{"title":"Is there an advantage of using genomic information to estimate gametic variances and improve recurrent selection in animal populations?","authors":"Jean-Michel Elsen, Jérôme Raoul, Hélène Gilbert","doi":"10.1186/s12711-025-00953-7","DOIUrl":null,"url":null,"abstract":"Gametic variances can be predicted from the outcomes of a genomic prediction for any genotyped individual. This is widely used in plant breeding, applying the utility criterion (UC). This paper aims to examine the conditions to use UC for recurrent selection in livestock. Here, the UC for a selection candidate is the linear combination of the expected value of the future progeny (half of the candidate’s breeding value) and its predicted gametic variance weighted by a coefficient $$\\theta$$ to be optimized. First, generalizing previous results, we derived analytically the ratio of the variance of the candidate’s gametic variance and that of half of the candidate’s breeding value. This ratio depends strongly on the number of quantitative trait loci (QTL) affecting the trait and, to a lesser extent, on the distribution of QTL allele frequencies: highly unbalanced frequencies and a limited number of QTL (< 10) favor higher values of the ratio. Then, changes in average breeding values and genetic variances when recurrent selection in a population of infinite size is applied were analytically derived and analyzed for selection up to 15 generations: in this ideal situation, after 5 to 10 generations (depending on $$\\theta$$ ), the expected breeding values were higher with selection on UC and the genetic variance was always higher than with selection on estimated breeding values. To describe the potential of the UC in more general situations, simulations were applied to a population of 1000 males and 1000 females, with various selection rates, numbers and allele frequencies of QTL, and $$\\theta$$ . These simulations were performed assuming independent QTL with known positions and effects. The best values for $$\\theta$$ (i.e. providing the best genetic progress) were generally lower than 1, limiting the weight on the gametic variance. As expected from the analytical derivations, the gain in genetic progress from using UC was greatest when there were few QTL and allele frequencies were unbalanced, but they barely exceeded 5%. We conclude that the key factor to choose selection on UC rather than on estimated breeding values is the ratio between the variance of the gametic standard deviations and the variance of the breeding values (GEBV), which should be carefully evaluated.","PeriodicalId":55120,"journal":{"name":"Genetics Selection Evolution","volume":"1 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genetics Selection Evolution","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12711-025-00953-7","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Gametic variances can be predicted from the outcomes of a genomic prediction for any genotyped individual. This is widely used in plant breeding, applying the utility criterion (UC). This paper aims to examine the conditions to use UC for recurrent selection in livestock. Here, the UC for a selection candidate is the linear combination of the expected value of the future progeny (half of the candidate’s breeding value) and its predicted gametic variance weighted by a coefficient $$\theta$$ to be optimized. First, generalizing previous results, we derived analytically the ratio of the variance of the candidate’s gametic variance and that of half of the candidate’s breeding value. This ratio depends strongly on the number of quantitative trait loci (QTL) affecting the trait and, to a lesser extent, on the distribution of QTL allele frequencies: highly unbalanced frequencies and a limited number of QTL (< 10) favor higher values of the ratio. Then, changes in average breeding values and genetic variances when recurrent selection in a population of infinite size is applied were analytically derived and analyzed for selection up to 15 generations: in this ideal situation, after 5 to 10 generations (depending on $$\theta$$ ), the expected breeding values were higher with selection on UC and the genetic variance was always higher than with selection on estimated breeding values. To describe the potential of the UC in more general situations, simulations were applied to a population of 1000 males and 1000 females, with various selection rates, numbers and allele frequencies of QTL, and $$\theta$$ . These simulations were performed assuming independent QTL with known positions and effects. The best values for $$\theta$$ (i.e. providing the best genetic progress) were generally lower than 1, limiting the weight on the gametic variance. As expected from the analytical derivations, the gain in genetic progress from using UC was greatest when there were few QTL and allele frequencies were unbalanced, but they barely exceeded 5%. We conclude that the key factor to choose selection on UC rather than on estimated breeding values is the ratio between the variance of the gametic standard deviations and the variance of the breeding values (GEBV), which should be carefully evaluated.
期刊介绍:
Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.