小柯机器人

荷兰阿姆斯特丹自由大学神经基因组学和认知研究中心Christiaan A. de Leeuw和Josefin Werme课题合作提出局部遗传相关性 (rg)分析的综合框架。相关论文于2022年3月14日发表在《自然—遗传学》杂志上。

他们介绍了 LAVA，这是一种用于局部 rg 分析的集成框架，除了测试两种表型之间的标准双变量局部 rgs 之外，还可以评估局部遗传力并使用偏相关和多元回归分析几种表型之间的条件遗传关系。应用于 25 种行为和健康表型，他们在整个基因组的双变量局部 rgs 中显示出相当大的异质性，这通常被全局 rg 模式所掩盖，并展示了他们的条件方法如何阐明更复杂、多变量的遗传关系。

据了解，rg分析用于识别可能具有共同遗传基础的表型。传统上，rg 是在全球范围内研究的，只考虑整个基因组共享信号的平均值，尽管当 rg 局限于特定基因组区域或不同基因座的相反方向时，这种方法可能会失败。当前用于局部 rg 分析的工具仅限于分析两种表型。

附：英文原文

Title: An integrated framework for local genetic correlation analysis

Author: Werme, Josefin, van der Sluis, Sophie, Posthuma, Danielle, de Leeuw, Christiaan A.

Issue&Volume: 2022-03-14

Abstract: Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs 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 rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations. 

DOI: 10.1038/s41588-022-01017-y

Source: https://www.nature.com/articles/s41588-022-01017-y

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
官方网址：https://www.nature.com/ng/
投稿链接：https://mts-ng.nature.com/cgi-bin/main.plex