Genetics Research

Research Papers

Bias in genomic predictions for populations under selection

Z. G. VITEZICAa1 c1, I. AGUILARa2, I. MISZTALa3 and A. LEGARRAa4

a1 Université de Toulouse, UMR 1289 TANDEM, INRA/INPT-ENSAT/ENVT, F-31326 Castanet-Tolosan, France

a2 Instituto Nacional de Investigación Agropecuaria Las Brujas, Canelones 90200, Uruguay

a3 Department of Animal and Dairy Science, University of Georgia, Athens, Georgia 30602, USA

a4 INRA, UR 631 SAGA, F-31326 Castanet-Tolosan, France

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.

(Received January 27 2011)

(Revised March 27 2011)

(Online publication July 18 2011)

Correspondence:

c1 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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