Linkage disequilibrium among commonly genotyped SNP variants detected from bull sequence
2017
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/.
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