Variance of gametic diversity and its application in selection programs
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The variance of gametic diversity (sigma(2)(gamete)) can be used to find individuals that more likely produce progeny with extreme breeding values. The aim of this study was to obtain this variance for individuals from routine genomic evaluations, and to apply gametic variance in a selection criterion in conjunction with breeding values to improve genetic progress. An analytical approach was developed to estimate sigma(2)(gamete) by the sum of binomial variances of all individual quantitative trait loci across the genome. Simulation was used to verify the predictability of this variance in a range of scenarios. The accuracy of prediction ranged from 0.49 to 0.85, depending on the scenario and model used. Compared with sequence data, SNP data are sufficient for estimating sigma(2)(gamete). Results also suggested that markers with low minor allele frequency and the covariance between markers should be included in the estimation. To incorporate sigma(2)(gamete) into selective breeding programs, we proposed a new index, relative predicted transmitting ability, which better utilizes the genetic potential of individuals than traditional predicted transmitting ability. Simulation with a small genome showed an additional genetic gain of up to 16% in 10 generations, depending on the number of quantitative trait loci and selection intensity. Finally, we applied sigma(2)(gamete) to the US genomic evaluations for Holstein and Jersey cattle. As expected, the DGAT1 gene had a strong effect on the estimation of sigma(2)(gamete) for several production traits. However, inbreeding had a small impact on gametic variability, with greater effect for more polygenic traits. In conclusion, gametic variance, a potentially important parameter for selection programs, can be easily computed and is useful for improving genetic progress and controlling genetic diversity.