Abstract
Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicted and true breeding values. Typically, for individuals (so-called test individuals), breeding values are predicted by means of markers, using marker effects estimated by fitting a ridge regression model to a set of training individuals. We present a theoretical expression for the accuracy; this expression is suitable for any configurations of LD between QTLs and markers. We also introduce a new accuracy proxy that is free of the QTL parameters and easily computable; it outperforms the proxies suggested in the literature, in particular, those based on an estimated effective number of independent loci (Me). The theoretical formula, the new proxy, and existing proxies were compared for simulated data, and the results point to the validity of our approach. The calculations were also illustrated on a new perennial ryegrass set (367 individuals) genotyped for 24,957 single nucleotide polymorphisms (SNPs). In this case, most of the proxies studied yielded similar results because of the lack of markers for coverage of the entire genome (2.7 Gb).
Highlights
During the last decades, investigators have mainly concentrated on linkage analysis to detect the regions of DNA, so-called quantitative trait loci (QTLs), responsible for quantitative variation
We propose to focus on mathematical properties of the accuracy based on the regression model called random regression best linear unbiased predictor (RRBLUP) or genomic best linear unbiased predictor (GBLUP)
With the help of simulated data, (a) reliability of the Theoretical accuracy from formula (5), (b) sensitivity to the regularization parameter λ, (c) the effects of a fixed TRN incidence matrix, (d) the pertinence of the proxy suggested by formula (6), and (e) a substitute for the effective number of segments
Summary
Investigators have mainly concentrated on linkage analysis to detect the regions of DNA, so-called quantitative trait loci (QTLs), responsible for quantitative variation. The linkage analysis (LA) is specific because it relies on family data and on pedigrees: segregation of a QTL is studied within a family by means of information related to the family. In this context, the most popular statistical method for QTL mapping is interval mapping [1]. The most popular statistical method for QTL mapping is interval mapping [1] It involves scanning the genome by means of genetic markers and testing for the presence/ absence of a QTL at every location in the genome. [6] detected QTLs responsible for a PLOS ONE | DOI:10.1371/journal.pone.0156086 June 20, 2016
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
