Werme Josefin, van der Sluis Sophie, Posthuma Danielle, de Leeuw Christiaan A
Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, the Netherlands.
Section Complex Trait Genetics, Department of Child and Adolescent Psychology and Psychiatry, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, the Netherlands.
Nat Genet. 2022 Mar;54(3):274-282. doi: 10.1038/s41588-022-01017-y. Epub 2022 Mar 14.
Genetic correlation (r) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, r is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the r is confined to particular genomic regions or in opposing directions at different loci. Current tools for local r analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local r analysis that, in addition to testing the standard bivariate local rs 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 rs across the genome, which is often masked by the global r patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.
遗传相关性（r）分析用于识别可能具有共同遗传基础的表型。传统上，r是在全局层面进行研究的，只考虑全基因组共享信号的平均值，不过当r局限于特定基因组区域或在不同位点呈相反方向时，这种方法可能会失效。当前用于局部r分析的工具仅限于对两种表型进行分析。在此，我们引入了LAVA，这是一个用于局部r分析的综合框架，除了测试两种表型之间的标准双变量局部r值外，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种行为和健康表型时，我们发现全基因组双变量局部r值存在相当大的异质性，而这种异质性常常被全局r模式所掩盖，并展示了我们的条件分析方法如何能够阐明更复杂的多变量遗传关系。
