for similar goals) on the remaining few breeds, resulting in the loss of genetic diversity in chickens. There was also consolidation of industrial breeding companies until only two layer and three broiler multinational conglomerates remain today (Besbes et al., 2007). These practices have not only further reduced the genetic base of our industrial breeds but also led to the displacement of local chicken populations by industrial lines in developing countries. While breeding companies claimed that large flock sizes and controlled inbreeding in industrial breeds may be comparable even to those of populations that were not subjected to intensive breeding (Delany, 2003), the breeders of white-egg layers have been concerned about reduced genetic variability and future response to selection (Hoffmann, 2005). The importance of  94  conservation of chicken genetic resources is advocated worldwide by the Food and Agriculture Organization (FAO; http://dad.fao.org/en/Home.htm). Within population diversity is an important component of population variation, especially in domesticated species (Caballero and Toro, 2002). On the other hand, diversity among populations can be assessed using genetic distance measures (Nei, 1972; Reynolds et al., 1983). The level of genetic diversity of the populations depends on the genetic structure of founders as well as the management that these populations have undergone. FAO defines animal genetic resources as populations with the highest genetic difference within a species or those carrying unique alleles. It is therefore recommended to conserve polymorphic populations with high levels of genetic differentiation, clear distinction among breeds, and low levels of admixture. There is a need to conserve polymorphic populations such as SQ and YW as part of conservation of animal genetic resources globally, and to preserve SK and TC populations as small populations for their unique genetic features. It is important to reconsider management strategies in these populations in terms of maintaining genetic variation within these populations. The genetic distance measures which are based on allele frequencies were in good agreement with the genetic diversity of these populations, and with what is known of the history of the populations. Evaluating the genetic variation and genetic relatedness in chicken populations that have been used for free run/free range production is an important step toward identifying and conserving unique genetic resources. Finding diversity in disease resistance genes in various populations can be an important factor to consider when developing breeding stocks and conserving populations (Fulton, 2004). Immune response has low to medium heritability and is easily influenced by environmental factors (Gibson and Bishop, 2005). Direct phenotypic selection for better immune response is therefore difficult in chickens. Expensive procedures and intensive labour are associated with reliable measurements of immune response (Gibson and Bishop, 2005). Knowledge of diversity of different mechanisms in immune response may help in designing disease resistance studies in the future and to provide fine structure mapping markers in the populations under study. The detection of linkage between DNA  95  markers and QTL associated with immune response is preferred in selection of individuals according to genotype based on marker-assisted selection (MAS). Disease resistance is therefore an important factor to consider when conserving genetic resources or developing breeding stocks for free run/free range production. Stocks with greater observed diversity in MHC genes could serve as a source of genetic variation in conservation and breeding programs. In populations with lower diversity of MHC genes, introgression may be more efficient in increasing the diversity of MHC haplotypes in industrial chickens. Nevertheless, the results could provide a basis for the inclusion of MHC markers for investigation of the genetic architecture of possible diversity of disease resistance in chickens. Haplotypic DNA sequence information for the MHC can be used to identify major molecular and evolutionary mechanisms in this region and this haplotype diversity information could be used as a framework for further analyses of disease associations. The results of the current study may be beneficial in designing breed management and developing conservation strategies for free run/free range populations in BC. Molecular markers used to evaluate the genetic diversity within and between populations is the first step in characterizing the various populations for compiling an inventory list that can be used in conservation purposes. Microsatellite genotyping data showed a high level of genetic differentiation between some of these populations. It is important to reconsider management strategies for these populations in terms of maintaining genetic variation within these populations. Information based on Ho and MNA using microsatellites, as the two indicators of genetic diversity, showed that except for the SK, non-industrial breeds are more heterogeneous than the industrial breeding stocks. Examining the genetic variability of the MHC region using LEI0258 marker, which is part of the adaptive immune system, showed that the two industrial populations seem to have less genetic variability indicated by Ho and MNA. The results of variability of some candidate genes associated with the innate immune system using SNP genotyping didn’t show a clear pattern to delineate industrial populations from populations of non-industrial  96  sources. Information obtained from this research concerning the variability of the adaptive and innate immune systems shows that while non-industrial populations may have higher general disease resistance, industrial breeding stocks may have better specific disease resistance because they carry more favorable alleles of some candidate genes for example Mx 1 and TVB genes due to their selection history. My results demonstrated that while the industrial populations were more inbred, they may not be as susceptible to common poultry diseases as some authors thought (Muir et al., 2008) Generally, the results of this thesis research should be useful to support decisions on conservation and further use of these populations in crossbreeding programs designed to create genetic stocks with improved adaptability and productivity in free run/range production systems. The information on genetic diversity of disease resistance in these populations of chickens may be usefull for developing the foundation population for selecting breeding stocks for free run/free range production with enhanced ability to resist an infectious disease outbreak. The development of high throughput single nucleotide polymorphism (SNP) assays like the new 60k Illumina SNP BeadChip can be used as new tools in whole genome association, genome-wide selection and assessing genetic diversity of potential breeds to keep for conservation in both layer and broiler lines.  5.5 Future research The results of this study add to the body of knowledge of genetic variability and population structure in chicken populations, but conservation of animal resources is a multidisciplinary approach and needs to consider other criteria such as politics, economic interests and cultural values along with the biological context for conserving a population. For this reason, there is a need for information on productive and phenotypic characteristics of these chickens. Therefore, information from DNA markers together with phenotypic performance data and history of populations could provide reliable guidelines for conservation decisions, and for designing breeding programs. A comprehensive characterization of chicken breeds collected from around the world is required to fill unanticipated breeding demands for production and research in the future.  97  Furthermore, using information regarding SNPs data to assess disease resistance, it will be worthwhile to conduct association studies using a candidate gene approach focused on genes involved in disease resistance in these chicken populations. Finally and most importantly, using high-throughput SNP genotyping like new 60k Illumina SNP BeadChip, will be of interest in assessing genetic diversity of potential breeds to keep for conservation. 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