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Cited by 389Epigenetics and DNA MethylationGenetic Associations and EpidemiologyGene expression and cancer classification

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A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies

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Lehne, Benjamin, Drong, Alexander W, Loh, Marie, Zhang, Weihua, Scott, William R, Tan, Sian-Tsung, Afzal, Uzma, Scott, James, Jarvelin, Marjo-Riitta, Elliott, Paul, McCarthy, Mark I, Kooner, Jaspal S, Chambers, John C·2015·Genome biology
DOI 10.1186/s13059-015-0600-xPMC PMC4365767
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Abstract

DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments.

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Figure 1 Marker call rates on the Y-chromosome. Distribution of call rates for 416 Y-chromosome markers in males (red points and red line) and females (green bars). Y-chromosome markers in females are represented in light green if their respective probes sequences are predicted to cross-hybridise with multiple genomic regions. Values greater than 80 are represented by numbers. (A) For a detection threshold P <0.05 more than 50% of Y-chromosome markers show non-zero call rates (call rate >0.05%) in females, even though females do not possess a Y chromosome. (B) For a detection threshold P <10−16 only 10% of Y-chromosome markers show non-zero call rates in females. Marker call rates in males (shown in red) are not materially affected by the more stringent detection P value threshold.
Figure 1 Marker call rates on the Y-chromosome. Distribution of call rates for 416 Y-chromosome markers in males (red points and red line) and females (green bars). Y-chromosome markers in females are represented in light green if their respective probes sequences are predicted to cross-hybridise with multiple genomic regions. Values greater than 80 are represented by numbers. (A) For a detection threshold P <0.05 more than 50% of Y-chromosome markers show non-zero call rates (call rate >0.05%) in females, even though females do not possess a Y chromosome. (B) For a detection threshold P <10−16 only 10% of Y-chromosome markers show non-zero call rates in females. Marker call rates in males (shown in red) are not materially affected by the more stringent detection P value threshold.CC BY · PubMed Central
Figure 2 Correcting for statistical inflation due to technical biases. Quantile-Quantile (QQ) plot for the comparison of 36 samples measured in duplicate reveals high statistical inflation before (Genomic Inflation Factor λraw = 12.74; brown points) and after quantile normalisation (λQN = 2.11; green points) due to technical biases. Batch-correction based on control probes removes technical biases and statistical inflation (λCP = 1.01; green points).
Figure 2 Correcting for statistical inflation due to technical biases. Quantile-Quantile (QQ) plot for the comparison of 36 samples measured in duplicate reveals high statistical inflation before (Genomic Inflation Factor λraw = 12.74; brown points) and after quantile normalisation (λQN = 2.11; green points) due to technical biases. Batch-correction based on control probes removes technical biases and statistical inflation (λCP = 1.01; green points).CC BY · PubMed Central
Figure 3 Prediction interval under the null hypothesis of no association. Quantile-quantile (QQ) prediction intervals for 1,000 permutations of the case–control status. The λ-value represents the median (2.5 percentile to 97.5 percentile) of all genomic inflation factors λ. (A) Quantile normalisation and adjustment for control probe PCs. Under no association we observe and overall statistical deflation, but also a high degree of statistical inflation for a small number of permutations. (B) Quantile normalisation, adjustment for control probe PCs, gender, age, white blood cells and PC1-5. Adjustments abolishes overall statistical deflation and results in a substantially more narrow prediction interval.
Figure 3 Prediction interval under the null hypothesis of no association. Quantile-quantile (QQ) prediction intervals for 1,000 permutations of the case–control status. The λ-value represents the median (2.5 percentile to 97.5 percentile) of all genomic inflation factors λ. (A) Quantile normalisation and adjustment for control probe PCs. Under no association we observe and overall statistical deflation, but also a high degree of statistical inflation for a small number of permutations. (B) Quantile normalisation, adjustment for control probe PCs, gender, age, white blood cells and PC1-5. Adjustments abolishes overall statistical deflation and results in a substantially more narrow prediction interval.CC BY · PubMed Central
Figure 4 Principal component analysis identifies global correlation patterns. We carried out a PCA of the methylation data (based on residuals after quantile normalisation and Control Probe Adjustment) to identify the primary patterns of covariation and used the PCs to explore relationships to biological factors such as measured (m) and estimated (e) white blood-cell subsets, gender, age and others. Colours represent P values for correlation of different factors with PCs 1 to 10.
Figure 4 Principal component analysis identifies global correlation patterns. We carried out a PCA of the methylation data (based on residuals after quantile normalisation and Control Probe Adjustment) to identify the primary patterns of covariation and used the PCs to explore relationships to biological factors such as measured (m) and estimated (e) white blood-cell subsets, gender, age and others. Colours represent P values for correlation of different factors with PCs 1 to 10.CC BY · PubMed Central
Figure 5 Workflow of CPACOR (incorporating Control Probe Adjustment and reduction of global CORrelation) for Epigenome-wide Association Studies using the Illumina HumanMethylation450 BeadChip. Signal intensities are filtered for multiple Quality Control criteria followed by quantile normalisation and calculation of beta values. In addition intensities from Illumina control probes are used to derive principal components (control-probe PCs). Based on beta values, proportions of white blood cell subpopulations are estimated and PCs are derived (from intermediary residuals; see Methods for details). To detect differential methylation regression analysis is performed for each methylation marker predicting disease status as a function of the (quantile normalised) beta value adjusted for technical and biological factors and PCs.
Figure 5 Workflow of CPACOR (incorporating Control Probe Adjustment and reduction of global CORrelation) for Epigenome-wide Association Studies using the Illumina HumanMethylation450 BeadChip. Signal intensities are filtered for multiple Quality Control criteria followed by quantile normalisation and calculation of beta values. In addition intensities from Illumina control probes are used to derive principal components (control-probe PCs). Based on beta values, proportions of white blood cell subpopulations are estimated and PCs are derived (from intermediary residuals; see Methods for details). To detect differential methylation regression analysis is performed for each methylation marker predicting disease status as a function of the (quantile normalised) beta value adjusted for technical and biological factors and PCs.CC BY · PubMed Central
Figure 6 Simulation analysis shows successive improvement of each stage of the CPACOR pipeline. We increased (‘spiked’) beta values of 100 randomly selected markers and determined the proportion of the spiked markers that were ranked among the top 100. The proportion of spiked markers achieving top 100 ranks is improved successively by each of the stages of the CPACOR pipeline.
Figure 6 Simulation analysis shows successive improvement of each stage of the CPACOR pipeline. We increased (‘spiked’) beta values of 100 randomly selected markers and determined the proportion of the spiked markers that were ranked among the top 100. The proportion of spiked markers achieving top 100 ranks is improved successively by each of the stages of the CPACOR pipeline.CC BY · PubMed Central

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Metadata

Authors
Lehne, B., Drong, A. W., Loh, M., Zhang, W., Scott, W. R., Tan, S.-T., Afzal, U., Scott, J., Jarvelin, M.-R., Elliott, P., McCarthy, M. I., Kooner, J. S., & Chambers, J. C.
Affiliations
  • Imperial College London (GB)
  • Centre for Human Genetics (GB)
  • Ealing Hospital NHS Trust (GB)
  • MRC Centre for Environment and Health (GB)
  • Imperial College Healthcare NHS Trust (GB)
Corresponding author
  • Lehne, Benjamin
  • Chambers, John C
ORCID
Venue
Genome biology
Publisher
Springer Science and Business Media LLC
Published
2015-02-15
Volume
16
Issue
1
Pages
37-37
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-015-0600-x
PMC
PMC4365767
OpenAlex
W4297674379
Cited by
380
References
31
Citations by year
2026: 22 · 2025: 40 · 2024: 42 · 2023: 43 · 2022: 43 · 2021: 54 · 2020: 43 · 2019: 36 · 2018: 26 · 2017: 16 · 2016: 12 · 2015: 2
FWCI
8.11
Type
journal article
Language
en
Update notices
correction (2016-04-21)
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (submittedVersion, cc-by)
  • Spiral (Imperial College London) (submittedVersion, other-oa)
  • Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
  • STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
Funders
  • Wellcome Trust (10371, 084723/Z/08/Z, 090532, 090532/Z/09/Z, 090532/Z/09/)
  • National Institute for Health Research Health Protection Research Unit
  • National Institute for Health and Care Research (NF-SI-0611-10136, RP-PG-0407-10371)
  • British Heart Foundation (090532/Z/09/Z, SP/04/002)
  • Imperial College London
  • Imperial College Healthcare NHS Trust
  • European Commission (279143)
  • MRC-PHE Centre for Environment and Health
  • Medical Research Council (G0900747 91070, Hub grant G0900747 91070, MR/L01341X/1, MR/K002414/1, G0601966, G0700931)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
DNA methylation, epigenome-wide association study, data quality control, data normalization, batch effect correction, permutation tests, null hypothesis, methylation quantitative trait loci, data analysis pipeline
MeSH terms
Humans, Software, Genome, Human, CpG Islands, DNA Methylation, Oligonucleotide Array Sequence Analysis, Quantitative Trait Loci, Epigenesis, Genetic, Genome-Wide Association Study, Epigenomics

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