A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms

Abstract

Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set. Genet. Epidemiol. 32:361-369, 2008.r 2008 Wiley-Liss, Inc.Key words: single nucleotide polymorphism; composite linkage disequilibrium; multiple testing correction; principal component analysis; eigenvalues INTRODUCTIONMultiple testing is a challenging issue for genetic data analysis. Candidate gene and genome-wide association studies involve statistical testing of not just a single hypothesis, but many. Even when the point-wise error rate (PWER, a p ) is set to a low level, the experiment-wise error rate (EWER, a e ) increases with the number of tests carried out. For this reason, strict significance thresholds have been recommended to control EWER [Risch and Merikangas, 1996]. However, an overly conservative approach may result in overlooking true positive signals, while an overly liberal criterion could produce excessive false positives. Šidák and Bonferroni corrections are popular approaches for controlling a e by specifying what a p values should be used for each individual test. The Šidák correction is calculated as a p ¼ 1 À ð1 À a e Þ 1=N , where N is the number of individual hypotheses to be tested [Šidák, 1967]. This correction assumes that the hypothesis tests are independent. Noting that ð1 À a p Þ N % 1 À Na p for small a p , we obtain the Bonferroni correction as Bonferroni, 1935Bonferroni, , 1936, which is an approximation to the Šidák correction.Recently, single nucleotide polymorphisms (SNPs), which are often densely genotyped, have become popular markers for genetic association studies. The closely spaced SNPs frequently yield high correlation because of extensive linkage disequilibrium (LD) among them [Wall and Pritchard, 2003]. Therefore, when association studies are conducted with many SNPs, the tests performed on each SNP are usually not independent, depending on the correlation structure among the SNPs. This violation of the independence assumption limits the Šidák and Bonferron...

Rapid and Robust Resampling-Based Multiple-Testing Correction with Application in a Genome-Wide Expression Quantitative Trait Loci Study

“…For approximation methods, we choose SimpleM (Gao et al 2008) to represent the approaches of estimating the number of independent tests, and SLIDE (Han et al 2009) to represent the approaches using the MVN framework. These two methods have been shown to be superior to other alternatives within their classes.…”

“…One approach is to estimate the effective number of independent tests from the eigenvalues of the correlation matrix of the SNPs (Cheverud 2001;Nyholt 2004;Li and Ji 2005;Gao et al 2008;Moskvina and Schmidt 2008;Pe'er et al 2008). Previous studies have shown that these methods may yield inaccurate results (Salyakina et al 2005;Han et al 2009).…”

Correction for multiple testing in a gene region

“…8 They found that extreme tail theory produced a more accurate estimate for M eff while still being computationally efficient. Cheverud and Nyholt's method is known to be overly conservative, 3,4 and Moskvina and Schmidt did not compare extreme tail theory with some of the more recent M eff methods as we do here. Thus, the relative merits of extreme tail theory versus more recently proposed M eff methods remain untested.…”

“…Gao et al's method estimates the M eff as the number of eigenvalues needed to explain a prespecified proportion of the sum of all of the eigenvalues. 3 They suggest that a threshold of 0.995 works well in most situations, although a higher or lower threshold would likely perform better depending on the LD structure of the gene region. We use Gao's recommendation of c ¼ 0.995 in our implementation.…”

“…A computationally efficient option, which is less conservative than the basic Bonferroni correction, is to calculate the effective number of independent SNPs (M effs ) in a gene region and to use this value in the Bonferroni correction. [1][2][3][4][5] One further option is to use extreme tail theory to explicitly calculate the probability of detecting a test statistic as large as or larger than the observed maximum test statistic in the gene region. 6 Although evaluations of methods to control for multiple testing exist, 6,7,8 the evaluations often have drawbacks.…”