Abstract
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.
Highlights
Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases
Genetic correlation analysis has become a routine procedure in post-genome-wide association studies (GWAS) analysis and was implemented in almost all large-scale GWASs published in the past few years
To quantify the extent of local genetic similarity in a genome region, where R is the index set for all single-nucleotide polymorphisms (SNPs) in the region, z1i and z2i are the association z-scores for the ith SNP with two traits, li is the linkage disequilibrium (LD) score for the ith SNP10, and θ controls the impact of LD
Summary
Genetic correlation analysis has quickly gained popularity in the past few years and provided insights into the genetic etiology of numerous complex diseases. 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. Recent methodological advances have enabled estimation of genetic correlation with GWAS summary statistics[10,11,23], making these approaches widely applicable to a large number of complex phenotypes. With these advances, genetic correlation analysis has become a routine procedure in post-GWAS analysis and was implemented in almost all large-scale GWASs published in the past few years. Our analysis implicates a collection of hub regions (small genome segments harboring local genetic correlations for multiple trait pairs) in the genome that underlie the risk for several of these traits
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