Efficient Implementation of the AI-REML Iteration for Variance Component QTL Analysis

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dc.creatorMishchenko, Kateryna
dc.creatorHolmgren, Sverker
dc.creatorRonnegard, Lars
dc.date2007-09-05
dc.date2008-02-11
dc.date.accessioned2026-07-07T09:19:30Z
dc.date.available2026-07-07T09:19:30Z
dc.descriptionRegions in the genome that affect complex traits, quantitative trait loci (QTL), can be identified using statistical analysis of genetic and phenotypic data. When restricted maximum-likelihood (REML) models are used, the mapping procedure is normally computationally demanding. We develop a new efficient computational scheme for QTL mapping using variance component analysis and the AI-REML algorithm. The algorithm uses an exact or approximative low-rank representation of the identity-by-descent matrix, which combined with the Woodbury formula for matrix inversion results in that the computations in the AI-REML iteration body can be performed more efficiently. For cases where an exact low-rank representation of the IBD matrix is available a-priori, the improved AI-REML algorithm normally runs almost twice as fast compared to the standard version. When an exact low-rank representation is not available, a truncated spectral decomposition is used to determine a low-rank approximation. We show that also in this case, the computational efficiency of the AI-REML scheme can often be significantly improved.
dc.description14 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/0709.0625
dc.identifierhttp://arxiv.org/abs/0709.0625
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154411
dc.subjectQuantitative Methods
dc.subjectOther Quantitative Biology
dc.titleEfficient Implementation of the AI-REML Iteration for Variance Component QTL Analysis
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