Genetic association analysis using weighted false discovery rate approach on Genetic Analysis Workshop 18 data.

Xin Qiu,Wei Xu,Osvaldo Espin-Garcia,Xiaowei Shen,Geoffrey Liu,Abul Kalam Azad

doi:10.1186/1753-6561-8-s1-s76

Abstract

In a genome-wide association study, association between disease trait and hundreds of thousands of genetic markers are tested. Several methods have been proposed to control the false discovery rate in such high-throughput data to adjust for multiple hypotheses testing. For Genetic Analysis Workshop 18, we applied the method of false discovery rate control with p value weighting on family-based association tests on quantitative trait to detect association between single-nucleotide polymorphisms (SNPs) and mean arterial pressure. This method can improve statistical power by incorporating independent but relevant information about the research objective. Using the real genetic and phenotype data of chromosome 3 from Genetic Analysis Workshop 18, 1 SNP from gene CACNA2D3 was found to have significant association with mean arterial pressure.

Highlights

Recent developments in technologies have made it possible to collect a large amount of data and perform thousands of statistical tests on the data
We define the quantitative phenotype as mean arterial pressure (MAP) [6], which can be determined by baseline systolic blood pressure (SBP) and diastolic blood pressure (DBP) as follows: Yi
We propose a weighted false discovery rate (WFDR) method to adjust p values for family-based association test, which we believe we will more powerful than false discovery rate (FDR)

Summary

Introduction

Recent developments in technologies have made it possible to collect a large amount of data and perform thousands of statistical tests on the data. A lot of methods have been proposed to control multiple testing. The family-wise error rate control method is too stringent. The false discovery rate (FDR) method proposed by Benjamini and Hochberg [1] is more powerful but it treats all the tests without any adjustment. Genovese et al proposed the weighted false discovery rate (WFDR) control method [2] to obtain an FDR-adjusted p value by incorporating prior information about the hypotheses. For genome-wide association studies, the p values for multiple testing can be adjusted by the results from previous genetic linkage study with improved power [2]

Methods

Results

Conclusion