Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data
© Chen et al.; licensee BioMed Central Ltd. 2013
Published: 23 January 2013
It is well known that DNA methylation, as an epigenetic factor, has an important effect on gene expression and disease development. Detecting differentially methylated loci under different conditions, such as cancer types or treatments, is of great interest in current research as it is important in cancer diagnosis and classification. However, inappropriate testing approaches can result in large false positives and/or false negatives. Appropriate and powerful statistical methods are desirable but very limited in the literature.
In this paper, we propose a nonparametric method to detect differentially methylated loci under multiple conditions for Illumina Array Methylation data. We compare the new method with other methods using simulated and real data. Our study shows that the proposed one outperforms other methods considered in this paper.
Due to the unique feature of the Illumina Array Methylation data, commonly used statistical tests will lose power or give misleading results. Therefore, appropriate statistical methods are crucial for this type of data. Powerful statistical approaches remain to be developed.
R codes are available upon request.
It is well known that DNA methylation has important effects on transcriptional regulation, chromosomal stability, genomic imprinting, and X-inactivation [1, 2]. It has been also shown to be associated with many human diseases, such as various types of cancer [3–11].
With the advances of BeadArray technology, genome-wide high-throughput methylation data can be easily generated by Illumina GoldenGate and Infinium Methylation Assays. After preprocessing steps, such as background correction and normalization, are applied to the raw fluorescent intensities, for each locus, from about 30 replicates in the same array a summarized β-value is generated as follows: , where M is the average signal from a methylated allele while U is that from unmethylated allele. The β -values are continuous numbers between 0 and 1, with 0 stands for totally unmethylated and 1 for completely methylated.
It has been shown that the β -value is rarely normally distributed [9, 12, 13]. Therefore the commonly used t-test for case control designs or ANOVA for multiple conditions are not the most powerful approaches when detecting differentially methylated loci. Observing this, Wang has proposed a model-based likelihood ratio test to detect differentially methylated loci for case and control data under the assumption that the β -value follows a three-component normal-uniform distribution . Wang showed that for some situations, their proposed test was better than the simple t-test based on simulation studies.
However, in their method, Wang did not consider the effect of age, which has been shown highly associated with methylation [14, 15]. Noticing the importance of age effect, one may use a linear regression with age included as a covariate when analyze methylation data with multiple conditions, such as cancer types. However, the underlying assumption of equal variances may not be satisfied . Therefore the commonly used linear regression method may not be appropriate.
In this paper, we consider methylation data with multiple conditions and propose a nonparametric method which incorporates the age effect in a way through the idea of combining p-values from independent tests [12, 16, 17]. More specifically, we first group subjects into several age groups based on their age; then for each age group, a nonparametric Kruskal-Wallis test is conducted for the given locus and the p-value is recorded. An overall p-value for that locus will be estimated through combining the p-values from all age groups. Using a real methylation data with three conditions and a simulation study, we show that the proposed test is more powerful than other methods, including linear regression.
Combined ANOVA test
Combined median test
Another nonparametric test is median test using the following statistic for each age group:
Combined welch test
Methods for combining p-values
Besides the Fisher method mentioned above, we also consider Z-test to combine p-values from independent tests. First we calculated the weighted Z statistic using individual p-values from each age group: , where n g is the total sample size in age group g and Φ is the cumulative distribution function (CDF) of the standard normal distribution. It is easy to see that this statistic has standard normal distribution under the null hypothesis. The overall p-value is calculated by 1- Φ(Z). Note that here we use one-sided test to obtain the overall p-value.
Estimated type I error rates at significance level 0.05 with 10000 replicates.
Distribution (sample sizes, parameters)
Beta (s = 30,30,30, a = 1,1,1, b = 2,2,2)
Beta (s = 30,30,30, a = 1,1,1, b = 10,10,10)
Beta (s = 30,30,30, a = 10,10,10, b = 1,1,1)
Beta (s = 30,30,30, a = 10,10,10, b = 10,10,10)
Beta (s = 20,30,40, a = 1,1,1, b = 2,2,2)
Beta (s = 20,30,40, a = 1,1,1, b = 10,10,10)
Beta (s = 20,30,40, a = 10,10,10, b = 1,1,1)
Beta (s = 20,30,40, a = 10,10,10, b = 10,10,10)
TN (s = 30,30,30, μ = 0.5,0.5, 0.5, σ2 = 0.1,0.1,0.1)
TN(s = 30,30,30, μ = 0.5, 0.5, 0.5, σ2 = 0.1,0.2,0.3)
TN (s = 20,30,40, μ = 0.5, 0.5, 0.5, σ2 = 0.1,0.1,0.1)
TN(s = 20,30,40, μ = 0.5, 0.5, 0.5, σ2 = 0.1,0.2,0.3)
Empirical power at significance level 0.05 with 10000 replicates.
Distribution (sample sizes, parameters)
Beta (s = 30, 30,30, a = 5,5,5,b = 20,25,30
Beta(s = 30, 30,30, a = 1.5,2,2.5, b = 20,20,20
Beta (s = 30, 30,30, a = 20,20,20, b = 1.5,2,2.5,
Beta (s = 20,30,40, a = 5,5,5, b = 20,25,30)
Beta (s = 20,30,40, a = 1.5,2,2.5, b = 20,20,20)
Beta (s = 20,30,40, a = 20,20,20, b = 1.5,2,2.5)
TN (s = 30, 30,30, μ = 0.45,0.5,0.55, σ2 = 0.2)
TN (s = 30, 30,30, μ = 0.45,0.5,0.55, σ2 = 0.1,0.2,0.3)
TN (s = 20,30,40, μ = 0.45,0.5,0.55, σ2 = 0.2)
TN (s = 20,30,40, μ = 0.45,0.5,0.55, σ2 = 0.1,0.2,0.3)
A real data set
We will use a real methylation data set, the United Kingdom Ovarian Cancer Population Study (UKOPS)  with 274 controls, 131 pre-treatment cases, and 135 post treatment cases, to compare the performance of the proposed test with others. Those methylation data were generated by the Illumina Infinium Huamn Methylaytion27 BeadChip and can be downloaded under accession number GSE19711 from the NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo).
Number of samples in age group by treatment group used in the paper after removing subjects with bs <4000 or coverage rate <95% or age >80.
Table 1 reports the estimated type I error rates from each method under different conditions. For most of the time, the estimated type I error rates are close to the nominal significance level as expected. Table 2 gives the empirical powers from each method. It can be seen that the non-parametric method of Mood's median test usually has the lowest powers in the simulations. None of the ANOVA, Welch and KW tests is uniformly most powerful. In words, their performances depend on the distributions from which the data are generated. From our simulation study, the KW test is usually as powerful as or more powerful than the ANOVA test. The true distributions of the β -value may vary from locus to locus; it is impossible to simulate all possible distributions. However, based on the observation of the real data, we know that the distributions of the β -value are far from the normal distribution, under which ANOVA is the best test. Therefore, we prefer nonparametric tests which are more robust.
Results from real data set
Number of significant differentially methylated loci detected for given cutoff p-value based on the real data.
Discussion and conclusions
Due to the unique feature of the β -value of methylation data, traditional statistical methods, such as linear regression and ANOVA test may not be appropriate. It has been shown that methylation is highly correlated with age; ignoring age effect may cause many false positives and/or false negatives. The effect of age may also not be linear; therefore we need a better way to account for this effect. In this paper, we use p-value combination method to deal with age effect. For each age group, we use nonparametric method to compare the treatment groups. It is important to find powerful and robust nonparametric methods for this sort of data. Although we found that KW method is more powerful than some other nonparametric methods for methylation data, it is desirable to find more powerful tests in this area. Furthermore, we want to point out that there are many other methods can be used to combine p-values [22, 23]; it may also be possible to find a more powerful method to combine p-values for Illumina Array Methylation data. However, based on our experiences, Fisher test is more robust and can be used in situations when a small portion of the p-values are very small; while the Z test is more powerful when the effect sizes are similar (e.g., the p-values don't differ much) for all of the age groups. Finally, although in this paper we use different cutoff p-values to compare the performance of tests, one may want to control the false positive rate. Several multiple comparison methods have been proposed for large scale data set to deal with the situations where the variables (loci) are not independent [24–28]. However, it remains to study which approach is more appropriate for the methylation data.
This article has been published as part of BMC Medical Genomics Volume 6 Supplement 1, 2013: Proceedings of the 2011 International Conference on Bioinformatics and Computational Biology (BIOCOMP'11). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/6/S1. Publication of this supplement has been supported by the International Society of Intelligent Biological Medicine.
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