Iteratively reweighted LASSO for mapping multiple quantitative trait loci

文献类型： 外文期刊

第一作者： Liu, Yongxin

作者： Liu, Yongxin;Yang, Runqing;Yang, Runqing

作者机构：

关键词： QTL mapping;IRLS;LASSO;multiple QTL model

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：11.622； 五年影响因子：10.288 ）

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收录情况： SCI

摘要： The iteratively reweighted least square (IRLS) method is mostly identical to maximum likelihood (ML) method in terms of parameter estimation and power of quantitative trait locus (QTL) detection. But the IRLS is greatly superior to ML in terms of computing speed and the robustness of parameter estimation. In conjunction with the priors of parameters, ML can analyze multiple QTL model based on Bayesian theory, whereas under a single QTL model, IRLS has very limited statistical power to detect multiple QTLs. In this study, we proposed the iteratively reweighted least absolute shrinkage and selection operator (IRLASSO) for extending IRLS to simultaneously map multiple QTLs. The LASSO with coordinate descent step is employed to efficiently estimate non-zero genetic effect of each locus scanned over entire genome. Simulations demonstrate that IRLASSO has a higher precision of parameter estimation and power to detect QTL than IRLS, and is able to estimate residual variancemore accurately than the unweighted LASSO based on LS. Especially, IRLASSO is very fast, usually taking less than five iterations to converge. The barley dataset from the North American Barley Genome Mapping Project is reanalyzed by our proposed method.

分类号： Q3

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