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United States Department of Agriculture

Agricultural Research Service

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Increasing long-term response by selecting for favorable minor alleles

Authors
item Sun, Chuanyu
item Vanraden, Paul

Submitted to: PLoS One
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 7, 2014
Publication Date: February 5, 2014
Citation: Sun, C., Van Raden, P.M. 2014. Increasing long-term response by selecting for favorable minor alleles. PLoS One. 9(2):e88510.

Interpretive Summary: Genomic selection increases short-term progress by using thousands of genetic markers, but can also increase inbreeding and limit long-term progress. Animals often are ranked on the sum of marker effects, ignoring frequencies of the marker alleles. This study derived simple, improved formulas to assign more weight to markers that have a favorable allele with low frequency. These methods are referred to as favorable minor allele (FMA) selection and improve long-term progress by preserving genetic variance, with little reduction in short-term progress. The formulas were applied to both simulated and real data. Optimal FMA selection provided only about 1% more long-term progress after 20 generations of selection, indicating little potential harm from emphasizing short-term genetic gain. With actual data, most animals that ranked higher with FMA selection had genes from another breed or unusual pedigrees. Direct limits on genomic inbreeding can also preserve genetic variation for future selection.

Technical Abstract: Long-term response of genomic selection can be improved by considering allele frequencies of selected markers or quantitative trait loci (QTLs). A previous formula to weight allele frequency of favorable minor alleles was tested, and 2 new formulas were developed. The previous formula used nonlinear weights based on square root of frequency of the favorable allele. The new formulas included a parameter delta to balance long- and short-term progress; one used simple linear weights instead of square root. The formulas were tested by simulation of 20 generations (population size of 3,000 for each generation) with direct selection on 3,000 QTLs (100 per chromosome). A QTL distribution with normally distributed allele effects and a heavy-tailed distribution were tested. Optimum delta from simulation was applied to data from Holstein, Jersey and Brown Swiss dairy cattle to compare differences of adjusted and official genomic evaluations. From simulation, optimum delta was 0.4 for the heavy-tailed QTL distribution but only 0.1 or 0.2 for a normal distribution. The previous formula (implied delta of 1.0) had slower response than unweighted selection in early generations and did not recover by generation 20. Long-term response was slightly greater with the new formulas than with unweighted selection; the linear formula may be best for routine use because of more progress in early generations compared to nonlinear formula. Official and adjusted U.S. evaluations based on actual genotypes and estimated marker effects were correlated by 0.994 for Holsteins and Jerseys and 0.989 for Brown Swiss using linear weighting of allele frequency. Correlations were lower (0.991, Holsteins; 0.986, Jerseys; 0.978, Brown Swiss) for nonlinear weighting. The difference between adjusted and official evaluations was highly correlated negatively with an animal’s average genomic relationship to the population. Strategies to reduce genomic inbreeding could achieve almost as much long-term progress as selection of favorable minor alleles, such that outcross individuals tended to have higher adjusted than traditional predictions.

Last Modified: 11/23/2014
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