Exploring Similarities and Differences Between Methods That Exploit Patterns of Local Genetic Correlation to Identify Shared Causal Loci Through Application to Genome-Wide Association Studies of Multiple Long Term Conditions

TY - JOUR

T1 - Exploring Similarities and Differences Between Methods That Exploit Patterns of Local Genetic Correlation to Identify Shared Causal Loci Through Application to Genome-Wide Association Studies of Multiple Long Term Conditions

AU - Darlay, Rebecca

AU - Shah, Rupal L.

AU - Dodds, Richard M.

AU - Nair, Anand T.N.

AU - Pearson, Ewan R.

AU - Witham, Miles D.

AU - Cordell, Heather J.

AU - Bartle, Victoria

AU - Cooper, Rachel

AU - Cordell, Heather J.

AU - Holding, Ray

AU - Marshall, Tom

AU - Matthews, Fiona E.

AU - Missier, Paolo

AU - Pearson, Ewan R.

AU - Plummer, Chris

AU - Robinson, Sian M.

AU - Sapey, Elizabeth

AU - Scharf, Thomas

AU - Sayer, Avan A.

AU - Singer, Mervyn

AU - Wason, James M.S.

N1 - Publisher Copyright: © 2025 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC.

PY - 2025/7

Y1 - 2025/7

N2 - Genetic correlation analysis can provide useful insight into the shared genetic basis between traits or conditions of interest. However, most genome-wide analyses only inform about the degree of global (overall) genetic similarity and do not identify the specific genomic regions that give rise to this similarity. Identification of the key genomic regions contributing to shared genetic correlation between traits could allow the genes in these regions to be prioritised for investigation of potential shared biological mechanisms. In recent years, several statistical tools (e.g. LAVA, ρ-HESS, SUPERGNOVA and LOGODetect) have been developed to investigate local (in contrast to global) genetic correlation. These tools partition the genome into multiple segments and provide estimates of the genetic correlation captured by each individual segment. We applied these tools to publicly available European ancestry genome-wide association study (GWAS) summary statistics for three pairs of commonly occurring conditions: hypertension with atrial fibrillation and flutter, hypertension with chronic kidney disease, and hypertension with type 2 diabetes. Despite each of the methods aiming to address the same question, the results were found to be inconsistent across tools, with some identified regions overlapping and others implicated only by a single tool. Computer simulations using genetic data from UK Biobank, carried out under known generating conditions, suggest that LAVA and, to a lesser extent, ρ-HESS, provide the most reliable identification of genuine shared genetic factors. A newly-developed tool, HDL-L, also performed highly competitively. Here we highlight the similarities and differences between the results obtained from these methods and discuss some potential reasons underlying these differences.

AB - Genetic correlation analysis can provide useful insight into the shared genetic basis between traits or conditions of interest. However, most genome-wide analyses only inform about the degree of global (overall) genetic similarity and do not identify the specific genomic regions that give rise to this similarity. Identification of the key genomic regions contributing to shared genetic correlation between traits could allow the genes in these regions to be prioritised for investigation of potential shared biological mechanisms. In recent years, several statistical tools (e.g. LAVA, ρ-HESS, SUPERGNOVA and LOGODetect) have been developed to investigate local (in contrast to global) genetic correlation. These tools partition the genome into multiple segments and provide estimates of the genetic correlation captured by each individual segment. We applied these tools to publicly available European ancestry genome-wide association study (GWAS) summary statistics for three pairs of commonly occurring conditions: hypertension with atrial fibrillation and flutter, hypertension with chronic kidney disease, and hypertension with type 2 diabetes. Despite each of the methods aiming to address the same question, the results were found to be inconsistent across tools, with some identified regions overlapping and others implicated only by a single tool. Computer simulations using genetic data from UK Biobank, carried out under known generating conditions, suggest that LAVA and, to a lesser extent, ρ-HESS, provide the most reliable identification of genuine shared genetic factors. A newly-developed tool, HDL-L, also performed highly competitively. Here we highlight the similarities and differences between the results obtained from these methods and discuss some potential reasons underlying these differences.

UR - https://www.scopus.com/pages/publications/105009646349

U2 - 10.1002/gepi.70012

DO - 10.1002/gepi.70012

M3 - Article

C2 - 40538118

AN - SCOPUS:105009646349

SN - 0741-0395

VL - 49

JO - Genetic Epidemiology

JF - Genetic Epidemiology

IS - 5

M1 - e70012

ER -