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Lookup NU author(s): Dr Rebecca DarlayORCiD, Dr Rupal ShahORCiD, Dr Richard DoddsORCiD, Adam Pearson, Professor Miles WithamORCiD, Professor Heather CordellORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC. 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.
Author(s): Darlay R, Shah RL, Dodds RM, Nair ATN, Pearson ER, Witham MD, Cordell HJ
Publication type: Article
Publication status: Published
Journal: Genetic Epidemiology
Year: 2025
Volume: 49
Issue: 5
Print publication date: 01/07/2025
Online publication date: 19/06/2025
Acceptance date: 23/05/2025
Date deposited: 14/07/2025
ISSN (print): 0741-0395
ISSN (electronic): 1098-2272
Publisher: Wiley
URL: https://doi.org/10.1002/gepi.70012
DOI: 10.1002/gepi.70012
PubMed id: 40538118
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