Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults
Background: fetal exposure to maternal smoking during pregnancy is associated with the development of non-communicable diseases in the offspring. It may induce such long-term effects through persistent changes in the DNA-methylome, which therefore holds the potential to be used as a biomarker of thi...
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ftsouthampton:oai:eprints.soton.ac.uk:443740 2024-02-11T10:07:07+01:00 Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults Rauschert, Sebastian Melton, Phillip E. Heiskala, Anni Karhunen, Ville Burdge, Graham Craig, Jeffrey M. Godfrey, Keith Lillycrop, Karen Mori, Trevor A. Beilin, Lawrence J Oddy, Wendy H Pennell, Craig Järvelin, Marjo-Riitta Sebert, Sylvain Huang, Rae-Chi 2020-08-29 text spreadsheet https://eprints.soton.ac.uk/443740/ https://eprints.soton.ac.uk/443740/1/Smoking_Score_Manuscript_Rauschert_final.docx https://eprints.soton.ac.uk/443740/2/Figure_1.pdf https://eprints.soton.ac.uk/443740/3/Figure_2.pdf https://eprints.soton.ac.uk/443740/4/Supplement_S1_revised_new_Rauschert.docx https://eprints.soton.ac.uk/443740/5/Copy_of_Supplement_S2_revised.xlsx en English eng https://eprints.soton.ac.uk/443740/1/Smoking_Score_Manuscript_Rauschert_final.docx https://eprints.soton.ac.uk/443740/2/Figure_1.pdf https://eprints.soton.ac.uk/443740/3/Figure_2.pdf https://eprints.soton.ac.uk/443740/4/Supplement_S1_revised_new_Rauschert.docx https://eprints.soton.ac.uk/443740/5/Copy_of_Supplement_S2_revised.xlsx Rauschert, Sebastian, Melton, Phillip E., Heiskala, Anni, Karhunen, Ville, Burdge, Graham, Craig, Jeffrey M., Godfrey, Keith, Lillycrop, Karen, Mori, Trevor A., Beilin, Lawrence J, Oddy, Wendy H, Pennell, Craig, Järvelin, Marjo-Riitta, Sebert, Sylvain and Huang, Rae-Chi (2020) Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults. Environmental Health Perspectives. (In Press) accepted_manuscript Article PeerReviewed 2020 ftsouthampton 2024-01-25T23:19:58Z Background: fetal exposure to maternal smoking during pregnancy is associated with the development of non-communicable diseases in the offspring. It may induce such long-term effects through persistent changes in the DNA-methylome, which therefore holds the potential to be used as a biomarker of this early life exposure. With reducing costs for measuring DNA-methylation, we aimed to develop a DNA-methylation score that can be used on adolescent DNA-methylation data and thereby generate a score for in utero smoke exposure. Methods: we used machine learning methods to create a score reflecting exposure to maternal smoking during pregnancy. This score is based on peripheral blood measurements of DNA methylation (Illumina’s Infinium HumanMethylation450 BeadChip). The score was developed and tested in the Raine Study with data from 995 Caucasian 17y-old participants using 10-fold cross-validation. The score was further tested and validated in independent data from the Northern Finland Birth Cohort 1986 (NFBC1986) (16y-olds) and 1966 (NFBC1966) (31y-olds). Further, three previously proposed DNA methylation scores were applied for comparison. The final score was developed with 204 CpGs using elastic net regression. Results: sensitivity and specificity values for the best performing previously developed classifier (‘Reese Score’) were 88% and 72% for Raine, 87% and 61% for NFBC1986 and 72% and 70% for NFBC1966, respectively; corresponding figures utilizing the elastic net regression approach were 91% and 76% (Raine), 87% and 75% (NFBC1986) as well as 72% and 78% for NFBC1966. Conclusion: we have developed a DNA methylation score for exposure to maternal smoking during pregnancy, outperforming the three previously developed scores. One possible application of the current score could be for model adjustment purposes or to assess its association with distal health outcomes where part of the effect can be attributed to maternal smoking. Further, it may provide a biomarker for fetal exposure to maternal smoking. Article in Journal/Newspaper Northern Finland University of Southampton: e-Prints Soton |
institution |
Open Polar |
collection |
University of Southampton: e-Prints Soton |
op_collection_id |
ftsouthampton |
language |
English |
description |
Background: fetal exposure to maternal smoking during pregnancy is associated with the development of non-communicable diseases in the offspring. It may induce such long-term effects through persistent changes in the DNA-methylome, which therefore holds the potential to be used as a biomarker of this early life exposure. With reducing costs for measuring DNA-methylation, we aimed to develop a DNA-methylation score that can be used on adolescent DNA-methylation data and thereby generate a score for in utero smoke exposure. Methods: we used machine learning methods to create a score reflecting exposure to maternal smoking during pregnancy. This score is based on peripheral blood measurements of DNA methylation (Illumina’s Infinium HumanMethylation450 BeadChip). The score was developed and tested in the Raine Study with data from 995 Caucasian 17y-old participants using 10-fold cross-validation. The score was further tested and validated in independent data from the Northern Finland Birth Cohort 1986 (NFBC1986) (16y-olds) and 1966 (NFBC1966) (31y-olds). Further, three previously proposed DNA methylation scores were applied for comparison. The final score was developed with 204 CpGs using elastic net regression. Results: sensitivity and specificity values for the best performing previously developed classifier (‘Reese Score’) were 88% and 72% for Raine, 87% and 61% for NFBC1986 and 72% and 70% for NFBC1966, respectively; corresponding figures utilizing the elastic net regression approach were 91% and 76% (Raine), 87% and 75% (NFBC1986) as well as 72% and 78% for NFBC1966. Conclusion: we have developed a DNA methylation score for exposure to maternal smoking during pregnancy, outperforming the three previously developed scores. One possible application of the current score could be for model adjustment purposes or to assess its association with distal health outcomes where part of the effect can be attributed to maternal smoking. Further, it may provide a biomarker for fetal exposure to maternal smoking. |
format |
Article in Journal/Newspaper |
author |
Rauschert, Sebastian Melton, Phillip E. Heiskala, Anni Karhunen, Ville Burdge, Graham Craig, Jeffrey M. Godfrey, Keith Lillycrop, Karen Mori, Trevor A. Beilin, Lawrence J Oddy, Wendy H Pennell, Craig Järvelin, Marjo-Riitta Sebert, Sylvain Huang, Rae-Chi |
spellingShingle |
Rauschert, Sebastian Melton, Phillip E. Heiskala, Anni Karhunen, Ville Burdge, Graham Craig, Jeffrey M. Godfrey, Keith Lillycrop, Karen Mori, Trevor A. Beilin, Lawrence J Oddy, Wendy H Pennell, Craig Järvelin, Marjo-Riitta Sebert, Sylvain Huang, Rae-Chi Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
author_facet |
Rauschert, Sebastian Melton, Phillip E. Heiskala, Anni Karhunen, Ville Burdge, Graham Craig, Jeffrey M. Godfrey, Keith Lillycrop, Karen Mori, Trevor A. Beilin, Lawrence J Oddy, Wendy H Pennell, Craig Järvelin, Marjo-Riitta Sebert, Sylvain Huang, Rae-Chi |
author_sort |
Rauschert, Sebastian |
title |
Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
title_short |
Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
title_full |
Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
title_fullStr |
Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
title_full_unstemmed |
Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
title_sort |
machine learning-based dna methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults |
publishDate |
2020 |
url |
https://eprints.soton.ac.uk/443740/ https://eprints.soton.ac.uk/443740/1/Smoking_Score_Manuscript_Rauschert_final.docx https://eprints.soton.ac.uk/443740/2/Figure_1.pdf https://eprints.soton.ac.uk/443740/3/Figure_2.pdf https://eprints.soton.ac.uk/443740/4/Supplement_S1_revised_new_Rauschert.docx https://eprints.soton.ac.uk/443740/5/Copy_of_Supplement_S2_revised.xlsx |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_relation |
https://eprints.soton.ac.uk/443740/1/Smoking_Score_Manuscript_Rauschert_final.docx https://eprints.soton.ac.uk/443740/2/Figure_1.pdf https://eprints.soton.ac.uk/443740/3/Figure_2.pdf https://eprints.soton.ac.uk/443740/4/Supplement_S1_revised_new_Rauschert.docx https://eprints.soton.ac.uk/443740/5/Copy_of_Supplement_S2_revised.xlsx Rauschert, Sebastian, Melton, Phillip E., Heiskala, Anni, Karhunen, Ville, Burdge, Graham, Craig, Jeffrey M., Godfrey, Keith, Lillycrop, Karen, Mori, Trevor A., Beilin, Lawrence J, Oddy, Wendy H, Pennell, Craig, Järvelin, Marjo-Riitta, Sebert, Sylvain and Huang, Rae-Chi (2020) Machine learning-based DNA methylation score for fetal exposure to maternal smoking: development and validation in samples collected from adolescents and adults. Environmental Health Perspectives. (In Press) |
op_rights |
accepted_manuscript |
_version_ |
1790605264853925888 |