Meta-Analysis of Multiple Regression Models and Rare Variant Gene-Gene Interaction Analysis for Next Generation Sequencing Data

• Applicant: Privatdozent Dr. Tim Becker
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: from 2007 to 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 54145271

The project has two major aims, both relating to gene-gene interaction. The first goal is to provide a meta-analysis framework for multiple regression analysis. The default application will be interaction analysis. The second major goal is to develop methods and provide analysis software for set-based interaction analysis of rare variants obtained from Next Generation Sequencing studies.Models for interaction analysis are typically defined by more than one parameter. Hence, extension of the standard GWAS meta-analysis framework is needed. Synthesis of regression slopes has recently been described and shall be carried over to genetic studies. A meta-analysis tool designed for multiple regression models shall be provided. The methodology depends on the availability of parameter covariance matrices. The framework shall be supported by extending existing own tools to provide these matrices. The statistical properties of the meta-analysis method shall be investigated. An international data analysis project is on the agenda.Rare variants and genetic interaction are potential explanations of the missing, broad-sense heritability of complex diseases. Therefore, it is a straightforward idea to integrate both ideas into a joint analysis framework. Our aim is to tackle the inevitably occurring high-dimensionality problem and computational challenges. Existing set-based methods, including burden tests as well as regression or variance component tests, shall be extended to the analysis of pairs of genes. Non-burden tests that model interaction terms shall be investigated. A one-degree-of-freedom test for supra-multiplicativity, recently proposed by us, shall be applied to improve power of set-based rare variant interaction analysis. All tests shall be implemented in a stand-alone tool that allows Genome-wide application. The power of different approaches shall be compared via a simulation study.

• DFG Programme: Research Grants