@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix skos: . vivo:departmentOrSchool "Medicine, Faculty of"@en, "Medical Genetics, Department of"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Soundara Rajan, Jeffy Rajan"@en ; dcterms:issued "2024-01-31T08:00:00Z"@en, "2020"@en ; vivo:relatedDegree "Master of Science - MSc"@en ; ns0:degreeGrantor "University of British Columbia"@en ; dcterms:description "Developmental coordination disorder is a neuromotor disability of unknown etiology that affects 5–6% of school-aged children. A major hallmark of DCD is difficulty in motor learning, as children with DCD struggle with learning new skills, planning of movement, adapting to change, and automatizing motor patterns. Evidence suggests DCD is highly heritable and phenotypically heterogeneous; however, little is known about the genetic basis of DCD. Hence, this study aims to investigate motor behaviors that reflect the core symptoms of human DCD in BXD recombinant inbred strains of mice and correlate phenotypic traits to the known genotypes of the lines of BXD mice using sophisticated bioinformatic tools in the hopes of finding underlying genetics of the disorder. A total of 12 different BXD inbred lines and the two parental strains (B6 and DBA) were phenotypically examined. We conducted three phases of phenotyping: a neurodevelopmental battery post-natal day (P1-P15), general motor testing (P60-P81), and a motor learning battery (P90-P120), designed to focus on similarities to the symptomology of the human condition of DCD. To date, we have found nine statistically significant QTLs and several suggestive QTLs associated with our measures of general and skilled motor function, which are defined at a particular chromosomal locus within a 1.5 LOD support interval of the QTL. Of 304 genes, we identified 14 candidate genes based on expression, function and polymorphisms within the mapped QTL intervals. Of these 14 candidates, four genes (Cp1x1, Idua, Nrip1, Ltn1) were classified as priority genes that met all our criteria and were believed to have the highest impact on phenotype. To date, no connections have been found between these candidate genes and DCD-related pathogenesis. However, we have identified overlapping loci with previously reported phenotypic data within our mapped QTL interval of motor phenotypes. The findings of this study provide novel insights into genes that may influence DCD-like motor behavior. In the long term, uncovered genes with associated variation in motor phenotypes could provide insights into genetic factors underlying DCD in the human population and help provide opportunities for early and tailored intervention in children at risk for developmental difficulties."@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/74768?expand=metadata"@en ; skos:note " INVESTIGATING MOUSE MOTOR ACTIVITY AND LEARNING BEHAVIOR USING QUANTITATIVE TRAIT LOCUS (QTL) ANALYSIS TO ELUCIDATE THE GENETIC UNDERPINNINGS OF DEVELOPMENTAL COORDINATION DISORDER (DCD) by Jeffy Rajan Soundara Rajan M.Phil., University of Madras, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2020 © Jeffy Rajan Soundara Rajan, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Investigating mouse motor activity and learning behavior using quantitative trait locus (QTL) analysis to elucidate the genetic underpinnings of developmental coordination disorder (DCD) submitted by Jeffy Rajan Soundara Rajan in partial fulfillment of the requirements for the degree of Master of Science in Medical Genetics Examining Committee: Dr. Daniel Goldowitz, Professor, Medical Genetics, Faculty of Medicine, UBC Supervisor Dr. Jill Zwicker, Associate Professor, Occupational Science and Occupational Therapy, Faculty of Medicine, UBC Medicine, UBC Supervisory Committee Member Dr. Jan M. Friedman, Professor, Medical Genetics, Faculty of Medicine, UBC Supervisory Committee Member Dr. Suzanne M.E. Lewis, Clinical Professor, Medical Genetics, Faculty of Medicine, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Catherine Van Raamsdonk, Associate Professor, Medical Genetics, Faculty of Medicine, UBC Supervisory Committee Member iii Abstract Developmental coordination disorder is a neuromotor disability of unknown etiology that affects 5–6% of school-aged children. A major hallmark of DCD is difficulty in motor learning, as children with DCD struggle with learning new skills, planning of movement, adapting to change, and automatizing motor patterns. Evidence suggests DCD is highly heritable and phenotypically heterogeneous; however, little is known about the genetic basis of DCD. Hence, this study aims to investigate motor behaviors that reflect the core symptoms of human DCD in BXD recombinant inbred strains of mice and correlate phenotypic traits to the known genotypes of the lines of BXD mice using sophisticated bioinformatic tools in the hopes of finding underlying genetics of the disorder. A total of 12 different BXD inbred lines and the two parental strains (B6 and DBA) were phenotypically examined. We conducted three phases of phenotyping: a neurodevelopmental battery post-natal day (P1-P15), general motor testing (P60-P81), and a motor learning battery (P90-P120), designed to focus on similarities to the symptomology of the human condition of DCD. To date, we have found nine statistically significant QTLs and several suggestive QTLs associated with our measures of general and skilled motor function, which are defined at a particular chromosomal locus within a 1.5 LOD support interval of the QTL. Of 304 genes, we identified 14 candidate genes based on expression, function and polymorphisms within the mapped QTL intervals. Of these 14 candidates, four genes (Cp1x1, Idua, Nrip1, Ltn1) were classified as priority genes that met all our criteria and were believed to have the highest impact on phenotype. To date, no connections have been found between these candidate genes and DCD-related pathogenesis. However, we have identified overlapping loci with previously reported phenotypic data within our mapped QTL interval of motor phenotypes. The findings of this study provide novel insights into genes that may influence DCD-like motor behavior. In the long term, uncovered genes with associated variation in motor phenotypes could provide insights into genetic factors underlying DCD in the human population and help provide opportunities for early and tailored intervention in children at risk for developmental difficulties. iv Lay Summary About one in 20 children in school has developmental coordination disorder (DCD), a condition that affects learning of motor skills and performance of daily tasks, such as tying shoelaces, printing, or playing sports. The causes of DCD are not known, but researchers suspected that genetics may play a part in the disorder. To explore the possible causes and genetics of DCD, I used a series of specially bred mouse line to measure the differences in DCD-like behavior on various motor tasks. I then looked to see if performance on these tasks was related to known genotypes of the different mice. In this study, I identified nine promising regions of the genome and 14 candidate genes (4 priority genes) that influence DCD-like behavior. The next step is to examine if these genes are associated with DCD in humans. v Preface I shared in the collection of behavioral data. I performed the QTL data analysis, and creation of all illustrations and figures of this study, except the following: Figure 1.1 Co-occurring conditions of Developmental Co-ordination Disorder (DCD) adapted from CanChild website. Figure 1.2 Generation of BXD recombinant inbred panel adapted from Gini and Hager (2012) publication. Kamaldeep Gill (PhD candidate supervised by Dr. Jill Zwicker) and I share the chapter 2 behavioral portion of the data collection. Eric Chow and Alex Pai, undergraduate student in the Goldowitz lab, were involved in the data analysis of the behavior tasks including gait analysis, horizontal ladder walking task and skilled reaching task. Portions of Chapter 2 have been published in: Gill, K., Soundara-Rajan, J., Goldowitz, D., Zwicker, J.G. (2020) Using a mouse model to gain insights into developmental coordination disorder. Genes, Brain, and Behavior, 19(4), e12647. As the second author, I contributed to the data collection, data analysis, and genetic portion of the manuscript. Dr. Jill Zwicker and Dr. Daniel Goldowitz were co-senior authors on this project and were involved in the design of research, data analysis and manuscript edits. Chapter 3 (sub-sections 3.2 and 3.3) and Chapter 4 contain unpublished data (focussed on genotype and phenotype association, behavior motor phenotypes measures and gait analysis) that are part of three different manuscripts in preparation. For these manuscripts, I will have the following responsibilities: For manuscript 1, which is centered around Chapter 3, sub-section 3.2, I will be largely responsible for the collection and analysis of the complex wheel behavioral data. For manuscript 2, which is centered around sub-section 3.3, I contributed to data collection, QTL analysis, and writing some of the first draft of the manuscript. Manuscript 3, which is vi centered around Chapter 4, will be the body of this thesis. I was responsible for data collection, analysis, and writing. Mice used in this study were housed, bred, and euthanized in accordance with ethical guidelines. All procedures were approved by the Animal Care Committee at the University of British Columbia (Animal Care Certificate: A17-0069). vii Table of Contents Abstract .................................................................................................................. iii Lay Summary ......................................................................................................... iv Preface ...................................................................................................................... v Table of Contents ................................................................................................. vii List of Tables ........................................................................................................... ix List of Abbreviations ............................................................................................ xii Acknowledgements ............................................................................................... xiv Dedication .............................................................................................................. xv Chapter 1 : Introduction ......................................................................................... 1 1.1 What is Developmental Coordination Disorder (DCD)? ............................................ 1 1.2 Prevalence of DCD ...................................................................................................... 2 1.3 Diagnostic criteria of DCD .......................................................................................... 3 1.4 Aetiology of DCD ....................................................................................................... 3 1.5 Co-morbidities associated with DCD .......................................................................... 5 1.6 Common environmental risk factors linked with DCD ............................................... 7 1.7 Genetic influences of DCD ......................................................................................... 8 1.8 Potential approaches for the study of genetic architecture underlying DCD ............ 10 1.9 QTL analysis with GeneNetwork as a tool ................................................................ 13 1.9.1 Quantitative trait locus analysis (QTL) ................................................................. 13 1.9.2 The BXD recombinant inbred (RI) panel as a tool for complex trait analysis ...... 15 1.9.3 Strategies for QTL gene(s) detection .................................................................... 18 1.10 Aim and objectives of the thesis ................................................................................ 19 Chapter 2 : Investigation of naturally occurring DCD-like phenotypic differences and regions in the genome contributing to those motor differences in BXD RI strains. ................................................................................................. 20 2.1 Introduction ............................................................................................................... 20 2.2 Materials and methods ............................................................................................... 22 2.2.1 Animals .................................................................................................................. 22 2.2.2 Behavioral Measures: Fox Neurodevelopmental Battery at Postnatal Day P1-P15 ............................................................................................................................... 23 2.2.2.1 Righting reflex ............................................................................................... 23 2.2.2.2 Negative geotaxis .......................................................................................... 23 2.2.2.3 Cliff aversion ................................................................................................. 24 2.2.2.4 Forelimb grasp ............................................................................................... 24 2.2.3 Behavioral Measures: Generalized tests of motor function at Postnatal Day P60-P81 ............................................................................................................................. 24 2.2.3.1 Gait analysis .................................................................................................. 24 2.2.3.2 Open field ...................................................................................................... 25 viii 2.2.3.3 Standard rotarod ............................................................................................ 25 2.2.4 Behavioral Measures: Tests of skilled motor function at Postnatal Day P90-P120 ........................................................................................................................... 26 2.2.4.1 Accelerating Rotarod ..................................................................................... 26 2.2.4.2 Horizontal ladder rung walking task ............................................................. 26 2.2.4.3 Skilled motor reaching task ........................................................................... 27 2.2.4.4 Complex wheel .............................................................................................. 27 2.2.5 Statistical analysis ................................................................................................. 28 2.2.6 QTL mapping and candidate gene analysis ........................................................... 28 Chapter 3 : Results ................................................................................................ 31 3.1 Underlying genetics of nervous system maturation .................................................. 31 3.2 Underlying genetics of postural control, locomotor activity, anxiety level and motor coordination ........................................................................................................................... 34 3.3 Underlying genetics of balance, fore- & hindlimb placement, skilled motor movements and learning ........................................................................................................ 42 3.4 Genes and SNPs within a significant QTL region ..................................................... 56 Chapter 4 : Discussion .......................................................................................... 59 4.1 Discussion and significance of findings .................................................................... 59 4.2 Research conclusion .................................................................................................. 71 4.3 Strength and weaknesses of the research ................................................................... 72 4.4 Future directions ........................................................................................................ 72 Bibliography .......................................................................................................... 73 Appendices ........................................................................................................... 100 Appendix A ......................................................................................................................... 100 Appendix B .......................................................................................................................... 101 Appendix C .......................................................................................................................... 102 ix List of Tables Table 1.1 DSM-5: Diagnostic criteria for developmental coordination disorder ............................ 3 Table 1.2 Types of validity used to measure the application of a rodent model to human disease ....................................................................................................................................................... 16 Table 2.1 Selection of BXD lines based on phenotype ................................................................. 21 Table 3.1 Fox neurodevelopmental battery results by strain ......................................................... 33 Table 3.2 Gait analysis parameter results by strain ....................................................................... 36 Table 3.3 Open field parameter results by strain ........................................................................... 38 Table 3.4 Rotarod parameter results by strain ............................................................................... 41 Table 3.5 Accelerating rotarod parameter results by strain ........................................................... 44 Table 3.6 Horizontal ladder rung walking task parameter results by strain .................................. 47 Table 3.7 Skilled reaching task parameter results by strain .......................................................... 50 Table 3.8 Complex wheel task parameter results by strain ........................................................... 53 Table 3.9 Behavioral Tests offered in Postnatal Day (P) P1-120 .................................................. 55 Table 3.10 Genes within the nine significant QTL regions ........................................................... 57 Table 3.11 Identification of the priority genes that meet criteria C- Presence of nonsynonymous SNPs in coding region of the gene ................................................................................................ 58 Table 4.1 Identification of the candidate genes that meet Criterion B- Functional implication in DCD-like behavior ........................................................................................................................ 61 Table 4.2 Identification of the family members with functional implication in motor behavior .. 68 x List of Figures Figure 1.1 Co-occurring conditions of Developmental Co-ordination Disorder (DCD) (based on CanChild website (canchild.ca) ....................................................................................................... 5 Figure 2.1 Workflow of the behavioral testing in Postnatal Day (P) P1-120. All three phases of testing are proposed based on the DCD-like behavior .................................................................. 22 Figure 2.2 Selection of analytical tools to study complex networks of genes, function and phenotypes ..................................................................................................................................... 30 Figure 3.1 Graphs illustrating average strain response time on Fox neurodevelopmental battery of tasks ........................................................................................................................................... 32 Figure 3.2 Graphs illustrating average performance for seven measures of gait patterns ............. 35 Figure 3.3 Genome-wide linkage map of stance duration, step cycle and PEP (top to bottom) on gait analysis to determine postural control .................................................................................... 37 Figure 3.4 Graphs illustrating average strain differences in open field parameters: total distance travelled, time spent in the center & periphery, baseline distance moving & not moving and velocity .......................................................................................................................................... 38 Figure 3.5 Genome-wide linkage map of velocity on open field task to determine locomotion and anxiety behavior ............................................................................................................................ 40 Figure 3.6 Graphs illustrating average strain response time on rotarod and performance improvement over the three-day testing period ............................................................................. 41 Figure 3.7 Graphs illustrating average strain performance in accelerating rotarod based on Latency to fall, Improvement in performance and Intertrial learning ........................................... 43 Figure 3.8 Genome-wide linkage map of performance improvement on accelerating rotarod to determine motor learning and balance .......................................................................................... 45 xi Figure 3.9 Graphs illustrating average strain performance for fore- & hindlimb placement accuracy, correction and impairment to determine motor learning ............................................... 46 Figure 3.10 Genome-wide linkage map of hindlimb and forelimb placement accuracy on horizontal ladder rung walking task .............................................................................................. 48 Figure 3.11 Graphs illustrating average strain performance on skilled reaching task based on first attempt success, total success and learning rate ............................................................................ 49 Figure 3.12 Genome-wide linkage map of first attempt success on skilled motor reaching task to determine motor learning .............................................................................................................. 51 Figure 3.13 Graphs illustrating average motor learning performance on complex wheel based on latency to fall, overall learning and online learning ...................................................................... 52 Figure 3.14 Genome-wide linkage map of online improvements on complex wheel task to determine motor learning difficulties ............................................................................................ 54 xii List of Abbreviations ACC Animal Care Committee ADHD Attention deficit hyperactivity disorder ADLs Activities of daily living AJs Adherens junctions ARC Activity regulated cytoskeletal-associated protein ASD Autism Spectrum Disorder CDCV Common Disease-Common Variant CDH10 Cadherin 10 CDRV Common Disease-Rare Variant Chr Chromosome CNS Central Nervous system CNVs Copy Number Variations CP1X1 Complexin 1 CRYBB1 Crystallin, beta B1 DARPP-32 Dopamine and cAMP-regulated phosphoprotein of 32 DCD Developmental Coordination Disorder DD Developmental Delay DNMs De novo Mutations DSM‐V Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition EPHX Epoxide hydrolase 2 FHL3 Fhl3 four and a half LIM domains 3 FOXP2 Forkhead-box-protein-P2 GPR20 G protein-coupled receptor 20 GWAS Genome wide association studies ID Intellectual disability IDUA Iduronidase, alpha-L KD Knockdown KO Knockout xiii LD Learning disabilities LOD Likelihood of the odds LRRC55 Leucine rich repeat containing 55 LRS Likelihood Ratio Statistic LTN1 Listerin E3 ubiquitin protein ligase 1 LYNX Ly6/ neurotoxin 1 M Mean (M) MATLAB MATrix LABoratory MPS Mucopolysaccharidoses NFIA Nuclear factor I/A NRIP1 Nuclear receptor interacting protein 1 P Postnatal day PEP Posterior extreme position PGAM5 Phosphoglycerate mutase family member 5 POLE Polymerase (DNA directed), epsilon 4 POU3F POU domain, class 3, transcription factor QTL Quantitative Trait Locus RI Recombinant inbred ROBO Roundabout guidance receptor SEM Standard Error of the Mean SEZ6 Seizure related 6 homolog like SLI Specific language impairment SNP Single nucleotide polymorphism SPARCL SPARC-like 1 SULF1 Sulfatase 1 xiv Acknowledgements My sincere gratitude to my supervisor, Dr. Daniel Goldowitz, for guiding me throughout my Master’s program, for teaching me how to think critically. Thank you for being very supportive and for giving me the tools and opportunities to do the things I am interested and passionate about. The completion of my thesis would not have been possible without his support, patience, motivation and immense knowledge. I would like to extend my heartfelt gratefulness to my supervisory committee members, Dr. Jill Zwicker and Dr. Catherine Van Raamsdonk for their expert assistance, invaluable feedback and suggestions by asking thought-provoking questions that challenged me to defend my work and encouraged me to think creatively. To the wonderful Goldowitz lab family who have contributed immensely to my research experience at CMMT. I would like to thank Joanna, Miguel, Ishita, Cheryl, Yuliya, Erin, Eric, Alex, and Maryam for been a source of friendship as well as a group for good advice. Special thanks to my friend Kamal, who have contributed immensely to my personal and work experience. I will never forget the long hours we spent together in the animal facility weaning and the testing mice. I would like to thank the CMMT mouse core technicians Kayla, Theresa, Qing, Yanbo for the mouse-related services. Honorable mention to all lines of inbred mice, I cannot describe how much I have enjoyed working with these little animals. Last but not least, to my wonderful family, my parents, my brother and my loving husband, thank you for being the pillar of strength in every step of my life, for making me believe in myself and for helping me achieve my dreams. I am forever indebted to you. Above all, I offer my sincere prayer of thanks to Almighty God for his infinite mercy on me in every second of my life. xv Dedication I dedicate this research to my loving family and all of my scientific mentors who kept me engaged, passionate, and curious 1 Chapter 1: Introduction 1.1 What is Developmental Coordination Disorder (DCD)? According to DSM-5, Developmental Coordination Disorder (DCD) is a heterogenous, neurodevelopmental condition broadly defined by deficiency in the development of motor coordination that challenges a child’s performance in many physical and everyday activities of daily living (ADLs), including academic achievement (American Psychiatric Association, American Psychiatric Association DSM-5 Task Force, Psychiatry Online Premium Package, 2013). The predominant characteristic evident in children with DCD is the presence of motor difficulties that impairs the acquisition and execution of motor-related skills which includes gross motor (e.g. running, climbing) and/or fine motor activities (e.g. dressing, closing buttons, tying shoelaces) (Visser, 2003; Wang, T., Tseng, Wilson, & Hu, 2009; Zwicker, Jill G., Suto, Harris, Vlasakova, & Missiuna, 2018), balance (Deconinck, Savelsbergh, De Clercq, & Lenoir, 2010), and activities of daily living (Bart, Jarus, Erez, & Rosenberg, 2011). Thus, individuals with DCD display deficits in postural control, sensorimotor coordination, and motor learning (Biotteau et al., 2019). Children with these deficits (lack of acquisition and execution of motor skills) are usually display more variable performance than that of typically developing peers (Cairney & Veldhuizen, 2013). The condition is idiopathic, hence reduction in child’s motor competence is not attributed to any known medical condition or neurologic dysfunction (e.g., cerebral palsy, muscular dystrophy, visual impairment or intellectual disability) (American Psychiatric Association, 2013; Cermak, 2001). In addition, children with DCD can experience significant secondary consequences of poor motor skills such as - physical, emotional and mental health concerns (e.g., obesity, cardiovascular diseases, depression, anxiety) (Caçola & Killian, 2018; Cairney et al., 2010; Cantell, Crawford, & Doyle-Baker, 2008; Faught, Hay, Cairney, & Flouris, 2005; Kwan, Cairney, Hay, & Faught, 2013; Lingam et al., 2012; Zwicker et al., 2013), including problems with self-worth and self-esteem (Yu et al., 2016; Zwicker et al., 2013) and restriction in social interaction and participation (Green, D. et al., 2011; Poulsen, Ziviani, Johnson, & Cuskelly, 2008; Skinner & Piek, 2001; Izadi-Naiafabadi, Ryan, Ghafooripoor, Gill, & Zwicker, 2019). Depending 2 on the severity of a child’s motor impairment or without sufficient intervention, these complications extend into adolescence in up to 70% of children with DCD (Kirby, Sugden, & Purcell, 2014). 1.2 Prevalence of DCD According to 2019 international guidelines, the prevalence of DCD varies from 2-20% of school-age children, with 50% of children born preterm likely to develop DCD (Edwards et al., 2011). The variability of prevalence largely depends on lack of awareness among clinicians (Gaines, Missiuna, Egan, & McLean, 2008; Kaplan, Wilson, Dewey, & Crawford, 1998; Tsiotra et al., 2006), lifestyle differences in various cultures (Tsiotra et al., 2006), differences in diagnostic tools used for evaluating motor performance (Lingam, Hunt, Golding, Jongmans, & Emond, 2009) and/or differences in terminology used (Polatajko, Fox, & Missiuna, 1995). Therefore, epidemiological information varies across diagnostic sites and countries, from 4.9-8.6% in Sweden (Kadesjo & Gillberg, 1999); 10% in Britain (Lingam et al., 2009); up to 15.6% in Singapore (Wright, Sugden, Ng, & Tan, 1994); 19% in Greece (Tsiotra et al., 2006); 22% in Australia (Cermak, 2001), 2.8% in Germany (Geuze, Sugden, & Chambers, 2005), with 5-6% as the commonly reported general prevalence of DCD (American Psychiatric Association, 2013). Higher prevalence rates across the globe suggest that there may be a difference in cut-off scores used for evaluating motor impairment in children with DCD (Polatajko, Fox, & Missiuna, 1995). DCD is more common in males as compared to females, with a sex ratio of approximately 3:1 (Missiuna et al., 2008) to 7:1 (Kadesjo & Gillberg, 1999) in clinical studies. One exception is a study by Girish and colleagues, who reported the prevalence of DCD in females (1.1%) was twice that of males (0.5%) in a population-based study (Girish, Raja, & Kamath, 2016). In another population-based study of children with DCD, the sex ratio was reported to have a more equal distribution between the sexes (Missiuna, Cairney, Pollock, Cousins, & MacDonald, 2009). The greater reported prevalance in males may be due to referral bias. However, in clinical populations, such as preterm infants, male infants tend to have poorer neurological outcomes, 3 suggesting that there may also be a biological basis for higher rates of DCD in boys (Kent et al., 2012). 1.3 Diagnostic criteria of DCD According to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5, (American Psychiatric Association, 2013), the diagnosis of children with DCD is assessed using the following four criteria: (Table 1.1). Table 1.1 DSM-5: Diagnostic criteria for developmental coordination disorder 1.4 Aetiology of DCD The underlying mechanisms of the motor problems in DCD are largely unclear. There are, however, some interesting clues about the aetiology of DCD from behavioral studies of information processing factors (i.e., disruption of perceptual and /or cognitive processing) and performance deficits involved in motor control systems (Wilson & McKenzie, 1998). The meta-analysis findings on information processing factors reported, regardless of whether a motor response was required, children with DCD have problem with visuospatial processing (Wilson & McKenzie, 1998). Similarly, in a meta-analysis, children with DCD were found to have deficits in Criterion A The motor coordination and performance skills must be below average according to the child’s chronological age and opportunity for learning Criterion B The motor impairment significantly and persistently interferes with daily life situations. Criterion C Onset of symptoms is manifested in the early developmental period Criterion D Difficulties with motor skills are not be related to intellectual disability, visual impairment or other neurological conditions (e.g. cerebral palsy, muscular dystrophy, degenerative disorders) 4 generating and monitoring internal models of movement (Wilson, Ruddock, Smits‐Engelsman, Polatajko, & Blank, 2013). It is unlikely to explain a complex disorder (Yu et al., 2016) like DCD as a single deficit. Therefore, other possible factors have been explored by researchers that include sensory processing dysfunction, attention and execution dysfunction, genetics, and environmental issues. Using motor skills data, Allen and Casey (2017) investigated sensory processing and integration. Of the total sample tested, they found 88% of children with DCD had differences in sensory processing and integration with a presentation of difficulties in social participation, hearing, body awareness, planning, balance and motion (Allen & Casey, 2017). However, sensory processing difficulties are not a core feature of DCD and are more likely a co-occurring condition. The study by Bernardi et al. (2017), showed that deficits in executive functioning is persistently observed in children with poor motor skills (DCD). However, it is unclear whether and to what extent the problems with executive functions (planning, time management, memory, and decision‐making) were altered by co-occurring disorders (or not) (Bernardi, Leonard, Hill, Botting, & Henry, 2017). Likewise, Tal-Saban and colleagues demonstrated high percentage of attention difficulties in young adults with DCD in comparison to typically developing young adults (Tal-Saban, Ornoy, and, Parush, 2014). It is also suspected that there is a familial component involved in motor coordination impairment, but little is known about the specific genetic factors or the relationship between genetic and environmental factors (Fliers et al., 2009). Given the heterogeneity of DCD, the cause of DCD is not limited to a single brain area or network (Kaplan, Crawford, Cantell, Kooistra, & Dewey, 2006; Zwicker, Missiuna, Harris, & Boyd, 2010; Zwicker, Missiuna, Harris, & Boyd, 2012). So far, the cerebellum (Debrabant, Gheysen, Caeyenberghs, Waelvelde, & Vingerhoets, 2013; Zwicker, Missiuna, & Boyd, 2009; Zwicker, Missiuna, Harris, & Boyd, 2011), parietal cortex (Kashiwagi, Iwaki, Narumi, Tamai, & Suzuki, 2009) and basal ganglia networks (Groenewegen, 2003) have been proposed as possible foci for the neural underpinnings of DCD. Another strong body of literature has linked the potential cause of DCD to atypical brain development (Brown-Lum & Zwicker, 2015; Caçola & Killian, 2018). For example, Brown-Lum & Zwicker (2015) revealed differences in brain activation patterns (i.e., fronto-central cortical regions and right dorsolateral prefrontal cortex) and established the notion that children with DCD are neurobiologically different and use different 5 patterns of neural activation during motor task performance than typically developing peers. However, to fully understand the etiology of DCD, more evidence is needed. 1.5 Co-morbidities associated with DCD The term co-morbidity refers to the presence of two or more disorders in the same individual. Neurodevelopmental disorders often present with co-morbidities, and studies have reported that a significant number of children diagnosed with DCD have also been diagnosed with other disorders such as attention deficit hyperactivity disorder (ADHD), autism spectrum disorder, (Martin, Neilson C., Piek, Baynam, Levy, & Hay, 2010), learning disabilities (LD) and / or specific language impairment (SLI) (Blank et al., 2019; Edwards et al., 2011; Larsen, Mortensen, Martinussen, & Nybo Andersen, 2013; King-Dowling, Missiuna, Rodriguez, Greenway, & Cairney, 2015; Zwicker, Missiuna, Harris, & Boyd, 2012). The co-morbidity affects the quality of life of children with DCD to a greater degree than the motor disorder. Hence, it is essential to understand, co- morbidity in DCD is the “rule” rather than the “exception” (Lingam et al., 2012). Figure 1.1 Co-occurring conditions of Developmental Co-ordination Disorder (DCD) (based on CanChild website (canchild.ca) 6 In the last few decades extensive research in this area has identified a high level of overlapping comorbidity between DCD and other neurodevelopmental disorders (Figure 1.1). More specifically, the comorbidity between DCD and ADHD has been particularly well explored. It has been estimated that up to 50% of children with DCD present with comorbid ADHD (Fliers et al., 2009; Piek & Skinner, 1999), alongside evidence suggesting a strong genetic component between two disorders (Fliers et al., 2009; Martin, Neilson C., Piek, & Hay, 2006). In addition, the co-occurrence with Autism Spectrum Disorders (ASD) has been recently explored and reported to be high with a remarkable overlap in symptoms (Cacola, Miller, & Williamson, 2017), in which 79% of children diagnosed with ASD were found to have defined movement impairments consistent with DCD (Green et al., 2009). In recent years, another area that has gained considerable interest is the link between specific language impairment (SLI) and DCD, in which over 32% of children with SLI presented with a severe motor difficulty and were more prone to have problems with receptive and expressive language problems (Flapper & Schoemaker, 2013). The co-occurrence of LD among children with DCD has been found to affect the severity of perceptual-motor dysfunction (Jongmans, Smits-Engelsman, & Schoemaker, 2003) and further confirmed (Schoemaker, Lingam, Jongmans, van Heuvelen, & Emond, 2013) by showing a higher risk for impairments in relation to ADL, attention, reading, handwriting, and social cognition than those with moderate motor difficulties. Other commonly associated problems are psychosocial difficulties (Zwicker et al., 2013) , obesity (Hendrix, Prins, & Dekkers, 2014) , joint hypermobility (Jelsma, Geuze, Klerks, Niemeijer, & Smits-Engelsman, 2013), poor physical fitness (Oudenampsen et al., 2013), and reduced involvement in day to day physical and social activities (Izadi-Najafabadi et al., 2019; Zwicker et al., 2013). Eventually, these problems can lead to internalising problems such as anxiety and depression in individuals with DCD (Cairney, Rigoli, & Piek, 2013; Missiuna et al., 2014), and more externalizing behaviours when compared to typically developing children (Zwicker, Harris, & Klassen, 2013). Cairney et al. (2010) and Cairney, Rigoli, and Piek (2013) have proposed an environmental stress hypothesis that suggests impairment of primary motor coordination might expose a child to multiple secondary stressors (e.g., inadequate participation in physical activities, obesity and frequent exposure to bullying) that collectively contribute to lower appraisals of oneself and, ultimately lead to mental health problems. The high co-occurence of DCD with other neurodevelopmental disorders suggests that there may be a shared susceptibility gene(s); this emphasizes the need for systematic phenotyping 7 when investigating the genetics of neurodevelopmental disorders. Furthermore, these data provide growing evidence supporting a genetic basis for DCD (Mosca et al., 2016). However, to better understand the aetiology of DCD, it is also important to understand the complex interplay between environmental and biological factors. 1.6 Common environmental risk factors linked with DCD In relation to diagnosis and intervention, environmental factors, such as physical, social and attitudinal environments (e.g. low-income/socio-economic backgrounds and low birth weight/low gestational age) (Edwards et al., 2011; Lingam et al., 2009), play a critical role in influencing movement in children. In recent years several studies have explored the risk factors of impairment through early environmental processes, particularly pre- and perinatal influences with decreasing gestational age in the aetiology of DCD. For example, the Avon longitudinal study of parents and children found a higher risk of DCD with a shorter gestational period (<37 weeks) and lower birthweight (<2500g) (Lingam et al., 2009). In the following year, the same group identified additional difficulties in attention, social skills, reading, and spelling (Lingam et al., 2010). In a systematic review of school-aged children, an increase of DCD was noted in children who were born very preterm (<1500g) compared to term-born children (Edwards et al., 2011). To further investigate possible aetiological factors in DCD, Pearsall-Jones and colleagues (2009) studied birth difficulties of second born twins that were at high risk of oxygen perfusion problems. This study reported twice as many second- as compared to first-born twins meet the criteria for a motor disorder. Second born twins also reached lower scores on 1 min Apgar (Appearance, Pulse, Grimace, Activity, and Respiration), gross motor scores and bimanual dexterity (i.e., hand dominance). Overall, seven of the nine twins who met criteria for DCD experienced perinatal oxygen perfusion problems. Presently it is not clear whether movement difficulties in these twins leads to perinatal oxygen perfusion problems (Pearsall-Jones, Piek, Rigoli, Martin, & Levy, 2009), or if they are due to the prenatal complications to the brain, heart or lungs (Morley, 2005). Another important factor to consider is obesity. Typically, children with DCD show limited physical activity/movement and therefore are prone to being overweight; this further leads to impaired performance in day-to-day activities and mental health problems(Hendrix et al., 2014; Ussher, Owen, Cook, & Whincup, 2007). Using body mass index (BMI), (Cairney, 8 Hay, Faught, and Hawes (2005) found higher prevalence of overweight, obese and impairment in motor ability in boys with DCD by approximately 19.2%, but there was no difference observed in girls when compared to the typically developing children. In contrast, other studies revealed no sex difference of DCD proportion within BMI-groups (Cairney et al., 2010; Hondt, Deforche, Bourdeaudhuij, & Lenoir, 2009). 1.7 Genetic influences of DCD It is generally agreed that DCD is highly heritable (~70%) (Lichtenstein, Carlstrom, Rastam, Gillberg, & Anckarsater, 2010; Martin, Piek, & Hay, 2006) however, it is surprising to find that only a few studies have explored the genetic profiles of individuals with DCD. In a genome-wide association study (GWAS). Fliers et al., (2012) investigated the association of motor coordination problem with genes expressed in nerve tissue and skeletal muscle. Even though, GWAS could not obtain significant findings, further bioinformatic analysis showed an association between coordination problems in ADHD and genes involved in neurite outgrowth and skeletal muscle function. The study also highlighted that motor impairment may extend beyond the neurological level and be obvious at the muscular level too. Other genetic findings focused on exploring mechanisms of movement dysfunction in individuals with specific structural abnormalities. For example, researchers investigated behavioral phenotypes of individuals presenting with 7q11.23 duplication syndrome (Dup7) (Hanson et al., 2015) and 16p.11.2 deletion (Morris et al., 2015). Although the focus of these studies was on Williams syndrome and autism-related characteristics, a broad range of developmental issues, including DCD, was found among patients with this particular deletion and duplication (Hanson et al., 2015; Morris et al., 2015). Moreover, an increase in co-occurring problems (Bishop, 2002; Lichtenstein et al., 2010; Martin et al., 2006) further complicates the understanding of aetiology of DCD. In a twin-based study, (Bishop (2002) suggested a shared genetic link between motor and language difficulties, and Martin et al. (2006) found a strong additive genetic component between ADHD and DCD. Specifically, Lichtenstein and his colleagues validated, when ASD was diagnosed in a monozygotic twin, the probability of co-occurrence in the other twin was ~ 15% (Martin et al., 2006) for ADHD and ~ 12% for DCD (Lichtenstein et al., 2010). 9 Several highly heritable neurodevelopmental disorders are often associated with deficits in fine motor skills (Bhat, Landa, & Galloway, 2011; Meyer & Sagvolden, 2006). Molecular genetic research suggests that the heritable variation in components of the dopaminergic (DAergic) system leads to poor cognitive performance and motor learning (Costa, 2007; Faraone & Mick, 2010). Likewise, Qian et al. (2013) examined the effect of naturally occurring genetic variation in the dopamine (DA) system on the acquisition and performance of fine motor skills in mice. The results supported the notion of genetically induced variation in frontostriatal DAergic neurotransmission contributes to differences in motor skill learning (Qian, Chen, Forssberg, & Diaz Heijtz, 2013). Mosca et al. (2016) was the first to test the genetics of a clearly defined sample of 82 children with DCD, with and without co-occurring ADHD and reading disorder. They examined copy number variations (CNVs) and structural variations within the genome and found greater genomic variation, with estimates around 26% of the DCD cohort displaying rare de novo CNVs, and 64% inherited CNVs from a parent who also had a neurodevelopmental disorder. The study also identified an enrichment of duplications for brain expressed genes that overlap with other neurodevelopmental disorders including 16p11.2, 22q11.2, SHANK3, GAP43, RBFOX1, FHIT and PTPRN2. There was also presence of DCD cohort specific variations (e.g. SHANK3) that provided strong evidence supporting shared susceptibility genes for DCD and other neurodevelopmental disorders (Mosca et al., 2016). To further improve our understanding of shared aetiology and complex diseases, an excellent model system is necessary to identify overlapping genes and pathways across neurodevelopmental disorders. For example, mouse models of 16p11.2 and 22q11.2 CNV demonstrated social behavior deficits, which in turn were identified to be associated with neuropsychiatric disorders, such as intellectual disability (ID), ASD and ADHD (Arbogast et al., 2016; Hiroi, Hiramoto, Harper, Suzuki, & Boku, 2012). These findings provide compelling evidence to start unravelling the genetic aetiology of DCD. 10 1.8 Potential approaches for the study of genetic architecture underlying DCD A genome is defined as the complete haploid genetic complement of a typical cell that includes all of its genes. Each genome carries variants and mutations that are shared across or specific populations to contribute a common or rare disease. Hence, gene identification in relation to a specific disorder is quite challenging. Traditionally, the variations within families are screened to identify links between genes and disorders. In order to narrow down the genes within significant QTLs to a few candidate genes, we would need to test several siblings from the same family. Considering a complex disorder like DCD there are other possible approaches - Mendelian traits, gene dosage, contribution of same gene to distinct disorders, common disease-common variant hypothesis, common disease-rare variant hypothesis, copy number variants (CNVs) and genome-wide approach can be used (Newbury, 2019). Mendelian traits: A Mendelian trait is one that exhibits a pattern of inheritance that is passed down by dominant or recessive alleles of a single defective gene. In human genetics, for example, Huntington’s disease, follows a dominant pattern of inheritance where an affected person passes the disease allele to the offspring; there is 50% chance the offspring will inherit the disease allele (Mendelian dominant trait). Cystic fibrosis, on other hand, demands two copies of a disease allele for an individual to express the phenotype. Typically, the parents of an affected individual are not affected but act as gene carriers and have a 25% risk of having an affected offspring. The alleles for certain conditions (e.g., muscular dystrophy) are much more common in men than they are in women due to their X-linked inheritance pattern and have a 50% chance for the offspring to inherit the disease allele. Such clear inheritance patterns are rarely reported in conditions like DCD. For example, a three-generation family is described to have severe developmental verbal dyspraxia inherited in an autosomal dominant fashion (Hurst, Baraitser, Auger, Graham, & Norell, 1990). The genome mapping approach led to the identification of a FOXP2 (Forkhead-box-protein-P2) mutation (Fisher, Vargha-Khadem, Watkins, Monaco, & Pembrey, 1998) in a critical region (Lai, Fisher, Hurst, Vargha-Khadem, & Monaco, 2001) of motor control that restricts their speech production. Mendelian and complex traits are two ends of a scale ranging from clearly monogenic, through oligogenic to polygenic, each probably induced by other environmental and gene-gene interaction components (Botstein & Risch, 2003; Dean, 2003). 11 Gene dosage: A change in gene dosage by gene insertions or deletions can have significant phenotypic consequences (e.g., FOXP2). In mice, homozygous knockout (KO) of FOXP2 showed severe motor impairment and premature death (Weiguo Shu et al., 2005), whereas heterozygous knockout (KO) of FOXP2 displayed developmental delays (Fujita, Tanabe, Shiota, Ueda, Suwa, Momoi, & Momoi, 2008). Thus, FOXP2 is a critical gene for survival and reduced levels lead to disorder. Copy number variants (CNVs) are structural variants involving alterations in the number of copies of specific regions of DNA, which may result in duplication or insertional transpositions (gains), deletion (losses), or complex rearrangements of the genome sequence that typically range from kilobases to megabases in length (Feuk et al., 2006; Iafrate et al., 2004). Most CNVs are harmless in a healthy individual. However, some have a higher likelihood with a large burden of CNVs of causing human diseases, including neurodevelopmental disorders (e.g., intellectual disability, schizophrenia, autism and bipolar disorder) (Girirajan et al., 2011; Green et al., 2016; Stefansson et al., 2014). Several hotspots for structural variants have been also identified on chromosomes 1, 3, 15, 16 and 22 as a target risk of neurodevelopmental disorder (Torres, Barbosa, & Maciel, 2016). Recent findings found that the rate of large CNVs in individuals with dyslexia and developmental language disorder have modest increase when compared to control groups (Gialluisi et al., 2016; Simpson et al., 2015), leading some researchers to suspect an association between disease severity and CNV burden. Contribution of same gene to distinct disorders: Human genetic studies have commonly shown that mutations in the same gene or same genomic region can increase the risk of multiple disorder (Zhu, Need, Petrovski, & Goldstein, 2014). For instance, mutation in DCDC2 gene has been functionally linked at the cellular level to dyslexia, sclerosing cholangitis and deafness (Girard et al., 2016; Schumacher et al., 2006). The CNTNAP2 gene is another excellent example of a single gene implicated in multiple disorders including autism spectrum disorder (ASD), schizophrenia, intellectual disability, dyslexia and language impairment, which were believed to share similar underlying genetic mechanisms (Rodenas-Cuadrado, Ho, & Vernes, 2014). Common disease-common variant hypothesis: It is evident that in recent decades the diverse knowledge of the effects of genetic variation upon disease has led many researches to revisit the 12 way in which genetic disorders are understood. The common disease-common variant (CDCV) hypothesis predicts a few common allelic variants that contribute to the genetic variance in disease susceptibility (Reich & Lander, 2001). These common alleles are not population specific but are present at >1% minor allele frequency in multiple populations. Therefore, it is likely to broadly assay the whole human genome using SNP-based genotyping platforms with as few as several hundred thousand markers (Altshuler et al., 2010; Hardy & Singleton, 2009). Following this difference in allele frequencies between disease case and control cohorts provides evidence for disease association (e.g., hypertension) (Abraham & Cho, 2012). Common disease-rare variant hypothesis: In the case of a common disease-rare variant (CDCV) model the variant effects may be large in a few individuals but are not common enough to explain much variance or result in genome-wide significance (Gibson, 2012). The rare variants are not suggested to be necessary or sufficient to cause a disorder but will carry an increased risk over common variants (e.g., autism) (Newbury, 2019). Most of the variance for complex disease is due to moderate-high penetrant rare variants (allele frequency <1% in the population). Affected individuals carry a large number of these rare allele variants. Unaffected individuals can carry one or more risk alleles but do not meet the threshold to develop disease. In recent years, a larger fraction of rare genetic variation in common neurodevelopmental and psychiatric diseases is targeted by de novo paradigms (Alonso-Gonzalez, Rodriguez-Fontenla, & Carracedo, 2018; Vissers et al., 2010). This method typically captures a subset of rare de novo changes that have occurred within the last generation. Since de novo changes are not inherited, they escape selective pressure and this may explain why devastating disorders (e.g., autism and schizophrenia) persist. Genome-wide approach: A genome-wide association approach is a powerful tool that involves rapidly scanning hundreds of thousands to millions of markers across the genomes of many individuals for investigating the genetic architecture of human disease susceptibility. The potential success of a GWAS depends on: (1) sample size; (2) the total number of loci affecting the trait; (3) distribution of effect size and allele frequency at those loci; (4) the panel of genome-wide variants used; and (5) heterogeneous nature of the trait or disease (Visscher et al., 2017). Despite these challenges, researchers already have reported considerable success for many neurodevelopmental disorders using this strategy (e.g., ADHD and attention-related traits) (Hawi et al., 2015), although not yet for DCD. In fact, GWAS in ADHD emphasized association of 13 specific biological pathways (Alemany et al., 2015) by supporting the notion that the same gene may contribute to clinically distinct disorders. Quantitative trait locus (QTL) mapping on the other hand is a method used to study the genome for QTLs, which are chromosomal segments harboring genes regulating the trait of interest (Grisel, 2000). This technique acts as an excellent addition to GWAS; combining the two approaches helps to provide the crucial output to unravel individual genes that contribute to the phenotype of interest (Miles & Wayne, 2008). By considering these factors along with a baseline understanding of the comorbidities, genetic, and environmental aspects of DCD, QTL mapping allows a directed approach to the genetic study of this disorder by maximising the likelihood of successful gene mapping. 1.9 QTL analysis with GeneNetwork as a tool 1.9.1 Quantitative trait locus analysis (QTL) QTL mapping is an invaluable preclinical gene mapping tool for examining the genetic variation of a complex quantitative trait (Falconer & Mackay, 1996; Williams, Strom, Zhou, & Yan, 1998). Complex traits are typically composed of several QTLs, contributing to the trait’s heritability in an uneven fashion. The principal goal of QTL mapping is to identify associations between the variation at the phenotypic level (trait data) and variation at genetic level (marker data) in terms of the number, positions, effects and interaction of QTL (Miles & Wayne, 2008). Usually, quantitative traits depend on diverse factors and are determined by multiple genes and environmental conditions, so one or many QTLs can control a phenotype or a trait. The probability of success in QTL mapping depends on the number of genes that affect the trait, its genetic nature (dominant, recessive or additive) and proportion of phenotypic variance owing to genetic variance of the trait (Abiola et al., 2003). The mapping population and quality of the genetic map is critical to the power and accuracy of QTL mapping. In experimental organisms, a larger number of lines in a mapping population helps to measure more accurately the phenotype associated with each genotype, which in turn yield greater power to unravel QTL detection and mapping (Belknap, 1998). Combining mouse QTLs and human GWAS has have spotted novel 14 candidates and pathways showing the translational value of experimental data from recombinant inbred (RI) populations (Andreux et al., 2012; Ashbrook, Williams, Lu, & Hager, 2015). To maximize the potential of the QTL analysis, the process begins with two or more parental strains that differ genetically and phenotypically with regard to the trait of interest. These parental strains are crossed to produce different lines of recombinant strains that contain unique fractions of the parental alleles in the genome. Single nucleotide polymorphisms (SNPs) and simple sequence repeats (microsatellites) are examples of genetic markers frequently used to differentiate between the two parental alleles (Miles & Wayne, 2008). Once the genotype and phenotype data are collected from each recombinant strain, linkage analysis is then carried out to dissect associations between loci and phenotypes in a lineage of individuals with known degree of relatedness (i.e., genetic markers that are linked to a QTL influencing a trait will segregate with the disease phenotype whereas, unlinked genetic markers will not significantly associate with a disease phenotype value). The likelihood ratio statistic (LRS) is used to measure the strength of association between quantitative phenotypic variations and genetic differences with respect to genotype markers. The higher the LRS, the greater the probability that the particular interval harbor the QTL influencing the phenotype. A permutation test (Doerge & Churchill, 1996) is carried out to evaluate the precision of the QTL location by multiple test corrections across genetic markers to achieve a genome-wide corrected p-value. This test randomly reassigns trait values from thousands of permutations across the strains compared to the LRS scores from the original data. Significance level (p=0.05) is determined when the properly arranged original data set is corelated with a high LRS score in more than 95% of the permuted data sets. One way to determine the confidence intervals for the position of the QTL is by using a nonparametric bootstrap method (Visscher, Thompson, & Haley, 1996). To identify the genetic cause of variation in the phenotype, GeneNetwork (GN) generates thousands of bootstrap samples by randomly withdrawing trait values with replacement from the original data set, remapping each and keeping track of the distribution of the QTL locations associated with the highest LRS scores (Visscher et al., 1996). The best locations produce the 15 yellow bars plotted in a bootstrap histogram on some of the QTL maps. The results vary between runs due to the random generation of the samples. Alternatively, 1.5-LOD (likelihood of the odds) support interval method or LRS (likelihood ratio statistics) is widely used to measure the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus by providing ∼95% confidence interval coverage for the location of a QTL (Lander & Botstein, 1989; Manichaikul, Dupuis, Sen, & Broman, 2006). 1.9.2 The BXD recombinant inbred (RI) panel as a tool for complex trait analysis Mouse models are a commonly used experimental platform (Rosenthal & Brown, 2007) to explore genetic, molecular, and behavioral aspects of human diseases. Mice provide one of the few systems in which behavioral abnormalities and potential therapeutics can be validated before translating to human health (Porges, 2006; Silverman, Yang, Lord, & Crawley, 2010a). In order to target a neurodevelopmental-related behavioral phenotype like DCD, an array of well-established assays and a suitable mouse model are considered as a gold standard, featuring a rigorous integration of face validity, construct validity and predictive validity (Gill, Rajan, Goldowitz, & Zwicker, 2020; Silverman, Yang, Lord, & Crawley, 2010b; van der Staay, F J, Arndt, & Nordquist, 2009) (Table 1.2). A variety of animal reference populations can be utilized for QTL mapping, but RI strains are commonly used (Gini & Hager, 2012). RI strains serve as an excellent renewable resource; therefore, a large homogeneous sample of animals can be genotyped any number of times. In addition, using similar set of RI strain helps one to examine different complex traits, correlate two independent traits to look for common genetic determinants, or simply to assess the wealth of reproducibility and compare findings across different labs or at different time periods (Chesler, Lu, Wang, Williams, & Manly, 2004; Wang, Williams, Manly, 2003). 16 Table 1.2 Types of validity used to measure the application of a rodent model to human disease In the past two decades, a large number of genetic loci influencing complex behaviors have been mapped using inbred strains (Flint, 2003). The BXD strains are a group of RI lines, generated by paring two inbred strains C57B6J and DBA/2J (F0) which differ at approximately 4.8 million SNPs to acquire the F1 generation (Figure 1.2). Then the F1 heterozygous generations were sibling-mated to produce the F2 generation and the successive offspring pairs were further intercrossed for more than 20 generations to produce inbred lines in which 99% of the mouse genome is isogenic (Bailey, 1971; Wade & Daly, 2005; Williams, Qi, & Lu, 2001). Since, RI strains are genetically stable for the long-term across generations, many are commercially available for basic and clinical research. Other popular panels of RI lines are AKXD (inbred parents AKR/J and DBA/2J), AXB-BXA (inbred parents C57BL/6J and A/J), and AKXL (inbred parents AKR/J and C57L/J). Type of validity Criteria Face Validity The symptoms of the animal model mimic the human condition Predictive Validity The ability of a rodent behavioral test to predict the effect of an individual’s future test performance from prior test performance. Construct Validity The similar etiological factors underlying the disorder between the animal and the human condition that it models 17 Figure 1.2 Generation of BXD recombinant inbred panel (based on Gini & Hager, 2012). Recombinant inbred (RI) strains are produced by systematic intercrossing between two parental inbred strains that differ both genetically and phenotypically in the trait of interest. Due to several advantages of RI lines (Plomin, McClearn, Gora-Maslak, & Neiderhiser, 1991; Williams et al., 2001), the BXD family of mice has been frequently chosen to map complex quantitative traits like behavioral differences (Ashbrook et al., 2018; Knoll, Jiang, & Levitt, 2018; Philip et al., 2010), neuroanatomical traits (Belknap, Phillips, & O'Toole, 1992; Rosen et al., 2009), cancer susceptibility (Grizzle et al., 2002), genetics (Mozhui et al., 2011) , and pharmacological responses to drugs and toxins (Belknap et al., 1993; Rulten, Ripley, Hunt, Stephens, & Mayne, 2006). Most importantly, the BXD lines are recognised as the largest genetic reference panel with a total of 160 publicly available genome-wide molecular profiles (Hager, Lu, Rosen, & Williams, 18 2012; Peirce, Lu, Gu, Silver, & Williams, 2004) and over 7,000 publicly available phenotypic measurements from single molecules to complex behavioral repertoires which are measured under standard or various environmental exposures (Diessler et al., 2018; Jung, Brownlow, Pellegrini, & Jankord, 2017; Mulligan et al., 2018; Rodrigues et al., 2017; Théberge et al., 2019). Over the years, several high-density genetic maps with millions of SNPs and thousands of microsatellite repeats were catalogued as a free online resource in GeneNetwork to the scientific community. 1.9.3 Strategies for QTL gene(s) detection QTL analysis has the advantage of being able to detect both major and minor genetic effects. A series of findings consistently showed that a single QTL can explain 1-5% of the phenotypic variance (Valdar et al., 2006), and a large amount of quantitative variation can be explained by a limited number of allele effect on observed phenotypes (Farrall, 2004). Expansions in QTL mapping made possible by the improvement of high-resolution maps of the RI strains with ~5 million segregating variants, similar to human populations (International HapMap 3 Consortium et al., 2010) has greatly increased the precision of QTL (Drinkwater & Gould, 2012). Hence, the accessibility of the genomic sequence shifted the focus from the recognition of a QTL-bearing chromosomal segments to the issue of narrowing a QTL interval down to a single gene or even a single nucleotide polymorphism. Usually, mapping of trait genes demands an intensive effort and rely upon evidence from diverse sources (Flint, Valdar, Shifman, & Mott, 2005). Therefore, using the online tools available at GeneNetwork, the genes within the 95% QTL confidence interval are evaluated based on three criteria: (1) gene expression within a relevant region and cells of interests; (2) relevant gene functions from previous literature; and (3) the presence of polymorphisms (i.e., non-synonymous SNPs) as the phenotypic difference is assumed to be associated by genetic variation and considered as good candidate genes. Discovery of these polymorphisms may allow for an enhanced understanding of neurodevelopmental disorders and serve as a major advance in neurobiological research. 19 1.10 Aim and objectives of the thesis The overarching goal of this thesis is to begin to unravel the genetic aetiology of DCD using mice as a model system and QTL as an analytic tool. 1. The first approach aims to investigate naturally occurring DCD-like phenotypic differences in BXD recombinant inbred strain through various motor analyses. 2. The second part aims to identify regions in the genome contributing to motor differences and prioritize candidate genes in these mapped regions. 20 Chapter 2: Investigation of naturally occurring DCD-like phenotypic differences and regions in the genome contributing to those motor differences in BXD RI strains. 2.1 Introduction DCD is a neurodevelopmental disorder that affects the acquisition and execution of age-appropriate gross and /or fine motor skills and significantly interferes with daily routines (American Psychiatric Association, 2013). DCD is highly comorbid with other developmental disorders with a complex interaction of genetic and environmental factors. Notably, 50% of children with DCD may meet the criteria for ADHD (Watemberg, Waiserberg, Zuk, & Lerman‐Sagie, 2007). Recent evidence demonstrated a strong genetic association between these two disorders (Fliers et al., 2009; Mosca et al., 2016) with an association of possible coordination problems in ADHD and genes involved in skeletal muscle (Fliers et al., 2009). Over the years, findings from population-based studies demonstrate higher heritability rates among DCD ~ 70%, ADHD ~ 60–90% and ASD ~ 80–90% (Chen et al., 2017; Lichtenstein et al., 2010; Martin et al., 2006; Sandin et al., 2017), supporting the idea that genetic factors are an important consideration in neurodevelopmental disorders. Although there are well established behavioral models available for many neuropsychiatric disorders (e.g., anxiety, depression, schizophrenia, autism, addiction, ADHD, posttraumatic stress disorder) and neurodegenerative disorders (e.g. Parkinson's, Alzheimer's, and Huntington's diseases; stroke; and normal aging), none exist for DCD. In the present study, the RI QTL approach (see Chapter 1) was used to evaluate the correlation of genome-to-phenome effect of 12 BXD family of lines (BXD1, 15, 27, 28, 32, 40, 45, 65a, 69, 75, 81, 86) and parental strain (C57B6J and DBA/2J) (Wang et al., 2016). The selection of each BXD lines was solely based on their behavior performance level (High ® Medium ® Low) in relation to the key symptoms of DCD on common assays (e.g., accelerated rotarod, open field) found in GeneNetwork (Philip et al., 2010) (see Table 2.1). Hence, these inbred strains are known to have core motor difficulties in posture, cerebellar involvement, motor coordination, and motor learning. A wide range of replicable assays in relation to DCD behavior were administered at the postnatal ages (P)1-P120, in an attempt to evaluate predictive modeling 21 of BXD reference population and compared for genetic correlations and common QTLs to tease apart the genetic architecture of DCD. Table 2.1 Selection of BXD lines based on phenotype Since DCD complications extend at least into adolescence in up to 30–70% of affected people (Kirby et al., 2014), it is important to evaluate motor development impairments at major timepoints of life in the mouse model. The RI model system was used as an appropriate experimental cohort and an excellent computational panel to measure the behavior phenotypes of human DCD through three major timepoints to target specific phenotype over three distinct phases (Figure 2.1). Record ID Phenotype Strain(s) 10921 Cerebellum volume BXD1/TyJ; BXD15/TyJ; BXD27/TyJ 10921; 11819 Cerebellum volume; Dowel test BXD28/TyJ 11819 Dowel test BXD32/TyJ 10921 Cerebellum volume BXD40/TyJ 11014 Open field behavior BXD45/RwwJ; BXD65a/RwwJ 11004 Rotarod performance BXD69/RwwJ; BXD75/RwwJ 11014; 11005 Open field behavior; Improvement in rotarod BXD81/RwwJ 11004; 11819 Rotarod performance; Dowel test BXD86/RwwJ 22 Figure 2.1 Workflow of the behavioral testing in Postnatal Day (P) P1-120. All three phases of testing are proposed based on the DCD-like behavior [d- days, w- week, cons.- consecutive] 2.2 Materials and methods 2.2.1 Animals Twelve BXD (1, 15, 27, 28, 32, 40, 45, 65a, 69, 75, 81, 86) recombinant inbred (RI) lines together with C57BL/6J (B6) and DBA/2J (D2) were used. The total number of experimental animals used in this study ranged between 129 to 288 mice per task, with almost equal numbers of males and females. The mice were examined between postnatal age P1 to P120. These strains were selected for the following reasons: (1) the BXDs are one of the largest murine mapping panels that constitute a reproducible and high-resolution mapping panel with ~5 million segregating variants, similar to human populations (International HapMap 3 Consortium et al., 2010); (2) the genetic analysis of numerous complex physiologic phenotypes has shown motor performance ranging from excellent to poor among various BXD lines; (3) both parents of the BXD family – C57BL/6J and DBA/2J – have been sequenced and these two strains differ at approximately 4.8 million SNPs; and (4) BXDs are excellent long-term resource. This feature of a genetic reference population (GRP) helps one to examine different traits on the same RI strains, correlate traits to look for common genetic determinants, and compare findings across different labs. The breeding animals were purchased from the Jackson Laboratory. All experiments were conducted in 23 accordance with the guidelines of the Canadian Council of Animal Care, and all protocols were approved by the UBC Animal Care Committee (ACC). 2.2.2 Behavioral Measures: Fox Neurodevelopmental Battery at Postnatal Day P1-P15 In phase I, a comprehensive battery of reflexes was studied on each pup every day for 15 days with a total of 3 trials per day to examine postnatal maturation of nervous system and behaviour in the neonatal mouse (Fox, 1965). As per the DSM-5 DCD diagnostic criteria, motor difficulties should not be related to any neurological conditions (American Psychiatric Association, 2013) . Abnormal neurodevelopmental reflexes are predictive signs of infants or mice with a high risk for neurodevelopmental disorders (Nguyen, Armstrong, & Yager, 2017). Each reflex parameter from the phase one was replicated based on human postural reflexes such as the palmar grasp and protection extension reflexes (Futagi, Toribe, & Suzuki, 2012; Mandich, Simons, Ritchie, Schmidt, & Mullen, 1994), and labyrinthine and body righting reflexes (Baloh, 1989). The phase I testing contained four distinct reflex assays: righting reflex, negative geotaxis, cliff aversion and forelimb grasp. 2.2.2.1 Righting reflex The mouse pup was placed dorsally on a surface to restore its normal prone position (Fox, 1965). An upper limit of 30 seconds is placed on this task, such that if a pup cannot right itself in this time period a score of 30 seconds is given. Typically, the average age for the appearance of surface righting is postnatal day 1-10 (Heyser, 2004). 2.2.2.2 Negative geotaxis Individual pups were placed on a 45° slope with the tip of the head end facing downward (Fox, 1965). The duration it took each pup to move themselves in the upward position was recorded up to maximum of 30 seconds to perform the test. Typically, the appearance of the negative geotaxis reflex occurs at postnatal day 7 with a range from postnatal day 6-15 (Fox, 1965; Heyser, 2004). 24 2.2.2.3 Cliff aversion The mouse pup was placed on the edge of a raised small box with only the digits of their forepaws and their snout positioned over the edge of a cliff (Fox, 1965). The duration it took each pup to turn away from the edge and secure itself was recorded up to a maximum of 30 seconds. If there was no response after 30 sec, the test was terminated. If the pup fell off the edge, a single additional trial was given. Typically, the appearance of cliff aversion reflex occurs between postnatal day 1-10. 2.2.2.4 Forelimb grasp Individual pups were allowed to grasp a thin rod and remain suspended for few seconds. The test was successfully performed if the pup could hold the rod up to 1 second. Typically, the average age of appearance of the forelimb grasp is at postnatal day 7 with a range from postnatal day 7-13 (Fox, 1965). 2.2.3 Behavioral Measures: Generalized tests of motor function at Postnatal Day P60-P81 The functional motor tasks in phase II were studied to measure motor impairments in mice. A wide-range of tests such as gait analysis, a standard rotarod, and open field was carried out on each BXD lines of mice together with parental strains at various times between P60-P81. 2.2.3.1 Gait analysis Gait analysis was performed on a weekly basis for a total of 3 weeks to measure comprehensive movement features such as static, stance and dynamic parameters. The main advantage of this method is that differences in the children’s gait problems can be directly interpreted to the mouse parameters. On the testing day, mice were placed on a walking apparatus (designed according to Mendes et al., 2015) and allowed to traverse freely across a narrow walkway for a minimum of two seconds. This method was based on the reflection of light within a transparent material through an optical effect termed total internal reflection. Foot contact disrupts this effect causing frustrated Total Internal Reflection (fTIR), a technique that generates 25 scattered light and gets detected by a high-speed video camera. The floor is made of acrylic plastic surrounded by LED lights, thus producing a touch sensor which is viewed using a mirror placed at 45-degree angle below the walking surface. Recorded videos are further analyzed using MATLAB (MATrix LABoratory) and Mouse walker (an integrated hardware and software system that provides a comprehensive and quantitative description of walking behavior of rodents). In this thesis, the following phenotypic measures have been used to address movement differences in balance and coordination of an experimental cohort: body speed (mm/sec), stance duration (sec), swing duration (sec), step cycle (sec), duty factor, leg combination index (%) and posterior extreme position (body units) have been used to address movement differences in balance and coordination of an experimental cohort. 2.2.3.2 Open field The open field task is commonly used to measure general locomotor activity and exploration in rodents, particularly anxiety-like behavior in a novel and habituated environment (Brooks & Dunnett, 2009). This task can be likened to children with DCD, who tend to withdraw from physical activities. An open field chamber measuring 50 cm x 50 cm x 12 cm was used. The chamber was constructed from opaque acrylic plastic. For standard open field analysis, individual mice were placed into the chamber and allowed to explore the environment for 10 minutes; movements were digitally recorded with a camera placed directly above the chamber and analysed by image tracking system Ethovision XT 7.0 software (Noldus Information Technology, USA). In the present study, the variables recorded include the total distance travelled, velocity, time spent in center and periphery, and time spent moving and not moving. 2.2.3.3 Standard rotarod The rotarod test is one of the most widely used methods to evaluate motor coordination and balance in rodents. In this thesis, the rotarod test was performed using the Ugo Basile Rota-Rod model 47600. On the testing day, mice were placed in individual compartment of rotarod. The rotation speed of the rotating rod was manually set at 18 rpm over 5 minutes. Each trial lasted up to a maximum of 5 minutes in duration or until the animal fell off the rotating rod; trials occurred over 3 consecutive days with 3 trials per day. In the present study, the duration of time spent on 26 the rotating rod was scored, to measure differences in strain performance in motor coordination, motor planning and balance. 2.2.4 Behavioral Measures: Tests of skilled motor function at Postnatal Day P90-P120 Motor learning, the third and final phase of behavior testing, was attempted to replicate the presentation of motor learning impairment similar to DCD. It is often reported that children with DCD have difficulty in adapting in new settings, are unable to recognize repeated sequences, have delayed acquisition of motor skills or cannot adjust to changes in external task requirements (Caçola, P., 2014; Geuze, Rh & Kalverboer, 1987; Gheysen, Van Waelvelde, & Fias, 2011; Missiuna, 1994). Therefore, at postnatal age (P) 90, skilled motor learning was assessed through the accelerated rotarod, horizontal rung walking task, complex wheel task, and skilled motor reaching task. 2.2.4.1 Accelerating Rotarod An accelerating rotarod was used to evaluate motor learning and balance (Jones & Roberts, 1968). The task was performed using the Ugo Basile Rota-Rod. On the testing day, mice were placed in an individual compartment against the rod’s direction of movement. The rotarod was setup to run at constant acceleration (4 to 40rpm over 2 mins) for 3 trials with a 30 minutes inter-trial rest over 3 consecutive days. In this thesis, the duration of time spent on the accelerating rotating rod and improvement in motor performance and motor learning were scored within and across three test days to assess motor impairments. 2.2.4.2 Horizontal ladder rung walking task The horizontal ladder rung walking task was used to measure the skilled fore- and hindlimb accuracy. This test required the mice to walk along a horizontal ladder on which the spacing of the rungs was irregular. The apparatus was 1m in length, constructed with clear plexiglass with metal rungs spaced at 1 cm intervals to create the walking platform located 30cm above the base of the apparatus. Individually, animals (P90) were habituated to the apparatus for 2 days prior to testing 27 with fixed rungs. On the third day, uneven spacing was created in a 30cm portion in the middle of the length of the ladder by the removal of rungs. Animals were placed at the start end of the apparatus to walk the length of the horizontal ladder rung. A video camera was used to record the animal walking across the unevenly spaced novel rung pattern on each test day. In this thesis, parameters of the number of missed steps/ errors, correction, and correct limb placement were manually tabulated to measure improvements in fore- and hindlimb motor learning performance across 3 consecutive test days with 5 trials per day. 2.2.4.3 Skilled motor reaching task The skilled motor reaching task was designed based on a publication by Qian and his colleagues (Qian et al., 2013) to measure skilled forelimb movements (i.e., reaching behavior) and learning behavior. This task required the animal to stretch for a food pellet through a slot in front of a plexiglass box, and to grasp and retrieve the pellet with a single forelimb. To increase motivation for learning, prior to and during skilled reaching training animals underwent a common restricted diet for 19 days until they reach 85–90% of their baseline body weight. To train the mice to the food pellets, each mouse received 30 pellets (20mg precision-weight, purified rodent tablets, Sandown Scientific), 8 hours prior to daily feeding and training for 1 week. Upon training start day, only regular Purina Rodent Chow was provided. Two days prior to 10 uninterrupted days of testing, mice were placed in the reaching box with food pellets to familiarize each animal to the training box and to identify preferred forelimb for reaching the pellets through an open slot. During the subsequent 10 test days, animals underwent daily 20-min sessions consisting of 30 separate trails, one single pellet per trial. In this thesis, impairments in motor learning were determined by measuring the percentage of food pellet acquired on their first try (first attempt success), total success (regardless of number of tries taken to acquire the food), and rate of learning. 2.2.4.4 Complex wheel The complex wheel task was designed based on a publication by Nagai et al. (2017) to measure novel learning via blocked practice (i.e. performing a single skill over and over) over 3 trials/day for 4 consecutive days. This task was a hybrid of accelerated rotarod and horizontal rung walking task. On the test day, the mice were placed on top of the stationary wheel, which was 28 gradually increased in acceleration from 0 to 40rpm, over the course of 10 minutes. The irregular fixed walking pattern was created by removing 20 rungs from a 50-rung cylinder. The mice did not receive any habituation or pretraining using the complex or regular wheel. To encourage the mouse to keep running on the top of the heel, a sponge was placed in behind the mouse with a small space between the wheel and the sponge. In this thesis, the time spent on complex wheel profile was measured to determine the overall improvements and intertrial learning of irregular walking pattern. 2.2.5 Statistical analysis Statistical analyses were conducted for the Fox Neurodevelopmental Battery, motor coordination, and motor learning using IBM SPSS Statistics version 25.0. Behavioral measurements were analysed using one-way analysis of variance (ANOVA). Repeated measures ANOVA was run for motor learning tasks. The threshold for statistical significance was set as p ≤0.05 and the data were presented as the mean (M) ± standard error of the mean (SEM). In order to determine which strains, differ significantly from each other, post-hoc tests were performed. Bonferroni correction was done to account for multiple comparisons and prevent data from false positives. 2.2.6 QTL mapping and candidate gene analysis All behavioral trait data were uploaded in GeneNetwork (www.genenetwork.org), an open access online database which contains BXD strain genomic information. Correlational analyses of genome-wide interval mapping and behavioral measures were conducted for detailed investigation (Figure 2.2). These analyses classified the BXD strains in line with their genotypes using distinct chromosomal markers and compared them individually with the phenotypic variables. The likelihood ratio statistic (LRS) was computed to assess the strength of genotype–phenotype associations and identify QTLs which refer to genomic regions that contain one or more sequence variants that modulate a phenotypic trait. A test of 2000 permutations was performed to evaluate the statistical significance of associations. The bootstrap test was implemented to identify QTL 29 location. A significant QTL is determined by the LRS (likelihood ratio statistics) value that corresponds to a genome-wide p-value of less than or equal to 0.05, whereas a suggestive QTL represents the LRS value that corresponds to a genome-wide p-value of less than or equal to 0.63. The suggestive threshold represents 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. According to GeneNetwork, the suggestive threshold is a permissive threshold that is valuable because it calls recognition to loci that may be worth follow-up. Confidence intervals around the significant LRS score peaks were calculated using 1.5 logarithm of the odds score. The QTL maps were all generated using GeneNetwork. The expression, functional and phenotypic information for each of the genes located within the significant QTLs (Bello, Smith, & Eppig, 2015; Eppig, et al., 2015), was surveyed using Allen Brain Atlas, Mouse Genome Informatics (MGI) database and PubMed. Secondly, literature searches were conducted to determine if each gene under the significant QTL peak had a previously reported role in motor skills and learning. Lastly, GeneNetwork variant browser was used to identify polymorphism information specifically nonsynonymous polymorphism. It is also attempted to identify previously published overlapping loci containing abnormal phenotypes within our mapped QTL interval using MGI resource. 30 Figure 2.2 Selection of analytical tools to study complex networks of genes, function and phenotypes (A) Genome scan: the output of the interval map with a chromosome number and megabase position displayed at the top and bottom of the graphical map. The LRS scores and chromosomal location of the marker plotted on the y-axis and x-axis, respectively. Blue lines plotted as the LRS. The horizontal pink and grey lines represent the threshold for significant and suggestive linkage scores. (B) Chromosome specific genome scan. Red and green lines plotted as the additive coefficient for the B allele and D allele across the genome. The yellow seismograph tracks represent SNPs that differ between the two parental strains. 31 Chapter 3: Results 3.1 Underlying genetics of nervous system maturation Approximately 14-25 neonatal mice per strain at postnatal age (P1) were tested for 15 consecutive days on a comprehensive battery of reflexes to examine postnatal maturation of nervous system and behavior (Fox, 1965). In negative geotaxis, each pup showed a variable performance on time to move themselves from a downward slope to the upward position. A significant difference was observed between B6 and DBA parental strains (Fig 3.1; Table 3.1). Similarly, in cliff aversion each pup displayed variable performance on time to turn away from the edge to a secured side. The difference persisted for almost whole testing period (day 1 to day 15) and a significant difference was observed between B6 and BXD86 strains (Fig 3.1; Table 3.1). Contrary to negative geotaxis and cliff aversion, surface righting showed less variability in performance in time taken to restore a pup’s position from dorsal to its normal prone position. A significant difference was observed between BXD65a and BXD86 strains (Fig 3.1; Table 3.1). In forelimb grasp, strains displayed less variability in time to hold the rod and remain suspended above platform, with no significant difference between strains (Fig 3.1; Table 3.1). The onset of each reflex was validated based on previous behavior evidence (Feather-Schussler & Ferguson, 2016; Fox, 1965; Heyser, 2004). On day 2, negative geotaxis appeared; cliff aversion and surface righting appeared right from day 1-10 and lastly forelimb grasp started from day 3. 32 Figure 3.1 Graphs illustrating average strain response time on Fox neurodevelopmental battery of tasks Negative geotaxis, cliff aversion, surface righting and forelimb grasp over the first 15 postnatal days. The B6 and DBA parental lines are in dotted lines. The wide range of colors show individual strain performance. Highlighted lines with dot symbol indicate significant strain performance. 33 Table 3.1 Fox neurodevelopmental battery results by strain * indicates that this strain is significantly different from B6 (p<0.05) ! indicates that this strain is significantly different from DBA (p<0.05) ^ indicates that this strain is significantly different from BXD65a (p<0.05) ~ indicates that this strain is significantly different from BXD86 (p<0.05) One-way analysis of variance (ANOVA) was performed to detect significant inter-strain differences on time taken for each pup to perform the reflex task up to a maximum of 30 seconds. Analyses that yielded P ≤ 0.05 were considered significant. There was a statistically significant difference between the 14 strains in all reflexes: negative geotaxis [(F(13,274) = 11.154, P<.001)], cliff aversion [(F(13,274) = 12.785, P<.001)], surface righting [(F(13,274) = 8.807, P<.001)] and forelimb grasp [(F(13,274) = 1.877, P<.033)]. The QTL mapping for all sensorimotor reflex measurements was performed separately using 12 BXD RI lines and parental strains. Genome-wide QTL analysis did not identify any significant QTL but identified suggestive QTL for righting reflex in Chromosome 17, a single suggestive QTL for cliff aversion in Chr 4 and Chr 5, and a QTL in Chr 10 for forelimb grasp (Appendix A). 34 3.2 Underlying genetics of postural control, locomotor activity, anxiety level and motor coordination In phase II, 236-287 experimental mice were used at P60-81 to undergo a series of behavior tasks: rotarod, open field and gait analysis. These tasks were employed to investigate motor coordination and locomotor activity. To evaluate motor coordination, specifically postural control, gait analysis was performed. The performance in gait was determined by measuring body speed, leg combination, duty factor, step cycle, swing duration, stance duration and posterior exterior position. When comparing all strains, BXD15, BXD27, and BXD86 display the most variable performance of all gait parameters (Fig. 3.2). These strains displayed lower body speed and stance duration with a longer step cycle and duty factor. To detect a between strain differences in gait parameters, ANOVA was performed. Statistically significant differences among the strains tested were identified for the following parameters: body speed [(F(13,236) = 10.124, P<.001)], leg combination [(F(13,236) = 15.003, P<.001)], stance duration [(F(13,236) = 23.536, P<.001)], swing duration [(F(13,236) = 15.737, P<.001)], posterior exterior position (i.e., position of the leg relative to the end of stance phase; [(F(13,236) = 10.737, P<.001)], step cycle [(F(13,236) = 23.536, P<.001)] and duty factor (i.e., proportion of the step cycle where the leg is in contact with the ground; [(F(13,236) = 13.934, P<.001)]. Post hoc tests for all gait parameters manifested significant between-strain performance (e.g. BXD15 and BXD45) across testing period (Table 3.2). 35 Figure 3.2 Graphs illustrating average performance for seven measures of gait patterns 36 Table 3.2 Gait analysis parameter results by strain $ indicates that this strain is significantly different from BXD15 (p<0.05) # indicates that this strain is significantly different from BXD45 (p<0.05) The QTL mapping for postural control phenotypes was performed separately for each parameter using 12 BXD RI lines and the parental strains. Overall, the genome-wide QTL map identified three significant QTLs in gait parameters: stance duration, step cycle, and posterior exterior position (Fig. 3.3). For stance duration, a significant QTL was located to the distal end on Chr 4 [Trait ID_20971] and spanned from 124.33 to 125.46 Mb with an LRS score of 16.00. Another significant QTL for step cycle parameter was identified to the distal end on Chr 4 from 124.33 to 125.35 Mb with an LRS score of 15.76 [Trait ID_21427]. Lastly, a significant QTL for PEP had also been mapped to the distal end to the Chr 4 and it spanned from 124.35 to 125.3 Mb with an LRS score of 16.44 [Trait ID_21410]; posterior exterior position also had a suggestive QTL at Chr 2. Other parameters such as body speed (two QTLs at Chr 10 and Chr 16), leg combination (three QTLs at Chr 5, 16 and 19) and duty factor (four QTLs at Chr 10, 4, 16 and 19) displayed multiple suggestive QTLs. 37 Figure 3.3 Genome-wide linkage map of stance duration, step cycle and PEP (top to bottom) on gait analysis to determine postural control The overall blue trace shows the LRS (A) The genome-wide QTL map showing a significant QTL found on chromosome 4 for stance duration, step cycle and PEP. (B) Interval QTL map of chromosome 4 using three test week performance using bootstrap analysis. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes] To evaluate locomotor activity (Carola et al. 2002), the open field test was performed. The performance in open field was determined by measuring the total distance travelled, time spent in the center and periphery, time spent moving versus not moving and velocity (Fig 3.4). 38 Figure 3.4 Graphs illustrating average strain differences in open field parameters: total distance travelled, time spent in the center & periphery, baseline distance moving & not moving and velocity Table 3.3 Open field parameter results by strain 39 * indicates that this strain is significantly different from B6 (p<0.05) ! indicates that this strain is significantly different from DBA (p<0.05) $ indicates that this strain is significantly different from BXD15 (p<0.05) Ω indicates that this strain is significantly different from BXD27 (p<0.05) ∂ indicates that this strain is significantly different from BXD32 (p<0.05) ¥ indicates that this strain is significantly different from BXD40 (p<0.05) ^ indicates that this strain is significantly different from BXD65a (p<0.05) As seen in Table 3.3, BXD65a (4726.35 ± 134.21) mice travelled the most distance whereas BXD27 (1942.85 ± 105.88) travelled the least distance across three testing periods. A significant strain difference was seen on time spent in the center and periphery: B6 (71.85 ± 6.69) spent higher duration in center whereas DBA (13.72 ± 1.97) stayed mostly in the periphery. When comparing the strain baseline performance, BXD40 (518.92 ± 4.799) mice displayed continuous movement whereas BXD27 (251.61 ± 14.81) spent the least time moving. For the parameter velocity, parental strains showed baseline significant differences in velocity with BXD32 (21.3 ± 3.04) being significantly faster than BXD27 (5.3 ± 0.64). To determine whether there were any statistically significant differences between or within strains performance on open field, repeated measures ANOVA was used. There was a statistically significant difference identified for all open field parameters as follows: total distance traveled [(F(13,269) = 11.030, P<.001)]; [(F(19.345, 400.283) = 3.983, P<.001)], time spent in the center [(F(13,248) = 1.609, P<.022)]; [(F(23.286, 481.83) = 2.845, P<.001)] and periphery [(F(13,269) = 34.798, P<.001)]; [(F(24.311, 503.06) = 3.069, P<.001)], velocity [(F(13,274) = 7.479, P<.001)] and time spent moving [(F(13,287) = 15.248, P<.001)] and not moving [(F(13,274) = 24.730, P<.001)]. Post hoc tests using the Bonferroni correction of open field parameters indicate that there was a significant difference across all strains in learning. The QTL mapping for behavior traits related to the open field test were performed separately for each parameter using 12 BXD RI lines and parental strains. The genome-wide QTL map for velocity showed a significant QTL location on Chr 4 [Trait ID_20371] (Fig. 3.5). This QTL spans a fairly small region (96.2 to 100.4 Mb) on Chr 4 with an LRS value of 17.13. Moreover, an additional significance location was identified on chromosome 5 that spans from 40 103.75 to 113.49 Mb region with an LRS value of 17.13. Meanwhile, for other parameters, total distance traveled, time spent in the center and periphery, time spent moving and not moving, several suggestive QTLs were identified: three suggestive QTLs found on Chr 18 and Chr 9 for both time spent moving and not moving; two suggestive QTLs identified on Chr 10 and Chr 9 for time spent in center and a single QTL identified on Chr 6 for total distance traveled. Figure 3.5 Genome-wide linkage map of velocity on open field task to determine locomotion and anxiety behavior The blue trace shows the LRS for velocity on open field task. (A) The genome-wide QTL map showing a significant QTL found on Chromosome 4 and 5. (B1 & B2) Interval QTL map of chromosome 4 and 5 using baseline velocity data. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes]. To evaluate motor coordination, the rotarod test was used at constant speed (18 rpm). The performance of rotarod was determined by measuring the latency to fall over the course of the testing period up to the maximum of 120 seconds and the level of improvements (motor learning) was compared by measuring the motor difference between last day (day 3) and baseline (day 1) performance (Fig. 3.6). 41 Figure 3.6 Graphs illustrating average strain response time on rotarod and performance improvement over the three-day testing period Table 3.4 Rotarod parameter results by strain * indicates that this strain is significantly different from B6 (p<0.05) # indicates that this strain is significantly different from BXD45 (p<0.05) ≈ indicates that this strain is significantly different from BXD69 (p<0.05) ∏ indicates that this strain is significantly different from BXD75 (p<0.05) 42 Overall (Table 3.4), BXD45 (115.56 ± 1.49) strain stayed the longest on the rotarod while BXD75 (86.79 ± 5.44) had the shortest latency to fall. When the number of trials increased, performance improved in BXD69 (54.18 ± 5.47), whereas B6 (-1.5 ± 6.53) showed the least improvement. Strain differences were assessed by repeated measures ANOVA using mean performance on rotarod. Statistically significant differences between strains were identified for latency to fall [(F(13,277) = 4.176, P<.001)] and improvements in performance [(F(13, 277) = 5.749, p<.001)]. In addition, there was a significance difference within strains over the course of testing period. Post hoc tests using the Bonferroni correction of rotarod parameters indicate that there was a significant difference across all strains except BXD28 and BXD32. The QTL mapping for motor coordination was performed separately for each parameter using 12 BXD RI lines and parental strains. Genome-wide QTL map identified suggestive QTL for latency to fall in Chr 1 and multiple suggestive QTLs in Chr 5 and Chr 6 for performance improvement (Appendix B). 3.3 Underlying genetics of balance, fore- & hindlimb placement, skilled motor movements and learning In phase III, between 42-272 adult experimental mice were tested at P90-120 with a spectrum of behavior tasks: accelerated rotarod, horizontal ladder walking task, skilled reaching task and complex wheel to investigate motor learning. To evaluate motor learning and balance, accelerating rotarod was performed at varying velocities (4 to 40rpm). The performance on the accelerating rotarod was determined by measuring the latency to fall, performance improvement and online learning over the course of the testing period up to the maximum of 2 minutes (Fig 3.7). In case of latency to fall, BXD81 (21.72 ± 3.462) strains displayed a reduced ability to continuously adapt to change in velocity (4-40 rpm) and showed motor learning impairment over baseline (day 1) and last day (day 3) of testing. The measurement of performance improvement identified the strains that showed improvement to adapt change in velocity by reaching plateau or not. The strains in which improvements did not reach a significance showed difficulty in motor learning. The result showed B6 (114.48 ± 1.873) 43 stayed the longest on the accelerating rotarod by running at a maximum RPM (40 rpm), whereas BXD75 (61.60 ± 5.307) did not reach plateau. This indicates BXD75 strain have difficulty in motor learning. Lastly, short-term improvements within the same day of testing (Trial 3-Trial 1) were assessed. The results indicated that all strains displayed less variability on short-term improvements and lacked statistical significance between them. Figure 3.7 Graphs illustrating average strain performance in accelerating rotarod based on Latency to fall, Improvement in performance and Intertrial learning 44 Table 3.5 Accelerating rotarod parameter results by strain * indicates that this strain is significantly different from B6 (p<0.05) ! indicates that this strain is significantly different from DBA (p<0.05) ∏ indicates that this strain is significantly different from BXD75 (p<0.05) å indicates that this strain is significantly different from BXD81 (p<0.05) To determine between or within strain differences in accelerating rotarod measures (latency to fall, improvement in motor learning and short-term motor learning), an ANOVA was conducted. Statistically significant differences were achieved between strains for the parameters, latency to fall [(F(13,272) = 2.936, P<.001)] and improvement in performance [(F(13,273) = 19.818, P<.001)] in motor learning. No statistical significance (F(13,272) = 1.001, p<.450) was found in short-term motor learning. Repeated measures for the performance improvement parameter found statistical significance [(F(25.60,535.52) = 2.331, p<.001)] within subjects (strains) over each testing trial. Post hoc tests for time spent on accelerated rotarod parameter indicated BXD1 (p<.021), BXD28 (p<.020), and BXD81 (p<.021) strains showed higher significance when compared to other strains. The QTL mapping for motor learning and balance phenotypes was performed using 12 BXD RI lines and parental strains. The genome-wide QTL map for the performance improvement parameter identified a significant QTL to the distal end of Chr 15 at 73.69 to 75.5 Mb with an LRS 45 score of 22.53 [Trait ID_20096] (Fig. 3.8) and suggestive QTLs on latency to fall and online learning parameters. Figure 3.8 Genome-wide linkage map of performance improvement on accelerating rotarod to determine motor learning and balance The blue trace shows the LRS for performance improvement on accelerating rotarod. (A) The genome-wide QTL map showing a significant QTL found on Chromosome 15. (B) Interval QTL map of chromosome 15 using performance improvement data over three days of testing period. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes] The horizontal ladder rung walking task was used to evaluate fore- and hind- limb placement accuracy, limb placement correction and error in fine and gross motor performance. The performance on the horizontal ladder rung walking task was determined by measuring overall learning to investigate if strains are able to learn with less errors across the five trials each day in fore- and hindlimb placements on the irregular walking pattern. As shown in Figure 3.9 and Table 3.6, the level of motor learning on final day performance was measured. The results indicated, with an increase in number of trials, the performance of BXD81 (0.98 ± 0.003) performance 46 improved with a reduced number of errors. In contrast, the performance of BXD69 (0.95 ± 0.006) showed the least improvement. The number of corrections that were made over the course of the testing period revealed less variability across strains and showed no statistically significant differences. Finally, forelimb (fine motor) and hindlimb (gross motor) impairment was compared using the correct placement measurement to identify impaired strains with motor skills. The results indicated that BXD81 (0.005 ± 0.001) had trouble in with motor performance. Figure 3.9 Graphs illustrating average strain performance for fore- & hindlimb placement accuracy, correction and impairment to determine motor learning 47 Table 3.6 Horizontal ladder rung walking task parameter results by strain ¥ indicates that this strain is significantly different from BXD40 (p<0.05) ≈ indicates that this strain is significantly different from BXD69 (p<0.05) å indicates that this strain is significantly different from BXD81 (p<0.05) Repeated measures ANOVA was conducted to determine between or within strain differences in fore- & hindlimb placement accuracy, limb placement correction and error in fine and gross motor performance. There was no statistically significant difference identified between [F(13, 219)= 5.028, p < .230] or within [F(101.64, 1712.2)= 1.347, p < 0.074] strain performance in fore- & hindlimb placement accuracy. The number of corrections that were made over the testing period identified no significance between lines [(F(13, 218) = 1.967, p < .105)], whereas there was significance was obtained within strains [F(98.52, 1652.1)= 1.711, p < 0.001)]. Assessment of motor impairment showed significance between strains [(F(13, 219)= 3.378, p < .001)] but displayed no significant difference within strains [(F(104.83, 1765.98)= 1.441, p < 0.142)]. Post hoc testing revealed BXD69, and BXD81 are highly significant when compared to other strains on irregular walking pattern (Table 3.6). 48 The genome-wide QTL map of overall learning in fore- and hindlimb performance showed a significant QTL mapped to the distal end of Chr 16 [Trait ID_21425] (Fig 3.10) This QTL spans a fairly small region 68.2 to 78.2 Mb & 85.18 to 87.8 Mb of Chr 16 with an LRS value of 19.32. Additionally, there was a presence of suggestive QTL on Chr 9 and 17 for correction and error rate. Figure 3.10 Genome-wide linkage map of hindlimb and forelimb placement accuracy on horizontal ladder rung walking task The blue trace shows the LRS for day 3 accurate limb placement on horizontal ladder walking task. (A) The genome-wide QTL map showing a significant QTL found on chromosome 16. (B) Interval QTL map of chromosome 16 using overall learning rate data. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05 The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes] The skilled motor reaching task was used to determine skilled motor learning by measuring the first attempt success, total success and learning rate (Fig 3.11). The first attempt success was calculated by taking the percentage of success rate in grasping the food pellet on their first attempt. The results indicate that B6 (15.11 ± 3.94) successfully obtained the food pellets on 49 their first try, whereas BXD86 (1.94 ± 1.08) was poorer in grasping the pellet on their first attempt and displayed significantly higher impairments in motor learning, but displayed no statistical significance across strains (Table 3.7). Total success parameter was used to assess if mice could grasp a food pellet and place it into its mouth regardless of the number of forelimb attempts. The results showed that BXD86 (4.73 ± 2.21) had difficulty in grasping the food pellets despite given a number of attempts but displayed no statistical significance across strains (Table 3.7). Overall learning was calculated by measuring differences on the first 2 days and the last 2 days. The results clearly showed BXD27 (-0.13 ± 0.5) mice have a disability in motor learning but displayed no statistical significance across strains (Table 3.7). Figure 3.11 Graphs illustrating average strain performance on skilled reaching task based on first attempt success, total success and learning rate 50 Table 3.7 Skilled reaching task parameter results by strain A repeated measures ANOVA with a Greenhouse-Geisser correction showed that the mean first attempt success and total success were statistically significant within test days and strain [F(1.88, 247.91)= 5.868, p <.001)], [(F(67.04, 216.59)= 1.824, p <.001)].When comparing between strain differences, a significant difference was observed for first attempt success parameter [(F(13,42)= 2.230, p<.025)], but no significance was achieved between stains [(F(13, 42)= 1.955, p<.051)] on total success parameter and learning rate [(F(13, 42)= 1.685, p<.101)]. Post hoc tests using Bonferroni correction indicated no significant differences across strains (p<1.000) for grasping of food pellets in all three parameters. The QTL mapping for skilled motor movements was performed separately for each parameter first attempt success, total success and learning rate using 12 BXD RI lines and parental strains. The genome-wide QTL map identified a significant QTL for first attempt success (Fig. 3.12). The significant QTL was located at the proximal end of Chr 15 [Trait ID_21415] and spans about 17.75 to 19.50 Mb with an LRS score of 12.50. In addition, two suggestive QTLs for total success and learning rate were found at Chr 15 and 5. 51 Figure 3.12 Genome-wide linkage map of first attempt success on skilled motor reaching task to determine motor learning The blue trace shows the LRS for the first advance of the forelimb towards the food over 10 days of performance on skilled motor reaching task. (A) The genome-wide QTL map showing a significant QTL found on Chromosome 15. (B) Interval QTL map with bootstrap analysis of chromosome 4 over the test data. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes] To evaluate novel motor learning, the complex wheel task was conducted. The performance in the complex wheel was determined by measuring latency to fall, overall learning and intertrial learning within the same day of testing (Fig. 3.13). The B6 (59.26 ± 4.54) strain stayed the longest on complex wheel and BXD75 (7.52 ± 0.99) had the shortest latency to fall (Table 3.8). With an increase in trials, there was significant improvement in the performance of DBA mice (15.48 ±3.722) while BXD15 (3.15 ± 1.05) mice showed the least improvement in accuracy when compared to the baseline and final day of testing. The BXD1 displayed short-term (i.e. intertrial) improvement within the same day of testing (10 ± 3.36) whereas BXD75 (-0.25 ± 0.25), BXD40 (-9.5 ± 4.42) and BXD86 (6.28 ± 3,44) displayed least improvement in time spent on irregularly placed highly complex rung pattern. 52 Figure 3.13 Graphs illustrating average motor learning performance on complex wheel based on latency to fall, overall learning and online learning 53 Table 3.8 Complex wheel task parameter results by strain * indicates that this strain is significantly different from B6 (p<0.05) ∏ indicates that this strain is significantly different from BXD75 (p<0.05) To access motor learning performance, repeated measures ANOVA was conducted. The output for latency to fall performance showed no significance within test days and strain [(F(31.12, 275.33)= 1.445, p<.065)], whereas, there was a presence of significant differences between strains [(F(13,115)= 16.728, p<.001)] on time spent on complex wheel. Post hoc tests using the Bonferroni correction indicated B6 and BXD75 lines were statistically significant when compared to other strains (Table 3.8). ANOVA for overall learning parameter showed no significant between-strain differences [(F(13,115)= 1.178, p<.304)] but there was a statistical significance observed in online learning performance [(F(13,115)= 2.214, p<.013)]. Post hoc tests identified no statistically significant differences on improvements in motor learning across four testing days and assessment on improvements within same day of testing (Table 3.8). The QTL mapping on short-term motor learning difficulty identified a significant QTL at the proximal end of Chr 1 [Trait ID_21492] and spans about 12.8 to 14.65 Mb with an LRS score 54 of 16.27 (Fig. 3.14).The latency to fall and overall improvements in learning displayed suggestive QTLs on Chr 2 and 4. Once the QTL influencing the DCD-like behavior traits were localized at specific chromosomal regions containing marker loci (Table 3.1), next step is to narrow down the genes within significant QTLs (Table 3.9). Figure 3.14 Genome-wide linkage map of online improvements on complex wheel task to determine motor learning difficulties The blue trace shows the LRS on improvements in accuracy within the same day of testing (Trial 3- Trial 1). (A) The genome-wide QTL map showing a significant QTL found on Chromosome 1. (B) Interval QTL mapping was generated on chromosome 1 over the course of four testing day performance. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. [asterisk (*) indicates significant QTL; down arrow (↓) indicates significant QTL interval with genes] 55 Table 3.9 Behavioral Tests offered in Postnatal Day (P) P1-120 The threshold for statistical significance in the maximum LRS column given in parentheses for each region. Each gene specific to significant QTL interval was given in the number of genes column. (green- significant; orange- suggestive; grey- no significant or suggestive threshold) 56 3.4 Genes and SNPs within a significant QTL region In total, there are 304 genes mapped within nine significant QTL regions (Table 3.2). For the gait parameters tested, stance duration located 22 genes within the Chr 4 interval of 124.33 – 125.46 Mb; step cycle parameter located 22 genes within the Chr 4 interval of 124.33 – 125.35Mb; posterior extreme position parameter located 22 genes located at Chr 4 in the QTL interval of 124.35 – 125.3 Mb. Meanwhile, the velocity in open field identified two defined significant QTLs in Chr 4 and Chr 5. There was a total of 27 genes located at Chr 4 in the QTL interval of 96.2 – 100.4 Mb and 176 genes located at Chr 5 in the QTL interval of 103.75 – 113.49 Mb. Accelerated rotarod identified a significant QTL on Chr 15 and identified 17 genes within the QTL interval of 73.69 – 75.5 Mb. In addition, accuracy in fore- & hindlimb placement located 33 genes at Chr 16 in the QTL interval of 68.2 – 78.2 Mb and 15 genes at Chr 16 in the QTL interval of 85.18 – 87.8 Mb. To determine the skilled motor movements, the first attempt success was measured, and identified 4 genes at Chr 15 in the QTL interval of 17.75 – 19.50 Mb. The motor learning performance on the complex wheel identified a total of 10 genes at Chr 1 in the QTL interval of 12.8 – 14.65 Mb. 57 Table 3.10 Genes within the nine significant QTL regions 58 Table 3.11 Identification of the priority genes that meet criteria C- Presence of nonsynonymous SNPs in coding region of the gene Lastly, the strongest candidate genes were explored based on the presence of non-synonymous polymorphism (Table 3.11). The overall results indicated that the Chr 5 QTL (103.75 – 113.49 Mb) had a total of 176 genes with SNPs, of which two were identified to have a non-synonymous polymorphism. Likewise, the Chr 16 QTL (68.2 – 78.2 Mb & 85.18 – 87.8 Mb) had a total of 48 genes that contained SNPs, of which two were non-synonymous polymorphism. 59 Chapter 4: Discussion 4.1 Discussion and significance of findings Given the fact of the heterogenous nature of DCD and the limited information on the underlying neural mechanism, it is difficult to assess the genetic causes of this disorder based upon current information. One way to approach the multifactorial genetic causation of DCD is to treat this disorder as a complex trait combined with modeling it in a rodent system. This study was developed by taking advantage of available robust bioinformatics tools, sequencing and expression data at GeneNetwork (http://www.genenetwork.org) (Mulligan, Mozhui, Prins, & Williams, 2017) and the evidence supporting the hereditary component of DCD (Lichtenstein et al., 2010; Martin et al., 2006). Hence, this thesis aimed to unravel DCD-like behaviors in the mouse model system using QTL analysis as a tool. QTL mapping was carried out to screen the whole mouse genome and locate the chromosomal regions (i.e., QTLs) that harbor genes regulating DCD-like behavior. The BXD recombinant inbred strains of mice were used as a mapping reference population to dissect associations between loci and behavioral difference in complex DCD-like phenotypic traits. The selection of each BXD line was based on the available scientific reports with respect to DCD-like motor performance over common assays (Chapter 2, Table 2.1) in the scientific online database GeneNetwork. Various behavioral phenotypes were measured over three major timepoints in mice involving the neurodevelopmental battery (Phase I, P1-P15), general motor function (Phase II, P60-P81), and motor learning (Phase III, P90-P120) to explore motor impairments found in children with DCD (Chapter 2, Figure 2.1). In phase I, statistically significant differences in sensorimotor reflexes were observed in all reflex tasks. Meanwhile, genome-wide mapping was performed on neurodevelopmental battery phenotypes using genotype data available at GeneNetwork. The LRS was run to assess genotype-phenotype association on neurodevelopmental battery phenotypes. Despite the finding of statistically significant differences across all reflex tasks, the mapping of phase I phenotypes (i.e., neurodevelopmental battery) did not identify any significant QTLs. 60 Analysis of phase II (i.e., general motor function) demonstrated statistically significant inter-strain differences in balance, motor coordination, and locomotion activity for the behavior assays of gait, open field and rotarod. The mapping for these general motor phenotypes identified three significant QTLs for variability in stance duration, step cycle and PEP (posterior exterior position) parameters of gait, two significant QTL peaks for the velocity parameter in the open field and no significant QTLs for rotarod. Lastly, in phase III, skilled motor function phenotypes from motor learning, fore- and hindlimb placement, and skilled motor movements were analysed at postnatal age P90-120 through the accelerated rotarod, horizontal ladder walking, skilled reaching, and complex wheel tasks. Overall, statistically significant inter-strain differences were found among skilled motor function phenotypes. The following parameters had no significant differences: - single day learning in the accelerating rotarod, latency to fall off the complex wheel, and overall success rate in grasping the food pellet. The QTL analysis of skilled motor learning phenotypes identified four significant QTL peaks, one per each of the following parameters: motor improvement in accelerating rotarod, overall learning in fore- and hindlimb performance in horizontal ladder rung walking task, first attempt success in skilled motor reaching task, and online learning in complex wheel (Chapter 3. Table 3.1). Once significant QTL peaks were identified at a specific chromosome location (in this case Chr 1, 4, 15 & 16), the defined QTLs were further narrowed within 1.5 LOD support interval of the QTL (i.e., region around the SNP showing significant evidence for linkage and SNPs in overlapping chromosomal region between phenotype) to locate the list of genes. Normally, QTL peaks commonly contain a number of genes ranging from a few to dozens. In this study, a total of 304 genes (including sequence tags and Riken clones) were identified within the nine significant QTLs (Chapter 3, Table 3.1). As described in the Materials and Methods section, a series of criteria were employed to prioritize promising candidates based on three criteria: (A) the gene is expressed in central nervous system (CNS) and/or skeletal muscle; (B) there is a functional implication in DCD-like behavior; and (C) presence, and number, of nonsynonymous SNPs in coding region of the gene (Table 4.2). The expressed sequence tags and Riken clones within the gene list were not evaluated since they are currently non-annotated in terms of expression pattern and function (Theberge et al., 2019). Of all the genes observed across nine significant QTLs, 143 genes met 61 criterion A (Appendix C); 14 genes met criterion B (Chapter 4. Table 4.1); and four genes met criterion C and are therefore considered as priority genes to explore (Chapter 3, Table 3.11) in relation to the clinical presentation of DCD. Tissue-specific expression is an important factor for determining the role of genes in a given disorder. The initial assessment of the 304 gene expression profile datasets was initially to determine expression in each tissue relative to other tissues using databases such as Allen brain atlas, NCBI, MGI and OMIM.The motor coordination problems are significant for DCD diagnosis and are likely associated with genes involved in nerve tissue and skeletal muscle (Fliers et al., 2012); hence I focused on 143 genes that had higher expression in CNS and skeletal muscles (Appendix C). Table 4.1 Identification of the candidate genes that meet Criterion B- Functional implication in DCD- like behavior Secondly, the functional role of genes in criterion B was explored using MGI and NCBI resource and identified the following genes: Nfia, sparcl1, Crybb1, Gpr20, Arc, Lynx1, Robo1, Robo2, Cdh10, Sulf1, Cp1x1, Idua, Nrip1, and Ltn1. Following exploration of these genes motor functional roles, each gene was further compared to available evidence from a clinical perspective 62 of DCD. Overall, five genes - Nfia (nuclear factor I/A), Sparcl1 (SPARC-like 1), Crybb1 (crystallin, beta B1), Cp1x1 (complexin 1) and Idua (iduronidase, alpha-L) were identified from the open-field parameter “velocity” to measure locomotion activity and anxiety-like behavior. Literature findings suggested Nfia (nuclear factor I-A) is a protein coding gene, and the gene expression analysis of Nfia-deficient mice displayed delay in brain development, especially oligodendrocyte maturation during the early postnatal period (Wong et al., 2007). Problems with oligodendrocyte maturation can lead to improper myelination of axons and defective electrical transmission in motor neurons. With respect to DCD, Brown-Lum & Zwicker (2015) reported impaired maturation of the oligodendrocyte lineage in white matter tracts associated with motor development of specific brain regions (i.e., corticospinal tract, cerebellum, and corpus callosum). These findings suggest that the activation of different regions of the brain during functional tasks and differences in white matter microstructure are implicated in children with DCD (Brown-Lum & Zwicker, 2015). Sparcl1 is a secreted glycoprotein associated with SPARC family of matricellular proteins. Evidence suggests abnormal Sparcl1 expression is associated with neurological and psychiatric disorders (e.g., depression, autism, and schizophrenia), suggesting that Sparcl1 may be important in regulation of healthy CNS function (Jacquemont et al., 2006; Risher et al., 2014; Seddighi et al., 2017; Zhurov et al., 2012). Similarly, DCD is known to be frequently linked with anxiety, depression, and other mental health concerns (Lingam et al., 2012). The Crybb1 (crystallin, beta B1) gene, one of the candidate genes for the velocity parameter, codes for a structural constituent of the eye lens. Spadero et al. (2015) examined the behavioral effect of Crybb1 knockdown (Crybb1 KD) to test if fear-related anxiety in mice was linked to Crybb1 expression. The knockdown of Crybb1 decreased anxiety-like behavior in mice when placed in activity-monitoring chamber (Spadaro et al., 2015). Children with DCD are known to have higher risk of developing symptoms of anxiety and depression than typically developing children (Draghi, Cavalcante Neto, Rohr, Jelsma, & Tudella, 2019). They also tend to avoid participation in physical activity. 63 Complexins are small, regulatory synaptic proteins. Research suggests, complexin I (Cp1x1) has important role in motor and exploratory behaviors (Glynn, Drew, Reim, Brose, & Morton, 2005), whereas complexin II was involved in learning (Freeman & Morton, 2004; Glynn, Bortnick, & Morton, 2003). Knockout of adult Cp1x1-/- mice exhibited profound abnormalities in tasks that require postural skills and complex coordinated movement in locomotion, walking, running and rearing, and tasks reflecting social interaction (Drew, Kyd, & Morton, 2007) and emotional reactivity (Glynn et al., 2005). Children with DCD are prone to have poor postural control, tend to trip, bump and fall, and also appear to have delay in actions and coordinated responses (Caçola & Lage, 2019). Lastly, Idua (iduronidase, alpha-L) was identified as a candidate gene from the open-field task. Deficiency in the Idua protein was associated with mucopolysaccharidoses (MPS) disorder. Recently, Kim et al. (2015) generated an Idua knockout (KO) mouse to study the pathogenesis of joint and locomotion symptoms in MPS I and demonstrated deficits that may be related to a DCD diagnosis in children (Geuze, 2005) such as significant decrease in grip strength, motor balance, and learning performance (Kim et al., 2015). For the parameter performance improvement in changing velocity on accelerating rotarod, three genes were identified: Gpr26 (G protein-coupled receptor 26), Arc (activity-regulated cytoskeleton-associated protein) and Lynx1 (Ly6/neurotoxin 1). The G protein-coupled receptor 26 (Gpr26) gene encodes a G protein-couple receptor protein, deficiency of Gpr26 in mice are found to be display increased anxiety and depression-like behaviors (Zhang et al., 2011). Likewise, activity-regulated cytoskeleton-associated protein (Arc) is a plasticity protein. Using a rodent model Penrod et al. (2019) found that knockout of the Arc gene produces mice with reduced anxiety-like behavior, depressive-like behavior, and novelty discrimination (Penrod et al., 2019). Higher levels of anxiety and depressive symptoms have been reported in children, teenagers and young adults with DCD (Pearsall-Jones, Piek, Rigoli, Martin, & Levy, 2011; Sigurdsson, van Os, & Fombonne, 2002; Skinner & Piek, 2001). The Ly6/neurotoxin 1 (Lynx1) gene was identified in the accelerated rotarod QTL. It is a protein-coding gene that acts as an endogenous modulator of nicotinic acetylcholine receptors in the mammalian CNS. Using lynx1KO mice, Miwa et al. (2006) studied the effect of treatment with a chronic course of nicotine on motor learning. The rotarod test was used as a test for motor 64 learning. The results showed the aged KO mice showed motor improvement when treated with nicotine, suggesting a role for nicotinic acetylcholine receptors in motor improvement (Miwa et al., 2006). Four genes – Robo1 (roundabout guidance receptor 1), Robo2 (roundabout guidance receptor 2), Nrip1 (nuclear receptor interacting protein 1) and Ltn1 (listerin E3 ubiquitin protein ligase 1) were identified from the QTL for the horizontal ladder rung task that measured overall learning in fore- & hindlimb placement accuracy. Robo1 is a neuronal axon guidance receptor gene belonging to the immunoglobulin family of Robo proteins that have been suggested to be involved in dyslexia (Hannula-Jouppi et al., 2005) and autism (Anitha et al., 2008). Candidate gene analysis within a four-generation family of dyslexic individuals revealed a rare balanced chromosomal translocation in Robo1 along with other chromosomal loci (Hannula-Jouppi et al., 2005). A growing body of evidence (Kaplan, Crawford, Wilson, & Dewey, 1997; Pitcher, Piek, & Hay, 2003) suggests that children who present with motor deficits will tend to have a co-occurring condition [in this case- DCD (23%), ADHD (8%) or dyslexia (19%)] and/ or overlapping signs of problems [ADHD-DCD (10%) & dyslexia-DCD (22%)] (Kirby, 2005). Gene expression analysis in peripheral lymphocytes of the autistic patient group identified a decrease in Robo1 and Robo2 expression suggesting a possible role in the pathogenesis of autism (Anitha et al., 2008). Nrip1 (nuclear receptor interacting protein 1) is another candidate gene based on the criteria of candidate gene selection. This gene encodes a nuclear receptor transcriptional coregulator that is extensively expressed in brain, particularly in the cerebellum, cortex, olfactory bulbs and hippocampus (Chih-Hao Lee, Chatchai Chinpaisal, & Li-Na Wei, 1998). In a mouse model study, Nrip1-deficient mice displayed severe cognitive impairments with an increased behavior response to stress when compared with wild-type mice, suggesting an important role in cognition (Duclot et al., 2012). Recent literature has also described cognitive limitations in some children with DCD, specifically related to an information processing system - attention, planning, execution, visual-perceptual, and learning deficits (Asonitou, Koutsouki, Kourtessis, & Charitou, 2012; Leonard, Bernardi, Hill, & Henry, 2015; Ricon, 2010; Wilson, P. H., Maruff, & Lum, 2003). 65 Listerin E3 ubiquitin protein ligase 1 (Ltn1) belongs to the ribosome quality control complex (RQC) pathway. Using N-ethyl-N-nitrosourea (ENU) mutagenesis screening, Chu et al. (2009) identified a recessive mutation in Ltn1. Newborn homozygous mutant lister mice exhibited progressive neurological and motor dysfunction that was attributed to altering listerin expression in motor and sensory neurons and neuronal processes in the brainstem and spinal cord. The neurons within this region are known to be involved in locomotion and muscle coordination thus causing progressive weakness of the hind limbs and eventually loss of locomotion in the mutant animal (Chu et al., 2009). The hallmark of DCD is a marked impairment in fine and gross motor skills, coordination and motor planning. Weakness in these skills can negatively impact day to day motor abilities in children (Biotteau et al., 2019). The Cdh10 (cadherin 10) gene, which belongs to the cadherin family of calcium-dependent glycoproteins, was identified within a significant QTL peak for first attempt success in skilled motor movement. Cadherins are cell adhesion molecules that play an important role in morphogenesis and functional differentiation of the central nervous system. Using various different study designs, abnormalities in critical domains of cadherin variants have been associated with a number of neurodevelopmental disorders, such as ASD (Crepel et al., 2014; O'Roak et al., 2012; Wang, K. et al., 2009), ADHD (Arias Vasquez et al., 2011; Salatino‐Oliveira et al., 2015), intellectual disability (Ghoumid et al., 2017; Taskiran et al., 2017), and epilepsy (Smith, L. et al., 2018). More specifically, Cadherin-10 gene is genetically linked to autism (Smith et al., 2017). Lastly, Sulf1 (sulfatase 1) gene was identified as a candidate gene from the QTL for the online motor learning parameter in the complex wheel test. Sulf1, is an endosulfatase that has been found to be highly expressed in skeletal muscles (Morimoto-Tomita, Uchimura, Werb, 2002) and slightly lower in whole brain (Kikuno et al., 1999). The absence of Sulf1 expression in the nervous system of adult mice leads to an impaired neurite outgrowth of cerebellar and hippocampal neurons, brain development, synaptic plasticity, learning and motor activity (Kalus et al., 2009). Changes in synaptic connections are long considered essential for novel motor skill learning (Harms, Rioult-Pedotti, Carter, & Dunaevsky, 2008. Recently, it was demonstrated that aberrant plasticity caused by dopamine deficiency can result in disrupted motor control by specifically targeting the learned motor skill (Zhuang, 2012). Therefore, with the cognitive difficulties in 66 children with DCD, the synapse connection and synaptic networks were suspected to alter the brain regions including cerebellum, prefrontal cortex, and striatum of these children (Deng et al., 2014). After understanding functional roles of genes within significant chromosomal regions, I focused on determining the number of promising genes with a meaningful polymorphism in mice. For this process, I inputted individual gene symbols within into a SNP variant browser. On the whole, the most interesting and strongest genes within the Chr5 QTL that met all the selection criteria that included Cplx1 (complexin 1) and Idua (iduronidase, alpha-L) gene that include two independent non-synonymous polymorphisms within mouse SNP variant browser. Likewise, the genes within the Chr16 QTL that also met all the criteria included Nrip1 (Nuclear receptor interacting protein 1) and Ltn1 (Listerin E3ubiquitin protein ligase 1) gene that encompassed two and six non-synonymous polymorphisms. Selection of only nsSNPs has its own restrictions. They usually have lesser frequency than synonymous SNP; therefore, the probability of having false positive is quite common in a small population. The ability to conclude whether a given polymorphism can confer disease susceptibility or not is of great importance for the early detection of affected individuals with a high risk of developing a particular disease and would open the way for diagnosis and targeted therapeutics. The potential impact of non-synonymous single nucleotide polymorphism (nsSNP) can be predicted through in silico, in vitro and in vivo approaches (Kucukkal, Yang, Chapman, Cao, & Alexov, 2014). For example, the assessment of in silico techniques is based on machine learning methods, statistical potentials, evolutionary sequence, and biophysics-based analysis. The commonly used assessment of in vitro techniques is SNP genotyping and functional SNP screening methods. For in vivo analysis, animal models would be an ideal way to study the effects of polymorphism and gene editing can be used to introduce individual nsSNPs within the genome (Kucukkal et al., 2014). In systems biology, crosstalk of genes between signaling pathways is common; as a result, one gene can play different functions. To further complicate things, some of these genes contribute to more than one disorder (e.g., Shank3 gene has been associated with both autism and schizophrenia) (Gauthier et al., 2010; Peça et al., 2011). To date, there are >75% of the ~20,000 genes annotated in the human genome have not had variation in them tied to any disease trait 67 (Posey et al., 2019). Thus, the majority of the human genome remains functionally unexplored and this lack in functional annotation knowledge was studied as dark genome (Lloyd et al., 2020). While considering all these factors, first I attempted to screen the list of genes (i.e., 143 genes) that passed criterion A (i.e., the gene is expressed in the CNS and/or skeletal muscle) but did not pass Criterion B (functional implication in DCD-like motor behavior). Initial screening identified a total of 129 genes that had no annotated roles in motor function. Next, I explored if any of the 129 genes had family members with a functional role in motor coordination and learning behavior. The results yielded a total of 5 gene families (Pou3f, Ephx, Sez, Lrrc, Pole) from criterion A genes with no functional roles but having family members that are involved in motor function (i.e., Pou3f3, Ephx2, Sez6, Lrrc8d and Pole4) (Table 4.2). Based on research evidence, the functional cell-based analyses of de novo variants in Pou3f3 (POU domain class 3 transcription factor 3) has been suggested to be implicated in neurodevelopmental disorder with an overlapping phenotypic spectrum that includes ASD, hypotonia, developmental delays, intellectual disability, and speech and language problems (Snijders Blok et al., 2019). Knockout of the Ephx2 (epoxide hydrolase 2) gene was reported to improve motor coordination when compared to wild-type mice post brain injury (Strauss, Gruzdev, & Zeldin, 2013). In behavioral tests, Sez6 (seizure-related gene 6) null mice demonstrated abnormalities in exploratory, motor and cognitive function (Gunnersen et al., 2007). Using in situ hybridization, Zhang and his colleagues suggested that Lrrc55 (leucine rich repeat containing protein 55) may have a potential role in motor learning related functions (Zhang et al., 2018). Investigation of gait pattern in Pole4 (polymerase epsilon) mutant mice on rotarod displayed a loss of motor function and coordination (Bellelli et al., 2018). When compared with an international mouse phenotyping consortium (IMPC) perspective, the results of the gene family screening tentatively suggest that all members of significant genes families (Pou3f3, Ephx2, Sez6, Lrrc55 and Pole4) may confer motor functional role. Altogether, these findings support that specific genes and/or gene networks with functional implication in motor behavior might possibly be associated with the development and function of neural circuits that are implicated with DCD. It is worth mentioning that a vast number of gene-phenotype relationships could be unreported (Meehan et al., 2017). In addition, most of the published studies only focus on genes that are well-annotated and ignore many potentially 68 important genes that are less well understood (Stoeger, Gerlach, Morimoto, & Nunes Amaral, 2018). Table 4.2 Identification of the family members with functional implication in motor behavior Next, I determined if there were any intersections or overlaps between our data and previously reported data within our mapped QTL regions. The MGI resource was used for this assessment due to its up-to-date and complete catalogue that identifies genetic variants (i.e., including naturally occurring SNP and QTL variants) and mutations, including those spontaneously occurring, induced, or genetically engineered, for a variety of phenotypic traits (Eppig, 2017). This database typically integrates genetic, genomic, and biological data about the laboratory mouse to facilitate the study of new disease models and therapeutic interventions in human health and disease. Hence, we took advantage of this bioinformatics resource and queried individual significant QTL locations for each parameter. We first inputted a significant QTL location (Chr 4: 124.33 to 125.35 Mb) for gait parameters “stance duration, step cycle and posterior exterior position” that involved measuring postural control (i.e., motor coordination). The phenotype, alleles, and disease models query output generated various mutations (e.g., targeted, transgenic, endonuclease-mediated) and phenotype data. The chromosomal location of the gait QTL was also associated with identified abnormal phenotypes that included startle reflex response (Le Roy, Perez‐Diaz, Cherfouh, & Roubertoux, 1999) and grooming response. Startle response is an early motor behaviour in the first year of infant life and is typically called a primitive reflex. These reflexes neither interfere with nor contribute to motor development (Bartlett, 1997). Eventually, primitive reflexes are replaced by a mature pattern of postural reflexes (e.g., righting reflex) that control balance, coordination and sensory motor development (i.e., when primitive reflexes disappear, postural and definitive motor actions are gained) (https://occupationaltherapychildren.com). 69 Secondly, we queried a significant open field QTL location [Chr 4 (96.2 to 100.4 Mb) and Chr 5 (103.75 to 113.49 Mb)] in MGI database. The phenotype, alleles and disease models query output generated various phenotypes including anxiety (Nakamura et al., 2003), locomotion activity (Koyner, Demarest, McCaughran Jr, Cipp, & Hitzemann, 2000) , body weight (Le Roy et al., 1999) and cerebellum pattern fissures (Le Roy-Duflos, 2001). Based on the literature evidence, it may be possible that these phenotypes are interrelated. For example, to understand the mechanism that underlie motor deficits in Down syndrome (DS), Galante et al. (2009) generated a Tc1 (trans-species aneuploid mouse line) mouse model. These mice exhibited a range of abnormalities, such as higher level of spontaneous locomotor activity, reduced level of anxiety, difference in gait, reduction in body weight, and motor coordination deficits and balance in the rotarod and static-rod tests (Galante et al., 2009). These deficits in gross motor coordination and weight status are found in children with DCD, such that increased body mass is associated with lowered motor coordination (D’ Hondt et al., 2009), and impaired locomotor skills (Barnett et al., 2016). To explore the physiological function of testicular orphan nuclear receptor 4 (TR4) in the CNS, Chen et al. (2005) generated TR4−/− mice by homologous recombination in embryonic stem cells. The postnatal TR4-/- cerebellum showed abnormalities in foliation with a behavioral defect in motor coordination suggesting that TR4 plays an important role in cerebellar function (Chen, Collins, Uno, & Chang, 2005). As the cerebellum is important for developing automatic movement control and the ongoing monitoring of movements, researchers have reported that both of these functions are affected in DCD. Generally, quantitative traits are multifactorial and are often influenced by environmental conditions and several polymorphic genes, so collective impact of many genes located at several QTLs provide the genetic influence on a trait or a behavioral phenotype(s) (Abiola et al., 2003). Sometimes a cluster of closely linked polymorphic genes is responsible for the quantitative variation of a trait. When the genomic positions of mapped QTL coincide, the gene for which expression has been detected is considered to be a possible candidate gene for the QTL affecting the phenotypic trait. 70 Next, I queried the significant accelerating rotarod QTL [Chr 15: (73.69 to 77.5 Mb)] in the MGI database. The phenotype, alleles and disease models query output generated phenotypes that included synaptic plasticity (Plath et al., 2006) and dopamine loss (Sedelis, Hofele, Schwarting, Huston, & Belknap, 2003). To investigate if these phenotypes are connected to skilled motor learning. Piochon et al. (2014) conducted a literature review and identified cerebellum-dependent motor coordination and learning impairment in mice with a 15q11-13 duplication. In addition, they pointed out deficits in synaptic plasticity and pruning are potential causes for motor problems (Piochon et al., 2014). Investigation of the significant QTL location [Chr 16; (68.2 to 78.2 Mb; 85.18 to 78.2 Mb) for horizontal ladder walking task generated phenotypes that included diet-induced obesity (Singer et al., 2004) and cardiac development (Lee, Chang, Bali, Chen, & Yan, 2011). Literature assessment on phenotype relatedness with motor behavior deficits found that obesity exerts detrimental effects on cognitive and motor control capabilities across lifespan (Wang, X. et al., 2016). In a diet-induced obesity mouse model, decrease in motor coordination and increase in slipping was observed on rotarod and beam walking task (Griffin et al., 2010; Lee, Wu, Shi, & Zhang, 2015). They also reported altered locomotion and gait speed (Takase, Tsuneoka, Oda, Kuroda, & Funato, 2016). However, to our knowledge association of cardiac phenotype with skilled motor performance has not been reported in mouse studies. Then, I inputted the significant QTL location for skilled motor reaching task [Chr 15 (17.75 to 19.50 Mb)]. The phenotype, alleles and disease models query output generated stress response (Thifault et al., 2008) and variation in tail growth phenotypes (Rocha, Eisen, Dale Van Vleck, & Pomp, 2004). The relation between stress-associated anxiety and motor dysfunction is supported by the observation that mouse strains bred for high anxiety traits manifested exaggerated motor skill impairments (e.g., walking, reaching and grasping) in comparison to less anxious mouse strains (Lepicard et al., 2000; Lepicard et al., 2003; Metz, Jadavji, & Smith, 2005). No association has been reported between tail growth variation and motor skill learning. Last but not least, a significant QTL location (Chr 1; 2.8 to 14.65 Mb) for the complex wheel test was associated with organ development (Ratzka et al., 2008) and variation in 71 reproductive organ size (Shorter et al., 2017) phenotypes that are not obviously associated with motor skills. Overall, the investigation of previously reported studies within significant mapped QTL identified overlapping loci for other annotated abnormal phenotypes that may be associated with motor-related phenotypes. Altogether, this study found nine significant chromosomal regions contributing to DCD motor-related phenotypes. Using bioinformatic resources, I identified 14 candidate genes in those QTL regions (Nfia, sparcl1, Crybb1, Gpr20, Arc, Lynx1, Robo1, Robo2, Cdh10, Sulf1, Cp1x1, Idua, Nrip1, Ltn1), including four priority genes (Cp1x1, Idua, Nrip1, Ltn1). In addition, overlapping loci with abnormal phenotypes (e.g., anxiety, locomotion activity) were observed at some of the significant mapped regions. The most promising candidate genes within the QTLs contain nonsynonymous sequence polymorphisms that may be involved in the regulation of motor phenotypes. To date, no connections have been found between these candidate genes and DCD-related etiology. Therefore, in the future, it will be of interest to determine if these molecules play a role in DCD phenotype regulation by performing genetic-association and functional studies that help to illuminate the etiology of DCD. 4.2 Research conclusion Impaired motor skills are key features of DCD and genetics have been proposed as an important factor to understand this neurodevelopmental disorder. To dissect the genetic basis of DCD, we took advantage of the natural variation in motor phenotypes and genotypes in the recombinant inbred mouse strains. The quantitative measures of a phenotype-driven, genome-wide approach was used to effectively identify several significant QTLs for general motor and skilled motor analyses that provide starting points to identify intriguing candidate genes that may influence DCD-like motor behavior. Moreover, in the longer term, uncovered candidate genes that are associated with variation in these phenotypes of BXD RI mice could shed light on genetic factors underlying DCD in humans. Finding motor deficits at an earlier stage in child development could have a significant impact on later motor and social skill development by permitting the use of tailored early interventions to improve outcomes. 72 4.3 Strength and weaknesses of the research To the best of my knowledge, this is the first study that attempts to understand a neurodevelopmental disorder like DCD by using the behavioral testing of the BXD lines of mice. The research findings of this work contribute to a collection of novel supplementary measures – gait phenotypes (i.e., leg combination index, duty factor, stance and swing duration, step cycle, posterior exterior position) – to the growing body of online scientific databases, GeneNetwork and MPD. These measures have the potential to help researchers evaluate relationships among variables and to assemble networks of associations. Most importantly, this study has identified nine significant QTL peaks, 14 candidate genes and four priority genes in regard to DCD-like motor phenotypes using a powerful mouse resource and bioinformatic tools. A major limitation of this thesis is the use of a relatively small number of experimental groups (12 BXD inbred lines and parental strains). When using RI lines, the location of a QTL and its detection are largely influenced by the total number of genotypes tested. However, by increasing the number of experimental lines that are examined, one can obtain greater power in the mapping resolution to detect accurate location of a QTL (Belknap, 1998). Also, studies using fewer than 30 RI strains, generally have a higher probability of failing to detect reliable QTLs (Wang et al., 2014). Nevertheless, I was able to find several significant and suggestive QTLs. 4.4 Future directions Thirty more inbred lines will be further selected from the expanded lines of BXD mice and tested for skilled motor learning phenotypes in order to improve the sensitivity and resolution of this study. Although there is still a long road ahead for the translation of these findings to clinical setting, the findings of this study have the potential to improve our knowledge of the genetics of DCD. In this study, I have identified 9 promising regions of the genome and 14 candidate genes (4 priority genes) that influence DCD-like behavior. 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Perinatal and neonatal predictors of developmental coordination disorder in very low birthweight children. Archives of Disease in Childhood, 98(2), 118-122. doi:10.1136/archdischild-2012-302268 100 Appendices Appendix A Genome-wide linkage map of surface righting reflex, cliff aversion and forelimb grasp on the Fox Neurodevelopmental Battery to measure sensorimotor reflexes The overall blue trace shows the LRS. The genome-wide QTL map showing suggestive QTLs on Chromosome 17, 4, 5 & 10 for surface righting reflex, cliff aversion and forelimb grasp. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. 101 Appendix B Genome-wide linkage map of Latency to fall and Performance improvement on standard rotarod to measure motor coordination and balance The overall blue trace shows the LRS. The genome-wide QTL map showing suggestive QTLs on Chromosome 17, 4, 5 & 10 for surface righting reflex, cliff aversion and forelimb grasp. The lower gray horizontal line represents suggestive LRS genome-wide threshold at p ≤ 0.63. The upper pink horizontal line represents significant LRS genome-wide threshold at p ≤ 0.05. The bottom orange marks indicate SNP density. 102 Appendix C 103 104 105 106 107 List of genes that met the criterion A Overall, a total of 143 genes were expressed in central nervous system (CNS) and/or skeletal muscle "@en ; edm:hasType "Thesis/Dissertation"@en ; vivo:dateIssued "2020-11"@en ; edm:isShownAt "10.14288/1.0391967"@en ; dcterms:language "eng"@en ; ns0:degreeDiscipline "Medical Genetics"@en ; edm:provider "Vancouver : University of British Columbia Library"@en ; dcterms:publisher "University of British Columbia"@en ; dcterms:rights "Attribution-NonCommercial-NoDerivatives 4.0 International"@* ; ns0:rightsURI "http://creativecommons.org/licenses/by-nc-nd/4.0/"@* ; ns0:scholarLevel "Graduate"@en ; dcterms:title "Investigating mouse motor activity and learning behavior using quantitative trait locus (QTL) analysis to elucidate the genetic underpinnings of developmental coordination disorder (DCD)"@en ; dcterms:type "Text"@en ; ns0:identifierURI "http://hdl.handle.net/2429/74768"@en .