Abstract
Genome-wide association studies often involve testing hundreds of thousands of single-nucleotide polymorphisms (SNPs). These tests may be highly correlated because of linkage disequilibrium among SNPs. Multiple testing correction ignoring the correlation among markers, as is done in the Bonferroni procedure, can cause loss of power. Several multiple testing adjustment methods accounting for correlations among tests have been developed and have shown improved power compared to the Bonferroni procedure. These methods include a Monte Carlo (MC) method and a method of computing p-values adjusted for correlated tests. The objective of this study is to apply these two multiple testing methods to genome-wide association study of the Genetic Analysis Workshop 16 rheumatoid arthritis data from the North American Rheumatoid Arthritis Consortium, to compare the performance of these two methods to the Bonferroni procedure in identifying susceptibility loci underlying rheumatoid arthritis, and to discuss the strengths and weaknesses of these methods. The results show that both the MC method and p-values adjusted for correlated tests method identified more significant SNPs, thus potentially have higher power than the corresponding Bonferroni methods using the same test statistics as in the MC method and p-values adjusted for correlated tests, respectively. Simulation studies demonstrate that the MC method may have slightly higher power than the p-values adjusted for correlated tests method.
Highlights
Genome-wide association studies (GWAS) for complex diseases involve multiple hypothesis testing
And Boehnke [1] proposed a method of computing p-values adjusted for correlated tests (p_ACT) by numerical integration of the asymptotic multivariate normal distribution of the test statistics
By using the Bonferroni procedure, we identified 634 and 589 significant single-nucleotide polymorphisms (SNPs) for the 22 chromosomes based on the statistics defined in Eqs. (1) and (3), respectively
Summary
Genome-wide association studies (GWAS) for complex diseases involve multiple hypothesis testing. To handle the correlation among test statistics, a permutation method [3] was proposed based on estimation of the joint distribution of test statistics This approach is computationally intensive and not (page number not for citation purposes). Lin [2] proposed a Monte Carlo (MC) sampling approach based on approximating the joint distribution of test statistics This method does not require repeated analyses of simulated datasets as in the permutation method, and is much less computationally demanding. And Boehnke [1] proposed a method of computing p-values adjusted for correlated tests (p_ACT) by numerical integration of the asymptotic multivariate normal distribution of the test statistics This approach is very computationally efficient and attains even greater speed. We compared the performance of these three procedures by simulation studies
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