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Statistical methods to detect pleiotropy in human complex traits
Open accessAbstract
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.
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“However, the topic has received renewed attention, given the pervasiveness of pleiotropy that has been uncovered through genome-wide association studies (GWAS) [8–10].”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [8]
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Additional file 4 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
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Additional file 3 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
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Additional file 5 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
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Additional file 1 of Multi-trait multi-locus SEM model discriminates SNPs of different effects
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Metadata
- Authors
- Hackinger, S., & Zeggini, E.
- Affiliations
- Wellcome Sanger Institute (GB)
- Corresponding author
- Zeggini, Eleftheria
- Venue
- Open Biology
- Publisher
- The Royal Society
- Published
- 2017-11
- Volume
- 7
- Issue
- 11
- ISSN
- 2046-2441 (ISSN-L)
- PMC
- PMC5717338
- OpenAlex
- W2766556645
- Cited by
- 143
- References
- 155
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 12 · 2023: 19 · 2022: 36 · 2021: 15 · 2020: 23 · 2019: 17 · 2018: 7
- FWCI
- 1.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Open Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome Trust (WT098051)
- 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 and phenotypic traits in livestock
- Keywords
- pleiotropy, multi-trait analysis, genetic overlap, complex traits, biological pathways, statistical analysis
- MeSH terms
- Humans, Quantitative Trait, Heritable, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Pleiotropy
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