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

Rebecca Darlay; Rupal L Shah; Richard M Dodds; Anand T N Nair; Ewan R Pearson; Miles D Witham; Heather J Cordell; ADMISSION Research Collaborative

doi:10.1002/gepi.70012

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

Genet Epidemiol. 2025 Jul;49(5):e70012. doi: 10.1002/gepi.70012.

Authors

Rebecca Darlay¹, Rupal L Shah¹, Richard M Dodds², Anand T N Nair³, Ewan R Pearson³, Miles D Witham^{2

4}, Heather J Cordell¹; ADMISSION Research Collaborative

Collaborators

ADMISSION Research Collaborative:
Victoria Bartle⁵, Heather J Cordell¹, Ray Holding⁵, Tom Marshall⁶, Fiona E Matthews⁷, Paolo Missier⁸, Ewan R Pearson⁹, Elizabeth Sapey^{10

11}, Thomas Scharf¹, Mervyn Singer^{12

13}, James M S Wason¹⁴

Affiliations

¹ Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK.
² Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
³ Division of Diabetes, Endocrinology and Reproductive Biology University of Dundee, Dundee, UK.
⁴ NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Tyne and Wear NHS Foundation Tust and Newcastle University, Newcastle upon Tyne, UK.
⁵ Public Coinvestigator, ADMISSION Research Collaborative, Newcastle upon Tyne, UK.
⁶ Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
⁷ Research and Enterprise Office, University of Hull, Hull, UK.
⁸ School of Computer Science, University of Birmingham, Birmingham, UK.
⁹ Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, UK.
¹⁰ PIONEER Hub, University of Birmingham, Birmingham, UK.
¹¹ Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
¹² University College London Hospitals NHS Foundation Trust, London, UK.
¹³ Bloomsbury Institute for Intensive Care Medicine, University College London, London, UK.
¹⁴ Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.

Abstract

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.

MeSH terms

Atrial Fibrillation / genetics
Computer Simulation
Diabetes Mellitus, Type 2 / genetics
Genetic Loci
Genetic Predisposition to Disease
Genome-Wide Association Study* / methods
Humans
Hypertension / genetics
Models, Genetic
Polymorphism, Single Nucleotide
Renal Insufficiency, Chronic / genetics
White
White People / genetics

Grants and funding

This study was funded in whole, or in part, by the Wellcome Trust (Grant number 219424/Z/19/Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The ADMISSION research collaborative is funded by the Strategic Priority Fund "Tackling multimorbidity at scale" programme (grant number MR/V033654/1). This funding is delivered by the UKRI Medical Research Council and the National Institute for Health and Care Research in partnership with the UKRI Economic and Social Research Council and in collaboration with the UKRI Engineering and Physical Sciences Research Council. The views expressed in this publication are those of the author(s) and not necessarily those of the Medical Research Council, the National Institute for Health and Care Research, or the Department of Health and Social Care. MDW also acknowledges support from the National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre and the Multiple Long-term Conditions Cross-NIHR Collaboration (MLTC CNC).