Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

Veronika V. Odintsova; Valerie Rebattu; Fiona A. Hagenbeek; René Pool; Jeffrey J. Beck; Erik A. Ehli; Catharina E.M. van Beijsterveldt; Lannie Ligthart; Gonneke Willemsen; Eco J.C. de Geus; Jouke Jan Hottenga; Dorret I. Boomsma; Jenny van Dongen; BIOS Consortium

doi:10.3389/fpsyt.2021.688464

Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

Veronika V. Odintsova^*
, Valerie Rebattu
, Fiona A. Hagenbeek
, René Pool
, Jeffrey J. Beck
, Erik A. Ehli
, Catharina E.M. van Beijsterveldt
, Lannie Ligthart
, Gonneke Willemsen
, Eco J.C. de Geus
, Jouke Jan Hottenga
, Dorret I. Boomsma
, Jenny van Dongen
, BIOS Consortium

^*Corresponding author for this work

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

Abstract

We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

Original language	English
Article number	688464
Pages (from-to)	1-17
Number of pages	17
Journal	Frontiers in Psychiatry
Volume	12
Issue number	July
Early online date	29 Jul 2021
DOIs	https://doi.org/10.3389/fpsyt.2021.688464
Publication status	Published - Jul 2021

Bibliographical note

Publisher Copyright:
© Copyright © 2021 Odintsova, Rebattu, Hagenbeek, Pool, Beck, Ehli, van Beijsterveldt, Ligthart, Willemsen, de Geus, Hottenga, Boomsma and van Dongen.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

We would like to thank the twins and their family members for their participation. Funding. We acknowledge funding from the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI?NL, 184.033.111) and the BBMRI-NL funded BIOS Consortium (NWO184.021.007); and Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies project (ACTION). ACTION received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no 602768. The Netherlands Twin Register is supported by grant NWO 480-15-001/674: Netherlands Twin Registry Repository: researching the interplay between genome and environment, the Avera Institute for Human Genetics and by multiple grants from the Netherlands Organization for Scientific Research (NWO). Genotyping was made possible by grants from NWO/SPI 56-464-14192, Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health, Rutgers University Cell and DNA Repository, the Avera Institute, Sioux Falls (USA) and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995) and European Research Council (ERC-230374). Epigenetic data were generated at the HUMAN GENOMICS FACILITY (HUGE-F) at EUR. JD was supported by NWO Large Scale infrastructures, X-Omics (184.034.019). DB acknowledges the Royal Netherlands Academy of Science Professor Award (PAH/6635). We acknowledge funding from the Netherlands Organization for Scientific Research (NWO): Biobanking and Biomolecular Research Infrastructure (BBMRI–NL, 184.033.111) and the BBMRI-NL funded BIOS Consortium (NWO184.021.007); and Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies project (ACTION). ACTION received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no 602768. The Netherlands Twin Register is supported by grant NWO 480-15-001/674: Netherlands Twin Registry Repository: researching the interplay between genome and environment, the Avera Institute for Human Genetics and by

Funders	Funder number
Sioux Falls
European Commission
Avera Institute
Seventh Framework Programme	602768, 230374, 480-15-001/674
Aggression in Children
European Research Council	184.034.019
Avera Institute for Human Genetics	NWO/SPI 56-464-14192
National Institutes of Health	1RC2 MH089951, 1RC2 MH089995, MH081802, R01 HD042157-01A1
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	184.034.019, 184.033.111
Royal Netherlands Academy of Science	PAH/6635
BBMRI-NL	NWO184.021.007

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

birth weight
BMI
DNA methylation
maternal smoking
methylation scores
multi-omics prediction
polygenic scores
smoking

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10.3389/fpsyt.2021.688464

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Cite this

Odintsova, V. V., Rebattu, V., Hagenbeek, F. A., Pool, R., Beck, J. J., Ehli, E. A., van Beijsterveldt, C. E. M., Ligthart, L., Willemsen, G., de Geus, E. J. C., Hottenga, J. J., Boomsma, D. I., van Dongen, J., & BIOS Consortium (2021). Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles. Frontiers in Psychiatry, 12(July), 1-17. Article 688464. https://doi.org/10.3389/fpsyt.2021.688464

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title = "Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles",

abstract = "We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6\%) of which 54.4\% was captured by the MS. The two predictors captured 16\% of former vs. never smoking initiation variance (MS:15.5\%, PGS: 0.5\%), 17.7\% of prenatal maternal smoking variance (MS:16.9\%, PGS: 0.8\%), 11.9\% of BMI variance (MS: 6.4\%, PGS 5.5\%), and 1.9\% of birth weight variance (MS: 0.4\%, PGS: 1.5\%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6\%), where the MSs contributed 1.5\%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.",

keywords = "birth weight, BMI, DNA methylation, maternal smoking, methylation scores, multi-omics prediction, polygenic scores, smoking",

author = "Odintsova, \{Veronika V.\} and Valerie Rebattu and Hagenbeek, \{Fiona A.\} and Ren{\'e} Pool and Beck, \{Jeffrey J.\} and Ehli, \{Erik A.\} and \{van Beijsterveldt\}, \{Catharina E.M.\} and Lannie Ligthart and Gonneke Willemsen and \{de Geus\}, \{Eco J.C.\} and Hottenga, \{Jouke Jan\} and Boomsma, \{Dorret I.\} and \{van Dongen\}, Jenny and \{BIOS Consortium\}",

note = "Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2021 Odintsova, Rebattu, Hagenbeek, Pool, Beck, Ehli, van Beijsterveldt, Ligthart, Willemsen, de Geus, Hottenga, Boomsma and van Dongen. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = jul,

doi = "10.3389/fpsyt.2021.688464",

language = "English",

volume = "12",

pages = "1--17",

journal = "Frontiers in Psychiatry",

issn = "1664-0640",

publisher = "Frontiers Media S.A.",

number = "July",

}

Odintsova, VV, Rebattu, V, Hagenbeek, FA , Pool, R, Beck, JJ, Ehli, EA, van Beijsterveldt, CEM, Ligthart, L, Willemsen, G, de Geus, EJC, Hottenga, JJ, Boomsma, DI , van Dongen, J & BIOS Consortium 2021, 'Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles', Frontiers in Psychiatry, vol. 12, no. July, 688464, pp. 1-17. https://doi.org/10.3389/fpsyt.2021.688464

TY - JOUR

T1 - Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

AU - Odintsova, Veronika V.

AU - Rebattu, Valerie

AU - Hagenbeek, Fiona A.

AU - Pool, René

AU - Beck, Jeffrey J.

AU - Ehli, Erik A.

AU - van Beijsterveldt, Catharina E.M.

AU - Ligthart, Lannie

AU - Willemsen, Gonneke

AU - de Geus, Eco J.C.

AU - Hottenga, Jouke Jan

AU - Boomsma, Dorret I.

AU - van Dongen, Jenny

AU - BIOS Consortium

N1 - Publisher Copyright: © Copyright © 2021 Odintsova, Rebattu, Hagenbeek, Pool, Beck, Ehli, van Beijsterveldt, Ligthart, Willemsen, de Geus, Hottenga, Boomsma and van Dongen. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/7

Y1 - 2021/7

N2 - We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

AB - We examined the performance of methylation scores (MS) and polygenic scores (PGS) for birth weight, BMI, prenatal maternal smoking exposure, and smoking status to assess the extent to which MS could predict these traits and exposures over and above the PGS in a multi-omics prediction model. MS may be seen as the epigenetic equivalent of PGS, but because of their dynamic nature and sensitivity of non-genetic exposures may add to complex trait prediction independently of PGS. MS and PGS were calculated based on genotype data and DNA-methylation data in blood samples from adults (Illumina 450 K; N = 2,431; mean age 35.6) and in buccal samples from children (Illumina EPIC; N = 1,128; mean age 9.6) from the Netherlands Twin Register. Weights to construct the scores were obtained from results of large epigenome-wide association studies (EWASs) based on whole blood or cord blood methylation data and genome-wide association studies (GWASs). In adults, MSs in blood predicted independently from PGSs, and outperformed PGSs for BMI, prenatal maternal smoking, and smoking status, but not for birth weight. The largest amount of variance explained by the multi-omics prediction model was for current vs. never smoking (54.6%) of which 54.4% was captured by the MS. The two predictors captured 16% of former vs. never smoking initiation variance (MS:15.5%, PGS: 0.5%), 17.7% of prenatal maternal smoking variance (MS:16.9%, PGS: 0.8%), 11.9% of BMI variance (MS: 6.4%, PGS 5.5%), and 1.9% of birth weight variance (MS: 0.4%, PGS: 1.5%). In children, MSs in buccal samples did not show independent predictive value. The largest amount of variance explained by the two predictors was for prenatal maternal smoking (2.6%), where the MSs contributed 1.5%. These results demonstrate that blood DNA MS in adults explain substantial variance in current smoking, large variance in former smoking, prenatal smoking, and BMI, but not in birth weight. Buccal cell DNA methylation scores have lower predictive value, which could be due to different tissues in the EWAS discovery studies and target sample, as well as to different ages. This study illustrates the value of combining polygenic scores with information from methylation data for complex traits and exposure prediction.

KW - birth weight

KW - BMI

KW - DNA methylation

KW - maternal smoking

KW - methylation scores

KW - multi-omics prediction

KW - polygenic scores

KW - smoking

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UR - https://www.scopus.com/pages/publications/85112403815#tab=citedBy

U2 - 10.3389/fpsyt.2021.688464

DO - 10.3389/fpsyt.2021.688464

M3 - Article

AN - SCOPUS:85112403815

SN - 1664-0640

VL - 12

SP - 1

EP - 17

JO - Frontiers in Psychiatry

JF - Frontiers in Psychiatry

IS - July

M1 - 688464

ER -

Predicting Complex Traits and Exposures From Polygenic Scores and Blood and Buccal DNA Methylation Profiles

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Persistent URL (handle)

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