ArticleGenetic epidemiology2020
Population genetic simulation study of power in association testing across genetic architectures and study designs.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variance-component-based meta-analysis of gene-environment interactions for rare variants.G3 (Bethesda, Md.) · 2021Pooled it
- Genetic Diversity and Marker-Trait Associations in Commercial Cultivars and WeedyPlants (Basel, Switzerland) · 2026Article
- Inference of population demographic history captures differing evolutionary signals based on the number of individuals in the dataset.bioRxiv : the preprint server for biology · 2026Article
- Formal Statistical Replication Analysis in Lung Cancer Genome-Wide Association Studies.medRxiv : the preprint server for health sciences · 2025Article
- Comparison of mixed model based approaches for correcting for population substructure with application to extreme phenotype sampling.BMC genomics · 2022Article
- Lessons Learned from Bugs in Models of Human History.American journal of human genetics · 2020Article
- Population genetic simulation study of power in association testing across genetic architectures and study designs.Genetic epidemiology · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.