Selection of cattle SNP markers using selective-sequencing experimental design and statistical learning

Authors: Bakshy, Kiranmayee; SCHNABEL, ROBERT - University Of Missouri; Bickhart, Derek

Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 12/7/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Recent advances in sequencing technologies made it conceivable to comprehensively catalog validated genetic variations in human population samples, creating a groundwork to understand disease and evolution. However, non-model organisms lack such a gold standard genetic variants data set, which directly impacts the accuracy of future genomic selection. Moreover, to ensure for more accurate future population studies, such as genome-wide association studies (GWAS), there is a need to identify and include rare variants that are more frequently associated with diseases than the high frequency variants which are commonly employed to design the currently available single nucleotide polymorphism (SNP) chips. We implemented a selective-sequencing experiment to select for a minimum number of animals that represent almost 85% of the homozygous haplotypes found in the National Holstein Database. By exploiting the homozygous nature of selected haplotype sequence from these individuals, we were able to curate a list of high quality, lower-frequency variant sites for use in variant-detection modeling. GATK Variant quality score recalibration was performed on whole genome sequence data from 172 Holstein sires using this new SNP variant dataset to model true positive variant locations. Given the paucity of gold standards for cattle variant-detection model training, we suggest that this method will result in more reliable variant calls in future cattle resequencing projects.