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dc.contributor.authorGreenwood, Mark C.
dc.contributor.authorVsevolozhskaya, Olga
dc.contributor.authorZaykin, Dmitri
dc.contributor.authorWei, Changshuai
dc.contributor.authorLu, Qing
dc.date.accessioned2014-11-21T19:33:14Z
dc.date.available2014-11-21T19:33:14Z
dc.date.issued2014
dc.identifier.citationVsevolozhskaya, Olga A., Dmitri V. Zaykin, Mark C. Greenwood, Changshuai Wei, and Qing Lu. “Functional Analysis of Variance for Association Studies. Edited by Zhi Wei. PLoS ONE 9, no. 9 (September 22, 2014): e105074. doi:10.1371/journal.pone.0105074.en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/8728
dc.descriptionhttp://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.description.abstractWhile progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.en_US
dc.description.sponsorshipThis work was supported by a National Institute on Drug Abuse T32 research training program grant award (T32DA021129), QL's NIDA Mentored Research Scientist Development Award (K01DA033346), and by DVZ's Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences. The content is the sole responsibility of the authors and does not necessarily represent the official views of Michigan State University, the National Institute on Drug Abuse, the National Institute of Environmental Health Sciences or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.publisherPublic Library of Scienceen_US
dc.subjectMathematicsen_US
dc.subjectStatisticsen_US
dc.titleFunctional Analysis of Variance for Association Studiesen_US
dc.typeArticleen_US
mus.citation.extentfirstpagee105074en_US
mus.citation.issue9en_US
mus.citation.journaltitlePLoS ONEen_US
mus.citation.volume9en_US
mus.identifier.categoryPhysics & Mathematicsen_US
mus.identifier.doi10.1371/journal.pone.0105074en_US
mus.relation.collegeCollege of Letters & Science
mus.relation.collegeCollege of Letters & Sciencesen_US
mus.relation.departmentMathematical Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US


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