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Science & Technology

LOGODetect: A Scan Statistical Methodology for Detection of Local Genetic Correlations

By 6th August 2021No Comments

The following study was conducted by Scientists from Center for Statistical Science, Tsinghua University, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, China; Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA; MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China. Study is published in Nature Communications Journal as detailed below.

Nature Communications; Volume 12, Article Number: 2033 (2021)

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.


Nature Communications



Guo, H., Li, J.J., Lu, Q. et al. Detecting local genetic correlations with scan statistics. Nat Commun 12, 2033 (2021).