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Bias in genomic predictions for populations under selection

  • Z. G. VITEZICA (a1), I. AGUILAR (a2), I. MISZTAL (a3) and A. LEGARRA (a4)
Abstract
Summary

Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.

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Corresponding author
*Corresponding author: UMR 1289 TANDEM, ENSAT, Avenue de l'Agrobiopole, Postal Box 32607, 31326 Auzeville Tolosane, France. E-mail: zulma.vitezica@ensat.fr
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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

C. Y. Chen , I. Misztal , I. Aguilar , A. Legarra & W. M. Muir (2011). Effect of different genomic relationship matrices on accuracy and scale. Journal of Animal Science, in press.

R. L. Quaas (1988). Additive genetic models with groups and relationships. Journal of Dairy Science 71, 13381345.

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Genetics Research
  • ISSN: 0016-6723
  • EISSN: 1469-5073
  • URL: /core/journals/genetics-research
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