SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

Genome Biology

Yuchang Wu¹  Xiaoyuan Zhong¹  Qiongshi Lu¹  Yiliang Zhang²  Hongyu Zhao²  Boyang Li²  Kunling Huang³  Brittany G. Travers⁴  Yixuan Ye⁵  Zhaolong Yu⁵  Wei Liu⁵  James J. Li⁶  Donna M. Werling⁶ 

[1] Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison;Department of Biostatistics, Yale School of Public Health;Department of Statistics, University of Wisconsin-Madison;Occupational Therapy Program in the Department of Kinesiology, University of Wisconsin-Madison;Program of Computational Biology and Bioinformatics, Yale University;Waisman Center, University of Wisconsin-Madison;

关键词: GWAS;    Local genetic covariance;    Eigen decomposition;    Autism spectrum disorder;    Chromatin modifiers;   

DOI  :  10.1186/s13059-021-02478-w

来源: DOAJ

【 摘 要 】

Abstract Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

【 授权许可】

Unknown