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 five phenotypically distinct but genetically correlated psychiatric disorders, we identified 49 non-overlapping genome regions associated with multiple disorders, including multiple hub regions showing concordant effects on more than two disorders. Our method addresses critical limitations in existing analytic strategies and may have wide applications in post-GWAS analysis.

LAVA: An integrated framework for local genetic correlation analysis

“…Due to often high levels of LD between nearby SNPs, global r g methods cannot easily be translated to a local scale; but methods aimed at estimating local r g have also started to emerge (Rho-Hess 19 , SUPERGNOVA 22 , LOGOdetect 23 ). To our knowledge, however, no existing tool currently offers the opportunity to model the local genetic relations between more than two phenotypes simultaneously.…”

Quantifying concordant genetic effects of de novo mutations on multiple disorders

“…Finally, 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 57 , identify and quantify common factors underlying multiple traits 58,59 , estimate causal effects among different traits 60 , and identify pleiotropic genomic regions through hypothesis-free scans 24 . 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 mixedeffects models leverage genome-wide association profiles to quantify the correlation between additive genetic components of multiple complex traits [17][18][19][20] . 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 [21][22][23][24] . Here, we introduce EncoreDNM (Enrichment correlation estimator for De Novo Mutations), a novel statistical framework that leverages exome-wide DNM counts, including genes that do not reach exome-wide statistical significance in single-disorder analysis, to estimate concordant DNM associations between disorders.…”

Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

“…Genetic correlation estimation methods can be classified as methods requiring individual-level data 6,[10][11][12][13] and methods that use GWAS summary statistics as input [3][4][5][14][15][16][17][18][19] . Restricted maximum likelihood (REML) is the most common approach among individual-level-data-based methods where genetic correlation is estimated as one of the (co)variance component parameters of LMM.…”

“…Cross-trait linkage disequilibrium (LD) score regression (LDSC) is the first method that uses GWAS summary statistics alone as input to estimate genetic correlation 3 . Built upon LDSC, methods have been developed to estimate annotation-stratified 4 , local [14][15][16] , and trans-ethnic 17 genetic correlation from GWAS summary statistics. Zhang et al 15 showed that most existing methods are based on the idea of minimizing the "distance" between the empirical and theoretical covariance matrices of marginal z-scores obtained from GWASs of two phenotypes.…”