Pleiotropy in complex traits: challenges and strategies
Open accessAbstract
Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
At some pleiotropic loci, there is no shared causal SNP; instead, different SNPs are causal for different traits.
“However, recent studies have shown that at some pleiotropic loci there is no shared causal SNP, but instead different SNPs are causal for the different traits. These loci are associated with multiple traits but there is no shared causal genetic variant behind the associations [9, 15].”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [15]
The method used to detect cross-phenotype association is one of several approaches.
“the method we applied to detect cross-phenotype association, which combines an all-subsets fixed-effects GWAS meta-analysis with a Bayesian method for evaluating the best-fit configuration of genotype-phenotype associations, is one of several approaches (Solovieff et al., 2013).”
From Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders (2019) · cites this paper as Solovieff et al., 2013
Examples of exposure sets where multivariable Mendelian randomization has been used include lipid fractions (such as high-density lipoprotein cholesterol, LDL-cholesterol, and triglycerides) and body composition measures (such as fat mass and fat-free mass).
“Examples of exposure sets where multivariable Mendelian randomization has been used include lipid fractions (such as high-density lipoprotein cholesterol, LDL-cholesterol, and triglycerides) 82 , and body composition measures (such as fat mass and fat-free mass) 83 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [82]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 136 · Cited by 1,085
Part of this paper’s reference list could not be split into single entries.
Research works printed but not linked: 3
- [8]8. Stearns FW. One hundred years of pleiotropy: a retrospective. Genetics. 2010;186:767–773. doi: 10.1534/genetics.110.122549. This is a historical review of pleiotropy. · DOI
- [9]9. Wagner GP, Zhang J. The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms. Nature Rev. Genet. 2011;12:204–213. doi: 10.1038/nrg2949. This excellent Review discusses pleiotropy in model organisms and the implications for evolution. 10.Kendler KS, Neale MC, Kessler RC, Heath AC, Eaves LJ. Major depression and generalized anxiety disorder. Same genes, (partly) different environments? Arch. Gen. Psychiatry. 1992;49:716–722. doi: 10.1001/archpsyc.1992.01820090044008. [DOI] [PubMed] [Google Scholar] 11.Criswell LA, et al. Analysis of families in the Multiple Autoimmune Disease Genetics Consortium (MADGC) collection: the PTPN22 620W allele associates with multiple autoimmune phenotypes. Am. J. Hum. Genet. 2005;76:561–571. doi: 10.1086/429096. [DOI] [PMC free article] [PubMed] [Google Scholar] 12.Eaton WW, Rose NR, Kalaydjian A, Pedersen MG, Mortensen PB. Epidemiology of autoimmune diseases in Denmark. J. Autoimmun. 2007;29:1–9. doi: 10.1016/j.jaut.2007.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar] 13.Sivakumaran S, et al. Abundant pleiotropy in human complex diseases and traits. Am. J. Hum. Genet. 2011;89:607–618. doi: 10.1016/j.ajhg.2011.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar] · DOI
- [37]37. Voight BF, et al. Plasma HDL cholesterol and risk of myocardial infarction: a Mendelian randomisation study. Lancet. 2012;380:572–580. doi: 10.1016/S0140-6736(12)60312-2. This paper presents an example of Mendelian randomization using results from GWASs. 38.Hung RJ, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature. 2008;452:633–637. doi: 10.1038/nature06885. [DOI] [PubMed] [Google Scholar] 39.Thorgeirsson TE, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452:638–642. doi: 10.1038/nature06846. [DOI] [PMC free article] [PubMed] [Google Scholar] 40.Chanock SJ, Hunter DJ. Genomics: when the smoke clears. Nature. 2008;452:537–538. doi: 10.1038/452537a. [DOI] [PubMed] [Google Scholar] 41.Lee SH, Yang J, Goddard ME, Visscher PM, Wray NR. Estimation of pleiotropy between complex diseases using SNP-derived genomic relationships and restricted maximum likelihood. Bioinformatics. 2012;28:2540–2542. doi: 10.1093/bioinformatics/bts474. [DOI] [PMC free article] [PubMed] [Google Scholar] 42.Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42:121–130. [PubMed] [Google Scholar] 43.Lange C, Silverman EK, Xu X, Weiss ST, Laird NM. A multivariate family-based association test using generalized estimating equations: FBAT-GEE. Biostatistics. 2003;4:195–206. doi: 10.1093/biostatistics/4.2.195. [DOI] [PubMed] [Google Scholar] 44.Liu J, Pei Y, Papasian CJ, Deng HW. Bivariate association analyses for the mixture of continuous and binary traits with the use of extended generalized estimating equations. Genet. Epidemiol. 2009;33:217–227. doi: 10.1002/gepi.20372. [DOI] [PMC free article] [PubMed] [Google Scholar] 45.Lee PH, et al. Modifiers and subtype-specific analyses in whole-genome association studies: a likelihood framework. Hum. Hered. 2011;72:10–20. doi: 10.1159/000327158. [DOI] [PMC free article] [PubMed] [Google Scholar] 46.Hartley SW, Monti S, Liu CT, Steinberg MH, Sebastiani P. Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction. Front. Genet. 2012;3:176. doi: 10.3389/fgene.2012.00176. [DOI] [PMC free article] [PubMed] [Google Scholar] 47.O’Reilly PF, et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE. 2012;7:e34861. doi: 10.1371/journal.pone.0034861. [DOI] [PMC free article] [PubMed] [Google Scholar] 48.Zhang H, Liu CT, Wang X. An association test for multiple traits based on the generalized Kendall’s tau. J. Am. Stat. Assoc. 2010;105:473–481. doi: 10.1198/jasa.2009.ap08387. [DOI] [PMC free article] [PubMed] [Google Scholar] 49.Ott J, Rabinowitz D. A principal-components approach based on heritability for combining phenotype information. Hum. Hered. 1999;49:106–111. doi: 10.1159/000022854. [DOI] [PubMed] [Google Scholar] 50.Lange C, et al. A family-based association test for repeatedly measured quantitative traits adjusting for unknown environmental and/or polygenic effects. Stat. Appl. Genet. Mol. Biol. 2004;3 doi: 10.2202/1544-6115.1067. Article 17. [DOI] [PubMed] [Google Scholar] 51.Klei L, Luca D, Devlin B, Roeder K. Pleiotropy and principal components of heritability combine to increase power for association analysis. Genet. Epidemiol. 2008;32:9–19. doi: 10.1002/gepi.20257. [DOI] [PubMed] [Google Scholar] 52.Ferreira MA, Purcell SM. A multivariate test of association. Bioinformatics. 2009;25:132–133. doi: 10.1093/bioinformatics/btn563. [DOI] [PubMed] [Google Scholar] · DOI · Paper page
- Authors
- Solovieff, N., Cotsapas, C., Lee, P. H., Purcell, S. M., & Smoller, J. W.
- Affiliations
- Massachusetts General Hospital (US)
- Broad Institute (US)
- Corresponding author
- Smoller, Jordan W.
- Venue
- Nature Reviews Genetics
- Published
- 2013-06-11
- Volume
- 14
- Issue
- 7
- Pages
- 483-495
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC4104202
- OpenAlex
- W1996299724
- Cited by
- 1,085
- References
- 136
- Citations by year
- 2026: 54 · 2025: 86 · 2024: 85 · 2023: 62 · 2022: 100 · 2021: 99 · 2020: 98 · 2019: 98 · 2018: 99 · 2017: 87 · 2016: 85 · 2015: 62 · 2014: 56 · 2013: 13
- FWCI
- 8.84
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (R01MH079799, K24MH094614)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- pleiotropy, complex traits, genetic variants, genome-wide association study, autoimmune disease, cancer, neuropsychiatric disorders, research design
- MeSH terms
- Alleles, Chromosome Mapping, Genotype, Humans, Models, Genetic, Phenotype, Genetic Variation, Multivariate Analysis, Genetic Predisposition to Disease, Genetic Diseases, Inborn, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
