Complex trait methylation scores in the prediction of major depressive disorder

Miruna C. Barbu; Carmen Amador; Genetics of DNA Methylation Consortium (GoDMC); Alex S.F. Kwong; Xueyi Shen; Mark J. Adams; David M. Howard; Rosie M. Walker; Stewart W. Morris; Josine L. Min; Chunyu Liu; Jenny van Dongen; Mohsen Ghanbari; Caroline Relton; David J. Porteous; Archie Campbell; Kathryn L. Evans; Heather C. Whalley; Andrew M. McIntosh

doi:10.1016/j.ebiom.2022.104000

Complex trait methylation scores in the prediction of major depressive disorder

Miruna C. Barbu^*, Carmen Amador, Genetics of DNA Methylation Consortium (GoDMC), Alex S.F. Kwong, Xueyi Shen, Mark J. Adams, David M. Howard, Rosie M. Walker, Stewart W. Morris, Josine L. Min, Chunyu Liu, Jenny van Dongen, Mohsen Ghanbari, Caroline Relton, David J. Porteous, Archie Campbell, Kathryn L. Evans, Heather C. Whalley, Andrew M. McIntosh

^*Corresponding author for this work

Epidemiology

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Background: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. Methods: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPAC_adults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). Findings: Each trait MS was significantly associated with its corresponding phenotype in GS (β_range=0.089–1.457) and in ALSPAC (β_range=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (β_range=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MS_p-value<0.01–0.5; β_range=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. Interpretation: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (p_nominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (N_ALSPAC=565; N_GS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. Funding: Wellcome Trust.

Original language	English
Article number	104000
Journal	EBioMedicine
Volume	79
DOIs	https://doi.org/10.1016/j.ebiom.2022.104000
Publication status	Published - May 2022

Bibliographical note

Acknowledgments
This research was funded in whole, or in part, by the Wellcome Trust [216767/Z/19/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Generation Scotland is currently supported by the Wellcome Trust [216767/Z/19/Z] and by the Wellcome Trust Investigator Award in Science 01/06/2021 to 31/05/26 ‘Exploiting genomic approaches to identify the environmental basis of depression’. (Reference: 220857/Z/20/Z) to McIntosh AM (PI). The DNA methylation profiling and data preparation was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI for both grants: McIntosh AM) and through funding from NARSAD (Ref: 27404; PI: Dr DM Howard and Ref: 21956; PI Dr Kathryn Evans) and the Royal College of Physicians of Edinburgh (Sim Fellowship; PI: Dr HC Whalley). Genotyping of the GS:SFHS samples was funded by the MRC and Wellcome Trust [104036/Z/14/Z]. Generation Scotland also receives support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Dr DM Howard is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z). Dr M Barbu is supported by a Guarantors of Brain Non-clinical Post-Doctoral Fellowship.

The UK Medical Research Council and Wellcome (Grant Ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Part of this data was collected using REDCap, see the REDCap website for details https://projectredcap.org/resources/citations/).

Publisher Copyright: © 2022 The Authors

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Barbu, M. C., Amador, C., Genetics of DNA Methylation Consortium (GoDMC), Kwong, A. S. F., Shen, X., Adams, M. J., Howard, D. M., Walker, R. M., Morris, S. W., Min, J. L., Liu, C., van Dongen, J., Ghanbari, M., Relton, C., Porteous, D. J., Campbell, A., Evans, K. L., Whalley, H. C., & McIntosh, A. M. (2022). Complex trait methylation scores in the prediction of major depressive disorder. EBioMedicine, 79, Article 104000. https://doi.org/10.1016/j.ebiom.2022.104000

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title = "Complex trait methylation scores in the prediction of major depressive disorder",

abstract = "Background: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. Methods: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). Findings: Each trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089–1.457) and in ALSPAC (βrange=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01–0.5; βrange=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. Interpretation: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. Funding: Wellcome Trust.",

author = "Barbu, {Miruna C.} and Carmen Amador and {Genetics of DNA Methylation Consortium (GoDMC)} and Kwong, {Alex S.F.} and Xueyi Shen and Adams, {Mark J.} and Howard, {David M.} and Walker, {Rosie M.} and Morris, {Stewart W.} and Min, {Josine L.} and Chunyu Liu and {van Dongen}, Jenny and Mohsen Ghanbari and Caroline Relton and Porteous, {David J.} and Archie Campbell and Evans, {Kathryn L.} and Whalley, {Heather C.} and McIntosh, {Andrew M.}",

note = "Acknowledgments This research was funded in whole, or in part, by the Wellcome Trust [216767/Z/19/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Generation Scotland is currently supported by the Wellcome Trust [216767/Z/19/Z] and by the Wellcome Trust Investigator Award in Science 01/06/2021 to 31/05/26 {\textquoteleft}Exploiting genomic approaches to identify the environmental basis of depression{\textquoteright}. (Reference: 220857/Z/20/Z) to McIntosh AM (PI). The DNA methylation profiling and data preparation was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI for both grants: McIntosh AM) and through funding from NARSAD (Ref: 27404; PI: Dr DM Howard and Ref: 21956; PI Dr Kathryn Evans) and the Royal College of Physicians of Edinburgh (Sim Fellowship; PI: Dr HC Whalley). Genotyping of the GS:SFHS samples was funded by the MRC and Wellcome Trust [104036/Z/14/Z]. Generation Scotland also receives support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Dr DM Howard is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z). Dr M Barbu is supported by a Guarantors of Brain Non-clinical Post-Doctoral Fellowship. The UK Medical Research Council and Wellcome (Grant Ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Part of this data was collected using REDCap, see the REDCap website for details https://projectredcap.org/resources/citations/). Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2022",

month = may,

doi = "10.1016/j.ebiom.2022.104000",

language = "English",

volume = "79",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

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Barbu, MC, Amador, C, Genetics of DNA Methylation Consortium (GoDMC), Kwong, ASF, Shen, X, Adams, MJ, Howard, DM, Walker, RM, Morris, SW, Min, JL, Liu, C, van Dongen, J, Ghanbari, M, Relton, C, Porteous, DJ, Campbell, A, Evans, KL, Whalley, HC & McIntosh, AM 2022, 'Complex trait methylation scores in the prediction of major depressive disorder', EBioMedicine, vol. 79, 104000. https://doi.org/10.1016/j.ebiom.2022.104000

TY - JOUR

T1 - Complex trait methylation scores in the prediction of major depressive disorder

AU - Barbu, Miruna C.

AU - Amador, Carmen

AU - Genetics of DNA Methylation Consortium (GoDMC)

AU - Kwong, Alex S.F.

AU - Shen, Xueyi

AU - Adams, Mark J.

AU - Howard, David M.

AU - Walker, Rosie M.

AU - Morris, Stewart W.

AU - Min, Josine L.

AU - Liu, Chunyu

AU - van Dongen, Jenny

AU - Ghanbari, Mohsen

AU - Relton, Caroline

AU - Porteous, David J.

AU - Campbell, Archie

AU - Evans, Kathryn L.

AU - Whalley, Heather C.

AU - McIntosh, Andrew M.

N1 - Acknowledgments This research was funded in whole, or in part, by the Wellcome Trust [216767/Z/19/Z]. For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. Generation Scotland is currently supported by the Wellcome Trust [216767/Z/19/Z] and by the Wellcome Trust Investigator Award in Science 01/06/2021 to 31/05/26 ‘Exploiting genomic approaches to identify the environmental basis of depression’. (Reference: 220857/Z/20/Z) to McIntosh AM (PI). The DNA methylation profiling and data preparation was supported by Wellcome Investigator Award 220857/Z/20/Z and Grant 104036/Z/14/Z (PI for both grants: McIntosh AM) and through funding from NARSAD (Ref: 27404; PI: Dr DM Howard and Ref: 21956; PI Dr Kathryn Evans) and the Royal College of Physicians of Edinburgh (Sim Fellowship; PI: Dr HC Whalley). Genotyping of the GS:SFHS samples was funded by the MRC and Wellcome Trust [104036/Z/14/Z]. Generation Scotland also receives support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Dr DM Howard is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Reference 213674/Z/18/Z). Dr M Barbu is supported by a Guarantors of Brain Non-clinical Post-Doctoral Fellowship. The UK Medical Research Council and Wellcome (Grant Ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Part of this data was collected using REDCap, see the REDCap website for details https://projectredcap.org/resources/citations/). Publisher Copyright: © 2022 The Authors

PY - 2022/5

Y1 - 2022/5

N2 - Background: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. Methods: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). Findings: Each trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089–1.457) and in ALSPAC (βrange=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01–0.5; βrange=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. Interpretation: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. Funding: Wellcome Trust.

AB - Background: DNA methylation (DNAm) is associated with time-varying environmental factors that contribute to major depressive disorder (MDD) risk. We sought to test whether DNAm signatures of lifestyle and biochemical factors were associated with MDD to reveal dynamic biomarkers of MDD risk that may be amenable to lifestyle interventions. Methods: Here, we calculated methylation scores (MS) at multiple p-value thresholds for lifestyle (BMI, smoking, alcohol consumption, and educational attainment) and biochemical (high-density lipoprotein (HDL) and total cholesterol) factors in Generation Scotland (GS) (N=9,502) and in a replication cohort (ALSPACadults, N=565), using CpG sites reported in previous well-powered methylome-wide association studies. We also compared their predictive accuracy for MDD to a MDD MS in an independent GS sub-sample (N=4,432). Findings: Each trait MS was significantly associated with its corresponding phenotype in GS (βrange=0.089–1.457) and in ALSPAC (βrange=0.078–2.533). Each MS was also significantly associated with MDD before and after adjustment for its corresponding phenotype in GS (βrange=0.053–0.145). After accounting for relevant lifestyle factors, MS for educational attainment (β=0.094) and alcohol consumption (MSp-value<0.01–0.5; βrange=-0.069–0.083) remained significantly associated with MDD in GS. Smoking (AUC=0.569) and educational attainment (AUC=0.585) MSs could discriminate MDD from controls better than the MDD MS (AUC=0.553) in the independent GS sub-sample. Analyses implicating MDD did not replicate across ALSPAC, although the direction of effect was consistent for all traits when adjusting for the MS corresponding phenotypes. Interpretation: We showed that lifestyle and biochemical MS were associated with MDD before and after adjustment for their corresponding phenotypes (pnominal<0.05), but not when smoking, alcohol consumption, and BMI were also included as covariates. MDD results did not replicate in the smaller, female-only independent ALSPAC cohort (NALSPAC=565; NGS=9,502), potentially due to demographic differences or low statistical power, but effect sizes were consistent with the direction reported in GS. DNAm scores for modifiable MDD risk factors may contribute to disease vulnerability and, in some cases, explain additional variance to their observed phenotypes. Funding: Wellcome Trust.

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DO - 10.1016/j.ebiom.2022.104000

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VL - 79

JO - EBioMedicine

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M1 - 104000

ER -

Complex trait methylation scores in the prediction of major depressive disorder

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