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Heijmans, B. T.

Publications and source records attributed to Heijmans, B. T..

3 recordsLinked to original sources

Why mediation analysis trumps Mendelian randomization in population epigenomics studies of the Dutch Famine

Our recent analysis of genome-wide DNA methylation data in men and women exposed to the Dutch Famine met passionate criticism by several researchers active on Twitter. It also prompted a more reasoned letter by Richmond and colleagues. At the core of the debate is the proper interpretation of findings from a mediation analysis. We used this method to identify specific DNA methylation changes that statistically provide a link between prenatal exposure to famine and adult metabolic traits. Our critics first argue that our results do not suggest mediation but reverse-causation, where famine-induced metabolic traits drive changes in DNA methylation. We rebut this scenario in a simulation study showing that our test of mediation was unlikely to become statistically significant in the case of reverse-causation. Some critics then argue that Mendelian randomization provides the sole path to correct inference. This belief misses a crucial point: DNA methylation, especially when measured in peripheral blood, is not likely to be a causal mediator from a biological point of view. It could however be a proxy of epigenetic regulation changes in specific tissues, for example at the level of transcription factor binding. If so, a Mendelian randomization approach using genetic variants affecting local DNA methylation in blood will be disconnected from the underlying biological mechanism and is bound to yield false-negative results. Our new simulation studies strengthen the original reasoning that the relationship between prenatal famine and metabolic traits is statistically mediated by specific DNA methylation changes while the specific molecular mechanism awaits elucidation.

genomics

Epigenetic selection and the DNA methylation signatures of adverse prenatal environments

Maternal adversity is associated with long-term physiological changes in offspring. These are believed to be mediated through epigenetic mechanisms, including DNA methylation (DNAm). Changes in DNAm are often interpreted as damage or as part of plastic responses of the embryo. We propose that selection on stochastic DNAm differences generated during epigenetic reprogramming after fertilization contributes to the effects of maternal adversity on DNAm. Using a mathematical model of epigenetic reprogramming in the early embryo we predict that this \"epigenetic selection\" will generate a characteristic reduction in variance of DNAm at selected loci in populations exposed to maternal adversity. We tested this prediction using DNAm data from a human cohort prenatally exposed to the Dutch Famine and confirmed the reduction in DNAm variance, suggesting that epigenetic selection may have occurred. Epigenetic selection should be considered as a possible mechanism linking adversity in pregnancy to offspring health and may have implications for the likely effectiveness of intervention strategies.

epidemiology

Genome-wide identification of directed gene networks using large-scale population genomics data

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 x 10-10), among which transcription factors were overrepresented (P = 3.3 x 10-7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

genomics