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Estimating inbreeding in an inbred line of Japanese quail (Coturnix Japonica) using pedigree and microsatellite… Kim, Shin Hun 2006

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Estimating inbreeding in an inbred line of Japanese quail (Coturnix Japonica) using pedigree and microsatellite analyses by Shin Hun Kim B.Agriculture, Korea University, 2004 A THESIS SUBMITED IN PARTIAL F U L F I L L M E N T OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE F A C U L T Y OF G R A D U A T E STUDIES (Genetics) THE UNIVERSITY OF BRITISH C O L U M B I A September, 2006 © Shin Hun Kim, 2006 A B S T R A C T Accurately estimating the level of inbreeding in a population is essential because inbreeding reduces fitness, fertility, viability, hatchability, and other production traits in populations. Inbreeding has been estimated by analyses of pedigrees and genetic markers. The objective of this study was to evaluate the accuracy of the two methods in an inbred line of Japanese quail. The inbred line was derived from a random bred (QO) line and maintained for 17 generations by pedigreed matings of brothers to groups of sisters. Data from analysis of 14 microsatellite markers, GUJ0001, GUJ0024, GUJ0030, GUJ0034, GUJ0040, GUJ0044, GUJ0057, GUJ0059, GUJ0060, GUJ0065, GUJ0068, GUJ0070, GUJ0071, and GUJ0085, were obtained. Pedigree data were used to calculate the inbreeding coefficient (FIT), which is the level of inbreeding based on a reference ancestor. From the microsatellite locus data, the population differentiation (Fsr) of the two lines caused by inbreeding was calculated as FST = ]-(HE(INB)/HE(RAND)), where / /^ /A^and HE(RAND) are the expected heterozygosity of the inbred and the random bred lines, respectively. The FIT was then calculated as FIT -FIS+(1-FIS)*FST, where Fis is the level of inbreeding within the inbred line. The heterozygosity observed by analysis of the microsatellite markers of the randombred and inbred lines was 0.430 and 0.207, respectively, and the number of alleles was 3.29 and 1.93, respectively, demonstrating that genetic diversity was reduced in the inbred line. The FIT of the inbred line from the pedigree and microsatellite marker analyses was 0.687 (±0.069) and 0.567 (±0.329), respectively. These results suggest that pedigree analysis seems to be more accurate than n microsatellite marker analyses without multi-generation genotyping for estimating inbreeding in an inbred line of Japanese quail. Further studies will be required to investigate reasons for the high standard deviation of microsatellite marker analyses. in T A B L E OF CONTENTS Abstract i i Table of Contents iv List of Tables vii List of Figures viii List of Abbreviations ix Acknowledgements x Co-Authorship Statement : xi Chapter 1 Introduction And Overview 1 1.1 The Japanese Quail 1 1.1.1 Classification and history of Japanese quail 1 1.1.2 Characteristics of Japanese quail 2 1.1.3 Japanese quail as a food source and for research 2 1.2 Genetic Diversity 4 1.2.1 Importance of genetic diversity 4 1.2.2 Inbreeding and heterozygosity 5 1.2.3 Inbreeding depression 7 1.2.4 Inbreeding and purging 9 1.2.5 Inbreeding and F statistics 10 1.3 Genetic Markers 13 1.3.1 Microsatellite markers 13 1.3.2 Genetics of microsatellites 13 1.3.3 Microsatellite mutation 14 1.3.4 The Mutation models of microsatellites 15 iv 1.3.5 Microsatellites and other markers 16 1.4 Objectives of Thesis 19 1.5 Literature Cited 20 Chapter 2 Estimating inbreeding in an inbred line of Japanese quail (Coturnix Japonica) using pedigree and microsatellite analyses 33 2.1 Introduction 33 2.2 Methods 35 2.2.1 FIT calculation 35 2.2.2 Experimental populations 36 2.2.3 Pedigree analysis 37 2.2.4 D N A extraction 37 2.2.5 Microsatellite markers 38 2.2.6 Microsatellite PCR product analyses 39 2.2.7 Microsatellite data analysis 39 2.3 Results 40 2.4 Discussion 41 2.5 Literature Cited 54 Chapter 3 Conclusion and General Discussion 58 3.1 Amplification of Markers 58 3.2 The Genetic Diversity and The Level of Inbreeding in The Lines 59 3.3 Comments on Future Research 60 3.4 Literature Cited 62 v APPENDICES 1 24 Japanese quail microsatellite markers (Kayang et al, 2002)..63 2 PCR optimization 64 3 Polyacrylamide gel production 65 4 For GUJ0068, allele size of 242 observed only in the inbred line 66 5 Annealing temperature 68 6 Laboratory Chemical Safety Certificate 70 7 CCAC/NIAUT Program Certificate 71 8 Literature Cited 72 VI LIST OF TABLES Table 2.1. List of 14 Japanese quail microsatellite loci used for final analysis and their size range and annealing temperature described by Kayang et al. (2002) 45 Table 2.2. Profiles of 14 Japanese quail microsatellite markers amplified in the random bred line (QO) 46 Table 2.3. Profiles of 14 Japanese quail microsatellite markers amplified in the inbred line.47 Table 2.4. The summary of the analysis done by G D A (vl.O: Lewis and Zaykin, 2001) of the 14 Japanese quail microsatellite markers amplified in the random bred line and in the inbred line 48 Table 2.5. FIT of 2 individuals in the last generation in the pedigree of the inbred line calculated by ENDOG (v3.0: Gutierrez and Goyache, 2005) 48 Vll LIST OF FIGURES Figure 2.1. The less intensive inbreeding scheme used at the Quail Genetic Resource Centre (QSRC) of the University of British Columbia 49 Figure 2.2. The pedigree of 44614-1 in Table 2.5 50 Figure 2.3. Polyacrylamide gels, analyzed by RFLPscan (LiCor Inc., Lincoln, NEB), showing PCR products for the most polymorphic locus, GUJ0030, in both lines of Japanese quail 51 A. GUJ0030 in the randombred line B. GUJ0030 in the inbred line Figure 2.4. Polyacrylamide gels showing PCR products for GUJ0034, which was polymorphic in the randombred line and monomorphic in the inbred line 52 C. GUJ0034 in the randombred line D. GUJ0034 in the inbred line Figure 2.5. Polyacrylamide gels showing PCR products for GUJ0068 having many non-emplified products 53 E. GUJ0068 in the randombred line F. GUJ0068 in the inbred line vi i i LIST OF ABBREVIATIONS AFLPs amplified fragment length polymorphism DAD-IS Domestic Animal Diversity Information System FAO Food and Agriculture Organization of the United Nations GUJ Gifu University Japanese quail HWE Hardy-Weinberg equilibrium I A M infinite alleles model IBD identical by descent PCR polymerase chain reaction QO Quebec QGRC Quail Genetic Resource Centre RAPDs random amplified polymorphic DNAs RFLPs restriction fragment length polymorphisms S M M stepwise mutation model SNPs single nucleotide polymorphisms SSRs simple sequence repeats STRs short tandem repeats I X A C K N O W L E D G E M E N T S I would like to sincerely appreciate Dr. Fred Silversides, my thesis supervisor who supported me and provided me with good environment and facilities for me to be able to focus on the research in Agassiz, and who guided me with thesis work. I would also like to thank Dr. Kimberly Cheng, my thesis co-supervisor, for his support, guidance, wonderful supervision, and help during this study. It was him who has helped me from the beginning to find right direction for my study and for my program. I am also thankful to Dr. Carol Ritland for her continuing assistance and a great deal of technical support with the experiment, and Dr. Kermit Ritland for his kind advice and sincere participation in the study. This study was funded by Agriculture and Agri-Food Canada (BC). I would like to express my special appreciation to Ji-En Kim who gave me lots of encouragement, advice, and support, whenever I needed any, and Karen Gilchrist and Nadia Kim for their great help, support, and encouragement. I would like to express my sincere gratitude to my parents, Mr. Gwang-Su Kim and Mrs. Gab-Soon Kim, and my brother arid sister for their support including financial support, guidance, advice, prayers, encouragement, and great teaching until now. Finally, this thesis is dedicated to my almighty GOD, who is always with me and still kindly taking care of me and my family. Co-Authorship Statement Dr. Fred Silverside, Dr. Kim Cheng, and Dr. Kermit Ritland designed the research program. I performed all the research including the experiment and the data analyses for this thesis, while Dr. Fred Silverside, Dr. Kim Cheng, and Dr. Kermit Ritland, guided me during the research, giving me advice and help with how to deal with the data analyses, and Dr. Carol Ritland helped me with technical problems for the experimental works. Finally, Dr. Cheng contributed to preparing for the manuscript. x i CHAPTER 1 Introduction A n d Overview 1.1 The Japanese quail 1.1.1 Classification and history of Japanese quail Japanese quail (Coturnix japonica) belongs to the order Galliformes and the family Phasianidae. The Japanese quail, originally domesticated in the 11th century as a pet song bird (Crawford, 1990), used to be considered a subspecies of the European or common quail (Coturnix coturnix) and was designated as Coturnix coturnix japonica (Westmore, 1952). However, because of taxonomic evidence in vocalizations, sympatry, sexual isolation and hybrid inviability, the Japanese quail is now considered to be a different species from the common quail and has been given a new designation, Coturnix japonica (Howard and Moore, 1984). In spite of the widest natural distribution of any galliform bird and its adaptability to a wide range of ecological conditions (Cheng et al, 1992), because of the loss of breeding habitat, Japanese quail have had extensive population reduction in the last 3 decades (Kimura, 1991). The Japanese quail is originally from Japan, China, Korea and Indochina (Taka-Tsukasa, 1935), and was introduced into Hawaii in 1921 (Schwartz and 1 Schwartz, 1949). After the Second World War, there was an attempt to rebuild the Japanese quail industry with the few remaining domesticated birds available from Korea, China and Taiwan, and from wild populations (Howes, 1964; Wakasugi, 1984). 1.1.2 Characteristics of Japanese quail The Japanese quail is slightly sexually dimorphic in that females are larger than males, and phylogenetically closely related to the chicken (Stock & Bunch, 1982). Both Japanese quail and chickens have 2n — 78 chromosomes and a similar genome length of 1.2*109 bp, consisting of morphologically distinct macrochromosomes (1-8 and the ZW sex chromosomes) and microchromosomes. Indeed, Shibusawa et al. (2001) found highly conserved chromosome homology between Japanese quail and chickens and very few chromosome rearrangements after divergence of the two species (Shibusawa et ai, 2001). 1.1.3 Japanese quail as a food source and for research Japanese quail are kept for their eggs and meat. Egg production is popular in Japan and South-East Asia, whereas meat is the main product in Europe (Minvielle, 1998). The consumption of Japanese quail has been increasing in the United States and in Canada 2 (Paulson et al, 1989). Because of its small size, inexpensive rearing requirements, rapid maturation compared with other domesticated poultry, and adaptability to a wide range of husbandry conditions, Japanese quail have been used in many studies including behavioural (Mills & Faure, 1991), developmental (Le Douarin et ai, 1997), physiological (Balthazart etai, 2003), genetic (Jones et al, 1991) and inbreeding (Mizutani, 2002), and its eggs are frequently used in studies of pathology (Halldin et al, 2003) and virology (Swain et al, 1997). As to genetic markers for Japanese quail, cross-species amplification has been applied using microsatellites of chicken (Pang et al, 1999), Japanese quail specific microsatellites have been developed (Kayang et al, 2000; Kayang et al, 2002), and linkage maps of amplified fragment length polymorphism (AFLP) markers (Roussot et al, 2003) and that of microsatellite markers (Kayang et al, 2004) of Japanese quail have been published. Some of the studies (Pang et al, 1999; Kayang et al, 2000; Kayang et al, 2002) investigated genetic diversity of Japanese quail. 3 1.2 Genetic diversity 1.2.1 Importance of genetic diversity When many species are being investigated in an attempt to estimate the level of genetic diversity, many studies have shown that we have lost a great deal of genetic diversity. For example, Frankham et al. (2002) pointed out that 50% of vertebrate animal species and 12% of all plants are vulnerable to extinction. In poultry, about 50% of the poultry breeds registered in FAO based Domestic Animal Diversity Information System (DAD-IS) were at risk (Hoffmann, 2005). Pisenti et al. (1999) reported the elimination of more than 238 poultry research stocks between 1984 and 1998 in the USA and Canada, almost 40% of the US stocks and 60% of the Canadian stocks. Seventy five percent of eliminated stocks were chickens, eleven percent were turkeys, eleven percent were Japanese quail, and four percent were game birds or waterfowl. The University of British Columbia used to hold the largest collection of mutations and unique lines of Japanese quail in the world, which included 12 specialized lines and 27 mutation lines. By 2003, the collection had been reduced to 9 lines and specialized lines, and transferred to Agriculture and Agri-Foods Canada's research station in Agassiz, BC, Canada (Cheng, 2003). 4 Keeping a high level of genetic diversity, which will be a reservoir of genetic material in the future is very important because a low level of genetic diversity in a population may reduce fitness and viability by increasing inbreeding and thus increasing homozygosity and could even lead species to extinction. Moreover, a low level of genetic diversity can make species too vulnerable to the outbreak of certain diseases. For example, when a disease swept through several captive cheetah colonies, it caused 50%-60% mortality over a 3-year period (Avise, 2004). In this respect, for endangered and captive species, good management will be required to reduce the loss of genetic diversity, inbreeding depression, or even extinction. 1.2.2 Inbreeding and heterozygosity Inbreeding is the mating of closely related individuals. Inbreeding changes genotypes by increasing the frequency of homozygotes and decreasing that of heterozygotes compared to random mating, leading to a reduction in fitness. The negative effect of inbreeding on fitness has made inbreeding a concern in genetic conservation because many populations of endangered or captive species may be small. In large random mating, populations with no mutation, migration or selection, allele and genotype frequencies reach 5 an equilibrium, which is called the Hardy-Weinberg equilibrium (HWE). In mathematical model, the HWE can be referred as p2 + 2pq + q2 = 1, where p and q are the relative frequency for alleles A l and A2, respectively. If mating occurs between two related parents, the proportions of the equilibrium change by the degree of inbreeding (inbreeding coefficient). Not just does parental relatedness cause inbreeding, but genetic drift can also do so in small populations. Genetic drift, which is allelic fluctuation from generation to generation, has major impacts on small populations. The allelic frequency can be large in small populations, and can even result in fixation or loss of an allele, leading to a high level of homozygosity. On the other hand, if the population is large enough, for example if the size is 500 or more (Haiti and Clark, 1997), random genetic drift hardly has any impact on the population. However, migration or gene flow between populations will reduce the chance of genetic drift and inbreeding (Barton and Whitlock, 1997). Previous studies have shown that inbreeding is common both in captive and in wild populations (Wright, 1977; Ralls et al, 1986; Charlesworth and Charlesworth, 1987; Thornhill, 1993; Keller, 1998). Briefly, there are two ways to measure inbreeding, calculating inbreeding coefficient (F) from pedigree analysis (Hartl and Clark, 1997), and estimating F from heterozygosity by using genetic 6 markers (Galeuchet et al, 2005). 1.2.3 Inbreeding depression Because inbreeding increases homozygosity and decreases heterozygosity, it causes inbreeding depression along with genetic drift, which reduces fitness (Keller and Waller, 2002; Frankham et al, 2002) and because deleterious mutations occur frequently, increased homozygosity will increase the genetic load in the population (Keller and Waller, 2002). Inbreeding causes inbreeding depression via two different mechanisms: homozygotes of deleterious recessive or partially recessive alleles reduce fitness, and overdominance, where the fitness of heterozygotes is higher than that of homozygotes. These mechanisms produce different outcomes and inbreeding depression resulting from homozygosity of deleterious alleles can be more easily removed by selection than from that caused by the elimination of overdominance (Lande and Schemske, 1985; Charlesworth and Charlesworth, 1987; Holsinger, 1991). There are studies supporting both, dominance-based (Moll et al, 1964; Hallauer and Miranda, 1985) and overdominance-based (Apirion and Zohary, 1961; Hallauer and Miranda, 1985) inbreeding depression. Various studies have reported that inbreeding depression is widely spread (Bulmer, 1973; Gibbs and Grant, 1989; Rosenfield and 7 Bielefeldt, 1992; Coltman et al, 1998). Frankel and Soule (1981) pointed out that an increase in inbreeding by 10% accounts for a reduction of up to 5 to 10% in fitness components. Miglior et al. (1992) found regression coefficients of -9.84 kg, -0.55 kg, and -0.0011% for milk, fat, and fat percentage per 1% increase of inbreeding in Jersey cattle. In 3 White Leghorn lines selected for different egg production traits (egg weight, egg number, and egg number and egg weight combination), Sewalem et al. (1999) showed that an increase in inbreeding of the hen and embryo resulted in decreasing reproductive fitness such as egg number, egg fertility, and mean hatchability. In other studies with the Great Tit, mortality was, on average, 27.7% among inbreeders and 16.2% among outbreeders (Greenwood et ai, 1978), and van Noordwijk and Scharloo (1981) showed that a 10% increase in inbreeding coefficient decreased the viability of eggs by 7.5%. However, several mechanisms have evolved to avoid inbreeding in animals, such as, extra pair copulation (Byrne et ai, 2003; Zeh and Zeh, 2006), and purging (Kirkpatrick and Jarne, 2000; Boakes and Wang, 2005). However, previous studies have reported that the severity of inbreeding depression is affected by environmental factors (Keller et al, 2002; Vermeulen and Bijlsma, 2004; Gallardo and Neira, 2005). Therefore, inbreeding depression can be underestimated in captive or laboratory animals because normal environment for these 8 animals is not as harsh as in nature (Hedrick and Kalinowski, 2000). 1.2.4 Inbreeding and purging Because most inbreeding depression is caused by many partially recessive and deleterious alleles, it is possible that those alleles with less fitness can be purged by natural selection (Templeton and Read, 1984; Crow, 1993; Hedrick, 1994). With overdominance partially recessive and deleterious alleles will remain in a heterozygote, leaving themselves less chances to be purged (Wang, 2000). However, even though a lot of studies have supported that purging decreases inbreeding depression in various species, it seems that purging reacts in each species differently, because various studies showed diverse results regarding purging (Kirkpatrick and Jarne, 2000; van Oosterhout et ai, 2000; Crnokrak and Barrett, 2002). For example, even though Crnokrak and Barrett (2002) found frequent and substantial purging in various species using three measures: (1) changes in inbreeding depression, (2) changes in fitness components of inbred lines relative to original outbred line, and (3) purged population (outcrossed inbred lines) trait means as a function of ancestral out bred trait means, they mentioned that adaptation to laboratory conditions may have caused rebounds in fitness, not 9 purging. In another case, although van Oosterhout et al. (2000) found considerable purging during inbreeding, they found out that the genetic load still remained higher than that of many outbreeding species. 1.2.5 Inbreeding and F statistics A population has a hierarchical population structure. To investigate the effect of population substructure, Wright (1921) presented F statistics, Fis, FST, and FIT. In small populations, the rates of inbreeding will be relatively high, because inbreeding is the mating of closely related individuals. Therefore, the genetic differentiation among subpopulations estimated by F statistics can be related to the inbreeding effect resulting from population substructure (Hartl and Clark, 1997), because the smaller population within a subpopulation will have a greater chance to mate to their relatives. Fis is measure of genetic variance inbreeding coefficient of an individual (I) relative to the subpopulation (S), FST is the effect of the subpopulation (S) compared to the total population (T), and FIT is the inbreeding coefficient of an individual (I) relative to the total population (T) (Wikipedia, 2006). The followings are the equations: F i S = 1-(H,/HS) 10 FST = 1-(HS/HT) FIT = 1-(HI./HT), where Hi is the observed heterozygosity averaged across all population fragments, Hs is the expected Hardy-Weinberg heterozygosity averaged across all population fragments, and H T is the expected Hardy-Weinberg heterozygosity for the total population (Frankham et al, 2004). The inbreeding coefficient F i S or F is the probability that an individual inherits both alleles at a locus that are identical by descent. The effects of inbreeding can be explained using F by the general formulation of P = p2 + Fpq, H = 2pq(l -F), and Q = q2 + Fpq, where P is the frequency of A1A1, Q that of A2A2, and H that of A1A2 in the next generation and where p and q are the relative frequency for alleles A l and A2, respectively (Wright, 1931). Therefore, F can be calculated as F=l-H/2pq or F=l-Ho/He, where Ho is observed heterozygosity and He expected heterozygosity, when expected heterozygosity is calculated by He = 1-Xpi, where pi is the frequency of the ith allele (Frankham et al, 2004). F S T , the fixation index, is the probability that two alleles drawn randomly from a subpopulation are identical by descent (Frankham et al, 2002) and measures the type of inbreeding where there is a reduction in average heterozygosity among the subpopulations 11 (Hartl and Clark, 1997). Allele frequencies among subpopulations can be affected by a lot of factors, such as random genetic drift, gene flow among subpopulations, and natural selection. Frankham et al. (2002) mentioned that high rates of gene flow among subpopulations decrease FST, but low rates of gene flow among subpopulations will diverge the gene pools of the subpopulations and lead to inbreeding, increasing FST- According to Wright (1978), FST of 0 to 0.05 of may be considered little genetic differentiation, 0.05 to 0.15 is moderate genetic differentiation, 0.15 to 0.25 is high genetic differentiation, and FST above 0.25 shows very great genetic differentiation. F] T, the heterozygosity of the inbred organisms relative to the total population, is the most inclusive measure of all inbreeding measures (Hartl and Clark, 1997). Hartl and Clark (1997) pointed out that FIT shows both the effects of mating between close relatives within a subpopulation and the accumulated inbreeding resulting from mating between remote relatives at all levels of the population hierarchy. With Fis = l-(Hi/Hs), FST = 1-(H S/HT), and FIT = 1-(HI/HT), the following equation can be derived: 1-FIT = (1-FIS)(1-FST) (Hartl and Clark, 1997). 12 1.3 Genetic markers 1.3.1 Microsatellite markers Microsatellite markers, which are referred to as short tandem repeats (STRs) or simple sequence repeats (SSRs) are 1 to 6 non-coding nucleotide repeats, with Mono-, di-and trinucleotide repeats being the most common. Microsatellites, which are one of the most common and variable types of D N A sequence and are regarded as neutral markers, are found in either intergenic regions or introns (Toth et al, 2000). With the advent of the polymerase chain reaction, their high mutation rate (Jarne & Lagoda, 1996) and polymorphism have made microsatellites one of the most important markers in genetic research. 1.3.2 Genetics of microsatellites Microsatellites are said to account for 3% of the human genome (International Human Genome Sequencing Consortium, 2001), and it has been reported that microsatellite density tends to be positively related to genome size (Primmer et al, 1997; Toth et al, 2000). Bats, which have considerably smaller genomes than other mammals appear to have fewer microsatellites than other mammals (Van Den Bussche et al, 1995). Mice were shown to 13 have two or three times more microsatellites than humans (review: Ellegren, 2004). Primmer et al. (1997) found that more di-, tri-, and tetranucleotide repeats were observed in mammals than in birds, and the density of microsatellites in mammals was higher than in birds, which is attributed to the fact that genome size of birds is about one third of that of humans in terms of total base pairs (Bloom et al, 1993; Wachtel and Tiersch, 1993). 1.3.3 Microsatellite mutation Most microsatellites show high mutation rates, ranging from 10" to 10" per generation (Edwards et al, 1992), with the average being approximately 10 3 (Weber & Wong 1993; Jarne & Lagoda 1996), but mutation rates may vary among species (Ellegren, 2000), microsatellite length or allele size (Schug et al, 1997; Brinkmann et al, 1998; Brohede et al, 2002), with long alleles being generally more mutation prone than shorter ones, repeat types, with dinucleotide repeats having higher mutation rates than tri- and tetranucleotide repeats (Kimmel & Chakraborty, 1996; Schug et al, 1998), base composition of the repeat (Bachtrog et al, 2000), and flanking sequences (Glenn et al, 1996; Bachtrog et al, 2000). 14 1.3.4 The mutation models of microsatellites There are thought to be two possible mutation mechanisms for length changes of microsatellites, the replication slippage model (Levinson & Gutman, 1987), which occurs when two strands dissociate and then realign out of register and new replication will cause insertion or deletion of repeat units relative to the template strand, and the unequal crossing-over model which occurs during recombination in meiosis. However, it is most likely that replication slippage is the major mechanism for microsatellite mutations, because it has been shown in yeast that the primary mechanism of mutation is D N A slippage (Strand et al, 1993), even though occasional unequal crossing-over may affect the allele length (Wierdl et al, 1997). Schug et al. (1998) found no significant correlation between microsatellite mutation rate and recombination rate, which indicates that unequal crossing-over accounts for little microsatellite mutation. For the mutation models, the infinite alleles model (IAM; Kimura & Crow, 1964) and the stepwise mutation model (SMM; Kimura & Otha 1978) have been presented. In the I A M model, each mutation creates a novel allele, and in S M M , the more size difference, the more distantly related, because it is presumed that each mutation produces a novel allele either by adding or deleting a single repeat at a fixed rate. However, many changes have been 15 added to the simple S M M , because S M M is of limited help in explaining the observation that microsatellites tend to show an upper size limit (Review: Ellegren, 2004), and replication slippage can make more than one repeat mutation. An upper limit on allele sizes (Feldman et al, 1997; Stefanini & Feldman, 2000), and a mutational bias that favors large alleles mutating to smaller ones (Zhivotovsky, 1999; Calabrese & Durrett, 2003) have been introduced to the S M M . Another model, in which two mutational forces, length mutations and point mutations, work together in creating microsatellite repeat length differences (Bell & Jurka, 1997; Kruglyak et al, 1998) has been suggested to overcome the weaknesses of S M M . In this model, length mutations tend to increase repeats whereas point mutations break long repeats into smaller ones. 1.3.5 Microsatellites and other markers Allozyme or isozyme markers are allelic variants of enzymes which are composed of amino acids. Because enzymes are electrically charged, allelic variation due to mutation of any amino acids in the enzyme can be detected through electrophoresis. Before other genetic markers using D N A were popularized, allozyme markers were applied to investigate population structure (Spieth, 1975; Beckwitt and Chakraborty, 1980; Ochman et al, 1983). 16 Although, these markers require no DNA, D N A probes, or primers, their low abundance and low level of polymorphism have made D N A based markers more popular (Nybom, 2004). RAPD (Random amplified polymorphic DNA), A F L P (Amplified fragment length polymorphism) and microsatellite markers are PCR-based markers. RAPDs and AFLPs both use random D N A sequences as primers to detect the variation in flanking sites, but they are different in that AFLPs utilize RFLPs (Restriction fragment length polymorphisms) along with RAPDs. RAPDs and AFLPs are abundant in the genome and no sequence data are required to develop primers. The D N A based but non PCR based markers are RFLPs and SNPs (Single nucleotide polymorphisms). RFLPs are D N A fragments cut by restriction enzymes, followed by elctrophoresis and Southern blotting. RFLP is a good way to investigate point mutations (Nishimukai et al, 1996), which can be base exchanges, base deletions or insertions. SNPs are D N A sequence variations to nucleotide (A, G, C, and T) and are abundant and widespread in the genomes of many species. SNPs are used to investigate the relationship between common genetic variation and common heritable diseases (Pritchard and Cox, 2002; Klein et al, 2005). Although D N A based markers, such as RAPDs, AFLPs, RFLPs, and SNPs are more abundant and randomly distributed throughout genome than allozyme markers, 1 7 microsatellite markers have been used in many fields since they were developed for several reasons. First of all, microsatellites are widely distributed throughout the genome (Kashi et al, 1990; Weber, 1990), which makes them easy to isolate. Microsatellites are highly polymorphic due to different numbers of repeats compared to other markers (Rowe et al, 1997). While randomly amplified polymorphic D N A (RAPD) and amplified fragment length polymorphisms (AFLPs) are dominant markers, and SNP loci have a maximum of two alleles, microsatellites are codominant and can have many alleles, which means microsatellite markers are more informative. Studying inbreeding in a population is greatly enhanced by neutrality. Because neutral markers will not be subjected to selection, they make it easier to look at the effect of inbreeding. In this respect, microsatellite markers have advantage over allozyme markers, which may not be neutral (Van Oosterhout et al, 2004). Codon degeneracy contributes too little variation to make allozyme markers very useful. In addition, PCR-based microsatellite markers require only a small amount of DNA, unlike RFLP. For parentage assignment, microsatellites are the most extensively used (Parker et al., 1998) as well because a high level of polymorphism is required. Gerber et al. (2000) showed that from 100 to 200 dominant loci are similar to 10 highly polymorphic codominant markers for parentage assignment. In 18 comparing genetic variation among populations, fewer microsatellite loci are required than SNPs because microsatellite loci normally have many alleles, whereas two are normal for SNPs (Leberg, 1992). A l l in all, thanks to their advantages over other markers, microsatellites are one of the most suitable and well used markers to investigate population structures such as inbreeding coefficients, F, or FST- Bouzat and Johnson (2004) investigated the genetic structure of four closely spaced leks in lesser prairie-chickens with six microsatellite loci and found F S T =0.036 (P=0.002) and F I S=0.190-0.307 for the leks. Yang et al. (2003) reported Frr=0.330, Fis=0.274, and FST=0.077 on everage with 26 microsatellite loci in 18 Chinese indigenous pig breeds. 1.4 Objectives of thesis The objectives of this research were (1) to investigate the level of inbreeding of the inbred line derived from QO compared to the random bred line (QO) in Japanese quail, and (2) to evaluate the accuracy of the two methods, the pedigree and microsatellite markers for estimating the level of inbreeding in the inbred line. 19 LITERATURE CITED Amos, W., Worthington Wilmer J., Fullard K., Burg, T. 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In wild song sparrows, Keller (1998) reported a 17.5% reduction in annual survival rate of offspring of a full-sib mating. . Japanese quail (Coturnix japonica) are very susceptible to inbreeding depression (Sittmann et al, 1966) probably because of the heavy genetic load that they carry. None of the inbred lines tha't Sittmann et al. (1966) started by consecutive full-sib matings lasted beyond the third generation. Of the 17 full-sib mating inbred lines that Kulenkamp et al. (1973) started, only five lasted to the 5 t h generation. There has been no report of any inbred 1 A version of this chapter will be submitted for publication. 33 Japanese quail line surviving more than 8 generations of consecutive full-sib matings (Okimoto, R., University of Arkansas, pers comm; Wada, M . , Tokyo Medical and Dental University, pers comm). However, highly inbred lines are very useful in genome analysis and gene mapping (e.g. Hoti and Sillanpaa, 2006). At the Quail Genetic Resource Centre (QGRC) at the University of British Columbia, a less intensive inbreeding scheme, adapted from the one used for the development of the 1-420 inbred White Leghorn chicken line (Shoffnor et al, 1953), was applied to the development of an inbred Japanese quail line. This quail inbred line has gone through 17 generations of such inbreeding and still appears to be strong. It is therefore of interest to estimate the inbreeding coefficient of this population and compare that with what 8 generations of full-sib mating would have accomplished. There are several measures available to describe the genetic variability of populations. The average inbreeding coefficient estimated from pedigree information is a frequently chosen option. Due to the development of molecular genetics, it is also possible to estimate inbreeding coefficient based on genetic markers information (Keller and Waller, 2002). Genetic markers have been used extensively for estimating relatedness and level of inbreeding in populations in the absence of pedigree information (e.g. Vargo et al, 2003). Baumung and Solkner (2003) used a simulated study to compare measures of inbreeding 34 based on pedigree or microsatellite marker information. I found no empirical study of this kind. The aim of this study was (1) to compare genetic diversity between the inbred quail line and the randombred line from which the inbred line was derived, and (2) compare microsatellite markers with pedigree analyses for calculating the level of inbreeding. 2.2 Methods 2.2.1 FIT calculation To compare the two methods, pedigree and microsatellite marker analyses, for calculating the level of inbreeding in the inbred line of Japanese quail, FIT, the inbreeding coefficient of an individual relative to the total population, was calculated. Because the inbred line was derived from the randombred line (QO) in this study, the randombred line is the total population. Therefore, calculating inbreeding of an individual of the inbred line based on the randombred line is calculating FIT of an individual of the inbred line based on the randombred line. FIT can be estimated by (1-FIS)(1-FST)=1-FIT (Hard and Clark, 1997), where Fis is the level of inbreeding within the population, which in this case is the inbred line. FST can be derived from the equation of Ft=l-(Ht/Ho), where Ft is the accumulated 35 affect of inbreeding over time, Ht is the heterozygosity at generation t, and Ho is the initial heterozygosity (Frankham et al., 2004), as FST = 1-(HE(INB/HE(RAND)X where HE(INB) and HE(RAND) are the expected heterozygosity of the inbred and randombred strains, respectively. 2.2.2 Experimental populations In 1990, a Japanese quail line (QO) that has been previously selected for 45 days body weight (Caron et al, 1990) was acquired from the Deschambault Agricultural Research Station, Quebec, Canada. At the QGRC, the QO line was maintained as a randombred population with 48 males and 96 females per generation. In 1992, an inbred line was started from this population (Aggrey, 1994) using the breeding scheme described in Figure 2.1. While efforts were made to adhere to this scheme, in some generations there were less than four females per cage. At the time of this study, the inbred line has gone through its 17 th generation. 71 individuals were sampled from these two lines of Japanese quail (40 samples from QO and 31 samples from the inbred strain). 36 2.2.3 Pedigree analysis Two individuals, as less related as possible from each other, of the last generation of the inbred line were chosen and two pedigrees per each of them representing the maximum and the minimum possible coancestry matings were used (Table 2.5, and Figure 2.2). With the four pedigrees, FIT was calculated by ENDOG (v3.0: Gutierrez and Goyache, 2005) and the mean and sampled standard deviation were calculated from the 4 FIT values by following: this study, x * is the FIT of each pedigree, and *r is the mean of 4 FIT values of the pedigrees. 2.2.4 D N A extraction Fifty u.1 of blood was taken from each individual through the brachial vein. Genomic D N A was extracted following protocol used by J. Fulton (J. E. Fulton, Hy-Line International, pers comm). After centrifugation at 1500 rpm for 2 minutes, approximately 40ul of packed red blood cells from each sample were resuspended into 3 ml of lysis buffer (1M Tris-HCL pH 8.0, 5M NaCl, 0.5M disodium EDTA, dH 2 0) and approximately 200 (al of SDS was added and the tube was inverted immediately. The solution was digested with 16 u.1 1 N , where N is the number of chosen pedigrees, which is 4 in 37 of 20 mg/ml proteinase K (Fisher Scientific, https://wwwl.fishersci.com/) and shaken at 180 rpm for 1 hour and 60 rpm overnight at 37 • . The next day, approximately 2 ml of 5 M NaCI was added to each sample, which was vigorously shaken by hand, and centrifuged at 3500 rpm for 30 minutes. Approximately 4 ml of the supernatant from each sample was added to 8 ml of room temperature 95% ethanol. Crude D N A was washed with 1.5 ml of room temperature 70% ethanol and the ethanol was allowed to evaporate briefly. The D N A was stored in 1 ml of TE buffer (1M Tris pH 8.0, 0.5 M EDTA pH 8.0, dH 2 0). Approximately 20 ng/ul of genomic D N A was used for each PCR reaction. 2.2.5 Microsatellite markers 24 highly polymorphic markers (Appendix 1) were selected from the Japanese quail microsatellite markers developed by Kayang et al. (2002). These markers were screened based on polymorphism, wide distribution over the genome, and clear gel bands. Finally, 14 markers, GUJ0001, GUJ0024, GUJ0030, GUJ0034, GUJ0040, GUJ0044, GUJ0057, GUJ0059, GUJ0060, GUJ0065, GUJ0068, GUJ0070, GUJ0071, and GUJ0085, were selected. The characteristics of the markers are shown in Table 2.1. 38 2.2.6 Microsatellite PCR product analyses 0.5-2 [i\ of 20 ng/(l of genomic D N A was added to a mixture of 1.5mM of MgC12, 4.3-5.8 ul of dH20, 1 u l of 2.0 mM dNTP's (Roche LTD., ON, Canada), 0.5 ul of 1.0 pmol/(l forward and reverse primers (Operon Biotechnologies, Inc., Alabama, USA), 0.5 u,l of 1 pmol/(l M 13f-29 primer (LiCor), and Taq D N A Polymerase (Roche LTD., ON, Canada). After the PCR reactions, the products were analyzed on a LiCor 4200 (LiCor Inc., Lincoln, NEB). The bands were scored using RFLPscan (LiCor Inc., Lincoln, NEB). If the gel did not show any band, or showed faint, or non-specific bands, the annealing temperatures for the PCR reactions were adjusted ±2-3 °C or the amount of genomic D N A was either increased or decreased to get a better resolution. 2.2.7 Microsatellite data analysis The expected heterozygosity (He), the observed heterozygosity (Ho) and inbreeding coefficient (FiS) from the microsatellite information were calculated by Genetic Data Analysis, vl.O (GDA; Lewis and Zaykin, 2001). FIT of the inbred line based on the randombred line was calculated by the calculations of 2.2.1, and FIT per each locus was also calculated to estimate sampled standard deviation by following: 39 1 N S = \ N - 1 , where N is the number of microsatellite loci, which is 14 in this study, Xj~ is the Fi. T of each locus, and x is the mean of the Fj.T values of the loci. 2.3 Results The inbreeding coefficient, FIT, of the inbred line was 0.687 with the sampled standard deviation of 0.069 (Table 2.5). From microsatellite analysis, F S T was estimated as 0.497 and FIT was calculated as 0.567 with the sampled standard deviation of 0.329. The number of alleles for each locus was counted for the randombred line and the inbred line, and the means were 3.29 and 1.93, respectively (Tables 2.2, 2.3 and 2.4). In the randombred line, all of the markers were polymorphic or highly polymorphic. Four loci (GUJ0001, GUJ0024, GUJ0040, and GUJ0085) had two alleles, four loci (GUJ0044, GUJ0060, GUJ0065, and GUJ0068) had three alleles, and five loci (GUJ0034, GUJ0057, GUJ0059, GUJ0070, and GUJ0071) had four alleles. The locus, GUJ0030 had six alleles. For the inbred line, the loci were found to be either monomorphic or less polymorphic than the randombred line. Five loci (GUJ0001, GUJ0024, GUJ0034, GUJ0059, and GUJ0085) were monomorphic and six loci (GUJ0040, GUJ0057, GUJ0065, GUJ0068, GUJ0070, and GUJ0071) had two alleles, while three alleles were observed for GUJ0044 and GUJ0060, and GUJ0030 had four alleles. 40 The observed and expected heterozygosity for the randombred line and for the inbred line is shown in Table 2.2, 2.3, and 2.4. For most of the markers, the expected and observed heterozygosity for the inbred line was lower than for the randombred line. Both the mean observed and expected heterozygosity for the random bred line were approximately twice as high as the inbred line, with the mean observed heterozygosity for the random bred line being 0.430, and that for the inbred line being 0.207. The mean expected heterozygosity for the randombred line was 0.475 and that for the inbred line was 0.239. The estimated mean inbreeding coefficients (Fis) for the random bred line and for the inbred line were 0.095 and 0.140, respectively. Annealing temperatures for the markers are mentioned in Appendix 2. 2.4 Discussion The purpose of this research was to investigate the level of inbreeding in the inbred line and to compare the two methods (microsatellite markers and pedigree analysis) for estimating the level of inbreeding in Japanese quail. From the microsatellite marker analyses, the mean number of observed alleles in the randombred line was 3.29 and the mean observed heterozygosity was 0.430. The mean number of alleles per locus for the randombred line is higher than that reported by Pang et al. 41 (1999) (2.45 per locus) and Inoue-Murayama et al. (2001) (2.3 per locus) for their Japanese quail populations. Our result is more in line with that reported by Kayang et al. (2002) (3.7 per locus). Chang et al. (2005) reported 4.67 alleles per locus for the wild Japanese quail in China. Mean observed heterozygosity for the randombred line was higher than that (0.31) of the populations studied by Inoue-Murayama et al. (2001). Our randombred line had similar heterozygosity to those of Kayang et al. (2002) (0.423). In all three studies, the heterozygosity observed for domestic quail populations is lower than that reported for wild Japanese quail (0.66) (Chang et al, 2005). Cheng et al (1992) and Kimura and Fuiji (1989) used isozyme polymorphism to assess genetic variability in domestic and wild Japanese quail also reported higher proportion of polymorphic loci in wild populations than in domestic populations. The mean number of observed alleles in the inbred line was 1.93 and the mean observed heterozygosity was 0.207. These values were less than half of those for the randombred line and indicated that the inbreeding scheme applied was effective in reducing heterozygosity (Maeda and Hashiguchi, 1981; Curik et al, 2003; Markert et al, 2004; Slate et al, 2004). 42 The inbreeding coefficient (F] T ) of the inbred strain estimated from pedigree information was 0.687 (± 0.069). This would be comparable to inbreeding achieved by 5 generations of consecutive full-sib matings ( F ] T = 0.672). The less intensive inbreeding scheme would allow purging by natural selection to play a bigger role in shaping the population (Kulenkamp et al., 1973), and would allow the population to go into a higher degree of inbreeding without suffering the expected inbreeding depression (Ballou, 1997; Kirkpatrick and Jarne, 2000; Boakes and Wang, 2005). The inbreeding coefficient (FIT) of the inbred line estimated from microsatellite marker analyses was 0.567 (± 0.329), which shows that the high standard deviation makes microsatellite markers less accurate for estimating inbreeding in an inbred line of Japanese quail. Baumung and Solkner (2003) concluded from their simulated study that good pedigree information, traceable to the base population, would be a good measure of autozygosity. On the other hand, microsatellite information, without multi-generation genotyping, tends to over-estimate heterozygosity and provides a lower than true estimate of inbreeding coefficient. Length variation of sequences flanking the microsatellites (Angers and Bernatchez, 1997; Grimaldi and Crouau-Roy, 1997; Schug et ai, 1998) would cause individuals with identical microsatellite repeats to show different length alleles because of the 43 difference of the flanking region. Such flanking region can also generate "homoplasy", where individuals with different microsatellite repeats can show the same alleles because of the flanking region. Therefore, heterozygosity of microsatellite markers can underestimate inbreeding. Our empirical data seem to support the simulation carried out by Baumung and Solkner (2003). In many situations, especially when working with wild populations, good pedigree information is not available. Using poor pedigree that includes gaps and false parentages appears to have many weaknesses and may not be a good measure of autozygosity (Roughsedge et al, 2001; Baumung and Solkner, 2003). However, Baumung and Solkner (2003) argued that low quality pedigrees were still better indicator of autozygosity than microsatellite analysis. Never-the-less, in situations where no pedigree information is available, microsatellite analysis would still provide a comparable measure of autozygosity. In conclusion, we have (1) demonstrated that our less intensive inbreeding scheme is effective in developing inbred Japanese quail inbred line, and (2) provided empirical data to support the notion that whenever pedigree information is available, it would provide a better estimate of inbreeding coefficient than a single generation microsatellite information. 44 Table 2.1. List of 14 Japanese quail microsatellite loci used for final analysis and their size range and annealing temperature described by Kayang et al. (2002). Locus a name GenBank accession b number c Repeat array Forward primer (5-3') Reverse primer (5-3') Size range d (bp) T A GUJ0001 AB035652 (CA)7TG(CA)13 G A A G C G A A A G C C G A G C C A C A G C A C T T C G G A G C A C A G G A 231-239 56 GUJ0024 AB035834 (CA)I3AA(CA)3 T C A C A C C T T C G G G C T G A T C T A T G C G A C G G G G T G C C T T A A A 162-174 55 GUJ0030 AB035840 (CA)31 T G C A C C A A T C C C A G C T G T T T A A C G C A C A A T G G A A A G T G G G 167-179 64 GUJ0034 AB035844 (CA)9CG(CA)2 C G T A A C G G T C C A A T A T G G A T T C C A C G A T G C A G A G G T A T T T 219-241 55 GUJ0040 AB035850 (CA)12 G T T G A A G C T C C C A T C C C T C C A C A C C C C C A C G G T C T T T G C A 176-192 55 GUJ0044 AB035854 (CA)16 G C C T T G A A A C C T G A G T G A T C T G C A T T T C A G C A G C T C T C A G 180-220 55 GUJ0057 AB063125 (CA)12 G G A A T G G A A A A T A T G A G A G C C A G G T G T T A A A G T C C A A T G T 132-154 62 GUJ0059 AB063127 (CA)10 G A C A A A G T T A C A G C T A G G A G T A G G T G C G A A A A T C T C T G A C 207-219 50 GUJ0060 AB063128 (CA)9 A T G C T A T G G G A A C C T C A C T C T A T A A A G C A G G G G G A C A T G G 132-168 60 GUJ0065 AB063133 (CA)13 GCGTGCCATTTACTTCCCGG A G C C A G G A T G A C C A G G A A G G 109-131 55 GUJ0068 AB063136 (CA)13 T A G G A G A G G T C A C G A T T T G C A T C T T A A C T C G C C C A G C C T T 204-216 54 GUJ0070 AB063138 (CA)9 A A A C C C C A A A G A A G C T G T C C A C G T T G T C A C C A T C A G C T T G 196-206 54 GUJ0071 AB063I39 (CA)8 A G A T C C T G C T C C T G G A A T T G C A G C T G C A C T T A A T A C A G G C 160-178 54 GUJ0085 AB063153 (GT)14 A C A A C C A C T T C T C C A G C T A C GCTTGTGCTGCTGTTGCTAA 245-265 55 These 14 microsatellite markers are from 100 microsatellite markers Kayang et al. (2002) published. 3 G U J stands for G i fu University Japanese quail. b Genbank accession number c Repeat array at their 14 microsatellitemarkers as reported by Kayang et al. (2002). b & C Size range (bp) and T A : annealing temperature of 14 microsatellite markers reported by Kayang et al. (2002). 45 Table 2.2. Profiles of 14 Japanese quail microsatellite markers amplified in the random bred line(QO). Locus a name Al le le size (bp) b T C t A N d H 0 f H E g F h GUJ0001 230, 234 58 39 2 0.026 0.026 0.000 GUJ0024 162,164 58 39 2 0.000 0.144 1.000 GUJ0030 171, 173, 175, 177, 179, 181 64 40 6 0.425 0.692 0.389 GUJ0034 217, 233 ,237 ,239 55 40 4 0.450 0.683 0.344 GUJ0040 178, 182 55 31 2 0.419 0.455 0.080 GUJ0044 188, 198,216 57 28 3 0.536 0.649 0.177 GUJ0057 138, 144, 146, 152 64 24 4 0.667 0.696 0.043 GUJ0059 211 ,215 ,217 ,235 50 39 4 0.462 0.548 0.159 GUJ0060 246, 252, 258 60 38 3 0.737 0.590 -0.253 GUJ0065 118, 122, 134 53 32 3 0.688 0.589 -0.170 GUJ0068 208 ,212 ,218 58 25 3 0.440 0.489 0.102 GUJ0070 196, 202,204, 206 56 36 4 0.639 0.543 -0.179 GUJ0071 165, 167, 175, 181 54 37 4 0.486 0.490 0.007 GUJ0085 257,263 58 40 2 0.050 0.049 -0.013 T A : annealing temperature; N : the number of individuals amplif ied; A 0 : the number of observed alleles; H 0 : observed heterozygosity; H E : expected heterozygosity; F| S : inbreeding coefficient. 3 G U J stands for G i fu University Japanese quail. k product size according to L i C o r 4200 (L iCor Inc., L inco ln , N E B ) and R F L P s c a n (L iCo r Inc., L inco ln , N E B ) . c annealing temperature that showed amplification for each locus in the both lines. ^ the number of individuals that showed amplification for each locus in Q O line, e the number of observed alleles for each locus in Q O line. f observed heterozygosity for each locus in Q O line. g expected heterozygosity for each locus in Q O line. ^ inbreeding coefficient for each locus in Q O line. d, e, f, g, & h c a ] c u ] a t e d b y G D A ( v | Q. L e w j s a n d z a yki n ) 2001) 46 Table 2.3. Profiles of 14 Japanese quail microsatellite markers amplified in the inbred line. Locus Al le le size r l a b N° A o H o ' F , s 8 Name (bp) GUJ0001 230 13 1 0.000 0.000 0.000 GUJ0024 162 31 1 0.000 0.000 0.000 G U J 0 0 3 0 171, 173, 175, 177 31 4 0.484 0.515 0.061 GUJ0034 239 31 1 0.000 0.000 0.000 GUJ0040 178, 182 28 2 0.250 0.363 0.315 GUJ0044 188, 198,216 25 3 0.320 0.523 0.393 GUJ0057 146, 152 14 2 0.357 0.516 0.316 GUJ0059 211 31 1 0.000 0.000 0.000 GUJ0060 246, 252, 258 31 3 0.581 0.544 -0.068 GUJ0065 118, 122 27 2 0.370 0.352 -0.053 GUJ0068 212, 242 9 2 0.111 0.111 0.000 GUJ0070 202, 204 9 2 0.111 0.111 0.000 GUJ0071 165, 181 26 2 0.308 0.317 0.029 GUJ0085 257 31 1 0.000 0.000 0.000 N : the number of individuals amplified; A 0 : the number of observed alleles; H 0 : observed heterozygosity; H E expected heterozygosity; F | S : inbreeding coefficient. a G U J stands for Gifu University Japanese quail . b product size according to L i C o r 4200 ( L i C o r Inc., L inco ln , N E B ) and R F L P s c a n ( L i C o r Inc., L inco ln , N E B ) . c the number of individuals that showed amplification for each locus in the inbred line. d the number of observed alleles for each locus in the inbred line. e observed heterozygosity for each locus in the inbred line. f expected heterozygosity for each locus in the inbred line. g inbreeding coefficient for each locus in the inbred line. ° ' d ' 6 ' f ' & 8 calculated by G D A (v 1.0: Lewis and Zaykin , 2001). 4 7 Table 2.4. The summary of the analysis done by G D A (vl.O: Lewis and Zaykin, 2001) of the 14 Japanese quail microsatellite markers amplified in the random bred line and in the inbred line. Population N b A 0 C H o d H E 6 F,s f Randombred 34.86 3.29 0.430 0.475 0.095 Inbred 23.93 1.93 0.207 0.239 0.140 The random bred line is Q O and the inbred line was derived from Q O and maintained inbred for 17 generations. b N : average number of individuals amplified for each line, c A 0 : average number of observed alleles for each line. d H 0 : average observed heterozygosity calculated by G D A for each line. e H E : average expected heterosygosity calculated by G D A for each line. f F i S : inbreeding coefficient calculated by G D A for each line. Table 2.5. FIT of 2 individuals in the last generation in the pedigree of the inbred line calculated by ENDOG (v3.0: Gutierrez and Goyache, 2005). a Individual F , Tb 44614-1 0.746 44614-2 0.746 44228-1 0.644 44228-2 0.613 c Mean 0.687 (± 0.069) 2 individuals chosen in the last generation in the pedigree of the inbred line and two different pedigrees per each individual chosen to calculate F I T by E N D O G . b F i T per each pedigree of each individual calculated by E N D O G . c The mean F i T of the 4 F I T values. 48 Full-sib Brothers Full-sib Sisters from a related family 2c?x4$ 2 ( ? x 4 ? 2<?x4? Select the progeny from two most prolific cages and use males from one cage and females from the other to set up matings for the next generation 2c?x49 2<?x4? 2 ( ? x 4 ? 2c?x4$ Figure 2.1. The less intensive inbreeding scheme used at the Quail Genetic Resource Centre (QSRC) of the University of British Columbia. 4 9 mre 2.2. The pedigree of 44614-1 in Table 2.5. 50 Figure 2.3-2.5. Examples of polyacrylamide gel showing PCR products. Each lane represents a sample and the empty lanes either are non-emplified products or were unused for the analyses. B 200 175 Figure 2.3. Polyacrylamide gels, analyzed by RFLPscan (LiCor Inc., Lincoln, NEB), showing PCR products for the most polymorphic locus, GUJ0030, in both lines of Japanese quail. A. GUJ0030 in the randombred line B. GUJ0030 in the inbred line 51 D ~— ' V — - ••—-~~— — ' F * — _ 230 Figure 2.4. Polyacrylamide gels showing PCR products for GUJ0034, which was polymorphic in the randombred line and monomorphic in the inbred line. C. GUJ0034 in the randombred line D. GUJ0034 in the inbred line 52 E T V y 9 -an 230 255 Figure 2.5. Polyacrylamide gels showing PCR products for GUJ0068 having many non-emplified products. E. GUJ0068 in the randombred line F. GUJ0068 in the inbred line 53 LITERATURE CITED Aggrey, S. E. (1994) Studies on genetic drift and inbreeding in small populations under artificial selection. 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(2003) Colony and population genetic structure 56 of the Formosan subterranean termite, Coptotermes formosanus, in Japan. Mol . Ecol., 12:2599-608. 57 CHAPTER 3 Conclusion A n d General Discussion 3.1 Amplification of Markers In this research, 24 Japanese quail specific primer sets were chosen from 100 markers Kayang et al. (2002) reported, but 3 of GUJ0079, GUJ0080, and GUJ0082 did not show any PCR product. Besides, GUJ0029, GUJ0067, GUJ0074, GUJ0076, GUJ0089, and GUJ0097 were excluded because they showed non-specific or unscorable bands for so many individuals, or they had too much contamination between individuals during LiCor 4200 (LiCor Inc., Lincoln, NEB) analysis. Moreover, GUJ0047 was monomorphic for both lines, and was not considered further. As a result, for calculation of FIT, 14 of them, GUJ0001, GUJ0024, GUJ0030, GUJ0034, GUJ0040, GUJ0044, GUJ0057, GUJ0059, GUJ0060, GUJ0065, GUJ0068, GUJ0070, GUJ0071, and GUJ0085, were finally analyzed. Even though a total of 71 quail including 40 from the random bred line and 31 from the inbred line were sampled, not all the samples were used for the final analysis for many reasons: drop-outs or amplification failure and LiCor 4200 (LiCor Inc., Lincoln, NEB) gel conditions or contaminations between individuals, and the number of individuals for each locus is shown in Table 2.2 and 2.3. Therefore, the data from this research does not represent all 71 individuals, 58 and it must be important to take care of LiCor 4200 (LiCor Inc., Lincoln, NEB) gel condition and minimize contamination during LiCor 4200 (LiCor Inc., Lincoln, NEB) analyses to be cost effective. 3.2 The Genetic Diversity and The Level of Inbreeding in The Lines From the calculations of the number of alleles, the mean observed and expected heterozygosity, and Fis for both lines, it was found that the inbred line was more inbred than the randombred line, and the randombred line seemed to have a lower level of diversity than wild populations of Japanese quail. FIT calculated from the pedigree of the inbred line, 0.687 (± 0.069) on average and it was estimated 0.567 (± 0.329) from the 14 microsatellite loci analysis, which shows that the high standard deviation of microsatellite markers makes themselves less reliable for calculating inbreeding in the inbred line of Japanese quail, compared to pedigree analysis. Therefore, unless multi-genotyping is available with microsatellite markers, pedigree analysis will be more accurate for estimating inbreeding in an inbred line of Japanese quail. However, the result shows that microsatellite marker analyses can be another option for investigating the level of inbreeding in an inbred line of Japanese quail, when pedigree is not available. Moreover, the results show that the less 59 intensive inbreeding scheme at the Quail Genetic Resource Centre (QSRC) at the University of British Columbia seems efficient for generating an inbred line of Japanese quail because FIT of 5 l h generation through full-sib is calculated as 0.672. 3.3 Comments on Future Study It was shown that the less intensive inbreeding scheme at the QSRC was efficient for generating an inbred line of Japanese quail. However, achieving higher level of inbreeding with this inbred line using the less intensive inbreeding scheme is remained to be seen. This is because up to 8 t h generation of Japanese quail through full-sib mating has been generated and FIT for 6 t h, 7 t h, and 8 t h generation through full-sib mating is calculated as 0.734, 0.785, and 0.826, respectively. Next, even though we found that microsatellite marker and pedigree analyses were comparable methods for estimating the level of inbreeding in an inbred line of Japanese quail, because of the high standard deviation of the microsatellites, more inclusive studies will be required. It is suspected that purging by natural selection can account for the that thanks to the lack of neutrality of microsatellites that D N A segment are affecting to gene(s) expression directly or indirectly, or are linked to other functional gene(s) (DiFiglia et al, 1997; Hagerman and Hagerman, 2004; Moody et al., 2005; Mooers et al., 2005; Bauer et al., 60 2005; Vanita et al, 2006). Huntinton disease is an example, where large C A G repeat expansion causes neurodegeneration disease (DiFiglia et al., 1997). Another interesting research published by Elizabeth et al. (2005) is a report that difference of microsatellite repeats to 5' region of the prairie vole vasopressin la receptor (avprla) gene contributes to the change of socio-behavioral traits. Besides, microsatellite mutation can contribute to-the wrong interpretation of microsatellite analysis thanks to homoplasy caused by the variation of flanking site of the microsatellite, which can lead to underestimating inbreeding. The high standard deviation can be caused by these factors or it is possible that there are other unknown reasons. Therefore, future studies should be more comprehensive to be able to reduce any bias, to understand more about microsatellite markers such as microsatellite mutation or neutrality, and to be able to generalize that microsatellite marker and pedigree analyses are comparable methods to estimate inbreeding in an inbred line of Japanese quail. 61 LITERATURE CITED Bauer, P. O., Zumrova, A. , Matoska, V., Marikova, T., Krilova, S., Boday A., Singh, B., and Goetz, P. (2005) Absence of spinocerebellar ataxia type 3/Machado-Joseph disease within ataxic patients in the Czech population. Eur J Neurol, 12(11): 851-7. DiFiglia, M . , Sapp, E., Chase, K. O., Davies, S. W., Bates, G. P., Vonsattel, J. P., and Aronin, N . (1997) Aggregation of Huntingtin in Neuronal Intranuclear Inclusions and Dystrophic Neurites in Brain. Science, 277: 1990-1993. Elizabeth, A. , Hammock, D., and Young, L. J. (2005) Microsatellite Instability Generates Diversity in Brain and Sociobehavioral Traits. Science, 308: 1630-1634. Hagerman, P. J., and Hagerman, R. J. (2004) Fragile X-associated tremor/ataxia syndrome (FXTAS). Ment Retard Dev Disabil Res Rev, 10(1): 25-30. Kayang B.B., M . Inoue-Murayama, T. Hoshi, K. Matsuo, H. Takahashi, M . Minezawa, M . Mizutani, S. Ito. (2002) Microsatellite loci in Japanese quail and cross-species amplification in chicken and guinea fowl. Genet. Sel. Evol. 34: 233-253 Moody, J. A. , Famula, T. R., Sampson, R. C , and Murphy, K. E. (2005) Identification of microsatellite markers linked to progressive retinal atrophy in American Eskimo Dogs. Am J Vet Res, 66(11): 1900-2. Mooers, B. H. , Logue, J. S., and Berglund, J. A . (2005) The structural basis of myotonic dystrophy from the crystal structure of C U G repeats. Proc Natl Acad Sci USA, 102(46): 16626-31. Vanita, V , Singh, D., Robinson, P. N . , Sperling, K., and Singh, J. R. (2006) A novel mutation in the DNA-binding domain of M A F at 16q23.1 associated with autosomal dominant "cerulean cataract" in an Indian family. Am J Med Genet A, 140A(6): 558-566. 62 APPENDICES Appendix 1. 24 Japanese quail microsatellite markers (Kayang et al, 2002). Locus name Repeat array (5-3') Reverse primer (5-3') Size range (bp) T A N 0 N E H 0 H E GUJ0001 (CA)7TG(CA)13 GAAGCGAAAGCCGAGCCA CAGCACTTCGGAGCACAGGA 231-239 56 4 3.3 0.70 0.70 GUJ0024 (CA) 13 AA(CA)3 TCACACCTTCGGGCTGATCT ATGCGACGGGGTGCCTTAAA 162-174 55 6 4.3 0.80 0.77 GUJ0029 (CA)11CT(CA)2 GAGCATTTCTAGTCTGTCTC ATACACAGGCTAAGGAAACC 140-152 55 5 2.9 0.80 0.66 GUJ0030 (CA)31 TGCACCAATCCCAGCTGTTT AACGCACAATGGAAAGTGGG 167-179 64 5 4.2 0.35 0.76 GUJ0034 (CA)9CG(CA)2 CGTAACGGTCCAATATGGAT TCCACGATGCAGAGGTATTT 219-241 55 5 4.2 0.60 0.76 GUJ0040 (CA)12 GTTGAAGCTCCCATCCCTCC ACACCCCCACGGTCTTTGCA 176-192 55 4 2.3 0.20 0.56 GUJ0044 (CA)16 GCCTTGAAACCTGAGTGATC TGCATTTCAGCAGCTCTCAG 180-220 55 5 3.5 0.75 0.72 GUJ0047 (CA)23 GAGATAAGACTGGCTGGGGC TCACCGTGGCTGGCCAACTT 262-292 55 5 2.4 0.55 0.59 GUJ0057 (CA)12 GGAATGGAAAATATGAGAGC CAGGTGTTAAAGTCCAATGT 132-154 62 5 2.4 0.65 0.59 GUJ0059 (CA)10 GACAAAGTTACAGCTAGGAG TAGGTGCGAAAATCTCTGAC 207-219 50 5 3.4 0.85 0.71 GUJ0060 (CA)9 ATGCTATGGGAACCTCACTC TATAAAGCAGGGGGACATGG 132-168 60 5 1.6 0.40 0.38 GUJ0065 (CA)13 GCGTGCCATTTACTTCCCGG AGCCAGGATGACCAGGAAGG 109-131 55 5 2.3 0.55 0.57 GUJ0067 (CA)14 ACGTACGAGCTCAACATTTG GCGTGCATAAAGGCAACTTA 121-131 55 5 2.8 0.85 0.65 GUJ0068 (CA)13 TAGGAGAGGTCACGATTTGC ATCTTAACTCGCCCAGCCTT 204-216 54 5 3.6 0.60 0.72 GUJ0070 (CA)9 AAACCCCAAAGAAGCTGTCC ACGTTGTCACCATCAGCTTG 196-206 54 6 4.3 0.62 0.77 GUJ0071 (CA)8 AGATCCTGCTCCTGGAATTG CAGCTGCACTTAATACAGGC 160-178 54 6 2.0 0.30 0.49 GUJ0074 (CA)10 GTTGTCCTGGCTGAGATGGC GGGTTTGAGGGCTTGGGGTT 290-298 59 3 2.2 0.60 0.54 GUJ0076 (CA)4AA(CA)9 GTATCAGTGCATGCTCGTCC TCGAGGACTGGCTGGAAAAT 208-230 57 5 2.3 0.80 0.57 GUJ0079 (CA)12 GAAAGATAAGCATGAGTGAC GTTTTGGCATTCACTTCAGA 121-135 55 6 3.0 0.65 0.67 GUJ0080 (CA)9 TTGAAGGGACATAGGGAAGC GAAAACGGTGAAGTCTGGTG 151-167 54 6 4.2 0.35 0.76 GUJ0082 (CA)9 CTTGGAACACACGGGATGGC TTACCCCTCTTTTCCCCCCG 142-156 59 5 2.7 0.30 0.63 GUJ0085 (GT)14 ACAACCACTTCTCCAGCTAC GCTTGTGCTGCTGTTGCTAA 245-265 55 5 2.4 0.65 0.59 GUJ0089 (CA)12 CCAGTTTAAGCACCAGCATC TGGCAAGTAGTCGTGGAAGA 131-145 55 5 2.5 0.79 0.60 GUJ0097 (CA)14 GGATGCTCAGTGTGGAAAAG GAGCAAGAGGTGAGTGTTTC 131-157 55 5 3.6 0.40 0.72 # The locus code GUJ stands for Gifu University Japanese quail. T A : annealing temperature N 0 : observed number of alleles N E : effective number of alleles H 0 & H E : observed and expected heterozygosity 63 Appendix 2. PCR optimization PCR reactions were performed to seek for the optimal conditions for each primer set using 2% agarose gel. 2 % agarose gel analysis was tested at 100V for 30-45 minutes in the mini electrophoresis boxes (COSMO BIO Co., LTD.). If the agarose gel did not show any product or many non-specific bands were observed, PCR reactions were repeated with the annealing temperature difference of ±2-3 °C until it showed any significant specific bands. The PCR reactions were performed using the following protocol. 1 ul of 20 ng/u.1 Japanese quail genomic D N A was added into the cocktail of 1.5mM of M g C l 2 , 2 u.1 of 2mM dNTP's, 2ul of each of 10pmol/u,l both forward and reverse primers, and 10.4 [i\ of dH20, and 0.6 u.1 of 5 U/u.1 Taq D N A Polymerase. The PCR reactions was started at 94 °C for the first denaturation for 5 minutes, and then 30-40 cycles of each of denaturation at 94 °C for 1 minutes, annealing at optimized temperature for 45-60 seconds, and extention at 72 °C for 1 minutes, and followed by 72 °C for 5-10 minutes. 64 Appendix 3. Polyacrylamide gel production 7% of 25Cm Polyacrylamide gels for a LiCor 4200 were made with the following protocol. After mixing 12.6g of Urea (USB™, Ohio, USA), 4.2ml of Long Ranger® (FMC®, Maine USA), and 7.2ml of 5X TBE (Trisbase, Boric acid, and EDTA, with dH 2 0), d H 2 0 was added up to total volume of 30ml with 200 ill of 10% APS. And then, 10-20 u.1 of T E M E D (Sigma Chemical Co., M O USA) was added after the solution was filtered. 65 Appendix 4. For GUJ0068, allele size of 242 observed only in the inbred line. For GUJ0068, one allele, 242, was observed in the inbred line, which was not found in the random line (Figure 2.5). There are several possible reasons for this observation. First possibility is PCR error, where it is possible that the PCR products for the random bred line did not amplify the allele because of "false homozygosity" (Miller et al, 2002). The quality of research using microsatellite markers with PCR depends on several factors, which are D N A contamination, amplification failure, and allele drop-out (Miller et al, 2002; Piyamongkol et al, 2003). Miller et al. (2002) mentioned that polymerase error causes amplification failure and stochastic sampling error might be the cause of allele drop-out, which means when D N A is pipetted into PCR mixture and the concentration of D N A is very low, by accident, one copy may be copied more than the other, and this situation even causes "false homozygote". It has been shown that using stochastic simulation, low D N A concentration accounts for more than 50 % of the rate of drop-out (Taberlet et al, 1996). Yang et al. (2005) even argued that because of amplification process of PCR, tiny amounts of artifacts or contaminants can affect to the results. Many other studies have suspected about the accuracy of PCR (Wilhelm et al, 2000; Borst et al, 2004; Yang et al, 2005) as well. Another possibility is that several individuals which were dropped out in the random bred 66 line could actually have included the allele, 242. The last and most probable possibility is microsatellite mutation, because this allele was not observed in the research (Kayang et al, 2002) either, in which the range was 204-216. 67 Appendix 5. Annealing temperature The range of annealing temperature for 14 markers was from 50 °C to 64 ° C . Only 6 primer sets, GUJ0030, GUJ0034, GUJ0040, GUJ0059, GUJ0060, and GUJ0071 were amplified at the suggested annealing temperatures in this experiment. The annealing temperatures for other 8 primer sets had to be adjusted. One possible reason for the difference between the annealing temperatures for these 8 markers in this study and those of the study of Kayang et al (2002) could be mutations in the flanking regions of the microsatellite markers (Eggleston-Stott et al, 1997; Achmann et al, 2001). The studies (Eggleston-Stott et al, 1997; Achmann et al, 2001) commented that the mutation to the flanking site of a particular locus can actually cause null allele, which will appear to be homozygote even if it is heterozygote and reducing the annealing temperature for the mutated allele in Lipizzan horses increased the amplification and found the locus thought to be homozygote to be actually heterozygote. The concentration of M g C ^ can affect the amplification as well (Pang et al, 1999; Kayang et al, 2000, Kayang et al, 2002). However, the concentration of MgCh used in the research of Kayang et al (2002) was 1.5 mM, which is the same as in this research. Most of 68 the size ranges for the 14 primer sets from the LiCor 4200 analyses were within those published by Kayang et al. (2002) or were not very different from them (Table 2.2, Table 2.4 and Table 2.5). 69 LITERATURE CITED Achmann R., Curik, I., Dove, R, Kavar, T , Bodo, I., Habe, E , Marti, E., Solkner, J., and Brem., G. (2004) Microsatellite diversity, population subdivision and gene flow in the Lipizzan horse. Anim Genet, 35(4); 285-292. Borst, A. , Box, A . T. A. , Fluit, A . C. (2004) False-positive results and contamination in nucleic acid amplification assays: Suggestions for a prevent and destroy strategy. Eur J Clin Microbiol Infect Dis, 23(4): 289-99. Eggleston-Stott, M . L. , Delvalle, A. , Dileanis, S., Wictum, E., Bowling, A . T. (1997) A single base transversion in the flanking region of an equine microsatellite locus affects amplification of one allele. Animal Genetics, 28; 438-440. Kayang B.B., M . Inoue-Murayama, T. Hoshi, K. Matsuo, H. Takahashi, M . Minezawa, M . Mizutani, S. Ito. (2002) Microsatellite loci in Japanese quail and cross-species amplification in chicken and guinea fowl. Genet. Sel. Evol. 34: 233-253 Kayang, B. B., Inoue-Murayama, M . , Nomura, A. , Kimura, K., Takahashi, H. , Mizutani, M . , and Ito, S. (2000) Fifty microsatellite markers for Japanese quail. J. Hered., 91(6): 502-5. Miller, C. R., Joyce, R and Waits, L . R (2002) Assessing Allelic Dropout and Genotype Reliability Using Maximum Likelihood. Genetics 160:357-366. Pang, S. W. Y., Ritland, C , Carlson, J. E., and Cheng, K. M . (1999) Japanese quail microsatellite loci amplified with chicken-specific primers. Animal Genetics, 30; 195-199. Piyamongkol, W., Bermudez, M . G , Harper, J. C , and Wells, D. (2003) Detailed 72 investigation of factors influencing amplification efficiency and allele drop-out in single cell PCR: implications for preimplantation genetic diagnosis. Mol . Hum. Reproduction Vol.9, No.7: 411-420. Taberlet, P., Friffin, S., Goossens, B., Questiau, S., Manceau, V. et al. (1996) Reliable genotyping of samples with very low D N A quantities using PCR. Nucleic Acids Res. 24:3189-3194. Wilhelm, J., Hahn, M . , Pingoul, A . (2000) Influence of D N A target melting behavior on real-time PCR quantification, Clin. Chem. 46; 1738-1743. Yang, I., Kim, Y. H. , Byun, J. Y., and Park, S. R. (2005) Use of multiplex polymerase chain reactions to indicate the accuracy of the anneling temperature of thermal cycling. Analytical Biochemistry 338: 192-200. 73 

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