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Exact association test for small size sequencing data

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors
Lee, Joowon; Lee, Seungyeoun; Jang, Jin-Young; Park, Taesung
Issue Date
2018-04-20
Publisher
BioMed Central
Citation
BMC Medical Genomics, 11(Suppl 2):30
Keywords
NGS data analysisSmall size sequencing dataAssociation studyCMH statisticIPMNFisher’s exact test
Abstract
Background
Recent 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.

Methods
In 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.

Results
Application of our method to IPMN data successfully identified susceptible genes associated with progression of IPMN to pancreatic cancer.

Conclusions
Our 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.
ISSN
1755-8794
Language
English
URI
http://hdl.handle.net/10371/142491
DOI
https://doi.org/10.1186/s12920-018-0344-z
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Journal Papers (저널논문_통계학과)
College of Medicine/School of Medicine (의과대학/대학원)Surgery (외과학전공)Journal Papers (저널논문_외과학전공)
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