An integrated framework for local genetic correlation analysis
An integrated framework for local genetic correlation analysis
复制标题
DOI：
10.1038/s41588-022-01017-y
复制
发表时间：
2022-03-01
期刊：
影响因子：
30.8
通讯作者：
de Leeuw, Christiaan A.
中科院分区：
文献类型：
--
作者：
Werme, Josefin;van der Sluis, Sophie;de Leeuw, Christiaan A.
遗传相关（r（g））分析用于识别可能具有共同遗传基础的表型。传统上，r（g）是全局研究的，只考虑整个基因组的共享信号的平均值，尽管当r（g）局限于特定的基因组区域或在不同基因座的相反方向时，这种方法可能会失败。目前的局部r（g）分析工具仅限于分析两种表型。在这里，我们介绍LAVA，一个集成的框架，本地r（g）分析，除了测试两个表型之间的标准双变量本地r（g）的，可以评估本地遗传力和分析条件遗传关系之间的几个表型使用偏相关和多元回归。应用于25个行为和健康表型，我们在整个基因组的双变量局部r（g）中显示出相当大的异质性，这通常被全局r（g）模式所掩盖，并展示了我们的条件方法如何阐明更复杂的多变量遗传关系。行为和健康特征的应用程序确定当地的遗传异质性，并提供遗传中介和混淆的见解。
Genetic correlation (r(g)) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, r(g) is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the r(g) is confined to particular genomic regions or in opposing directions at different loci. Current tools for local r(g) analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local r(g) analysis that, in addition to testing the standard bivariate local r(g)s between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local r(g)s across the genome, which is often masked by the global r(g) patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.LAVA estimates multivariate local genetic relations, which enables conditional genetic analyses. Application to behavioral and health traits identifies local genetic heterogeneity and provides insights into genetic mediation and confounding.