A low-density SNP genotyping panel for the accurate prediction of cattle breeds. (15th October 2020)

• Record Type: Journal Article
• Authors: Reverter, Antonio; Hudson, Nicholas J; McWilliam, Sean; Alexandre, Pamela A; Li, Yutao; Barlow, Robert; Welti, Nina; Daetwyler, Hans; Porto-Neto, Laercio R; Dominik, Sonja
• Is Part Of: Journal of animal science. Volume 98:Number 11(2020)
• Publication Date: 2020-10-15
• Subjects: average Euclidean distance -- cattle -- genomic breed composition; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skaa337
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP ) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1, 243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500, 000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED ) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1 . For each panel, the genomic breed composition (GBC ) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost—only modest gains in accuracy are made as one increases panel density beyond this point.