ArticleBioinformatics (Oxford, England)2018
Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Rare-Variant Burden across Lysosomal Genes Implicates Sialylation and Ganglioside Metabolism in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Lack of association between G6PD variants and Parkinson disease.HGG advances · 2026Article
- Rare-variant association studies: When are aggregation tests more powerful than single-variant tests?American journal of human genetics · 2025Article
- Quantifying variant contributions in cystic kidney disease using national-scale whole-genome sequencing.The Journal of clinical investigation · 2024Article
- Rare genetic variants explain missing heritability in smoking.Nature human behaviour · 2022Article
- Whole Exome Sequencing Study Identifies Novel Rare Risk Variants for Habitual Coffee Consumption Involved in Olfactory Receptor and Hyperphagia.Nutrients · 2022Article
- The impact of rare germline variants on human somatic mutation processes.Nature communications · 2022Article
- Identifying therapeutic drug targets using bidirectional effect genes.Nature communications · 2021Article
- Exome Sequencing Identifies Abnormalities in Glycosylation and ANKRD36C in Patients with Immune-Mediated Thrombotic Thrombocytopenic Purpura.Thrombosis and haemostasis · 2021Article
- Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale.Nature genetics · 2020Article
- Group testing in mediation analysis.Statistics in medicine · 2020Article
- The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome.Current opinion in endocrine and metabolic research · 2020Article
- Pulling the covers in electronic health records for an association study with self-reported sleep behaviors.Chronobiology international · 2018Article
- Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits.Nature genetics · 2018Article
- Association of FAAH p.Pro129Thr and COMT p.Ala72Ser with schizophrenia and comorbid substance use through next-generation sequencing: an exploratory analysis.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)Article
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3 authors.
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Abstract
Motivation: Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus. Results: We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using these simplified power calculations, we develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from genome-wide association studies with rare variants. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power. Availability and implementation: A shiny application that allows a variety of Power Analysis of GEnetic AssociatioN Tests (PAGEANT), in R is made publicly available at https://andrewhaoyu.shinyapps.io/PAGEANT/. Contact: nilanjan@jhu.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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