Leveraging Sequence Data for High-Density Imputation and Genetic Defect Mapping in Ruminants

The recent increase in popularity of Single Nucleotide Polymorphism (SNP) chip technology has led to advancements in genomic prediction accuracy across many livestock species. Our ability to leverage genomic information when making predictions for animal performance has led to those predictions increasing in accuracy and driving more efficient genetic gain. While SNP chips are tremendously useful for that purpose, more dense genotyping that would be useful in mapping studies is costly and impractical for routine use in populations. This thesis focuses on optimizing a genotype imputation pipeline for increasing the density of genetic markers for use in downstream Genome Wide Association Studies (GWAS). This allows allow for lower density commercially-available SNP chips such as those with < 50,000 markers to be used in analyses that help fine-map complex trait associations. Another piece of our work leveraged whole genome sequencing to determine possible genetic causes of cleft palate in Boer goats. Sequencing unaffected parents and affected kids can help in identifying variants potentially linked to the defect. We worked to identify candidate variants and verify their impact on the phenotype to support the development of a genetic test for producers. Both studies used whole genome-sequencing data to help inform producer breeding decisions.