ArticleBriefings in bioinformatics2021
Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Mapping Gastroesophageal Reflux Disease and Coronary Artery Disease: A Comprehensive Analysis of Multivariable Mendelian Randomization and Shared Genetic Etiology.Clinical cardiology · 2025Pooled it
- Genetics of child aggression, a systematic review.Translational psychiatry · 2024Pooled it
- Impact of gallstone disease on the risk of stroke and coronary artery disease: evidence from prospective observational studies and genetic analyses.BMC medicine · 2023Pooled it
- Estimating genetic correlation jointly using individual-level and summary-level GWAS data.Nature communications · 2026Article
- Shared Genetic Architecture of Premenstrual Disorder and Postpartum Depression: Registry-Based and Genetic Evidence.Research square · 2026Article
- Integrative Observational and Genetic Evidence From the UK Biobank Supports Carotid Intima-Media Thickness as a Window Into Cardiac Remodeling.Journal of the American Heart Association · 2026Observational
- Genetic Correlation and Causal Inference Between Female Fat Distribution and Preeclampsia: An Integrative Genomic Study.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Exploring the Genetic Overlap Between Metabolic Traits and Anorexia Nervosa.Biological psychiatry global open science · 2026Article
- Gut Microbiome Mediates the Causal Link Between Autism Spectrum Disorder and Dietary Preferences: A Mendelian Randomization Study.International journal of molecular sciences · 2026Article
- Enlarged Perivascular Spaces (EPVS) and the Risk of Amyotrophic Lateral Sclerosis (ALS): Evidence for Overlapping Genetic Signals in White Matter Without Causal Links.Brain sciences · 2026Article
- Liability threshold model-based disease risk prediction based on electronic health record phenotypes.Nature genetics · 2025Article
- HIGH-DIMENSIONAL STATISTICAL INFERENCE FOR LINKAGE DISEQUILIBRIUM SCORE REGRESSION AND ITS CROSS-ANCESTRY EXTENSIONS.Annals of statistics · 2025Article
- Genome-Wide Association Studies of Delay Discounting and Impulsive Personality Traits in Children From the Adolescent Behavior and Cognitive Development Study.Genes, brain, and behavior · 2025Article
- Disentangling horizontal and vertical Pleiotropy in genetic correlation estimation: introducing the HVP model.Human genetics · 2025Article
- Genetic Correlation and Mendelian Randomization Analyses Support Causal Relationships Between Instant Coffee and Age-Related Macular Degeneration.Food science & nutrition · 2025Article
- MUTATE: a human genetic atlas of multiorgan artificial intelligence endophenotypes using genome-wide association summary statistics.Briefings in bioinformatics · 2025Article
- Elucidating shared genetic association between female body mass index and preeclampsia.Communications biology · 2025Article
- GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics.Nucleic acids research · 2025Article
- Investigating shared risk variants and genetic etiology between Alzheimer's disease and three stress-related psychiatric disorders: a large-scale genome-wide cross-trait analysis.Frontiers in aging · 2025Article
- The Use of AI for Phenotype-Genotype Mapping.Methods in molecular biology (Clifton, N.J.) · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Genetic correlation is the correlation of phenotypic effects by genetic variants across the genome on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlation based on data collected from genome-wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications due to the imprecision of LD obtained from reference panels. Our findings offer guidance on how to choose appropriate methods for genetic correlation estimation in post-GWAS analysis.
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