ArticleBioinformatics (Oxford, England)2016
Gene-set association tests for next-generation sequencing data.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The association between single-nucleotide polymorphisms within type 1 interferon pathway genes and human immunodeficiency virus type 1 viral load in antiretroviral-naïve participants.AIDS research and therapy · 2024Article
- Matrisome and Immune Pathways Contribute to Extreme Vascular Outcomes in Williams-Beuren Syndrome.Journal of the American Heart Association · 2024Article
- Whole exome sequencing in patients with Williams-Beuren syndrome followed by disease modeling in mice points to four novel pathways that may modify stenosis risk.Human molecular genetics · 2020Article
- Meta-Qtest: meta-analysis of quadratic test for rare variants.BMC medical genomics · 2019Article
- WISARD: workbench for integrated superfast association studies for related datasets.BMC medical genomics · 2018Article
- rqt: an R package for gene-level meta-analysis.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
motivationRecently, many methods have been developed for conducting rare-variant association studies for sequencing data. These methods have primarily been based on gene-level associations but have not been proven to be as effective as expected. Gene-set-level tests have shown great advantages over gene-level tests in terms of power and robustness, because complex diseases are often caused by multiple genes that comprise of biological gene sets.
resultsHere, we propose several novel gene-set tests that employ rapid and efficient dimensionality reduction. The performance of these tests was investigated using extensive simulations and application to 1058 whole-exome sequences from a Korean population. We identified some known pathways and novel pathways whose rare or common variants are associated with elevated liver enzymes and replicated the results in an independent cohort. AVAILABILITY AND IMPLEMENTATION: Source R code for our algorithm is freely available at http://statgen.snu.ac.kr/software/QTest CONTACT: tspark@stats.snu.ac.kr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 OpenQuestion graph.