Detecting local genetic correlations with scan statistics

Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. However, existing approaches oversimplify the shared genetic architecture between different phenotypes and cannot effectively identify precise genetic regions contributing to the genetic correlation. In this work, we introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent associations with multiple phenotypes through a scan statistic approach. It uses summary association statistics from genome-wide association studies (GWAS) as input and is robust to sample overlap between studies. Applied to seven phenotypically distinct but genetically correlated neuropsychiatric traits, we identify 227 non-overlapping genome regions associated with multiple traits, including multiple hub regions showing concordant effects on five or more traits. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis. Genetic correlation analyses give insight on complex disease, yet are limited by oversimplification. Here, the authors present LOGODetect, a method using summary statistics from genome-wide association studies to identify genomic regions with correlation signals across multiple phenotypes.

Automated summary

Genetic correlation analysis in complex diseases is important, but limited by oversimplification. LOGODetect is a method using summary statistics from genome-wide association studies to identify genomic regions with correlation signals across multiple phenotypes.