PCA-based bootstrap confidence interval tests for gene-disease association involving multiple SNPs
© Peng et al; licensee BioMed Central Ltd. 2010
Received: 6 December 2008
Accepted: 26 January 2010
Published: 26 January 2010
Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE) and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA) are preferable in this regard but their performance varies with methods of extracting principal components (PC s).
PCA-based bootstrap confidence interval test (PCA-BCIT), which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PC s, i.e., cases only(CAES), controls only(COES) and cases and controls combined(CES). Extraction of PC s with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test.
PCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.
Genetic association studies now customarily involve multiple SNPs in candidate genes or genomic regions and have a significant role in identifying and characterizing disease-predisposing variant(s). A critical challenge in their statistical analysis is how to make optimal use of all available information. Population-based case-control studies have been very popular and typically involve contingency table tests of SNP-disease association. Notably, the genotype-wise Armitage trend test does not require HWE and has equivalent power to its allele-wise counterpart under HWE[3, 4]. A thorny issue with individual tests of SNPs for linkage disequilibrium (LD) in such setting is multiple testing, however, methods for multiple testing adjustment assuming independence such as Bonferroni's[5, 6] is knowingly conservative. It is therefore necessary to seek alternative approaches which can utilize multiple SNPs simultaneously. The genotype-wise Armitage trend test is appealing since it is equivalent to the score test from logistic regression of case-control status on dosage of disease-predisposing alleles of SNP. However, testing for the effects of multiple SNPs simultaneously via logistic regression is no cure for difficulty with multicollinearity and curse of dimensionality. Haplotype-based methods have many desirable properties and could possibly alleviate the problem[11–14], but assumption of HWE is usually required and a potentially large number of degrees of freedom are involved[7, 11, 15–18].
It has recently been proposed that PCA can be combined with logistic regression test (LRT)[7, 16, 17] in a unified framework so that PCA is conducted first to account for between-SNP correlations in a candidate region, then LRT is applied as a formal test for the association between PC scores (linear combinations of the original SNPs) and disease. Since PC s are orthogonal, it avoids multicollinearity and at the meantime is less computer-intensive than haplotype-based methods. Studies have shown that PCA-LRT is at least as powerful as genotype- and haplotype-based methods[7, 16, 17]. Nevertheless, the power of PCA-based approaches vary with ways by which PC s are extracted, e.g., from genotype correlation, LD, or other kinds of metrics, and in principle can be employed in frameworks other than logistic regression[7, 16, 17]. Here we investigate ways of extracting PCs using genotype correlation matrix from different types of samples in a case-control study, while presenting a new approach testing for gene-disease association by direct use of PC scores in a PCA-based bootstrap confidence interval test (PCA-BCIT). We evaluated its performance via simulations and compared it with PCA-LRT and permutation test using real data.
where cov (F i , F j ) = 0, i ≠ j, and var(F1) ≥ var(F2) ≥ ⋯ ≥ var(F p ).
Methods of extracting PC s
Potentially, PCA can be conducted via four distinct extracting strategies (ES) using case-control data, i.e., 0. Calculate PC scores of individuals in cases and controls separately (SES), 1. Use cases only (CAES) to obtain loadings for calculation of PC scores for subjects in both cases and controls, 2. Use controls only (COES) to obtain the loadings for both groups, and 3. Use combined cases and controls (CES) to obtain the loadings for both groups. It is likely that in a case-control association study, loadings calculated from cases and controls can have different connotations and hence we only consider scenarios 1-3 hereafter. More formally, let (X1, X2, ⋯, X p ) and (Y1, Y2, ⋯, Y p ) be p-dimension vectors of SNPs at a given candidate region for cases and controls respectively, then we have,
Given a sample of N cases and M controls with p-SNP genotypes (X1, X2, ⋯, X N ) T , (Y1, Y2, ⋯, Y M ) T , and X i = (X1i, X2i, ⋯, x pi ) for the i th case, Y i = (Y1i, Y2i, ⋯, y pi ) for the i th control, a PCA-BCIT is furnished in three steps:
Step 1: Sampling
Replicate samples of cases and controls are obtained with replacement separately from (X1(b, X2(b), ⋯, X N (b)) T and (Y1(b, Y2(b), ⋯, Y M (b)) T , b = 1,2, ⋯, B (B = 1000).
Step 2: PCA
For each replicate sample obtained at Step 1, PCA is conducted and a given number of PC s retained with a threshold of 80% explained variance for all three strategies, expressed as and .
Step 3: PCA-BCIT
where is the percentile of , and is the percentile.
where is the percentile of , and is the percentile.
3c) Confidence intervals of cases and controls are compared. The null hypothesis is rejected if and do not overlap, which is and are statistically different, indicating the candidate region is significantly associated with disease at level α. Otherwise, the candidate region is not significantly associated with disease at level α.
Step 1: Sampling
The observed genotype frequencies in the study sample are taken to be their true frequencies in populations of infinite sizes. Replicate samples of cases and controls of given size (N, N = 100, 200, ⋯, 1000) are generated whose estimated genotype frequencies are expected to be close to the true population frequencies while both the allele frequencies and LD structure are maintained. Under null hypothesis, replicate cases and controls are sampled with replacement from the controls. Under alternative hypothesis, replicate cases and controls are sampled with replacement from the cases and controls respectively.
Step 2: PCA-BCITing
For each replicate sample, PCA-BCITs are conducted through the three strategies of extracting PC s as outlined above on association between PC scores and disease (RA).
Step 3: Evaluating performance of PCA-BCIT s
Repeat steps 1 and 2 for K ( K = 1000 ) times under both null and alternative hypotheses, and obtain the frequencies (P α ) of rejecting null hypothesis at level α (α = 0.05).
Performance of PCA-BCIT at level 0.05 with strategies 1-3†
Type I error
Armitage trend test on nine PTPN2 2 SNPs and RA susceptibility
PCA-BCIT, PCA-LRT and permutation test on real data
Sample characteristics of heroin-induced positive responses on first use
Cases (N= 91)
Controls (N= 245)
30.42 ± 7.65
30.93 ± 8.18
Age at onset (yrs)
26.29 ± 7.41
26.97 ± 7.89
Reason for first use of heroin
Armitage trend tests on nine OPRM1 SNPs and heroin-induced positive responses on first use
Count and frequency
Armitage trend test
In this study, a PCA-based bootstrap confidence interval test[19, 26–28] (PCA-BCIT) is developed to study gene-disease association using all SNPs genotyped in a given region. There are several attractive features of PCA-based approaches. First of all, they are at least as powerful as genotype- and haplotype-based methods[7, 16, 17]. Secondly, they are able to capture LD information between correlated SNPs and easy to compute with needless consideration of multicollinearity and multiple testing. Thirdly, BCIT integrates point estimation and hypothesis testing as a single inferential statement of great intuitive appeal and does not rely on the distributional assumption of the statistic used to calculate confidence interval[19, 26–29].
While there have been several different but closely related forms of bootstrap confidence interval calculations, we focus on percentiles of the asymptotic distribution of PC s for given confidence levels to estimate the confidence interval. PCA-BCIT is a data-learning method, and shown to be valid and powerful for sufficiently large number of replicates in our study. Our investigation involving three strategies of extracting PC s reveals that strategy 1 is invalid, while strategies 2 and 3 are acceptable. From analyses of real data we find that PCA-BCIT is more favourable compared with PCA-LRT and permutation test. It is suggested that a practical advantage of PCA-BCIT is that it offers an intuitive measure of difference between cases and controls by using the set of SNPs (PC scores) in a candidate region (Figure 3). As extraction of PC s through COES is more in line with the principle of a case-control study, it will be our method of choice given that it has a comparable performance with CES. Nevertheless, PCA-BCIT has the limitation that it does not directly handle covariates as is usually done in a regression model.
PCA-BCIT is both a valid and a powerful PCA-based method which captures multi-SNP information in study of gene-disease association. While extracting PC s based on CAES, COES and CES all have good performances, it appears that COES is more appropriate to use.
- SNP :
single nucleotide polymorphism
- HWE :
- LD :
- LRT :
logistic regression test
- PCA :
principle component analysis
- PC :
- ES :
- SES :
separate case and control extracting strategy (strategy 0)
- CAES :
case-based extracting strategy (strategy 1)
- COES :
control-based extracting strategy (strategy 2)
- CES :
combined case and control extracting strategy (strategy 3)
- BCIT :
bootstrap confidence interval test.
This work was supported by grant from the National Natural Science Foundation of China (30871392). We wish to thank Dr. Dandan Zhang (Fudan University) and NARAC for supplying us with the data, and comments from the Associate Editor and anonymous referees which greatly improved the manuscript. Special thanks to referee for the insightful comment that extraction of PC s with controls is line with the case-control principles.
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