Accurate ethnicity prediction from placental DNA methylation data

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. Electronic supplementary material The online version of this article (10.1186/s13072-019-0296-3) contains supplementary material, which is available to authorized users.

Identification of epigenome-wide DNA methylation differences between carriers of APOE ε4 and APOE ε2 alleles

“…ageing [13], obesity [14] and lipid levels [15]), as well as modifiers of APOE genotype effects (e.g. sex [16] and ethnicity [17,18]).…”

A cross-cohort analysis of autosomal DNA methylation sex differences in the term placenta

“…The datasets compiled in this step include GSE73375 (n=9, North Carolina, USA) (23), GSE75428 (n=289, Rhode Island Child Health Study, Rhode Island, USA) (24), GSE98224 (n=9, Toronto, Canada) (25), GSE74738, GSE100197, GSE108567, and GSE128827 (n=34, all Epigenetics in Pregnancy Complications Cohort, Vancouver, Canada) (26–29). These data were utilized as described in Yuan et al 2019, to generate PlaNET, the Placental DNAme Elastic Net Ethnicity Tool, which estimates metrics of genetic ancestry from placental DNAme datasets (28).…”

“…All samples from the discovery cohort (n=585) were subjected to routine filtering and normalization as described in Yuan et al 2019 (28). Briefly, CpGs removed included those targeted by non-specific probes (31, 32), known placental-specific non-variable CpGs (range of β values < 0.05 between the 10 th -90 th centile in all samples in this cohort) (33), and those with poor quality data (detection P value > 0.01 or bead count < 3 in more than 1% of samples) (34).…”

“…Data were normalized by the normal exponential out-of-band (noob) and beta mixture quantile (BMIQ) normalization methods from the R packages “wateRmelon” and “minfi”, respectively (35). Samples were assigned values in three continuous ancestry coordinates, reflecting the probability of each sample being similar to 1000 Genomes Project populations of African, Asian, and European descent (28). Following the development of PlaNET, probes targeting polymorphic loci were also removed from this cohort (31, 32), as were samples born before term (<37 weeks gestation) and/or affected by preeclampsia; this left 324,104 autosomal CpGs in 341 samples available for sex-specific DNAme analysis.…”

Large-scale placenta DNA methylation mega-analysis reveals fetal sex-specific differentially methylated CpG sites and regions

“…A total of 14 GEO datasets comprising 783 samples were included in the analysis, including 637 full term, 33 1st trimester, 72 2nd trimester, and 41 3rd trimester samples (Supplementary Table 1). We predicted the gestational age of these samples using the predictAge() function from the planet R package 53 to verify that our sample groups captured their intended points during the gestation period 54 .…”