Canonical transformation for multivariate mixed model association analyses

文献类型： 外文期刊

第一作者： Yang, Li'ang

作者： Yang, Li'ang;Yang, Li'ang;Zhang, Ying;Song, Yuxin;Yang, Runqing;Zhang, Hengyu

作者机构：

期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：5.574； 五年影响因子：5.662 ）

ISSN： 0040-5752

年卷期： 2022 年 135 卷 6 期

页码：

收录情况： SCI

摘要： Key message In extension of Single-RunKing to analyze multiple correlated traits, mvRunKing not only enlarged number of the analyzed phenotypes with canonical transformation, but also improved statistical power to detect pleiotropic QTNs through joint association analysis. Based on genomic variance-covariance matrices, we simplified multivariate mixed model association analysis to multiple univariate ones by using canonical transformation, and then individually implemented univariate association tests in the Single-RunKing. which enlarged number of the analyzed phenotypes. With canonical transformation back to the original scale, the association results would be biologically interpretable. Especially, we rapidly estimated genomic variance-covariance matrices with multivariate GEMMA and optimized separately the polygenic variances (or heritabilities) for only the markers that had large effects or higher significance levels in univariate mixed models, greatly improving computing efficiency for multiple univariate association tests. Beyond one test at once, joint association analysis for quantitative trait nucleotide (QTN) candidates can significantly increase statistical powers to detect QTNs. A user-friendly mvRunKing software was developed to efficiently implement multivariate mixed model association analyses.

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