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Accurate ethnicity prediction from placental DNA methylation data Yuan, Victor; Price, E. M.; Del Gobbo, Giulia; Mostafavi, Sara; Cox, Brian; Binder, Alexandra M.; Michels, Karin B.; Marsit, Carmen; Robinson, Wendy P.

Abstract

Background: The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. Results: Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers (n = 50 SNPs). PlaNET’s ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms (n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion: PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable.

Item Metadata

Title
Accurate ethnicity prediction from placental DNA methylation data
Creator
Yuan, Victor; Price, E. M.; Del Gobbo, Giulia; Mostafavi, Sara; Cox, Brian; Binder, Alexandra M.; Michels, Karin B.; Marsit, Carmen; Robinson, Wendy P.
Contributor
Children's Hospital (Vancouver, B.C.). Research Institute
Publisher
BioMed Central
Date Issued
2019-08-09
Description
Background: The influence of genetics on variation in DNA methylation (DNAme) is well documented. Yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches to address confounding by population stratification using DNAme data may not generalize to populations or tissues outside those in which they were developed. To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PlaNET (Placental DNAme Elastic Net Ethnicity Tool), using five cohorts with Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples that is also compatible with the newer EPIC platform. Results: Data from 509 placental samples were used to develop PlaNET and show that it accurately predicts (accuracy = 0.938, kappa = 0.823) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), and produces ethnicity probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP arrays (> 2.5 million SNP) and ancestry informative markers (n = 50 SNPs). PlaNET’s ethnicity classification relies on 1860 HM450K microarray sites, and over half of these were linked to nearby genetic polymorphisms (n = 955). Our placental-optimized method outperforms existing approaches in assessing population stratification in placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion: PlaNET provides an improved approach to address population stratification in placental DNAme association studies. The method can be applied to predict ethnicity as a discrete or continuous variable and will be especially useful when self-reported ethnicity information is missing and genotyping markers are unavailable.
Subject
Epigenetics; DNA methylation; Microarray; Placenta; Population stratification; Machine learning; Confounding; Association study
Genre
Article
Type
Text
Language
eng
Date Available
2019-08-09
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0380368
URI
http://hdl.handle.net/2429/71273
Affiliation
Medicine, Faculty of; Science, Faculty of; Other UBC; Non UBC; Medical Genetics, Department of; Statistics, Department of
Citation
Epigenetics & Chromatin. 2019 Aug 09;12(1):51
Publisher DOI
10.1186/s13072-019-0296-3
Peer Review Status
Reviewed
Scholarly Level
Faculty
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International (CC BY 4.0)