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摘要翻译：
基因组中影响复杂性状的区域，即数量性状位点(QTL)，可以通过遗传和表型数据的统计分析来识别。当使用受限最大似然(REML)模型时，映射过程通常需要计算。利用方差分量分析和AI-REML算法，提出了一种高效的QTL定位计算方案。该算法使用递归恒等式矩阵的精确或近似低秩表示，结合矩阵求逆的Woodbury公式，使AI-REML迭代体中的计算更加高效。对于IBD矩阵的精确低秩表示是先验可用的情况，改进的AI-REML算法通常比标准版本运行速度快近两倍。当没有精确的低秩表示时，使用截断谱分解来确定低秩近似。我们证明了同样在这种情况下，AI-REML格式的计算效率通常可以显著提高。
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英文标题：
《Efficient Implementation of the AI-REML Iteration for Variance Component
QTL Analysis》
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作者：
Kateryna Mishchenko, Sverker Holmgren and Lars Ronnegard
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最新提交年份：
2008
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Quantitative Methods 定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
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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PDF链接：
https://arxiv.org/pdf/0709.0625
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