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University of Florida Thesis Or Dissertation UNRAVELLING THE GENOMIC ARCHITECTURE OF BULL FERTILITY IN DAIRY CATTLE By YI HAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2016 © 2016 Yi Han To my parents and my grandfather ACKNOWLEDGMENTS I thank my advisor Dr. Francisco Penagaricano for giving me the opportunity to enter this field and his help throughout my research. I thank my committee members: Dr. Raluca Mateescu and Dr. Mauricio Elzo for being so kind, understanding and supportive. Finally, thanks to Mom, Dad and Ruohan for their encouragement and endless support. 4 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 8 LIST OF ABBREVIATIONS ............................................................................................. 9 ABSTRACT ................................................................................................................... 10 CHAPTER 1 INTRODUCTION .................................................................................................... 11 2 LITERATURE REVIEW .......................................................................................... 13 Bull Fertility ............................................................................................................. 13 Compensable Traits vs. Uncompensable Trait ....................................................... 13 Prediction of Sire Fertility ........................................................................................ 16 Fertility Estimation Using Field Data (in vivo) ................................................... 16 Technician non-return rates (NNR) ............................................................ 17 Estimated relative conception rate (ERCR) and agritech analytics (ATA) .. 17 Sire conception rate (SCR) ........................................................................ 18 Predictors of Fertility in vitro ............................................................................. 19 Making Predictions ........................................................................................... 21 The Genetic Basis of Bull Fertility ........................................................................... 22 Chromosomal Aberrations ................................................................................ 22 Numerical aberrations ................................................................................ 23 Structural aberrations ................................................................................. 23 Gene Identification ........................................................................................... 24 GWAS ........................................................................................................ 25 Pathway based analysis ............................................................................ 26 Novel Omic Technologies on Bull Fertility ............................................................... 27 3 UNRAVELLING THE GENOMIC ARCHITECTURE OF BULL FERTILITY IN HOLSTEIN CATTLE ............................................................................................... 32 Background ............................................................................................................. 32 Methods .................................................................................................................. 34 Phenotypic and Genotypic Data ....................................................................... 34 Statistical Methods for Genome-Wide Association Mapping ............................ 35 Genome-Wide Association Mapping Using ssGBLUP ..................................... 36 Genome-Wide Association Mapping Using Single Marker Regression (cGWAS) ....................................................................................................... 38 5 Gene Set Analysis ............................................................................................ 39 Results .................................................................................................................... 40 Whole Genome Association Analysis ............................................................... 40 Gene Set Analysis ............................................................................................ 42 Discussion .............................................................................................................. 44 Conclusion .............................................................................................................. 48 LIST OF REFERENCES ............................................................................................... 57 BIOGRAPHICAL SKETCH ............................................................................................ 67 6 LIST OF TABLES Table page 2-1 Summary of the strongest candidate genes found by GWAS ............................. 30 3-1 Most significant genetic markers associated with Sire Conception Rate. ........... 54 3-2 Gene Ontology (GO) Molecular Function terms significantly enriched with genes associated with Sire Conception Rate ..................................................... 55 3-3 MeSH terms significantly enriched with genes associated with Sire Conception Rate (SCR). ..................................................................................... 56 7 LIST OF FIGURES Figure page 3-1 Descriptive statistics for Sire Conception Rate (SCR). ....................................... 50 3-2 Manhattan plots showing the results of the genome-wide association mapping for Sire Conception Rate. ..................................................................... 51 3-3 Genomic regions (1.5 Mb) that explain more than 0.50% of the genetic variance for Sire Conception Rate. ..................................................................... 52 3-4 Gene Ontology Biological Process terms significantly enriched with genes associated with Sire Conception Rate. ............................................................... 53 8 LIST OF ABBREVIATIONS AI Artificial insemination AIPL Animal Improvement Programs Laboratory ATA Agritech Analytics CASA Computer-assisted semen analysis CDCB Council of Dairy Cattle Breeding CDDR Cooperative Dairy DNA Repository cGWAS Classical Genome-wide association studies ERCR Estimated relative conception rate GEBVs Genomic estimated breeding values GO Gene Ontology GSEA Gene set enrichment analysis HOST Hypo-osmotic swelling test IBD Identical-by-descent MeSH Medical Subject Headings NAAB National Association of Animal Breeders NNR Non-return rate PMI Plasma membrane integrity QTL Quantitative trait loci SCR Sire conception rate SNP Single-nucleotide polymorphisms ss-GBLUP Single step Genomic best linear unbiased predictor SSFS Service Sire Fertility Summary USDA United States Department of Agriculture 9 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science UNRAVELLING THE GENOMIC ARCHITECTURE OF BULL FERTILITY IN DAIRY CATTLE By Yi Han December 2016 Chair: Francisco Peñagaricano Major: Animal Sciences Fertility is considered an important economic trait in dairy cattle. Most studies have investigated cow fertility while bull fertility has received much less consideration. The main objective of this study was to perform a comprehensive genomic analysis in order to unravel the genomic architecture underlying sire fertility in dairy cattle. The analysis included the application of alternative genome-wide association mapping approaches and the subsequent use of diverse gene set enrichment tools. The association analyses identified at least eight genomic regions strongly associated with bull fertility. Most of these regions harbor genes, such as KAT8, CKB, TDRD9 and IGF1R, with functions related to sperm biology, including sperm development, motility and sperm-egg interaction. Moreover, the gene set analyses revealed many significant functional terms, including fertilization, sperm motility, calcium channel regulation, and SNARE proteins. Most of these terms are directly implicated in sperm physiology and male fertility. This study contributes to the identification of genetic variants and biological processes underlying sire fertility. These findings can provide opportunities for improving bull fertility via marker-assisted selection. 10 CHAPTER 1 INTRODUCTION A rapid raising demand for dairy products has inflicted on a comparatively small number of dairy cows in recent years (Potgieter, 2012). Multiple programs including nutrition, management and genetic selection have been used and successfully made a progress on milk production. However, traits associated with fitness, such as fertility are seldom taken into account in dairy cattle breeding program. In response to the intense selection for genetic merit of milk yield, a worldwide deleterious effect on fertility has become a major concern (Lucy, 2001a; Pryce and Veerkamp, 2001). This phenomenon happens to be both logical and natural (Berry et al., 2016). One possible explanation is that the transmission of mutations causing infertility has
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