Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

A unified genetic association test robust to latent population structure for a count phenotype

Full metadata record
DC FieldValueLanguage
dc.contributor.authorSong, Minsun-
dc.date.available2021-02-22T08:45:48Z-
dc.date.created2020-08-18-
dc.date.issued2018-09-
dc.identifier.issn0277-6715-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/4306-
dc.description.abstractConfounding caused by latent population structure in genome-wide association studies has been a big concern despite the success of genome-wide association studies at identifying genetic variants associated with complex diseases. In particular, because of the growing interest in association mapping using count phenotype data, it would be interesting to develop a testing framework for genetic associations that is immune to population structure when phenotype data consist of count measurements. Here, I propose a solution for testing associations between single nucleotide polymorphisms and a count phenotype in the presence of an arbitrary population structure. I consider a classical range of models for count phenotype data. Under these models, a unified test for genetic associations that protects against confounding was derived. An algorithm was developed to efficiently estimate the parameters that are required to fit the proposed model. I illustrate the proposed approach using simulation studies and an empirical study. Both simulated and real-data examples suggest that the proposed method successfully corrects population structure.-
dc.language영어-
dc.language.isoen-
dc.publisherWILEY-
dc.titleA unified genetic association test robust to latent population structure for a count phenotype-
dc.typeArticle-
dc.contributor.affiliatedAuthorSong, Minsun-
dc.identifier.doi10.1002/sim.7634-
dc.identifier.scopusid2-s2.0-85051184706-
dc.identifier.wosid000441008900004-
dc.identifier.bibliographicCitationSTATISTICS IN MEDICINE, v.37, no.20, pp.2954 - 2967-
dc.relation.isPartOfSTATISTICS IN MEDICINE-
dc.citation.titleSTATISTICS IN MEDICINE-
dc.citation.volume37-
dc.citation.number20-
dc.citation.startPage2954-
dc.citation.endPage2967-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaPublic, Environmental & Occupational Health-
dc.relation.journalResearchAreaMedical Informatics-
dc.relation.journalResearchAreaResearch & Experimental Medicine-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryPublic, Environmental & Occupational Health-
dc.relation.journalWebOfScienceCategoryMedical Informatics-
dc.relation.journalWebOfScienceCategoryMedicine, Research & Experimental-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.subject.keywordPlusGENOME-WIDE ASSOCIATION-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorcount phenotype-
dc.subject.keywordAuthorgenome-wide association studies-
dc.subject.keywordAuthorlatent population structure-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/full/10.1002/sim.7634-
Files in This Item
Go to Link
Appears in
Collections
이과대학 > 통계학과 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Song, Min Sun photo

Song, Min Sun
이과대학 (통계학과)
Read more

Altmetrics

Total Views & Downloads

BROWSE