Biology subjects

Idaghdour, Y.

Publications and source records attributed to Idaghdour, Y..

4 recordsLinked to original sources

bioRxiv2018

Intergenerational epigenetic inheritance in reef-building corals

MainThe notion that intergenerational or transgenerational inheritance operates solely through genetic means is slowly being eroded: epigenetic mechanisms have been shown to induce heritable changes in gene activity in plants 1,2 and metazoans 1,3. Inheritance of DNA methylation provides a potential pathway for environmentally induced phenotypes to contribute to evolution of species and populations 1-4. However, in basal metazoans, it is unknown whether inheritance of CpG methylation patterns occurs across the genome (as in plants) or as rare exceptions (as in mammals) 4. Here, we demonstrate genome-wide intergenerational transmission of CpG methylation patterns from parents to sperm and larvae in a reef-building coral. We also show variation in hypermethylated genes in corals from distinct environments, indicative of responses to variations in temperature and salinity. These findings support a role of DNA methylation in the transgenerational inheritance of traits in corals, which may extend to enhancing their capacity to adapt to climate change.

genetics↗

bioRxiv2017

Genomic and Environmental Contributions to Chronic Diseases in Urban Populations

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the GxE interactions in humans. We used a system biology approach to comprehensively assess the interactions between 1.6 million environmental exposure data, health, and expression phenotypes, together with whole genome genetic variation, for [~]1000 individuals from a founder-population in Quebec. We reveal a substantial impact of the urbanization gradient on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. In detail, air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits when controlling for genetic ancestry. Finally, we capture 34 clinically associated expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects chronic disease development, and that genetic variation, including rare variants, can modulate individuals response to environmental challenges.\n\nHighlightsO_LIFine scale environmental effects overpower those of ancestry on gene expression\nC_LIO_LIAir pollution (geographic and temporal) is associated with transcriptional response\nC_LIO_LIGene-by-environment interactions with air pollution include asthma associated loci\nC_LIO_LIInflammatory pathways and cardio-respiratory clinical traits are among those affected\nC_LI

genomics↗

bioRxiv2016

Constraints on eQTL fine mapping in the presence of multi-site local regulation of gene expression

Expression QTL (eQTL) detection has emerged as an important tool for unravelling of the relationship between genetic risk factors and disease or clinical phenotypes. Most studies use single marker linear regression to discover primary signals, followed by sequential conditional modeling to detect secondary genetic variants affecting gene expression. However, this approach assumes that functional variants are sparsely distributed and that close linkage between them has little impact on estimation of their precise location and magnitude of effects. In this study, we address the prevalence of secondary signals and bias in estimation of their effects by performing multi-site linear regression on two large human cohort peripheral blood gene expression datasets (each greater than 2,500 samples) with accompanying whole genome genotypes, namely the CAGE compendium of Illumina microarray studies, and the Framingham Heart Study Affymetrix data. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ~40% of over 3500 eGenes in both datasets, and the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. However, the concordance of specific signals between the two studies is only ~30%, indicating that expression profiling platform is a large source of variance in effect estimation. Furthermore, a series of simulation studies imply that in the presence of multi-site regulation, up to 10% of the secondary signals could be artefacts of incomplete tagging, and at least 5% but up to one quarter of credible intervals may not even include the causal site, which is thus mis-localized. Joint multi-site effect estimation recalibrates effect size estimates by just a small amount on average. Presumably similar conclusions apply to most types of quantitative trait. Given the strong empirical evidence that gene expression is commonly regulated by more than one variant, we conclude that the fine-mapping of causal variants needs to be adjusted for multi-site influences, as conditional estimates can be highly biased by interference among linked sites.

genetics↗

bioRxiv2016

Integrated Genomic Analysis of Mitochondrial RNA Processing in Human Cancers

Alterations to mitochondrial function and mutations in mitochondrial genes have been reported for a wide variety of cancers, however the mitochondrial transcriptome remains largely unexplored in cancer despite an emerging appreciation of the role that post-transcriptional regulation plays in the etiology of these diseases. Here, we quantify and assess changes to mitochondrial RNA processing in human cancers using integrated genomic analysis of RNA Sequencing and genotyping data from 1226 samples across 12 different cancer types. We find significant changes to m1A and m1G post-transcriptional methylation rates at functionally important positions in mitochondrial tRNAs in tumor tissues across all cancers. Pathways of RNA processing are strongly associated with methylation rates in normal tissues (P=2.85x10-27), yet these associations are lost in tumors. Furthermore, we report 18 gene-by-disease-state interactions where altered methylation rates occur under cancer status conditional on genotype, implicating genes associated with mitochondrial function or cancer (e.g. CACNA2D2, LMO2 and FLT3) and suggesting that nuclear genetic variation can potentially modulate an individuals ability to maintain unaltered rates of mitochondrial RNA processing under cancer status. Finally, we report a significant association between the magnitude of methylation rate changes in tumors and patient survival outcomes. These results highlight mitochondrial post-transcriptional events as a clinically relevant mechanism and as a theme for the further investigation of cancer processes, biomarkers and therapeutic interventions.

genomics↗