Linkage disequilibrium among commonly genotyped SNP variants detected from bull sequence.

Abstract

Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNPs genotyped by currently available arrays used for genomic selection. A number of variants detected from sequencing influential sires are likely to be causal, but noticeable improvements in prediction accuracy using imputed sequence variant genotypes have not been reported. Improvement in accuracy of predicted breeding values may be limited by the accuracy of imputed sequence variants. Using genotypes of SNPs on a high-density array and non-synonymous SNPs detected in sequence from influential sires of a multibreed population, results of this examination suggest that linkage disequilibrium between non-synonymous and array SNPs may be insufficient for accurate imputation from the array to sequence. In contrast to 75% of array SNPs being strongly correlated to another SNP on the array, less than 25% of the non-synonymous SNPs were strongly correlated to an array SNP. When correlations between non-synonymous and array SNPs were strong, distances between the SNPs were greater than separation that might be expected based on linkage disequilibrium decay. Consistently near-perfect whole-genome linkage disequilibrium between the full array and each non-synonymous SNP within the sequenced bulls suggests that whole-genome approaches to infer sequence variants might be more accurate than imputation based on local haplotypes. Opportunity for strong linkage disequilibrium between sequence and array SNPs may be limited by discrepancies in allele frequency distributions, so investigating alternate genotyping approaches and panels providing greater chances of frequency-matched SNPs strongly correlated to sequence variants is also warranted. Genotypes used for this study are available from https://www.animalgenome.org/repository/pub/;USDA2017.0519/.