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Epigenetics

Is ethnic DNA methylation just ancestry?

Galanter JM, Gignoux CR, Oh SS, et al. · eLife · 2017

Open access · cc by · source: Europe PMC

In 573 GALA II Latinos, shared ancestry explained a median 75.7% of ethnicity-linked methylation variance, but environmental signals (e.g., maternal smoking loci) still contributed.

How this study connects

Role on claims

Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.

  • Three quarters of the apparent environmental signal was genetic, and roughly a third of sites survived.

    Most methylation differences between ethnic sub-groups were explained by genetic ancestry. Ethnicity and ancestry associated with 916 and 194 CpGs respectively, with ancestry accounting for a median 75.7% of ethnicity-associated methylation variance; after adjustment, 314 of 834 ethnicity-associated sites remained.

    Evidence for the claim as stated.

  • What a methylation difference is taken to mean depends on the design that produced it. The ancestry study shows a population-level difference largely reducing to genotype, while the reprogramming study manipulates methylation directly and reads a functional consequence — so the two support very different inferences from the same kind of measurement.

    Evidence for the claim as stated.

Open questions

Tensions this paper is part of

From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.

  • Scope difference — different assays, populations, or outcomes

    What a methylation difference is taken to mean depends on the design that produced it. The ancestry study shows a population-level difference largely reducing to genotype, while the reprogramming study manipulates methylation directly and reads a functional consequence — so the two support very different inferences from the same kind of measurement.

Study at a glance

Design
Cross-sectional — EWAS of ethnicity vs DNA methylation in GALA II Latino participants with genetic ancestry estimates
N
N=573 · 573 participants with methylation data; 524 with genomic ancestry estimates (Puerto Rican n=220, Mexican n=276)
Population
Latino American GALA II participants (mostly Puerto Rican or Mexican origin) with dense genotyping and methylation arrays
Outcome
Ethnicity-associated CpG methylation, fraction explained by shared ancestry, and enrichment for environmentally associated loci

Structured fields used in claim comparison tables when every cited study has a complete layer.

Inspect source passages

Is ethnic DNA methylation just ancestry?

These passages occur verbatim in the stored paper text. They support the points listed below; they do not verify every statement in the explanation.

573 GALA II participants had methylation data (Puerto Rican 220; Mexican 276).

The study included 573 participants, the majority of whom self-identified as being either of Puerto Rican (n = 220) or Mexican origin (n = 276).

Shared ancestry explained a median 75.7% (IQR 45.8–92%) of ethnicity-associated methylation variance.

shared genomic ancestry accounted for a median of 75.7% (IQR 45.8% to 92%) of the variance in methylation associated with ethnicity.
Read passages in the original paper

Key findings

Ethnicity and ancestry associated with 916 and 194 CpGs. Ancestry accounted for a median 75.7% (IQR 45.8–92%) of ethnicity-associated methylation variance. After ancestry adjustment, 314/834 ethnicity sites remained. Ethnicity-associated sites were enriched for prior environmental associations, especially maternal smoking in pregnancy.

Methodology

Measured DNA methylation and genetic ancestry in GALA II Latino participants, mapped ethnicity-associated CpGs, estimated how much shared genomic ancestry explained those differences, and tested enrichment of environmentally associated sites.

Limitations

Cross-sectional methylation cannot prove which exposures caused residual ethnic differences; GALA II Latinos may not generalize to all populations.

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