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On the Accuracy of Genomic Selection

Author

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  • Charles-Elie Rabier
  • Philippe Barre
  • Torben Asp
  • Gilles Charmet
  • Brigitte Mangin

Abstract

Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicted and true breeding values. Typically, for individuals (so-called test individuals), breeding values are predicted by means of markers, using marker effects estimated by fitting a ridge regression model to a set of training individuals. We present a theoretical expression for the accuracy; this expression is suitable for any configurations of LD between QTLs and markers. We also introduce a new accuracy proxy that is free of the QTL parameters and easily computable; it outperforms the proxies suggested in the literature, in particular, those based on an estimated effective number of independent loci (Me). The theoretical formula, the new proxy, and existing proxies were compared for simulated data, and the results point to the validity of our approach. The calculations were also illustrated on a new perennial ryegrass set (367 individuals) genotyped for 24,957 single nucleotide polymorphisms (SNPs). In this case, most of the proxies studied yielded similar results because of the lack of markers for coverage of the entire genome (2.7 Gb).

Suggested Citation

  • Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0156086
    DOI: 10.1371/journal.pone.0156086
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    References listed on IDEAS

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    1. Peter Donnelly, 2008. "Progress and challenges in genome-wide association studies in humans," Nature, Nature, vol. 456(7223), pages 728-731, December.
    2. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
    3. Jennifer Spindel & Hasina Begum & Deniz Akdemir & Parminder Virk & Bertrand Collard & Edilberto Redoña & Gary Atlin & Jean-Luc Jannink & Susan R McCouch, 2015. "Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic," PLOS Genetics, Public Library of Science, vol. 11(2), pages 1-25, February.
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    Cited by:

    1. Charles‐Elie Rabier & Simona Grusea, 2021. "Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1001-1026, August.
    2. Julien Frouin & Axel Labeyrie & Arnaud Boisnard & Gian Attilio Sacchi & Nourollah Ahmadi, 2019. "Genomic prediction offers the most effective marker assisted breeding approach for ability to prevent arsenic accumulation in rice grains," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-22, June.
    3. Karim Karimi & Mehdi Sargolzaei & Graham Stuart Plastow & Zhiquan Wang & Younes Miar, 2019. "Opportunities for genomic selection in American mink: A simulation study," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.

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