publication . Article . 2019

In Epigenomic Studies, Including Cell-Type Adjustments in Regression Models Can Introduce Multicollinearity, Resulting in Apparent Reversal of Direction of Association

Sheila J. Barton; Sheila J. Barton; Phillip E. Melton; Phillip E. Melton; Philip Titcombe; Philip Titcombe; Robert Murray; Sebastian Rauschert; Karen A. Lillycrop; Karen A. Lillycrop; ...
Open Access English
  • Published: 01 Sep 2019 Journal: Frontiers in Genetics (issn: 1664-8021, Copyright policy)
  • Publisher: Frontiers Media S.A.
  • Country: United States
Abstract
Background: Association studies of epigenome-wide DNA methylation and disease can inform biological mechanisms. DNA methylation is often measured in peripheral blood, with heterogeneous cell types with different methylation profiles. Influences such as adiposity-associated inflammation can change cell-type proportions, altering measured blood methylation levels. To determine whether associations between loci-specific methylation and outcomes result from cellular heterogeneity, many studies adjust for estimated blood cell proportions, but high correlations between methylation and cell-type proportions could violate the statistical assumption of no multicollineari...
Subjects
free text keywords: epigenomics, houseman cell-type adjustments, statistical assumptions, multicollinearity, reversal of direction of association, Illumina 450K, Genetics, QH426-470, Original Research, Genetics(clinical), Molecular Medicine, Regression analysis, Methylation, DNA methylation, Regression, Biology, Variance inflation factor, Population, education.field_of_study, education, Andrology
Funded by
RCUK| Nutrition, Development and Lifelong Health: Studies in European Populations
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: MC_UU_12011/4
  • Funding stream: MRC
,
NHMRC| Determinants of Child Health and development: populations, partnerships, pathways and prevention
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 353514
  • Funding stream: Programs
,
NHMRC| Early Life Programming of Cardiovascular Disease
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 1053384
  • Funding stream: Early Career Fellowships
,
EC| LIFECYCLE
Project
LIFECYCLE
Early-life stressors and LifeCycle health
  • Funder: European Commission (EC)
  • Project Code: 733206
  • Funding stream: H2020 | RIA
,
NHMRC| Childhood Precursors of Adult Cardiovascular Disease, Obesity and Diabetes- 16 year follow up of a Longitudinal Cohort
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 403981
  • Funding stream: NHMRC Project Grants
20 references, page 1 of 2

Barton S. J.Crozier S. R.Lillycrop K. A.Godfrey K. M.Inskip H. M. (2013). Correction of unexpected distributions of P values from analysis of whole genome arrays by rectifying violation of statistical assumptions. BMC Genomics 14, 161. 10.1186/1471-2164-14-161 23496791 [OpenAIRE] [PubMed] [DOI]

Gujarati D.N.Porter D. C. (2009). Basic econometrics. New York: McGraw-Hill Irwin.

de Mello V. D.Pulkkinen L.Lalli M.Kolehmainen M.Pihlajamaki J.Uusitupa M. (2014). DNA methylation in obesity and type 2 diabetes. Ann. Med. 46 (3), 103–113. 10.3109/07853890.2013.857259 24779963 [PubMed] [DOI]

Du P.Zhang X.Huang C. C.Jafari N.Kibbe W. A.Hou L. (2010). Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 11, 587. 10.1186/1471-2105-11-587 [OpenAIRE] [DOI]

Garcia-Cardona M. C.Huang F.Garcia-Vivas J. M.Lopez-Camarillo C.Del Rio Navarro B. E.Navarro Olivos E. (2014). DNA methylation of leptin and adiponectin promoters in children is reduced by the combined presence of obesity and insulin resistance. Int. J. Obes. (Lond.) 38 (11), 1457–1465. 10.1038/ijo.2014.30 24549138 [OpenAIRE] [PubMed] [DOI]

Garcia A. H.Erler N. S.Jaddoe V. W. V.Tiemeier H.van den Hooven E. H.Franco O. H. (2017). 25-Hydroxyvitamin D concentrations during fetal life and bone health in children aged 6 years: a population-based prospective cohort study. Lancet Diabetes Endocrinol. 5 (5), 367–376. 10.1016/S2213-8587(17)30064-5 28259646 [OpenAIRE] [PubMed] [DOI]

Graham M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology 84 (11), 2809–2815. 10.1890/02-3114 [OpenAIRE] [DOI]

Holbrook J. D.Huang R. C.Barton S. J.Saffery R.Lillycrop K. A. (2017). Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies? Epigenomics. 9 (8), 1143–1150. 10.2217/epi-2017-0032 28749184 [OpenAIRE] [PubMed] [DOI]

Horvath S.Gurven M.Levine M. E.Trumble B. C.Kaplan H.Allayee H. (2016). An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 17 (1), 171. 10.1186/s13059-016-1030-0 27511193 [OpenAIRE] [PubMed] [DOI]

Houseman E. A.Accomando W. P.Koestler D. C.Christensen B. C.Marsit C. J.Nelson H. H. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 13, 86. 10.1186/1471-2105-13-86 [OpenAIRE] [DOI]

Houseman E. A.Kim S.Kelsey K. T.Wiencke J. K. (2015). DNA methylation in whole blood: uses and challenges. Curr. Environ. Health Rep. 2 (2), 145–154. 10.1007/s40572-015-0050-3 26231364 [OpenAIRE] [PubMed] [DOI]

Huang R. C.Burrows S.Mori T. A.Oddy W. H.Beilin L. J. (2015 a). Lifecourse adiposity and blood pressure between birth and 17 years old. Am. J. Hypertens. 28 (8), 1056–1063. 10.1093/ajh/hpu266 25600223 [OpenAIRE] [PubMed] [DOI]

Huang R. C.Garratt E. S.Pan H.Wu Y.Davis E. A.Barton S. J. (2015 b). Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Epigenetics 10 (11), 995–1005. 10.1080/15592294.2015.1080411 26646899 [OpenAIRE] [PubMed] [DOI]

Lillycrop K.Murray R.Cheong C.Teh A. L.Clarke-Harris R.Barton S. (2017). ANRIL promoter DNA methylation: A perinatal marker for later adiposity. EBioMedicine 19, 60–72. 10.1016/j.ebiom.2017.03.037 28473239 [OpenAIRE] [PubMed] [DOI]

Murray R.Bryant J.Titcombe P.Barton S. J.Inskip H.Harvey N. C. (2016). DNA methylation at birth within the promoter of ANRIL predicts markers of cardiovascular risk at 9 years. Clin. Epigenet. 8, 90. 10.1186/s13148-016-0259-5 [OpenAIRE] [DOI]

20 references, page 1 of 2
Abstract
Background: Association studies of epigenome-wide DNA methylation and disease can inform biological mechanisms. DNA methylation is often measured in peripheral blood, with heterogeneous cell types with different methylation profiles. Influences such as adiposity-associated inflammation can change cell-type proportions, altering measured blood methylation levels. To determine whether associations between loci-specific methylation and outcomes result from cellular heterogeneity, many studies adjust for estimated blood cell proportions, but high correlations between methylation and cell-type proportions could violate the statistical assumption of no multicollineari...
Subjects
free text keywords: epigenomics, houseman cell-type adjustments, statistical assumptions, multicollinearity, reversal of direction of association, Illumina 450K, Genetics, QH426-470, Original Research, Genetics(clinical), Molecular Medicine, Regression analysis, Methylation, DNA methylation, Regression, Biology, Variance inflation factor, Population, education.field_of_study, education, Andrology
Funded by
RCUK| Nutrition, Development and Lifelong Health: Studies in European Populations
Project
  • Funder: Research Council UK (RCUK)
  • Project Code: MC_UU_12011/4
  • Funding stream: MRC
,
NHMRC| Determinants of Child Health and development: populations, partnerships, pathways and prevention
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 353514
  • Funding stream: Programs
,
NHMRC| Early Life Programming of Cardiovascular Disease
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 1053384
  • Funding stream: Early Career Fellowships
,
EC| LIFECYCLE
Project
LIFECYCLE
Early-life stressors and LifeCycle health
  • Funder: European Commission (EC)
  • Project Code: 733206
  • Funding stream: H2020 | RIA
,
NHMRC| Childhood Precursors of Adult Cardiovascular Disease, Obesity and Diabetes- 16 year follow up of a Longitudinal Cohort
Project
  • Funder: National Health and Medical Research Council (NHMRC) (NHMRC)
  • Project Code: 403981
  • Funding stream: NHMRC Project Grants
20 references, page 1 of 2

Barton S. J.Crozier S. R.Lillycrop K. A.Godfrey K. M.Inskip H. M. (2013). Correction of unexpected distributions of P values from analysis of whole genome arrays by rectifying violation of statistical assumptions. BMC Genomics 14, 161. 10.1186/1471-2164-14-161 23496791 [OpenAIRE] [PubMed] [DOI]

Gujarati D.N.Porter D. C. (2009). Basic econometrics. New York: McGraw-Hill Irwin.

de Mello V. D.Pulkkinen L.Lalli M.Kolehmainen M.Pihlajamaki J.Uusitupa M. (2014). DNA methylation in obesity and type 2 diabetes. Ann. Med. 46 (3), 103–113. 10.3109/07853890.2013.857259 24779963 [PubMed] [DOI]

Du P.Zhang X.Huang C. C.Jafari N.Kibbe W. A.Hou L. (2010). Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform. 11, 587. 10.1186/1471-2105-11-587 [OpenAIRE] [DOI]

Garcia-Cardona M. C.Huang F.Garcia-Vivas J. M.Lopez-Camarillo C.Del Rio Navarro B. E.Navarro Olivos E. (2014). DNA methylation of leptin and adiponectin promoters in children is reduced by the combined presence of obesity and insulin resistance. Int. J. Obes. (Lond.) 38 (11), 1457–1465. 10.1038/ijo.2014.30 24549138 [OpenAIRE] [PubMed] [DOI]

Garcia A. H.Erler N. S.Jaddoe V. W. V.Tiemeier H.van den Hooven E. H.Franco O. H. (2017). 25-Hydroxyvitamin D concentrations during fetal life and bone health in children aged 6 years: a population-based prospective cohort study. Lancet Diabetes Endocrinol. 5 (5), 367–376. 10.1016/S2213-8587(17)30064-5 28259646 [OpenAIRE] [PubMed] [DOI]

Graham M. H. (2003). Confronting multicollinearity in ecological multiple regression. Ecology 84 (11), 2809–2815. 10.1890/02-3114 [OpenAIRE] [DOI]

Holbrook J. D.Huang R. C.Barton S. J.Saffery R.Lillycrop K. A. (2017). Is cellular heterogeneity merely a confounder to be removed from epigenome-wide association studies? Epigenomics. 9 (8), 1143–1150. 10.2217/epi-2017-0032 28749184 [OpenAIRE] [PubMed] [DOI]

Horvath S.Gurven M.Levine M. E.Trumble B. C.Kaplan H.Allayee H. (2016). An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 17 (1), 171. 10.1186/s13059-016-1030-0 27511193 [OpenAIRE] [PubMed] [DOI]

Houseman E. A.Accomando W. P.Koestler D. C.Christensen B. C.Marsit C. J.Nelson H. H. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 13, 86. 10.1186/1471-2105-13-86 [OpenAIRE] [DOI]

Houseman E. A.Kim S.Kelsey K. T.Wiencke J. K. (2015). DNA methylation in whole blood: uses and challenges. Curr. Environ. Health Rep. 2 (2), 145–154. 10.1007/s40572-015-0050-3 26231364 [OpenAIRE] [PubMed] [DOI]

Huang R. C.Burrows S.Mori T. A.Oddy W. H.Beilin L. J. (2015 a). Lifecourse adiposity and blood pressure between birth and 17 years old. Am. J. Hypertens. 28 (8), 1056–1063. 10.1093/ajh/hpu266 25600223 [OpenAIRE] [PubMed] [DOI]

Huang R. C.Garratt E. S.Pan H.Wu Y.Davis E. A.Barton S. J. (2015 b). Genome-wide methylation analysis identifies differentially methylated CpG loci associated with severe obesity in childhood. Epigenetics 10 (11), 995–1005. 10.1080/15592294.2015.1080411 26646899 [OpenAIRE] [PubMed] [DOI]

Lillycrop K.Murray R.Cheong C.Teh A. L.Clarke-Harris R.Barton S. (2017). ANRIL promoter DNA methylation: A perinatal marker for later adiposity. EBioMedicine 19, 60–72. 10.1016/j.ebiom.2017.03.037 28473239 [OpenAIRE] [PubMed] [DOI]

Murray R.Bryant J.Titcombe P.Barton S. J.Inskip H.Harvey N. C. (2016). DNA methylation at birth within the promoter of ANRIL predicts markers of cardiovascular risk at 9 years. Clin. Epigenet. 8, 90. 10.1186/s13148-016-0259-5 [OpenAIRE] [DOI]

20 references, page 1 of 2
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