Abstract
BackgroundGenomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information.MethodsA total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method.ResultsAbout 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers.ConclusionsUse of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy.
Highlights
Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals
Accuracies of direct genomic values (DGV) and their standard errors in the test population using different prediction methods with individual markers and haplotypes as predictors are shown in Table 2, along with the posterior mean of π from the BayesCπ method
The prediction method yielding the highest accuracy differed between traits: BayesBπ had the highest accuracy for protein yield, BayesCπ for mastitis and GBLUP for fertility when individual marker prediction was used
Summary
Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. It was originally assumed that the key feature of this method was that markers were in linkage disequilibrium (LD) with the quantitative trait loci (QTL) and explained most of the genetic variance [2]. The accuracy of genomic prediction increases when the markers explain more additive genetic relationships between individuals. Several simulation studies have shown that using haplotypes instead of individual markers increases the accuracy of genomic prediction [6,7,8]. De Roos et al [10] used ancestral haplotypes for genomic prediction in a Holstein population
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.
