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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #346764
Title: Quantification of genomic relationship from DNA pooled samples

Author
item Kuehn, Larry
item McDaneld, Tara
item Keele, John

Submitted to: World Congress of Genetics Applied in Livestock Production
Publication Type: Proceedings
Publication Acceptance Date: 10/9/2017
Publication Date: 5/7/2018
Citation: Kuehn, L.A., McDaneld, T.G., Keele, J.W. 2018. Quantification of genomic relationship from DNA pooled samples. In: Proceedings World Congress of Genetics Applied in Livestock Production. 11-16 Feb 2018. Auckland, New Zealand. Volume Electronic Poster Session - Theory to Application 2, p. 631. Availabe: www.wcgalp.org.

Interpretive Summary:

Technical Abstract: Use of DNA pooling for GWAS has been demonstrated to reduce genotypic costs up to 90% while achieving similar power to individual genotyping. Recent work has focused on use of DNA pooling to inform problems in genomic prediction. This study is designed to demonstrate the efficacy of estimating genomic relationship of pools to animals that constitute the pool and potentially related animals that are not members of the pool. A random sample of 32 liver samples were selected from two extreme phenotype classes (16 from each). For each phenotypic class, four subpools were formed; subpools were comprised of four different samples in a 1:2:3:4 ratio. Whole pools were derived from the subpools in two different arrangements again representing 1:2:3:4 ratios. Actual vs. intended representation of animals in the subpools and whole pools was assessed by multiple regression of pooling allele frequencies on animal genotypes. Pooling allele frequencies were used to derive genomic relationship between subpools or whole pools and animals in the other phenotypic classes. Pooling allele frequencies yielded similar genomic relationships to the average of individual genotypes. Weighting animals within subpools or whole pools by their estimated contribution as determined by regression increased the accuracy of the genomic relationship of the pool to animals outside the pool due to removal of pool construction error. Strategies to reduce pool construction error would reduce the cost of genome prediction without much sacrifice in accuracy as long as the strategy isn’t expensive or time-consuming. The accuracy of large pools is less compromised by pool construction error compared to small pools.