Linkage disequilibrium among commonly genotyped SNP variants detected from bull sequence

Summary Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNPs genotypedby currently available arrays used for genomic selection. A number of variants detected from sequencing influential siresare 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 SNPson a high-density array and non- synonymous SNPsdetected in sequence from influential siresof a multibreed population, results of this examination suggest that linkage disequilibriumbetween non- synonymousand array SNPsmay be insufficient for accurate imputation from the array to sequence. In contrast to 75% of array SNPsbeing strongly correlated to another SNPon the array, less than 25% of the non- synonymous SNPswere strongly correlated to an array SNP. When correlations between non- synonymousand array SNPswere strong, distances between the SNPswere greater than separation that might be expected based on linkage disequilibriumdecay. Consistently near-perfect whole-genome linkage disequilibriumbetween the full array and each non- synonymous SNPwithin 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 disequilibriumbetween sequence and array SNPsmay be limited by discrepancies in allele frequency distributions, so investigating alternate genotyping approaches and panels providing greater chances of frequency-matched SNPsstrongly correlated to sequence variants is also warranted. Genotypes used for this study are available from https://www.animalgenome.org/repository/pub/;USDA2017.0519/.