Time-dependent genetic effects on gene expression implicate aging processes

Gene expression is dependent on genetic and environmental factors. In the last decade, a large body of research has significantly improved our understanding of the genetic architecture of gene expression. However, it remains unclear whether genetic effects on gene expression remain stable over time. Here, we show, using longitudinal whole-blood gene expression data from a twin cohort, that the genetic architecture of a subset of genes is unstable over time. In addition, we identified 2213 genes differentially expressed across time points that we linked with aging within and across studies. Interestingly, we discovered that most differentially expressed genes were affected by a subset of 77 putative causal genes. Finally, we observed that putative causal genes and down-regulated genes were affected by a loss of genetic control between time points. Taken together, our data suggest that instability in the genetic architecture of a subset of genes could lead to widespread effects on the transcriptome with an aging signature

Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity

Systemic lupus erythematosus (SLE) diagnosis and treatment remain empirical and the molecular basis for its heterogeneity elusive. We explored the genomic basis for disease susceptibility and severity

Identifying novel regulatory effects for clinically relevant genes through the study of the Greek population

Abstract Background Expression quantitative trait loci (eQTL) studies provide insights into regulatory mechanisms underlying disease risk. Expanding studies of gene regulation to underexplored populations and to medically relevant tissues offers potential to reveal yet unknown regulatory variants and to better understand disease mechanisms. Here, we performed eQTL mapping in subcutaneous (S) and visceral (V) adipose tissue from 106 Greek individuals (Greek Metabolic study, GM) and compared our findings to those from the Genotype-Tissue Expression (GTEx) resource. Results We identified 1,930 and 1,515 eGenes in S and V respectively, over 13% of which are not observed in GTEx adipose tissue, and that do not arise due to different ancestry. We report additional context-specific regulatory effects in genes of clinical interest (e.g. oncogene ST7) and in genes regulating responses to environmental stimuli (e.g. MIR21, SNX33). We suggest that a fraction of the reported differences across populations is due to environmental effects on gene expression, driving context-specific eQTLs, and suggest that environmental effects can determine the penetrance of disease variants thus shaping disease risk. We report that over half of GM eQTLs colocalize with GWAS SNPs and of these colocalizations 41% are not detected in GTEx. We also highlight the clinical relevance of S adipose tissue by revealing that inflammatory processes are upregulated in individuals with obesity, not only in V, but also in S tissue. Conclusions By focusing on an understudied population, our results provide further candidate genes for investigation regarding their role in adipose tissue biology and their contribution to disease risk and pathogenesis

Passive and active DNA methylation and the interplay with genetic variation in gene regulation

DNA methylation is an essential epigenetic mark whose role in gene regulation and its dependency on genomic sequence and environment are not fully understood. In this study we provide novel insights into the mechanistic relationships between genetic variation, DNA methylation and transcriptome sequencing data in three different cell-types of the GenCord human population cohort. We find that the association between DNA methylation and gene expression variation among individuals are likely due to different mechanisms from those establishing methylation-expression patterns during differentiation. Furthermore, cell-type differential DNA methylation may delineate a platform in which local inter-individual changes may respond to or act in gene regulation. We show that unlike genetic regulatory variation, DNA methylation alone does not significantly drive allele specific expression. Finally, inferred mechanistic relationships using genetic variation as well as correlations with TF abundance reveal both a passive and active role of DNA methylation to regulatory interactions influencing gene expression. DOI:http://dx.doi.org/10.7554/eLife.00523.001

Population Variation and Genetic Control of Modular Chromatin Architecture in Humans Article Population Variation and Genetic Control of Modular Chromatin Architecture in Humans

Graphical Abstract Highlights SUMMARY Chromatin state variation at gene regulatory elements is abundant across individuals, yet we understand little about the genetic basis of this variability. Here, we profiled several histone modifications, the transcription factor (TF) PU.1, RNA polymerase II, and gene expression in lymphoblastoid cell lines from 47 whole-genome sequenced individuals. We observed that distinct cis-regulatory elements exhibit coordinated chromatin variation across individuals in the form of variable chromatin modules (VCMs) at sub-Mb scale. VCMs were associated with thousands of genes and preferentially cluster within chromosomal contact domains. We mapped strong proximal and weak, yet more ubiquitous, distal-acting chromatin quantitative trait loci (cQTL) that frequently explain this variation. cQTLs were associated with molecular activity at clusters of cis-regulatory elements and mapped preferentially within TF-bound regions. We propose that local, sequence-independent chromatin variation emerges as a result of genetic perturbations in cooperative interactions between cis-regulatory elements that are located within the same genomic domain

Properties of DNA methylation when associated to gene expression.

<p>(A) Number of all significant positive (pos-eQTMs) and negative (neg-eQTMs) correlations between DNA methylation and gene expression in fibroblasts (F), LCLs (L) and T-cells (T). A larger number of neg-eQTMs is found in all three cell-types, but an important part is composed of pos-eQTMs. (B) Percent of pos-eQTMs, neg-eQTMs and null sites that overlap with promoters, CpG island shores, CpG islands, gene bodies and enhancers. Pos-eQTMs are significantly depleted in promoter proximal regions in all cell-types. In most cases both positive and negative eQTMs are enriched for CpG island shores, gene bodies and enhancers, and depleted for CpG islands. One star indicates <i>P</i> < 0.05, two stars indicate <i>P</i> < 5E-04, Fisher’s exact test. (C) Correlation coefficients of eQTMs significant in both cell-types compared for all pair wise combinations of the three cell-types are plotted. The percentage of discordant cases (associations with opposite sign between any pair of cell-types) is indicated in the top left corner of each panel. Most eQTMs that are significant in any pair of cell-types have the same sign of association. (D) eQTM effect sizes are measured as the slope of the linear regression of expression given methylation on scaled values, and compared between cell-types for union of eQTMs. Black dots are significant eQTMs (same CpG-exon pair) in both the cell-types compared; orange, blue and purple dots are eQTMs significant only in fibroblast, LCL and T-cell, respectively, within the pair compared. Coefficient of determination <i>R</i>², reflecting the proportion of effect size variance in one cell-type explained by the other cell-type, is shown in the top left corner of each plot (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004958#pgen.1004958.s001" target="_blank">S1 Table</a>). This shows that there is a large amount of tissue-specific effects for correlations between DNA methylation and gene expression.</p

Allele-specific expression cell-type and individual effects.

<p>(A) Overlap of Allele-Specific Expression (ASE) across cell-types illustrated by the relative amount of ASE sites (<i>P</i> < 0.005) in each cell-type (x-axis) found in one, two or three cell-types within each individual (y-axis), requiring at least 30 reads per site and further sampling to exactly 30 reads. Number of ASE sites falling in each category is indicated in the squares. F, L and T stand for fibroblasts, LCLs and T-cells, respectively. 33–40% of the assayable heterozygous sites in the three cell-types of an individual are in ASE in at least two cell-types (see also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004958#pgen.1004958.s020" target="_blank">S13E Fig.</a>). (B) Distributions of allelic ratio distance (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004958#sec004" target="_blank">Materials and Methods</a>) between samples of different (DIFF) or same cell-types (Cell-tp) or individual (Indivl). All the pair wise differences between distributions have <i>P</i> < 2.2E-16 (Wilcoxon test). Allelic ratio distances between two cell-types of an individual are smaller than those between two individuals within one cell-type, and these are smaller than those between different individuals and different cell-types. This indicates a strong genetic load at the individual level, but also an important cell-type specific effect.</p

Summary of associations and allele-specific expression analyses in GenCord.

<p>Number of significant genes, methylation sites or assayable heterozygous sites in fibroblasts (F), LCLs (L) and T-cells (T). The association analyses on eQTL, mQTLs and eQTMs were previously reported [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004958#pgen.1004958.ref037" target="_blank">37</a>].</p><p>* FDR calculated as number of expected over number of observed based on the nominal P-value threshold.</p><p>Summary of associations and allele-specific expression analyses in GenCord.</p

Enrichment of eQTLs and mQTLs in distinct genomic regions.

<p>For distinct genomic regions, the proportion of overlapping eQTLs (A) and mQTLs (B) (both in red) was compared to the proportion of overlapping null SNPs (black, see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004958#sec004" target="_blank">Methods</a> for details). We found significant enrichment of eQTLs in CpG islands, exons and DNase I hypersensitive sites (HSs), and significant enrichment of mQTLs in enhancer and insulator marks, as well as significant depletion in last exons and introns. One star indicates <i>P</i> < 0.05, two stars indicate <i>P</i> < 5E-04, Fisher’s exact test.</p