Rare Variant Analysis of Next Generation Sequencing Studies with the Variable Binning / Variable Threshold (VB/VT) Collapsing Algorithm

• Applicant: Professor Michael Nothnagel, Ph.D., since 6/2016
• Subject Area: Epidemiology and Medical Biometry/Statistics
• Term: from 2015 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 279728737

Rare variants derived from next generation sequencing (NGS) studies are prime candidates to explain relevant portions of the so-called missing heritability of complex diseases. Respective statistical methods typically address multiple variants simultaneously and rely on a twofold parameterization of the analysis dataset: first, the choice of the threshold minor allele frequency to define rare and common variants, and, second, the definition of the SNP sets that shall be analyzed together (analysis bins). While the optimal definition of rareness can be obtained with the established variable threshold (VT) approach, a respective method for the choice of analysis bins was not proposed until now. This is an important methodological gap, since power is limited when selected analysis bins (exons, genes, haplotype blocks etc.) do not exactly correspond to associated regions. In a pilot study, we investigated the possibility to analyze all possible contiguous bins, i.e. bins with all possible combinations of start and end positions. While the theoretical number is high, in practice, the effective number of analysis bins is substantially lower for burden tests such as the collapsing method. We give the outline of an algorithm that efficiently identifies all effective bins, the variable binning (VB) algorithm. In addition, we plan to combine the VB algorithm with the VT approach. Adjustment for multiple testing shall be achieved within a Monte-Carlo simulation framework, adjustment for population stratification will be performed by localized genetic matching of cases and controls. Our goal is to provide an efficient implementation of the VB/VT algorithm that enables application to NGS studies as well as to combined GWAS/NGS studies on a Genome-wide scale. Power and validity of the algorithm will be evaluated via a simulation study and application to Exome-chip data and NGS data in collaboration with external partners.

• DFG Programme: Research Grants
• Ehemaliger Antragsteller: Privatdozent Dr. Tim Becker, until 5/2016