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arXiv · 0709.0625

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

Kateryna Mishchenko·Sverker Holmgren·Lars Ronnegard
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Abstract

Regions 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.

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Kateryna Mishchenko, Sverker Holmgren, Lars Ronnegard. 2008-02-11. Efficient Implementation of the AI-REML Iteration for Variance Component QTL Analysis. https://arxiv.org/abs/0709.0625

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