Accurate ethnicity prediction from placental DNA methylation data
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ABSTRACT:
PROVIDER: PRJNA529078 | ENA |
REPOSITORIES: ENA
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Project description:The influence of genetics on DNA methylation (DNAme) variation is well documented, yet confounding from population stratification is often unaccounted for in DNAme association studies. Existing approaches have been developed to address confounding by population stratification by directly using DNAme data, but have not been validated in additional human populations or tissues, such as the placenta. Results: To aid future placental DNAme studies in assessing population stratification, we developed an ethnicity classifier, PLANET (placental elastic net DNAme ethnicity classifier), on combined Infinium Human Methylation 450k BeadChip array (HM450k) data from placental samples. We used data from five North American cohorts from private and public repositories (n = 509) and show that PLANET can not only accurately predict (accuracy = 0.9379, kappa = 0.8227) major classes of self-reported ethnicity/race (African: n = 58, Asian: n = 53, Caucasian: n = 389), but can also produce probabilities that are highly correlated with genetic ancestry inferred from genome-wide SNP (>2.5 million SNP) and ancestry informative markers (n=50) data. We found that PLANET’s ethnicity classification relies on 1860 DNAme microarray sites, and over half of these were also linked to nearby genetic polymorphisms (n=955). Lastly, we found our placental-optimized method outperforms existing approaches in assessing population stratification in our placental samples from individuals of Asian, African, and Caucasian ethnicities. Conclusion: PLANET outperforms existing methods and heavily relies on the genetic signal present in DNAme microarray data. PLANET can be used to address population stratification in future placental DNAme association studies, and will be especially useful when ethnicity information is missing and genotyping markers are unavailable.
2020-10-14 | GSE128827 | GEO
Project description:BackgroundThe 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.ResultsData 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.ConclusionPlaNET 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.
| S-EPMC6688210 | biostudies-literature
Project description:Epienome-wide DNA methylation profiling of systemic lupus erythematosus (SLE). The Illumina HumanMethylation450K Beadchip was used to obtain DNA methylation profiles across approximately 450,000 CpGs in normal human blood samples from females. Samples included 33 non-SLE female patients (control) and 57 SLE female patients. SLE patients:- Ethnicity included 39 African americans and 18 European Americans. SLEDAI score ranged from 2-30. Non-SLE pateients:-Ethnicity indclued 17 African Americans and 16 European Americans, all with a SLEDAI score of zero.
2020-03-21 | GSE96879 | GEO
Project description:Accurate circadian time prediction from a single bulk and single-cell transcriptomic data by tauFisher
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Project description:Maternal obesity alters placental tissue function and morphology with a corresponding increase in local inflammation. We and others showed that placenta size, inflammation and fetal growth are regulated by maternal diet and obesity status. Maternal obesity alters placental DNA methylation which in turn could likely impact gene transcription of of proteins critical for normal fetal development. RNA-binding motif single-stranded interacting protein 1 (RBMS1) is expressed by the placenta and likely modulates DNA replication and transcription regulation. Serum RBMS1 protein concentration is increased with maternal obesity and RBMS1 gene expression in liver tissue is induced by a high-fat diet and inflammation. However, it is not yet known whether placental RBMS1 mRNA expression and DNA methylation are altered by maternal obesity.
2018-09-24 | GSE120062 | GEO
Project description:DNA Methylation data from frozen placental tissue
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Project description:Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Previous studies developed methylation clocks based on linear regression models on methylation array data. While accurate, these models are limited to fixed-rate changes in methylation levels across age. Moreover, the high cost of methylation arrays, compared to targeted-PCR sequencing, hinders widespread utility of such predictors. We present an AI-based alternative termed GP-age, which uses a non-parametric approach based on Gaussian Process Regression of a large cohort of ~12K blood methylomes. Given a new blood sample, methylation levels are compared to the cohort samples, which are then weighted to predict the query age. Using only 30 CpG sites, our approach outperforms state-of-the-art methylation clocks that use hundreds of sites, with a median error of 2.1 years (on held-out data). Our model was also applied to sequencing-based data yielding highly accurate predictions. Overall, we provide an accessible alternative to current array-based methylation clocks, with future applications in aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.
2023-01-20 | GSE207605 | GEO
Project description:Genome wide placental DNA methylation profiling of full term and preterm deliveries sampled from 5 full term deliveries and 4 preterm deliveries. The Illumina HumanMethylation450 Beadchip was used to obtain DNA methylation profiles across approximately 485,577 CpGs in formalin fixed samples. Samples included 4 placental tissues from 4 women with preterm delivery and 5 placental tissues from 5 women with full term delivery. 9 women's placental DNA (4 women had perterm deliveries and 5 women had full term deliveries) were hybridised to the Illumina HumanMethylation450 Beadchip
2016-07-25 | E-GEOD-49504 | biostudies-arrayexpress
Project description:In this study, we screened human placental samples for allele-specific methylation and subsequently novel imprinted genes associated with these regions. We used reduced representation bisulfite sequencing to identify partially methylated CpG islands (CGIs) in the human placental genome. We were able to delineate potential candidates for allele-specific methylation based on the calculation of a concordance statistic. Amongst the 28 regions chosen for validation based on high levels of expression, two regions were shown to exhibit allele-specific expression. Single base-resolution methylation analysis in the placental genome and RNA-Seq
2013-10-09 | E-GEOD-40955 | biostudies-arrayexpress
Project description:Transmicron: Accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens
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