title: A new testing strategy to identify rare variants with either risk or protective effect on disease.
abstract: Rapid 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.
authors.affiliation: Department of Biostatistics, Columbia University, New York, New York, United States of America. ii2135@columbia.edu
authors.forename: Iuliana
authors.initials: I
authors.lastname: Ionita-Laza
authors.forename: Joseph D
authors.initials: JD
authors.lastname: Buxbaum
authors.forename: Nan M
authors.initials: NM
authors.lastname: Laird
authors.forename: Christoph
authors.initials: C
authors.lastname: Lange
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D053487:DEAD-box RNA Helicases / Q000235:genetics; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000235:genetics; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000706:statistics & numerical data*; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006239:Haplotypes / Q000235:genetics; D006801:Humans; D000072640:Interferon-Induced Helicase, IFIH1; D012307:Risk Factors; D017422:Sequence Analysis, DNA
chemical_list: C452815:IFIH1 protein, human; D053487:DEAD-box RNA Helicases; D000072640:Interferon-Induced Helicase, IFIH1