ArticleBMC medical genomics2018
Exact association test for small size sequencing data.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Mutations in ammonia transporter RhBG that impair NHThe Journal of physiology · 2026Article
- Genome-wide analysis to uncover how Pocillopora acuta survives the challenging intertidal environment.Scientific reports · 2024Article
- SNP Alleles Associated With Low Bolting Tendency in Sugar Beet.Frontiers in plant science · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent statistical methods for next generation sequencing (NGS) data have been successfully applied to identifying rare genetic variants associated with certain diseases. However, most commonly used methods (e.g., burden tests and variance-component tests) rely on large sample sizes. Notwithstanding, due to its-still high cost, NGS data is generally restricted to small sample sizes, that cannot be analyzed by most existing methods.
methodsIn this work, we propose a new exact association test for sequencing data that does not require a large sample approximation, which is applicable to both common and rare variants. Our method, based on the Generalized Cochran-Mantel-Haenszel (GCMH) statistic, was applied to NGS datasets from intraductal papillary mucinous neoplasm (IPMN) patients. IPMN is a unique pancreatic cancer subtype that can turn into an invasive and hard-to-treat metastatic disease.
resultsApplication of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer.
conclusionsOur method is expected to identify disease-associated genetic variants more successfully, and corresponding signal pathways, improving our understanding of specific disease's etiology and prognosis.
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