Skip to main content
ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics, Breeding, and Animal Health Research » Research » Publications at this Location » Publication #333666

Research Project: GENETIC RESEARCH TO ENHANCE EFFICIENT AND SUSTAINABLE PRODUCTION OF BEEF CATTLE AND SHEEP

Location: Genetics, Breeding, and Animal Health Research

Title: Linkage disequilibrium among commonly genotyped SNP and variants detected from bull sequence

Author
item Snelling, Warren
item Kuehn, Larry
item Keel, Brittney
item Thallman, Richard - Mark
item Bennett, Gary

Submitted to: Animal Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/22/2017
Publication Date: 6/20/2017
Publication URL: http://handle.nal.usda.gov/10113/5801804
Citation: Snelling, W.M., Kuehn, L.A., Keel, B.N., Thallman, R.M., Bennett, G.L. 2017. Linkage disequilibrium among commonly genotyped SNP and variants detected from bull sequence. Animal Genetics. 48:516-522. https://doi.org/doi: 10.1111/age.12579.

Interpretive Summary: Promises of increased selection accuracy from genomic predictions using sequence instead of genetic marker (SNP) chips have not yet been fulfilled, perhaps because of difficulty assigning sequence from influential sires to relatives with SNP chip genotypes. Differences in allele frequency distributions for SNP on commonly available chips and variations detected in sequence (SQVAR) may limit the chances of strong correlations between chip and SQVAR needed to accurately determine sequence genotypes. Using genotypes of SNP on a high-density chip and SQVAR that code for protein changes detected from sequence of influential sires of a multibreed population, results of this study indicate correlations between coding SQVAR and close chip SNP may be too weak to accurately determine SQVAR genotypes using chip SNP surrounding each coding SQVAR. Using all SNP on the chip to infer each SQVAR might be more accurate. Alternatives to standard SNP chips, including SNP with allele frequency distributions similar to SQVAR, could also enable SQVAR to be inferred more accurately.

Technical Abstract: Genomic prediction utilizing causal variants could increase selection accuracy above that achieved with SNP genotyped by commercial assays. A number of variants detected from sequencing influential sires are likely to be causal, but noticable improvements in prediction accuracy using imputed sequence variant genotypes have not been reported. Improvement in accuracy of predicted breeding values may be limited by accuracy of imputed sequence variants. Using genotypes of single nucleotide polymorphisms (SNP) on a high-density array and non-synonymous SNP detected in sequence from influential sires of a multibreed population, results of this examination suggest that linkage disequilibrium between non-synonymous and array SNP may be insufficient for accurate imputation from the array to sequence. In contrast to 75% of array SNP strongly correlated to another SNP on the array, less than 25% of the non-synonymous SNP were strongly correlated to an array SNP. When correlations between non-synonymous and array SNP were strong, distances between the SNP 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 SNP may be limited by discrepancies in allele frequency distributions, so investigating alternate genotyping approaches and panels providing greater chances of frequency-matched SNP strongly correlated to sequence variants is also warranted.