Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

BMC Genetics

Research Article

Luc LG Janss¹  Daniel Sorensen¹  Guosheng Su¹  Rainer Roehe²  Dagmar NRG Kapell²  Cheryl J Ashworth³ 

[1] Faculty of Science and Technology, Department of Molecular Biology and Genetics, Aarhus University, DK-8830, Tjele, Denmark;Sustainable Livestock Systems Group, Scottish Agricultural College, West Mains Road, EH9 3JG, Edinburgh, UK;The Roslin Institute and R(D)SVS, The University of Edinburgh, Easter Bush, EH25 9RG, Midlothian, UK;

关键词: Genomic Selection;    Bayesian Analysis;    Heritabilities;    Quantitative Trait Loci;   

DOI  :  10.1186/1471-2156-13-42

 received in 2011-11-08, accepted in 2012-05-31,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundGenomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families.ResultsGenomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models.ConclusionsGenomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model.

【 授权许可】

Unknown   
© Kapell et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.