Pleiotropy in complex traits: challenges and strategies

By N. Solovieff, C. Cotsapas, Phil H. Lee, Shaun Purcell, Jordan W. Smoller. Nature reviews genetics, 2013

1,050 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b562f5c1c967d5582e72ccbbb7b1698e97f051f9

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Citations in the corpus

Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “The main concern for Mendelian randomization is the possible violation of the third exclusion restriction criterion, due to a genetic phenomenon called pleiotropy [18, 47], a.”
Functionally-informed fine-mapping and polygenic localization of complex trait heritability (2019, background): “Fourth, SNPs that were fine-mapped for multiple genetically uncorrelated traits may shed light on shared biological pathways 54 .”
Constraints Evolve: Context Dependency of Gene Effects Allows Evolution of Pleiotropy (2015, background): “The distribution of pleiotropy across genes and the effects of pleiotropy on variation have been a subject of multiple recent contributions and reviews (Solovieff et al. 2013, Stearns 2010, Stern 2000, Wagner & Zhang 2011) and are not discussed here.”
Evaluating the potential role of pleiotropy in Mendelian randomization studies (2018, background): “ropy, though it has also been called biological pleiotropy (106), Type I pleiotropy (107), developmental pleiotropy and selec-”
Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. (2015, background): “widespread.(90) The reliability of Mendelian randomization studies highly depends on genuine genetic instruments and thereby the ability to assess the validity of the genetic instruments via detailed metabolic phenotyping is of immense value.”
An integrated framework for local genetic correlation analysis (2022, background): “10–14, our multivariate approaches provide unbiased estimates and well-controlled type 1 error rates, although for some instances of the partial correlation with binary phenotypes, this requires filtering variants with a minor allele frequency (MAF) below 0.5%.”
Insulin resistance: Genetic associations with depression and cognition in population based cohorts (2019, neutral): “Pleiotropy reflects the overlap between the genetic architecture of two or more traits (Solovieff et al., 2013) and can be assessed using data from single-nucleotide polymorphism (SNP) genotyping.”
Mendelian randomization: genetic anchors for causal inference in epidemiological studies (2014, background): “Amongst its many names, the former has been termed ‘spurious pleiotropy’ (35,36), ‘mediated pleiotropy’ (37) or ‘type II pleiotropy’ (36); the latter ‘biological pleiotropy’ (37) or ‘type I pleiotropy’ (36).”
A machine learning-based framework to identify type 2 diabetes through electronic health records (2016, background): “A disease may be caused by the joint effects of multiple single nucleotide polymorphism (SNPs) (i.e. heterogeneity), while a SNP may lead to multiple diseases (i.e. pleiotropy) [32–34].”
Network biology concepts in complex disease comorbidities (2016, background): “These mutations Model 1 Model 2 Model 3 Model 4 Model 5Multifunctionality Nature Reviews | Genetics A B A B A B A Secondary B A B Type I Type II Type II • Molecular genetic • Developmental • Selectional • Developmental • Selectional • Adoptive • Parsimonous • Opportunistic • Combinatorial Biological”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “, 2017), pleiotropy (Solovieff et al., 2013; Yang et al., 2015) and linkage disequilibrium (LD) in human genome (The 1000 Genomes Project Consortium et al.”
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