Identification of causal factors for recessive lethals in dairy cattle with special focus on large chromosomal deletions

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: (i) 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. (ii) 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. (iii) deals with three main issues related to imputation of structural variants, in this case, large chromosomal deletions, e.g. availability of deletion genotypes, size of haplotype reference panel, and finally, imputation itself. To address the first two issues, this study describes a Gaussian mixture model-based approach where read-depth data from the variant call format (VCF) file is used to genotype a known deletion locus, without the need for raw sequence (BAM) file. Finally, it presents a pipeline for joint imputation of WGS variants along with large chromosomal deletions. (iv) describes genome-wide association studies for female fertility in three Nordic dairy cattle breeds using imputed WGS variants including large chromosomal deletions. This study is based on the analyses of eight fertility related traits using single-marker association, conditional and joint analyses. This study illustrates that inflation in association test-statistics could be seen even after correcting for population stratification using (genomic) principal components, and relatedness among the samples using genomic relationship matrices; however, this was known for traits with strong polygenic effects, among other factors. Finally, mapping of several new quantitative trait loci (QTL), along with the previously known ones, are reported in this study. This study also highlights the importance of including (imputed) large deletions for association mapping of fertility traits. (v) describes prediction of genomic breeding values for fertility using SNP array-chip genotypes, selected QTL and large chromosomal deletion. Using genomic best linear unbiased prediction (GBLUP) method with one or several genomic-relationship matrices derived from a set of selected markers, this study reports higher prediction accuracy compared with previous report. This study also highlights the influence of selecting markers with best predictability, especially for a breed with small training population, in accuracy of genomic prediction. The results demonstrate that large deletions in general have a high predictive performance.