Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
Open accessAbstract
Summary: Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case–control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of ~0.31 (SE 0.14, P = 0.024). Availability: Our methods, appropriate for both quantitative and binary traits, are implemented in the freely available software GCTA (http://www.complextraitgenomics.com/software/gcta/reml_bivar.html). Contact: hong.lee@uq.edu.au Supplementary Information: Supplementary data are available at Bioinformatics online.
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Genetic correlation was estimated with REML accounting for sampling variation, whereas transcriptomic effect correlation was estimated as sample Spearman correlation between DE point estimates, which is downwardly biased in a sample size dependent manner.
“A notable difference between the transcriptomic and genetic analyses in this study is that the genetic correlation was estimated with a REML approach that accounts for sampling variation30, whereas the transcriptomic effect correlation was estimated as the sample Spearman correlation between differential expression point estimates, which is downwardly biased in a sample size dependent manner (”
From Transcriptome-wide analysis of differential expression in perturbation atlases (2025) · cites this paper as [30]
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- Authors
- Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M., & Wray, N. R.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Lee, S.H.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-07-26
- Volume
- 28
- Issue
- 19
- Pages
- 2540-2542
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3463125
- OpenAlex
- W2098143048
- Cited by
- 635
- References
- 14
- Citations by year
- 2026: 21 · 2025: 37 · 2024: 45 · 2023: 34 · 2022: 42 · 2021: 59 · 2020: 55 · 2019: 56 · 2018: 50 · 2017: 48 · 2016: 61 · 2015: 57 · 2014: 36 · 2013: 32
- FWCI
- 23.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- EPubDev Repository (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- RUNE (Research UNE) (submittedVersion)
- Funders
- Wellcome Trust (076113)
- Medical Research Council
- Australian Research Council (FT0991360, DP1093502)
- National Health and Medical Research Council (613601, 613608, 613672, 1011506)
- National Institute of General Medical Sciences (GM 075091, R01GM075091)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- pleiotropy, genetic correlation, complex diseases, single nucleotide polymorphisms, genomic relationships, restricted maximum likelihood, linear mixed models, type 2 diabetes, hypertension, binary traits, case-control study, GCTA, Wellcome Trust Case Control Consortium
- MeSH terms
- Computer Simulation, Diabetes Mellitus, Type 2, Humans, Hypertension, Models, Genetic, Pedigree, Phenotype, Software, Likelihood Functions, Linear Models, Computational Biology, Quantitative Trait, Heritable, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study, Genetic Pleiotropy
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