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A low-density SNP genotyping panel for the accurate prediction of cattle breeds.

Authors
  • Reverter, Antonio1
  • Hudson, Nicholas J2
  • McWilliam, Sean1
  • Alexandre, Pamela A1
  • Li, Yutao1
  • Barlow, Robert3
  • Welti, Nina4
  • Daetwyler, Hans5
  • Porto-Neto, Laercio R1
  • Dominik, Sonja6
  • 1 CSIRO Agriculture & Food, 306 Carmody Rd., St. Lucia, Brisbane, QLD 4067, Australia. , (Australia)
  • 2 School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia. , (Australia)
  • 3 CSIRO Agriculture & Food, 39 Kessels Rd., Cooper Plains, Brisbane, QLD, Australia. , (Australia)
  • 4 CSIRO Agriculture & Food, Waite Rd., Urrbrae, SA, Australia. , (Australia)
  • 5 Agriculture Victoria Research, AgriBio, Bundoora, Victoria, Australia. , (Australia)
  • 6 CSIRO Agriculture & Food, Chiswick, New England Highway, Armidale, NSW, Australia. , (Australia)
Type
Published Article
Journal
Journal of animal science
Publication Date
Oct 15, 2020
Identifiers
DOI: 10.1093/jas/skaa337
PMID: 33057688
Source
Medline
Keywords
Language
English
License
Unknown

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 economised 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 sub-species. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied two 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 three benchmarking approaches: i) % error (expressed relative to the estimated GBC made from over 1 million SNP); ii) % breed mis-assignment (expressed relative to each individual's breed recorded); and iii) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest 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. © The Author(s) 2020. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: [email protected]

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