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000137632 0247_ $$2doi$$a10.1093/bioinformatics/btu496
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000137632 0247_ $$2ISSN$$a1460-2059
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000137632 037__ $$aDZNE-2020-03954
000137632 041__ $$aEnglish
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000137632 1001_ $$0P:(DE-HGF)0$$aChoi, Sungkyoung$$b0
000137632 245__ $$aFARVAT: a family-based rare variant association test.
000137632 260__ $$aOxford$$bOxford Univ. Press$$c2014
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000137632 520__ $$aIndividuals in each family are genetically more homogeneous than unrelated individuals, and family-based designs are often recommended for the analysis of rare variants. However, despite the importance of family-based samples analysis, few statistical methods for rare variant association analysis are available.In this report, we propose a FAmily-based Rare Variant Association Test (FARVAT). FARVAT is based on the quasi-likelihood of whole families, and is statistically and computationally efficient for the extended families. FARVAT assumed that families were ascertained with the disease status of family members, and incorporation of the estimated genetic relationship matrix to the proposed method provided robustness under the presence of the population substructure. Depending on the choice of working matrix, our method could be a burden test or a variance component test, and could be extended to the SKAT-O-type statistic. FARVAT was implemented in C++, and application of the proposed method to schizophrenia data and simulated data for GAW17 illustrated its practical importance.The software calculates various statistics for the analysis of related samples, and it is freely downloadable from http://healthstats.snu.ac.kr/software/farvat.won1@snu.ac.kr or tspark@stats.snu.ac.krsupplementary data are available at Bioinformatics online.
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000137632 650_2 $$2MeSH$$aFamily
000137632 650_2 $$2MeSH$$aGenetic Association Studies: methods
000137632 650_2 $$2MeSH$$aGenetic Variation
000137632 650_2 $$2MeSH$$aHumans
000137632 650_2 $$2MeSH$$aSchizophrenia: genetics
000137632 650_2 $$2MeSH$$aSoftware
000137632 7001_ $$0P:(DE-HGF)0$$aLee, Sungyoung$$b1
000137632 7001_ $$0P:(DE-HGF)0$$aCichon, Sven$$b2
000137632 7001_ $$0P:(DE-HGF)0$$aNöthen, Markus M$$b3
000137632 7001_ $$0P:(DE-2719)9000181$$aLange, Christoph$$b4$$udzne
000137632 7001_ $$0P:(DE-HGF)0$$aPark, Taesung$$b5$$eCorresponding author
000137632 7001_ $$0P:(DE-HGF)0$$aWon, Sungho$$b6
000137632 77318 $$2Crossref$$3journal-article$$a10.1093/bioinformatics/btu496$$b : Oxford University Press (OUP), 2014-07-29$$n22$$p3197-3205$$tBioinformatics$$v30$$x1460-2059$$y2014
000137632 773__ $$0PERI:(DE-600)1468345-3$$a10.1093/bioinformatics/btu496$$gVol. 30, no. 22, p. 3197 - 3205$$n22$$p3197-3205$$q30:22<3197 - 3205$$tBioinformatics$$v30$$x1460-2059$$y2014
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