ArticleGenetics2017
Controlling the Rate of GWAS False Discoveries.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revisiting the genome-wide significance threshold for common variant GWAS.G3 (Bethesda, Md.) · 2021Pooled it
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- CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.Genome biology · 2026Article
- Integrating natural variation through GWAS - genetics of drought and flood tolerance in grass pea reveal independent yet interconnected mechanisms.BMC plant biology · 2026Article
- Genome-wide association study to identification of candidate genes influencing key seed traits in Perilla frutescens.BMC plant biology · 2025Article
- Genome-wide association studies revealed partial genetic links between early vigour and precocity in macadamia.Horticulture research · 2025Article
- Rapid species-level metagenome profiling and containment estimation with sylph.Nature biotechnology · 2025Article
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- Bayesian Effect Size Ranking to Prioritise Genetic Risk Variants in Common Diseases for Follow-Up Studies.Genetic epidemiology · 2025Article
- Second-order group knockoffs with applications to genome-wide association studies.Bioinformatics (Oxford, England) · 2024Article
- Exploration of protein and genetic targets causing atrioventricular block: mendelian-randomization analyses based on eQTL data and pQTL data.BMC cardiovascular disorders · 2024Article
- BEATRICE: Bayesian fine-mapping from summary data using deep variational inference.Bioinformatics (Oxford, England) · 2024Article
- The benefits of permutation-based genome-wide association studies.Journal of experimental botany · 2024Article
- BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference.bioRxiv : the preprint server for biology · 2024Article
- Longitudinal study investigating the influence of COMT gene polymorphism on cortical thickness changes in Parkinson's disease over four years.Scientific reports · 2024Article
- Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies.Scientific reports · 2023Article
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection.The international journal of biostatistics · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the precise way in which discoveries are counted, and the performance of FDR-controlling procedures is satisfactory only if there is a one-to-one correspondence between what scientists describe as unique discoveries and the number of rejected hypotheses. The presence of linkage disequilibrium between markers in genome-wide association studies (GWAS) often leads researchers to consider the signal associated to multiple neighboring SNPs as indicating the existence of a single genomic locus with possible influence on the phenotype. This a posteriori aggregation of rejected hypotheses results in inflation of the relevant FDR. We propose a novel approach to FDR control that is based on prescreening to identify the level of resolution of distinct hypotheses. We show how FDR-controlling strategies can be adapted to account for this initial selection both with theoretical results and simulations that mimic the dependence structure to be expected in GWAS. We demonstrate that our approach is versatile and useful when the data are analyzed using both tests based on single markers and multiple regression. We provide an R package that allows practitioners to apply our procedure on standard GWAS format data, and illustrate its performance on lipid traits in the North Finland Birth Cohort 66 cohort study.
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