ArticlePloS one2013
The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 6 of them syntheses that pooled it.
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Who cites it
107 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- East Asian-specific and cross-ancestry genome-wide meta-analyses provide mechanistic insights into peptic ulcer disease.Nature genetics · 2023Pooled it
- Melatonin and aggressive behavior: A systematic review of the literature on preclinical and clinical evidence.Journal of pineal research · 2022Pooled it
- Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder.Human molecular genetics · 2016Pooled it
- Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium.Translational psychiatry · 2016Pooled it
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- Overlap between genetic variants associated with schizophrenia spectrum disorders and intelligence quotient: a systematic review.Journal of psychiatry & neuroscience : JPNPooled it
- A Type 2 Diabetes Subtype Responsive to ACCORD Intensive Glycemia Treatment.Diabetes care · 2021Trial
- Multi-organ imaging genetics: towards an integrated view of brain, body and brain diseases.Nature reviews. Neurology · 2026Review
- A new era of genome-wide association studies in the field of Alzheimer's disease and overlapping co-pathologies: lessons learned from a neuropathology-centered approach.Acta neuropathologica · 2026Review
- Increasing specificity in ADHD genetic association studies during childhood: use of the oxytocin-vasopressin pathway in attentional processes suggests specific mechanism for endophenotypes in the 2004 Pelotas birth (Brazil) cohort.European archives of psychiatry and clinical neuroscience · 2025Article
- Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.NAR genomics and bioinformatics · 2025Article
- Optimization of self- or parent-reported psychiatric phenotypes in longitudinal studies.Journal of child psychology and psychiatry, and allied disciplines · 2025Article
- Positive and negative affect, related mental health traits, and cognitive performance: shared genetic architecture and potential causality.medRxiv : the preprint server for health sciences · 2024Article
- Genome-wide discovery for biomarkers using quantile regression at biobank scale.Nature communications · 2024Article
- Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks.iScience · 2024Article
- Using Alternative Definitions of Controls to Increase Statistical Power in GWAS.Behavior genetics · 2024Article
- Longitudinal studies of bipolar patients and their families: translating findings to advance individualized risk prediction, treatment and research.International journal of bipolar disorders · 2024Review
- Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity.Briefings in bioinformatics · 2024Review
- Omada: robust clustering of transcriptomes through multiple testing.GigaScience · 2024Article
- Pharmacogenomics and response to lithium in bipolar disorder.Pharmacogenomics · 2024Review
47 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of "non-cases") reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.
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