Clique-Based Clustering of Correlated SNPs in a Gene Can Improve Performance of Gene-Based Multi-Bin Linear Combination Test

Yun Joo Yoo,Shelley B Bull,Sun Ah Kim

doi:10.1155/2015/852341

Abstract

Gene-based analysis of multiple single nucleotide polymorphisms (SNPs) in a gene region is an alternative to single SNP analysis. The multi-bin linear combination test (MLC) proposed in previous studies utilizes the correlation among SNPs within a gene to construct a gene-based global test. SNPs are partitioned into clusters of highly correlated SNPs, and the MLC test statistic quadratically combines linear combination statistics constructed for each cluster. The test has degrees of freedom equal to the number of clusters and can be more powerful than a fully quadratic or fully linear test statistic. In this study, we develop a new SNP clustering algorithm designed to find cliques, which are complete subnetworks of SNPs with all pairwise correlations above a threshold. We evaluate the performance of the MLC test using the clique-based CLQ algorithm versus using the tag-SNP-based LDSelect algorithm. In our numerical power calculations we observed that the two clustering algorithms produce identical clusters about 40~60% of the time, yielding similar power on average. However, because the CLQ algorithm tends to produce smaller clusters with stronger positive correlation, the MLC test is less likely to be affected by the occurrence of opposing signs in the individual SNP effect coefficients.

Highlights

Current genetic association studies aim to identify genetic variants responsible for a disease by investigating associations between single nucleotide polymorphisms (SNPs) and a trait of interest
Through extensive numerical power calculations for the multi-bin linear combination test (MLC) test under various causal-gene SNP-trait model scenarios, we show that the CLQ algorithm is highly suitable for incorporation into the MLC test
We reported that power of the MLC test depends on the correlation structure among SNPs and we postulated that clusters of strongly correlated SNPs with positive correlations benefit the test [13]

Summary

Introduction

Current genetic association studies aim to identify genetic variants responsible for a disease by investigating associations between single nucleotide polymorphisms (SNPs) and a trait of interest. When the gene is the unit of interest in the association analysis, gene-based analyses can be performed with multimarker methods using multi-SNP genotypes or haplotypes [6, 8,9,10]. SNP genotypes are analyzed in a multiple regression model and global statistics are constructed to represent the joint effects of multiple SNPs in a gene [3, 8, 12, 13] Some multimarker tests such as C-alpha [14], SKAT [11], and CMC tests [15] target rare variants with minor allele frequency (MAF) less than 1%. Multimarker tests such as SKAT-C [16] and the test by Curtis [10] can be applied to a combined set of rare (MAF < 1%), low frequency (1% ≤ MAF < 5%), and common (MAF ≥ 5%) variants

Methods

Results

Conclusion