Detecting local genetic correlations with scan statistics

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.

Genetic Correlation and Bidirectional Causal Association Between Type 2 Diabetes and Pulmonary Function

“…These findings provided support for a causal association between heritable traits. Since two-sample MR is prone to false-negative results, identification of more SNPs that proxy glycemic traits robustly and application of novel genetic correlation methods such as LOGODetect are needed to provide more conclusive results in future studies ( 44 ).…”

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

“…These methods can accurately map regional signals and control family-wise error rates during multiple testing of interrelated hypotheses. Scan statistics have been successfully applied to many areas including molecular biology 37 and human genetics 38 . The Scan module of DiffScan was developed with the goal to identify SVRs at nucleotide resolution in a data-adaptive fashion.…”

Quantifying concordant genetic effects of de novo mutations on multiple disorders

“…Second, genetic correlation methods based on GWAS summary data provided key motivations for the mixed-effects Poisson regression model in our study. Built upon genetic correlations, a plethora of methods have been developed in the GWAS literature to jointly model more than two GWAS ( Turley et al, 2018 ), identify and quantify common factors underlying multiple traits ( Grotzinger et al, 2019 ; Grotzinger et al, 2020 ), estimate causal effects among different traits ( Pickrell et al, 2016 ), and identify pleiotropic genomic regions through hypothesis-free scans ( Guo et al, 2021 ). Future directions of EncoreDNM include using enrichment correlation to improve gene discovery, learning the directional effects and the causal structure underlying multiple disorders, and dynamically searching for gene sets and annotation classes with shared genetic effects without pre-specifying the hypothesis.…”

“…Modeling ‘omnigenic’ associations as independent random effects, linear mixed-effects models leverage genome-wide association profiles to quantify the correlation between additive genetic components of multiple complex traits ( Lee et al, 2012 ; Bulik-Sullivan et al, 2015 ; Lu et al, 2017 ; Ning et al, 2020 ). These methods have identified ubiquitous genetic correlations across many human traits and revealed significant and robust genetic correlations that could not be inferred from significant GWAS associations alone ( Shi et al, 2017 ; Brainstorm, 2018 ; Guo et al, 2021 ; Zhang et al, 2021b ).…”