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A novel generalized ridge regression method for quantitative genetics
Dalarna University, School of Technology and Business Studies, Statistics. (Division of Computational Genetics, Swedish University of Agricultural Sciences, Uppsala)ORCID iD: 0000-0003-4390-1979
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala.
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-1057-5401
2013 (English)In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 193, no 4, 1255-1268 p.Article in journal (Refereed) Published
Abstract [en]

As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithmfor situations where the number of parameters largely exceeds the number of observations. The computationally demanding parts of the method depend mainly on the number ofobservations and not the number of parameters. The algorithm was implemented in the R package bigRR based on the previously developed package hglm. Using such an approach, a heteroscedastic effects model (HEM) was also developed, implemented and tested. Theefficiency for different data sizes were evaluated via simulation. The method was tested for a bacteria-hypersensitive trait in a publicly available Arabidopsis dataset including 84 inbred lines and 216 130 SNPs. The computation of all the SNP effects required less than10 seconds using a single 2.7 GHz core. The advantage in run-time makes permutationtest feasible for such a whole-genome model, so that a genome-wide significance threshold can be obtained. HEM was found to be more robust than ordinary RR (a.k.a. SNPBLUP) in terms of QTL mapping, because SNP specific shrinkage was applied instead of acommon shrinkage. The proposed algorithm was also assessed for genomic evaluation and was shown to give better predictions than ordinary RR.

Place, publisher, year, edition, pages
2013. Vol. 193, no 4, 1255-1268 p.
Keyword [en]
Heteroskedastic effects model, p>n problem, animal model, QTL mapping, R package: bigRR
National Category
Genetics
Research subject
Komplexa system - mikrodataanalys, General Microdata Analysis - methods
Identifiers
URN: urn:nbn:se:du-11792DOI: 10.1534/genetics.112.146720ISI: 000316937300018PubMedID: 23335338OAI: oai:DiVA.org:du-11792DiVA: diva2:602573
Available from: 2013-02-01 Created: 2013-02-01 Last updated: 2015-06-29Bibliographically approved

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Publisher's full textPubMedhttp://www.genetics.org/content/193/4/1255.abstract

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CiteExportLink to record
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