Biology subjects

Page, C. M.

Publications and source records attributed to Page, C. M..

3 recordsLinked to original sources

bioRxiv2022

The X-factor in ART: does the use of Assisted Reproductive Technologies influence DNA methylation on the X chromosome?

BackgroundAssisted reproductive technologies (ART) may perturb DNA methylation (DNAm) in early embryonic development. Although a handful of epigenome-wide association studies of ART have been published, none have investigated CpGs on the X chromosome. To bridge this knowledge gap, we leveraged one of the largest collections of mother-father-newborn trios of ART and non-ART (natural) conceptions to date to investigate DNAm differences on the X chromosome. Materials and MethodsThe discovery cohort consisted of 982 ART and 963 non-ART trios from the Norwegian Mother, Father, and Child Cohort Study (MoBa). The replication cohort consisted of 149 ART and 58 non-ART neonates from the Australian "Clinical review of the Health of adults conceived following Assisted Reproductive Technologies" (CHART) study. The Illumina EPIC array was used to measure DNA methylation (DNAm) in both datasets. In the MoBa cohort, we performed a set of X-chromosome-wide association studies ("XWASs" hereafter) to search for sex-specific DNAm differences between ART and non-ART newborns. We tested several models to investigate the influence of various confounders, including parental DNAm. We also searched for differentially methylated regions (DMRs) and regions of co-methylation flanking the most significant CpGs. For replication purposes, we ran an analogous model to our main model on the CHART dataset. Results and conclusionsIn the MoBa cohort, we found more differentially methylated CpGs and DMRs in girls than boys. Most of the associations persisted even after controlling for parental DNAm and other confounders. Many of the significant CpGs and DMRs were in gene-promoter regions, and several of the genes linked to these CpGs are expressed in tissues relevant for both ART and sex (testis, placenta, and fallopian tube). We found no support for parental infertility as an explanation for the observed associations in the newborns. The most significant CpG in the boys-only analysis was in UBE2DNL, which is expressed in testes but with unknown function. The most significant CpGs in the girls-only analysis were in EIF2S3 and AMOT. These three loci also displayed differential DNAm in the CHART cohort. Overall, genes that co-localized with the significant CpGs and DMRs are implicated in several key biological processes (e.g., neurodevelopment) and disorders (e.g., intellectual disability and autism. These connections are particularly compelling in light of previous findings indicating that neurodevelopmental outcomes differ in ART-conceived children compared to naturally-conceived.

genetics↗

bioRxiv2021

Cross-fitted instrument: a blueprint for one-sample Mendelian Randomization

Bias from weak instruments may undermine the ability to estimate causal effects in instrumental variable regression (IVR). We present here a simple solution for handling weak instrument bias by introducing a new type of instrumental variable called cross-fitted instrument (CFI). CFI splits the data at random and estimates the impact of the instrument on the exposure in each partition. The estimates are then used to perform an IVR on each partition. We adapt CFI to Mendelian randomization (MR) and term this adaptation Cross-Fitting for Mendelian Randomization (CFMR). A major advantage of CFMR is its use of all the available data to select genetic instruments, as opposed to traditional two-sample MR where a large part of the data is only used for instrument selection. Consequently, CFMR has the potential to enhance the power of MR in a meta-analysis setting by enabling an unbiased one-sample MR to be performed in each cohort prior to meta-analyzing the results across all the cohorts. In a similar fashion, CFMR enables a cross-ethnic MR analysis by accounting for ethnic heterogeneity, which is particularly important in consortia-led meta-analyses where the participating cohorts might be of different ethnicities. To our knowledge, there are currently no MR approach that can account for such heterogeneity. Finally, CFMR enables the application of MR to exposures that are rare or difficult to measure, which would normally preclude their analysis in the regular two-sample MR setting. Key messagesO_LIWe develop a new method to enable an unbiased one-sample Mendelian Randomization. C_LIO_LIThe new method provides the same power as the standard two-sample Mendelian Randomization approach and does not require summary statistics from a genome-wide association study in an independent cohort. C_LIO_LIOur approach enables a cross-ethnic instrumental variable regression to account for heterogeneity in a sample consisting of multiple ethnicities. C_LI

bioinformatics↗

bioRxiv2018

Variably methylated regions in the newborn epigenome: environmental, genetic and combined influences

BackgroundEpigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. We examined the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs), defined as consecutive CpGs showing the highest variability of DNAm in 4 independent cohorts (PREDO, DCHS, UCI, MoBa, N=2,934).\n\nResultsWe used Akaikes information criterion to test which factors best explained variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E) including maternal demographic, psychosocial and metabolism related phenotypes, genotypes in cis (G), or their additive (G+E) or interaction (GxE) effects. G+E and GxE models consistently best explained variability in DNAm of VMRs across the cohorts, with G explaining the remaining sites best. VMRs best explained by G, GxE or G+E, as well as their associated functional genetic variants (predicted using deep learning algorithms), were located in distinct genomic regions, with different enrichments for transcription and enhancer marks. Genetic variants of not only G and G+E models, but also of variants in GxE models were significantly enriched in genome wide association studies (GWAS) for complex disorders.\n\nConclusionGenetic and environmental factors in combination best explain DNAm at VMRs. The CpGs best explained by G, G+E or GxE are functionally distinct. The enrichment of GxE variants in GWAS for complex disorders supports their importance for disease risk.

genetics↗