Abstract
The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study.
Highlights
In the past several years, genome-wide association studies (GWAS) have achieved great successes in identifying hundreds of genetic variants that affect dozens of complex diseases
We conducted a 2-D scan for a GWAS data set from the North American Rheumatoid Arthritis Consortium (NARAC) supplied by Genetic Analysis Workshop 16 (GAW16)
Data cleaning For GAW16 data quality control, we excluded those singlenucleotide polymorphism (SNP) whose Hardy-Weinberg equilibrium p-values < 0.001 or minor allele frequencies < 0.01, and excluded SNPs or individuals with missing rates >10%
Summary
In the past several years, genome-wide association studies (GWAS) have achieved great successes in identifying hundreds of genetic variants that affect dozens of complex diseases. As multiple genetic variations and environmental risk factors are expected to jointly affect a complex phenotype, it is natural to ask whether there is any benefit from conducting a joint marker analysis, e.g., a systematic study of all possible pairwise interactions, versus single-marker analysis [1]. Both simulations [2,3] and analytical studies [4] indicate that an exhaustive two-dimensional (2-D) scan may have higher statistical power under certain genetic models, e.g., when certain epistatic effects exist. We propose a two-stage analysis strategy that incorporates single-marker analysis, 2-D scan, and a multiple marker analysis using LASSO to balance statistical power and computational feasibility in GWAS analysis
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