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.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).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.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.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.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.CC BY · PubMed Central

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Du, P., Kibbe, W. A., & Lin, S. M. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn224 · Cited by 2,028
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Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J. M., Delano, D., Zhang, L., Schroth, G. P., Gunderson, K. L., et al. (2011) · Genomics · doi:10.1016/j.ygeno.2011.07.007 · Cited by 1,727 · cited 6×
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Teschendorff, A. E., Marabita, F., Lechner, M., Bartlett, T. E., Tegner, J. N., Gómez-Cabrero, D., & Beck, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts680 · Cited by 1,686
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Kulis, M., & Esteller, M. (2010) · Advances in genetics · doi:10.1016/b978-0-12-380866-0.60002-2 · Cited by 1,617
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Chen, Y. A., Lemire, M., Choufani, S., Butcher, D. T., Grafodatskaya, D., Zanke, B. W., Gallinger, S., Hudson, T. J., & Weksberg, R. (2013) · Epigenetics · doi:10.4161/epi.23470 · Cited by 1,547
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Additional file 1 of Variability of multi-omics profiles in a population-based child cohort
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Additional file 1 of Associations of circulating folate, vitamin B12 and homocysteine concentrations in early pregnancy and cord blood with epigenetic gestational age: the Generation R Study
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Additional file 2 of Long-term impact of paediatric critical illness on the difference between epigenetic and chronological age in relation to physical growth
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Additional file 1 of Refining epigenetic prediction of chronological and biological age
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Additional file 1 of Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits
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Additional file 1 of Long-term impact of paediatric critical illness on the difference between epigenetic and chronological age in relation to physical growth
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Additional file 2 of Refining epigenetic prediction of chronological and biological age
Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., Wrobel, N., Campbell, A., Harris, S. E., Liewald, D. C. M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571943
Additional file 3 of Refining epigenetic prediction of chronological and biological age
Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., Wrobel, N., Campbell, A., Harris, S. E., Liewald, D. C. M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571946
Additional file 1 of Technical and biological sources of unreliability of Infinium probes on Illumina methylation microarrays
Nazarenko, T., Vavourakis, C. D., Jones, A., Evans, I., Schreiberhuber, L., Kastner, C., Ishaq-Parveen, I., Redl, E., Watson, A. W., Brandt, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27049192
Additional file 2 of Pre-adolescence DNA methylation is associated with BMI status change from pre- to post-adolescence
Wang, J., Zhang, H., Rezwan, F. I., Relton, C. L., Arshad, S. H., & Holloway, J. W. (2021) · Figshare · doi:10.6084/m9.figshare.14313719
Additional file 1 of Novel DNA methylation signatures of tobacco smoking with trans-ethnic effects
Christiansen, C., Castillo-Fernandez, J. E., Domingo‐Relloso, A., Zhao, W., El-Sayed Moustafa, J. S., Tsai, P.-C., Maddock, J., Haack, K., Cole, S. A., Kardia, S. L. R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14053459
Additional file 4 of Refining epigenetic prediction of chronological and biological age
Bernabeu, E., McCartney, D. L., Gadd, D. A., Hillary, R. F., Lu, A. T., Murphy, L., Wrobel, N., Campbell, A., Harris, S. E., Liewald, D. C. M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26571949
Additional file 1 of BMI trajectory in childhood is associated with asthma incidence at young adulthood mediated by DNA methylation
Rathod, R., Zhang, H., Karmaus, W. J. J., Ewart, S., Kadalayil, L., Relton, C. L., Ring, S. M., Arshad, S. H., & Holloway, J. W. (2021) · Figshare · doi:10.6084/m9.figshare.15047802
Additional file 1 of Leveraging locus-specific epigenetic heterogeneity to improve the performance of blood-based DNA methylation biomarkers
Miller, B. F., Pisanic II, T. R., Margolin, G., Petrykowska, H. M., Athamanolap, P., Goncearenco, A., Osei-Tutu, A., Annunziata, C. M., Wang, T.-H., & Elnitski, L. L. (2020) · Figshare · doi:10.6084/m9.figshare.13120690
Additional file 1 of Epigenome-wide association study of whole blood gene expression in Framingham Heart Study participants provides molecular insight into the potential role of CHRNA5 in cigarette smoking-related lung diseases
Yao, C., Joehanes, R., Wilson, R., Tanaka, T., Ferrucci, L., Kretschmer, A., Prokisch, H., Schramm, K., Gieger, C., Peters, A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14267103
Additional file 3 of Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth
Alfano, R., Zugna, D., Barros, H., Bustamante, M., Chatzi, L., Ghantous, A., Herceg, Z., Keski‐Rahkonen, P., de Kok, T. M. C. M., Nawrot, T. S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26560613
Additional file 1 of Epigenetic age acceleration is associated with cardiometabolic risk factors and clinical cardiovascular disease risk scores in African Americans
Ammous, F., Zhao, W., Ratliff, S. M., Mosley, T. H., Bielak, L. F., Zhou, X., Peyser, P. A., Kardia, S. L. R., & Smith, J. A. (2021) · Figshare · doi:10.6084/m9.figshare.14227988
Additional file 2 of Technical and biological sources of unreliability of Infinium probes on Illumina methylation microarrays
Nazarenko, T., Vavourakis, C. D., Jones, A., Evans, I., Schreiberhuber, L., Kastner, C., Ishaq-Parveen, I., Redl, E., Watson, A. W., Brandt, K., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27049195
Additional file 3 of Long-term impact of paediatric critical illness on the difference between epigenetic and chronological age in relation to physical growth
Verlinden, I., Coppens, G., Vanhorebeek, I., Güiza, F., Derese, I., Wouters, P. J., Joosten, K. F., Verbruggen, S. C. A. T., & Van den Berghe, G. (2023) · Figshare · doi:10.6084/m9.figshare.22602389
Additional file 2 of Epigenome-wide association study of dietary fatty acid intake
Lange de Luna, J., Nounu, A., Neumeyer, S., Sinke, L., Wilson, R., Hellbach, F., Matías‐García, P. R., Delerue, T., Winkelmann, J., Peters, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26681938
Additional file 1 of Pre-adolescence DNA methylation is associated with BMI status change from pre- to post-adolescence
Wang, J., Zhang, H., Rezwan, F. I., Relton, C. L., Arshad, S. H., & Holloway, J. W. (2021) · Figshare · doi:10.6084/m9.figshare.14313713
Additional file 1 of Epigenome-wide association study of dietary fatty acid intake
Lange de Luna, J., Nounu, A., Neumeyer, S., Sinke, L., Wilson, R., Hellbach, F., Matías‐García, P. R., Delerue, T., Winkelmann, J., Peters, A., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26681935
Additional file 1 of Fetal exposure to phthalates and bisphenols and DNA methylation at birth: the Generation R Study
Sol, C. M., Gaylord, A., Santos, S., Jaddoe, V. W. V., Felix, J. F., & Trasande, L. (2022) · Figshare · doi:10.6084/m9.figshare.21310664
Additional file 1 of Sex differences in the intergenerational link between maternal and neonatal whole blood DNA methylation: a genome-wide analysis in 2 birth cohorts
Hu, J., Xu, X., Li, J., Jiang, Y., Hong, X., Rexrode, K. M., Wang, G., Hu, F. B., Zhang, H., Karmaus, W. J., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22622806
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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: Loh, Marie
Zhang, Weihua
Scott, William R
Afzal, Uzma
Scott, James
Elliott, Paul
McCarthy, Mark I
Kooner, Jaspal S
Chambers, John C
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies

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