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Best Prediction of the Additive Genomic Variance in Random-Effects Models

dc.contributor.authorSchreck, Nicholas
dc.contributor.authorPiepho, Hans-Peter
dc.contributor.authorSchlather, Martin
dc.date.accessioned2019-11-06T13:51:50Z
dc.date.available2019-11-06T13:51:50Z
dc.date.issued2019de
dc.relation.ISSN1943-2631de
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/16621
dc.description.abstractThe additive genomic variance in linear models with random marker effects can be defined as a random variable that is in accordance with classical quantitative genetics theory. Common approaches to estimate the genomic variance in random-effects linear models based on genomic marker data can be regarded as estimating the unconditional (or prior) expectation of this random additive genomic variance, and result in a negligence of the contribution of linkage disequilibrium (LD). We introduce a novel best prediction (BP) approach for the additive genomic variance in both the current and the base population in the framework of genomic prediction using the genomic best linear unbiased prediction (gBLUP) method. The resulting best predictor is the conditional (or posterior) expectation of the additive genomic variance when using the additional information given by the phenotypic data, and is structurally in accordance with the genomic equivalent of the classical additive genetic variance in random-effects models. In particular, the best predictor includes the contribution of (marker) LD to the additive genomic variance and possibly fully eliminates the missing contribution of LD that is caused by the assumptions of statistical frameworks such as the random-effects model. We derive an empirical best predictor (eBP) and compare its performance with common approaches to estimate the additive genomic variance in random-effects models on commonly used genomic datasets.de
dc.language.isoengde
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectbest prediction; genetic variance; quantitative genetics; genomic variance; random-effects models; BLUP; whole-genome regressionde
dc.subject.ddc630
dc.titleBest Prediction of the Additive Genomic Variance in Random-Effects Modelsde
dc.typejournalArticlede
dc.identifier.doi10.1534/genetics.119.302324
dc.type.versionpublishedVersionde
dc.relation.pISSN0016-6731
dc.relation.eISSN1943-2631
dc.bibliographicCitation.volume213de
dc.bibliographicCitation.issue2de
dc.bibliographicCitation.firstPage379de
dc.bibliographicCitation.lastPage394de
dc.type.subtypejournalArticle
dc.description.statuspeerReviewedde
dc.bibliographicCitation.journalGeneticsde


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