Abstract
To date, the quantitative genetics theory for genomic selection has focused mainly on the relationship between marker and additive variances assuming one marker and one quantitative trait locus (QTL). This study extends the quantitative genetics theory to genomic selection in order to prove that prediction of breeding values based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance. We also assessed the efficiency of genomic selection in relation to phenotypic selection, assuming mass selection in an open-pollinated population, all QTLs of lower effect, and reduced sample size, based on simulated data. We show that the average effect of a SNP substitution is proportional to LD measure and to average effect of a gene substitution for each QTL that is in LD with the marker. Weighted (by SNP frequencies) and unweighted breeding value predictors have the same accuracy. Efficiency of genomic selection in relation to phenotypic selection is inversely proportional to heritability. Accuracy of breeding value prediction is not affected by the dominance degree and the method of analysis, however, it is influenced by LD extent and magnitude of additive variance. The increase in the number of markers asymptotically improved accuracy of breeding value prediction. The decrease in the sample size from 500 to 200 did not reduce considerably accuracy of breeding value prediction.
Highlights
Genomic selection is the process of identifying superior individuals based on breeding values predicted from the analysis of thousands of molecular marker loci and a limited number of phenotypic records (Meuwissen et al, 2001)
This study extends the quantitative genetics theory to genomic selection in order to prove that prediction of breeding values based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and quantitative trait locus (QTL), assuming dominance
This study extends the quantitative genetics theory for genomic selection to prove that prediction of breeding value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance
Summary
Genomic selection is the process of identifying superior individuals based on breeding values predicted from the analysis of thousands of molecular marker loci and a limited number of phenotypic records (Meuwissen et al, 2001). Because the statistical analysis involves a very large number of markers and relatively few observations, marker effects that comprise the genomic value cannot be simultaneously predicted by the least squares regression (Goddard and Hayes, 2007). Meuwissen et al (2001) showed that Bayesian methods were the most accurate to predict breeding values and identified quantitative trait loci (QTLs) with higher effects. Jannink et al (2010) considered that the paradigm of lower efficiency of marker-assisted selection in relation to phenotypic selection for quantitative traits can change with the establishment of genomic selection. Comparable results were obtained by Goddard (2009) and Grattapaglia and Resende (2011). Jannink et al (2010) considered that the paradigm of lower efficiency of marker-assisted selection in relation to phenotypic selection for quantitative traits can change with the establishment of genomic selection. Goddard (2009), observed higher efficiency of genomic selection in relation to phenotypic selection only for the short term. Grattapaglia and Resende (2011) stated that genomic selection in forestry breeding could be superior to pedigree-based BLUP selection when the cycle length for the first strategy was at least 75 % less than the cycle length by pedigree-based BLUP selection
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.