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000136182 0247_ $$2doi$$a10.1371/journal.pgen.1001289
000136182 0247_ $$2pmid$$apmid:21304886
000136182 0247_ $$2pmc$$apmc:PMC3033379
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000136182 0247_ $$2ISSN$$a1553-7404
000136182 037__ $$aDZNE-2020-02504
000136182 041__ $$aEnglish
000136182 082__ $$a610
000136182 1001_ $$0P:(DE-HGF)0$$aIonita-Laza, Iuliana$$b0$$eCorresponding author
000136182 245__ $$aA new testing strategy to identify rare variants with either risk or protective effect on disease.
000136182 260__ $$aSan Francisco, Calif.$$bPublic Library of Science$$c2011
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000136182 520__ $$aRapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.
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000136182 650_7 $$0EC 3.6.1.-$$2NLM Chemicals$$aIFIH1 protein, human
000136182 650_7 $$0EC 3.6.4.13$$2NLM Chemicals$$aDEAD-box RNA Helicases
000136182 650_7 $$0EC 3.6.4.13$$2NLM Chemicals$$aInterferon-Induced Helicase, IFIH1
000136182 650_2 $$2MeSH$$aAlgorithms
000136182 650_2 $$2MeSH$$aComputer Simulation
000136182 650_2 $$2MeSH$$aDEAD-box RNA Helicases: genetics
000136182 650_2 $$2MeSH$$aData Interpretation, Statistical
000136182 650_2 $$2MeSH$$aDiabetes Mellitus, Type 1: genetics
000136182 650_2 $$2MeSH$$aGenetic Predisposition to Disease
000136182 650_2 $$2MeSH$$aGenetic Testing: statistics & numerical data
000136182 650_2 $$2MeSH$$aGenetic Variation
000136182 650_2 $$2MeSH$$aGenome-Wide Association Study: statistics & numerical data
000136182 650_2 $$2MeSH$$aHaplotypes: genetics
000136182 650_2 $$2MeSH$$aHumans
000136182 650_2 $$2MeSH$$aInterferon-Induced Helicase, IFIH1
000136182 650_2 $$2MeSH$$aRisk Factors
000136182 650_2 $$2MeSH$$aSequence Analysis, DNA
000136182 7001_ $$0P:(DE-HGF)0$$aBuxbaum, Joseph D$$b1
000136182 7001_ $$0P:(DE-HGF)0$$aLaird, Nan M$$b2
000136182 7001_ $$0P:(DE-2719)9000181$$aLange, Christoph$$b3$$eLast author
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