Journal Article

DZNE-2020-02504

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A new testing strategy to identify rare variants with either risk or protective effect on disease.

Ionita-Laza, I. (Corresponding author) ; Buxbaum, J. D. ; Laird, N. M. ; Lange, C. (Last author)DZNE*

2011
Public Library of Science San Francisco, Calif.

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Please use a persistent id in citations: doi:10.1371/journal.pgen.1001289

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.

Keyword(s): Algorithms (MeSH) ; Computer Simulation (MeSH) ; DEAD-box RNA Helicases: genetics (MeSH) ; Data Interpretation, Statistical (MeSH) ; Diabetes Mellitus, Type 1: genetics (MeSH) ; Genetic Predisposition to Disease (MeSH) ; Genetic Testing: statistics & numerical data (MeSH) ; Genetic Variation (MeSH) ; Genome-Wide Association Study: statistics & numerical data (MeSH) ; Haplotypes: genetics (MeSH) ; Humans (MeSH) ; Interferon-Induced Helicase, IFIH1 (MeSH) ; Risk Factors (MeSH) ; Sequence Analysis, DNA (MeSH) ; IFIH1 protein, human ; DEAD-box RNA Helicases ; Interferon-Induced Helicase, IFIH1

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Contributing Institute(s):

1. U T4 Researchers - Bonn (U T4 Researchers - Bonn)

Research Program(s):

1. 345 - Population Studies and Genetics (POF3-345) (POF3-345)

Appears in the scientific report 2011

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