Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study

Bose, Maitreyee; Wu, Chong; Pankow, James S; Demerath, Ellen W; Bressler, Jan; Fornage, Myriam; Grove, Megan L; Mosley, Thomas H; Hicks, Chindo; North, Kari; Kao, Wen Hong; Zhang, Yu; Boerwinkle, Eric; Guan, Weihua

doi:10.1186/1471-2105-15-312

Cited by 94Epigenetics and DNA MethylationGene expression and cancer classificationGenetic Associations and Epidemiology

Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study

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Bose, Maitreyee, Wu, Chong, Pankow, James S, Demerath, Ellen W, Bressler, Jan, Fornage, Myriam, Grove, Megan L, Mosley, Thomas H +6 more·2014·BMC Bioinformatics

DOI 10.1186/1471-2105-15-312PMC PMC4180315

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Abstract

BACKGROUND: DNA methylation is a widely studied epigenetic phenomenon; alterations in methylation patterns influence human phenotypes and risk of disease. As part of the Atherosclerosis Risk in Communities (ARIC) study, the Illumina Infinium HumanMethylation450 (HM450) BeadChip was used to measure DNA methylation in peripheral blood obtained from ~3000 African American study participants. Over 480,000 cytosine-guanine (CpG) dinucleotide sites were surveyed on the HM450 BeadChip. To evaluate the impact of technical variation, 265 technical replicates from 130 participants were included in the study. RESULTS: For each CpG site, we calculated the intraclass correlation coefficient (ICC) to compare variation of methylation levels within- and between-replicate pairs, ranging between 0 and 1. We modeled the distribution of ICC as a mixture of censored or truncated normal and normal distributions using an EM algorithm. The CpG sites were clustered into low- and high-reliability groups, according to the calculated posterior probabilities. We also demonstrated the performance of this clustering when applied to a study of association between methylation levels and smoking status of individuals. For the CpG sites showing genome-wide significant association with smoking status, most (~96%) were seen from sites in the high reliability cluster. CONCLUSIONS: We suggest that CpG sites with low ICC may be excluded from subsequent association analyses, or extra caution needs to be taken for associations at such sites.

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The mild correlation among first-generation epigenetic clock age gaps could be due to large technical noise in DNA methylation arrays, which limits robustness across datasets.
“This could be due to the large degree of technical noise in DNA methylation arrays152–154, which limits the robustness of applying these models across datasets.”
From Measuring biological age using omics data (2022) · cites this paper as [152]

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Additional file 3 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097344
Additional file 2 of Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control
Gunasekara, C., MacKay, H., Scott, C. A., Li, S., Laritsky, E., Baker, M. S., Grimm, S. L., Jun, G., Li, Y., Chen, R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610221
Additional file 2 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097338
Additional file 5 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097356
Additional file 2 of Variability of multi-omics profiles in a population-based child cohort
Gallego-Paüls, M., Hernandéz-Ferrer, C., Bustamante, M., Basagaña, X., Barrera‐Gómez, J., Lau, C. E., Siskos, A., Vives-Usano, M., Ruiz-Arenas, C., Wright, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15033205
Additional file 1 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097329
Additional file 4 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L. L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097353
Letter to the editor: critical evaluation of the reliability of DNA methylation probes on the illumina MethylationEPIC v1.0 BeadChip microarrays
Hecker, J., Weiss, S. T., Lasky-Su, J. A., DeMeo, D. L., & Lange, C. (2024) · Epigenetics · doi:10.1080/15592294.2024.2411470
Additional file 7 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097371
Additional file 6 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687262
Additional file 14 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687241
Additional file 3 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687253
Additional file 7 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687265
Additional file 1 of Variability of multi-omics profiles in a population-based child cohort
Gallego-Paüls, M., Hernández-Ferrer, C., Bustamante, M., Basagaña, X., Barrera-Gómez, J., Lau, C., Siskos, A. P., Vives-Usano, M., Ruiz-Arenas, C., Wright, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15033202
Additional file 4 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687256
Additional file 8 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687268
Additional file 10 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687229
Additional file 2 of Extensive intratumor regional epigenetic heterogeneity in clear cell renal cell carcinoma targets kidney enhancers and is associated with poor outcome
Khoury, L. Y. E., Pan, X., Hlady, R. A., Wagner, R. T., Shaikh, S., Wang, L., Humphreys, M. R., Castle, E. P., Stanton, M. L., Ho, T. H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26585937
Additional file 6 of Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing
Huang, J., Bai, L., Cui, B., Wu, L., Wang, L., An, Z., Ruan, S., Yu, Y., Zhang, X., & Chen, J. (2020) · Figshare · doi:10.6084/m9.figshare.12097362
Additional file 1 of Systemic interindividual epigenetic variation in humans is associated with transposable elements and under strong genetic control
Gunasekara, C., Mackay, H. A., Scott, C. A., Li, S., Laritsky, E., Baker, M. S., Grimm, S. L., Jun, G., Li, Y., Chen, R., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610218
Additional file 1 of Extensive intratumor regional epigenetic heterogeneity in clear cell renal cell carcinoma targets kidney enhancers and is associated with poor outcome
Khoury, L. Y. E., Pan, X., Hlady, R. A., Wagner, R. T., Shaikh, S., Wang, L., Humphreys, M. R., Castle, E. P., Stanton, M. L., Ho, T. H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26585934
Additional file 9 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687271
Additional file 2 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687250
Additional file 1 of A systematic evaluation of normalization methods and probe replicability using infinium EPIC methylation data
Welsh, H., Batalha, C. M. P. F., Li, W., Mpye, K. L., de Souza-Pinto, N. C., Naslavsky, M. S., & Parra, E. J. (2023) · Figshare · doi:10.6084/m9.figshare.22618201
Additional file 13 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687238
Additional file 12 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687235
Additional file 5 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687259
Additional file 1 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687247
Additional file 11 of Batch-effect detection, correction and characterisation in Illumina HumanMethylation450 and MethylationEPIC BeadChip array data
Ross, J. P., van Dijk, S. J., Phang, M., Skilton, M. R., Molloy, P. L., & Oytam, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19687232

Evaluating Reliability of DNA Methylation Measurement
Cao, R., & Guan, W. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1994-0_2 · Cited by 1 · see citing works
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Reliability of DNA methylation measures using Illumina methylation BeadChip
Xu, Z., & Taylor, J. A. (2020) · Figshare · doi:10.6084/m9.figshare.12851687
Reliability of DNA methylation measures from dried blood spots and mononuclear cells using the HumanMethylation450k BeadArray
Dugué, P., English, D. R., MacInnis, R. J., Jung, C., Bassett, J. K., FitzGerald, L. M., Wong, E. M., Joo, J. E., Hopper, J. L., Southey, M. C., et al. (2016) · Scientific Reports · doi:10.1038/srep30317 · Cited by 85 · see citing works
Abstract MP32: Epigenome-Wide DNA Methylation Profiling in a CVD Cohort: The ARIC Study
Pankow, J. S., Demerath, E. W., Guan, W., Fornage, M., Mosley, T. H., & Bressler, J. (2013) · Circulation · doi:10.1161/circ.127.suppl_12.amp32
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CpGFilter: model-based CpG probe filtering with replicates for epigenome-wide association studies
Chen, J., Just, A. C., Schwartz, J. D., Hou, L., Jafari, N., Sun, Z., Kocher, J., Baccarelli, A., & Lin, X. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv577 · Cited by 34 · see citing works

Authors: Bose, M., Wu, C., Pankow, J. S., Demerath, E. W., Bressler, J., Fornage, M., Grove, M. L., Mosley, T. H., Hicks, C., North, K., Kao, W. H., Zhang, Y., Boerwinkle, E., & Guan, W.
Affiliations: University of Minnesota (US)
The University of Texas Health Science Center at Houston (US)
University of Mississippi (US)
University of North Carolina at Chapel Hill (US)
Johns Hopkins University (US)
College of Saint Benedict and Saint John's University (US)
Corresponding author: Guan, Weihua
ORCID: Wu, Chong
Pankow, James S
Demerath, Ellen W
Bressler, Jan
Fornage, Myriam
Grove, Megan L
Mosley, Thomas H
Hicks, Chindo
North, Kari
Zhang, Yu
Boerwinkle, Eric
Guan, Weihua
Venue: BMC Bioinformatics
Publisher: Springer Science and Business Media LLC
Published: 2014-09-19
Volume: 15
Issue: 1
Pages: 312-312
ISSN: 1471-2105 (ISSN-L)
DOI: 10.1186/1471-2105-15-312
PMC: PMC4180315
OpenAlex: W2024362135
Cited by: 86
References: 34
Citations by year: 2026: 2 · 2025: 7 · 2024: 9 · 2023: 13 · 2022: 25 · 2021: 8 · 2020: 9 · 2018: 4 · 2017: 4 · 2016: 2 · 2015: 3
FWCI: 1.23
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: BMC Bioinformatics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, cc-by)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
UNC Libraries
Funders: University of Minnesota
National Institutes of Health (HHSN268201100012C, HHSN268201100008C, HHSN268201100006C, HHSN26820- 1100005C, HHSN268201100007C, HHSN268201100011C, HHSN268201100010C, HHSN268201100009C)
National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201100007I, HHSN268201100007C, HHSN2682 01100008C, HH-SN-268201100006C, HHSN268201100011C, HHSN2682011-00005I, HHSN2682011-00009I, HHSN268201100005C, HHSN268201100005G, HHSN268201100010C, HHSN268201100011I, HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C, HHSN26820 1100009C, HHSN2682011-00008I, RC2HL102419)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: DNA methylation, technical replicates, measurement reliability, intraclass correlation coefficient, CpG sites, technical variability, smoking status, epigenetics, African American participants, Atherosclerosis Risk in Communities Study
MeSH terms: Female, Humans, Male, Middle Aged, Residence Characteristics, Reproducibility of Results, Cluster Analysis, CpG Islands, DNA Methylation, Genetic Predisposition to Disease, Oligonucleotide Array Sequence Analysis, Atherosclerosis, Epigenomics

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Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study

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