NNT : 2019 IAVF 0018 THESE DE DOCTORAT préparée à l’Institut des sciences et industries du vivant et de l’environnement (AgroParisTech) et Center for Quantitative Genetics and Genomics (Aarhus University) pour obtenir le grade de Docteur de l’Institut agronomique, vétérinaire et forestier de France Spécialité : Génétique animale École doctorale n°581 Agriculture, alimentation, biologie, environnement et santé (ABIES) par Md Mesbah UDDIN Identification of causal factors for recessive lethals in dairy cattle with special focus on large chromosomal deletions Etude de délétions chromosomiques et de variants génétiques responsables de mortalité embryonnaire chez les bovins laitiers Directeur de thèse : Didier BOICHARD et Goutam SAHANA Co-encadrement de la thèse : Bernt GULDBRANDTSEN, Mogens Sandø LUND et Aurélien CAPITAN Thèse présentée et soutenue à Foulum, (Danemark), le 17 Septembre 2019: Composition du jury : M. Just JENSEN, Professor, Aarhus University Président M. Didier BOICHARD, Senior Scientist, INRA Directeur de thèse M. Göran ANDERSSON, Professor, Swedish University of Agricultural Sciences (SLU) Rapporteur Mme Alessandra STELLA, Senior Researcher, National Research Council of Italy (CNR) Rapporteur M. Georg THALLER, Professor, Kiel University Rapporteur M. Claus Bøttcher JØRGENSEN, Professor, University of Copenhagen Rapporteur UMR 1313 Génétique Animale et Biologie Intégrative Center for Quantitative Genetics and Genomics AgroParisTech|INRA Department of Molecular Biology and Genetics 78350 Jouy-en-Josas Aarhus University, 8830 Tjele France Denmark Identification of causal factors for recessive lethals in dairy cattle with special focus on large chromosomal deletions Md Mesbah Uddin PhD Thesis This PhD thesis is submitted to the Graduate School of Science and Technology (GSST), Aarhus University, Denmark, and the Doctoral School ABIES (Agriculture Food Biology Environment Health), AgroParisTech, France, in fulfilment of requirements for the double PhD degrees under the Erasmus Mundus double degree program EGS-ABG. Supervisors Main supervisors Goutam Sahana Department of Molecular Biology and Genetics, Aarhus University, Denmark Didier Boichard Génétique Animale et Biologie Intégrative, AgroParisTech/INRA, France Co-supervisors Bernt Guldbrandtsen Department of Molecular Biology and Genetics, Aarhus University, Denmark Mogens Sandø Lund Department of Molecular Biology and Genetics, Aarhus University, Denmark Aurélien Capitan Génétique Animale et Biologie Intégrative, AgroParisTech/INRA, France Allice, France Acknowledgement Firstly, I am grateful to the EGS-ABG consortium for giving me the opportunity in this double doctorate program, hosted by two leading universities in the field—Aarhus University and AgroParisTech. Last four years was a life-changing and eye-opening journey for me with lots of academic, research, cultural and international experiences that I could not imagine without this Erasmus Mundus endeavor. I am proud and feel lucky to be an EGS-ABG graduate. I am thankful to all EGS-ABG colleagues, past and present, for their help throughout this journey. I am grateful to both of my PhD host institutes, QGG/Aarhus University and INRA/AgroParisTech, for offering this PhD. I am thankful to MBG/AU and GABI/INRA secretaries for their help regarding the administration. I have to mention one name, Karin Smedegaard (may she rest in peace). Her help and assistance started from receiving me from the Viborg train station, setting-up the MBG email, registering for social security, opening bank account … to visiting apartments and renting my first apartment in Viborg. I am grateful to her, will deeply miss her presence. I was lucky to have both Goutam and Didier as my PhD supervisors. I am thankful to them for their help and guidance throughout my PhD. It was an excellent learning experience for me. I really enjoyed and appreciated their prompt feedback on my work (both research results and writing) with detailed and to-the-point suggestions, and constructive criticisms when needed. I am indebted to Goutam and Didier for their untiring efforts to educate me, to correct my mistakes, and finally to shape this dissertation in a presentable manner (just within one week!). Specially, I want to thank Goutam under whose direct supervision I started my PhD journey and spent major part of the learning phase of my PhD. From day one, Goutam made it clear that I have to take charge of my PhD studies. No doubt, I was overwhelmed with this freedom and independence, but with his sincere supervision, I was able to navigate through my PhD, and gained enough confidence to pursue research topics from conception to dissemination. Before starting PhD, I had little experience working in Linux environment or with big data. I am indebted to Goutam for bearing with me, especially in the 1st year of my PhD where he allowed me to attend several courses and spend time to learn programming/bioinformatics techniques (even though, I had no apparent progress in my research project). Without that, I could not imagine pursuing a PhD in quantitative genetics (let alone completing it!). I am thankful to Didier for his supervision during the second half of my PhD. His mentorship was pleasant, delightful and enlightening. Despite his busy schedule, I always had access and undivided attention from him. Every meeting and discussion I had with him, whether research or life in general, ended with enlightenment and wisdom, which greatly helped my research and personal life. Lastly, my stay in GABI was stress-free, and I was always in vacation mood—could be the Paris effect!!! I am also thankful to my co-supervisors Bernt, Mogens and Aurelien. I am grateful to Bernt from whom I learned the ins and outs of the Linux cluster in QGG, whole-genome sequence analysis, various aspects of genetics (to name few). Particularly, when I was struggling to interpret results in light of population/evolutionary genetics, I cherish the memories of those one-to-one discussions with Bernt (that often lasted several hours in late afternoon), which greatly helped me in finishing my first PhD paper. I am thankful to Mogens for bringing a broader, applied, and real-life problem solving perspective of the research to the supervision team, which was the North Star in my PhD that helped me not to fell prey of hair-splitting type analysis, and guided me to pursue topics that are relevant to livestock breeding and genetics. I am also grateful to Mogens for his keen interest in ensuring an all- i round training for all QGG PhD fellows to prepare us for a better career in academia/research and/or industry. Finally, I am thankful to Aurelien from whom I learned a lot, especially, while working with the recessive lethal project. I admired his enthusiasm, friendly attitude and hands-on supervision approach; I really enjoyed working with him. Besides his guidance on mapping recessive lethals, I learned a lot on storytelling, and precise scientific writing—though it was lot of extra work to remove few of those “may be/could be” words (he would often say we have the data to check that...). I am thankful to Grum, my QGG Buddy, for his help in social and work life. I also thank my office mates Lingzhao and Bingjie in QGG, and Rabia and Margarita in GABI/INRA for their pleasant company and stimulating work environment. I am grateful to all QGG/Aarhus University and G2B/INRA colleagues for their support throughout this journey. Lastly, I am grateful to my family, parents, my wife Abeda, and son Abdullah, for their unconditional love, support and sacrifice. Without their amazing supports, this PhD would still be a dream. I dedicate this PhD Thesis to them. Md Mesbah Uddin 17 September 2019 ii Résumé in English Fertility is an economically important trait in dairy cattle. Fertility is defined as the genetic ability of a cow to show oestrus and conceive after insemination, and to resume breeding after calving. This trait had a negative trend in recent years, partly due to intensive selection for production-related traits—which has a negative genetic correlation with fertility—using few bulls with high genetic merits for production traits. Before genomics, it was difficult to achieve faster genetic progress for fertility using pedigree-based breeding scheme due to low heritability of this trait. Genomic selection scheme provides a solution to this hard-to-select problem, especially for traits with low heritability. Identification of causal variants for recessive lethal mutations, when possible, and selection of a set of predictive markers that successfully track such causal variants, is vital for making mating-decision (e.g. to avoid at- risk mating) and for additional increase in genetic gains using genomic selection. The overall aim of this PhD thesis is to identify causal variants for recessive lethal mutations and select a set of predictive markers that are in high linkage-disequilibrium with the causal variants for female fertility in dairy cattle. We addressed this broad aim under five articles/manuscripts that are presented through Chapter 2 to Chapter 6 in this thesis. Chapter 2 describes a systematic approach of mapping recessive lethals in French Normande cattle using homozygous haplotype deficiency (HHD). This study shows the influence of sample size, quality of genotypes, quality of (genotype) phasing and imputation, age of haplotype (of interest), and last but not the least, multiple testing corrections, on discovery and replicability of HHD results. It also illustrates the importance of fine-mapping with pedigree and whole-genome sequence (WGS) data, (cross-species) integrative annotation to prioritize candidate mutation, and finally, large-scale genotyping of the candidate mutation, to validate or invalidate initial results. Chapter 3 describes a high-resolution population-scale mapping of large chromosomal deletions from whole-genome sequences of 175 animals from three Nordic dairy breeds. This study employs three different approaches to validate identified deletions. Next, it describes population genetic properties and functional importance of these deletions. Finally, it illustrates deletion formation mechanisms based on the assembled sequence features at breakpoints.