ArticleBioinformatics (Oxford, England)2012
SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrating External Controls by Regression Calibration for Genome-Wide Association Study.Genes · 2024Article
- Analysis in case-control sequencing association studies with different sequencing depths.Biostatistics (Oxford, England) · 2020Article
- Likelihood-based complex trait association testing for arbitrary depth sequencing data.Bioinformatics (Oxford, England) · 2015Article
- Association analysis using next-generation sequence data from publicly available control groups: the robust variance score statistic.Bioinformatics (Oxford, England) · 2014Article
- BETASEQ: a powerful novel method to control type-I error inflation in partially sequenced data for rare variant association testing.Bioinformatics (Oxford, England) · 2014Article
- Population-Sequencing as a Biomarker for Sample Characterization.Journal of biomarkers · 2013Article
- Genetic association of cyclic AMP signaling genes with bipolar disorder.Translational psychiatry · 2012Article
- Imputation of rare variants in next-generation association studies.Human heredity · 2012Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationNext-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.
resultsIt is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods. AVAILABILITY: SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.html.
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Registered trials
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