Abstract
BackgroundIdentifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) arising from genetic background are often dependent. The existing methods, i.e., local index of significance (LIS) and pooled local index of significance (PLIS), were both proposed for modeling SNP dependence and assumed that the whole chromosome follows a hidden Markov model (HMM). However, the fact that SNP data are often collected from separate heterogeneous regions of a single chromosome encourages different chromosomal regions to follow different HMMs. In this research, we developed a data-driven penalized criterion combined with a dynamic programming algorithm to find change points that divide the whole chromosome into more homogeneous regions. Furthermore, we extended PLIS to analyze the dependent tests obtained from multiple chromosomes with different regions for GWAS.ResultsThe simulation results show that our new criterion can improve the performance of the model selection procedure and that our region-specific PLIS (RSPLIS) method is better than PLIS at detecting disease-associated SNPs when there are multiple change points along a chromosome. Our method has been used to analyze the Daly study, and compared with PLIS, RSPLIS yielded results that more accurately detected disease-associated SNPs.ConclusionsThe genomic rankings based on our method differ from the rankings based on PLIS. Specifically, for the detection of genetic variants with weak effect sizes, the RSPLIS method was able to rank them more efficiently and with greater power.
Highlights
Identifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS)
Simulation study we design the detailed simulation studies to illustrate the performance of our Adaptive criterion-based partitioning (ACP) method in model selection; thereafter we conducted simulation studies to compare the performance of the proposed region-specific PLIS (RSPLIS) with that of pooled local index of significance (PLIS) in GWAS
Simulations of the ACP method performance for model selection Simulations in this subsection were conducted to compare the performance of our ACP method with that of BICbased partitioning (BICP) method for selecting change points and the number of components
Summary
Identifying genetic variants associated with complex human diseases is a great challenge in genome-wide association studies (GWAS). The false discovery rate (FDR) for controlling such procedures, which was introduced in a seminal paper [1], is one of the most important methodological developments in multiple hypothesis testing and has played successful role in many large-scale multiple testing studies Such studies include multi-stage clinical trials, microarray experiments, brain imaging studies, and astronomical surveys, Wei and Li pointed out that genomic dependency information could significantly improve the efficiency of analysis of large-scale genomic data [12,13]. Sun and Cai [14] proposed a local index of significance (LIS) controlling procedure that uses a hidden Markov model (HMM) to represent the dependence structure, and has shown its optimality under certain conditions and its strong empirical performance This LIS procedure was extended to pooled local index of significance (PLIS) procedure for multiple-chromosome analysis [15], where the authors developed chromosome-specific HMMs for analysis of the SNP data arising from large-scale GWAS. Instead of HMM, Li [16] introduced a hidden Markov random field model (HMRFM) to account for LD when analyzing the SNP data from GWAS
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