Genetic Control Of Gene Expression Research Articles

Cell-type-specific and disease-associated expression quantitative trait loci in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis. Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA sequencing of lung tissue from 66 individuals with pulmonary fibrosis and 48 unaffected donors. Using a pseudobulk approach, we mapped expression quantitative trait loci (eQTLs) across 38 cell types, observing both shared and cell-type-specific regulatory effects. Furthermore, we identified disease interaction eQTLs and demonstrated that this class of associations is more likely to be cell-type-specific and linked to cellular dysregulation in pulmonary fibrosis. Finally, we connected lung disease risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression and implicates context-specific eQTLs as key regulators of lung homeostasis and disease.

Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells

BackgroundThe discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression.ResultsHere, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming.ConclusionsThis work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.

On the cross-population generalizability of gene expression prediction models.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/.

Genetic Effects on Enhancer Activity in Human Pancreatic Islets

Genome-wide association studies (GWAS) have identified >100 single nucleotide polymorphisms (SNPs) that modulate risk for type 2 diabetes (T2D) and related traits. Using functional genomic profiling, we and others have shown that T2D GWAS SNPs are significantly enriched to overlap pancreatic islet stretch enhancer chromatin states, implicating these regulatory elements as biological mediators of disease risk. Recent studies that integrate genetic variation and islet gene expression have identified expression quantitative trait loci (eQTL) that nominate target effector transcripts for T2D GWAS SNPs. However, the subset of causal regulatory SNPs that mediate genetic control of gene expression are unknown. To help close this gap in knowledge, we have performed capped analysis of gene expression (CAGE) on RNA enriched for active enhancers (eRNA-CAGE) across 40 human islet samples. Comparison of these profiles to previously published islet chromatin maps revealed significant enrichment for active chromatin states (P<0.001). Interestingly, we observe unbalanced bidirectional transcription at chromatin-defined intronic enhancer regions, where the predominant direction of enhancer transcription is concordant with the transcribed direction of the endogenous gene, suggesting interactions between gene and enhancer activity. One such example is an ABCC8 intronic enhancer that we previously functionally validated using a mouse transgenic reporter assay. We further performed genotyping and imputation, which allowed us to run an eRNA-QTL scan. We identified 4associations (P<0.for beta distribution adjusted P-value; QTLtools package), which are significantly enriched (P=7.3x10-7) to occur in islet active enhancer chromatin states. Notably, we found islet eRNA-QTL that overlap T2D GWAS SNPs and islet eQTL. Together, these results form a genetic map of human islet enhancer activity that is a foundation for interpreting regulatory mechanisms at T2D GWAS loci. Disclosure A. Varshney: None. S. Parker: None.

Genetic Control of Left Atrial Gene Expression Yields Insights into the Genetic Susceptibility for Atrial Fibrillation.

Genome-wide association studies have identified 23 loci for atrial fibrillation (AF), but the mechanisms responsible for these associations, as well as the causal genes and genetic variants, remain undefined. To identify the effect of common genetic variants on gene expression that might explain the mechanisms linking genome-wide association loci with AF risk, we performed RNA sequencing of left atrial appendages from a biracial cohort of 265 subjects. Combining gene expression data with genome-wide single nucleotide polymorphism data, we found that approximately two-thirds of the expressed genes were regulated in cis by common genetic variants at a false discovery rate of <0.05, defined as cis-expression quantitative trait loci. Twelve of 23 reported AF genome-wide association loci displayed genome-wide significant cis-expression quantitative trait loci, at PRRX1 (chromosome 1q24), SNRNP27 (1q24), CEP68 (2p14), FKBP7 (2q31), KCNN2 (5q22), FAM13B (5q31), CAV1 (7q31), ASAH1 (8p22), MYOZ1 (10q22), C11ORF45 (11q24), TBX5 (12q24), and SYNE2 (14q23), suggesting that altered expression of these genes plays a role in AF susceptibility. Allelic expression imbalance was used as an independent method to characterize the cis-control of gene expression. One thousand two hundred forty-eight of 5153 queried genes had cis-single nucleotide polymorphisms that significantly regulated allelic expression at a false discovery rate of <0.05. We provide a genome-wide catalog of the genetic control of gene expression in human left atrial appendage. These data can be used to confirm the relevance of genome-wide association loci and to direct future functional studies to identify the genes and genetic variants responsible for complex diseases such as AF.

Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease

BackgroundGenome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic obstructive pulmonary disease (COPD). However, many genetic variants show suggestive evidence for association but do not meet the strict threshold for genome-wide significance. Integrative analysis of multiple omics datasets has the potential to identify novel genes involved in disease pathogenesis by leveraging these variants in a functional, regulatory context.ResultsWe performed expression quantitative trait locus (eQTL) analysis using genome-wide SNP genotyping and gene expression profiling of lung tissue samples from 86 COPD cases and 31 controls, testing for SNPs associated with gene expression levels. These results were integrated with a prior COPD GWAS using an ensemble statistical and network methods approach to identify relevant genes and observe them in the context of overall genetic control of gene expression to highlight co-regulated genes and disease pathways. We identified 250,312 unique SNPs and 4997 genes in the cis(local)-eQTL analysis (5% false discovery rate). The top gene from the integrative analysis was MAPT, a gene recently identified in an independent GWAS of lung function. The genes HNRNPAB and PCBP2 with RNA binding activity and the gene ACVR1B were identified in network communities with validated disease relevance.ConclusionsThe integration of lung tissue gene expression with genome-wide SNP genotyping and subsequent intersection with prior GWAS and omics studies highlighted candidate genes within COPD loci and in communities harboring known COPD genes. This integration also identified novel disease genes in sub-threshold regions that would otherwise have been missed through GWAS.

Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis

BackgroundEfforts to improve sustainability in livestock production systems have focused on two objectives: investigating the genetic control of immune function as it pertains to robustness and disease resistance, and finding predictive markers for use in breeding programs. In this context, the peripheral blood transcriptome represents an important source of biological information about an individual’s health and immunological status, and has been proposed for use as an intermediate phenotype to measure immune capacity. The objective of this work was to study the genetic architecture of variation in gene expression in the blood of healthy young pigs using two approaches: an expression genome-wide association study (eGWAS) and allele-specific expression (ASE) analysis.ResultsThe blood transcriptomes of 60-day-old Large White pigs were analyzed by expression microarrays for eGWAS (242 animals) and by RNA-Seq for ASE analysis (38 animals). Using eGWAS, the expression levels of 1901 genes were found to be associated with expression quantitative trait loci (eQTLs). We recovered 2839 local and 1752 distant associations (Single Nucleotide Polymorphism or SNP located less or more than 1 Mb from expression probe, respectively). ASE analyses confirmed the extensive cis-regulation of gene transcription in blood, and revealed allelic imbalance in 2286 SNPs, which affected 763 genes. eQTLs and ASE-genes were widely distributed on all chromosomes. By analyzing mutually overlapping eGWAS results, we were able to describe putative regulatory networks, which were further refined using ASE data. At the functional level, genes with genetically controlled expression that were detected by eGWAS and/or ASE analyses were significantly enriched in biological processes related to RNA processing and immune function. Indeed, numerous distant and local regulatory relationships were detected within the major histocompatibility complex region on chromosome 7, revealing ASE for most class I and II genes.ConclusionsThis study represents, to the best of our knowledge, the first genome-wide map of the genetic control of gene expression in porcine peripheral blood. These results represent an interesting resource for the identification of genetic markers and blood biomarkers associated with variations in immunity traits in pigs, as well as any other complex traits for which blood is an appropriate surrogate tissue.

Deciphering the genetic control of gene expression following Mycobacterium leprae antigen stimulation.

Leprosy is a human infectious disease caused by Mycobacterium leprae. A strong host genetic contribution to leprosy susceptibility is well established. However, the modulation of the transcriptional response to infection and the mechanism(s) of disease control are poorly understood. To address this gap in knowledge of leprosy pathogenicity, we conducted a genome-wide search for expression quantitative trait loci (eQTL) that are associated with transcript variation before and after stimulation with M. leprae sonicate in whole blood cells. We show that M. leprae antigen stimulation mainly triggered the upregulation of immune related genes and that a substantial proportion of the differential gene expression is genetically controlled. Indeed, using stringent criteria, we identified 318 genes displaying cis-eQTL at an FDR of 0.01, including 66 genes displaying response-eQTL (reQTL), i.e. cis-eQTL that showed significant evidence for interaction with the M. leprae stimulus. Such reQTL correspond to regulatory variations that affect the interaction between human whole blood cells and M. leprae sonicate and, thus, likely between the human host and M. leprae bacilli. We found that reQTL were significantly enriched among binding sites of transcription factors that are activated in response to infection, and that they were enriched among single nucleotide polymorphisms (SNPs) associated with susceptibility to leprosy per se and Type-I Reaction, and seven of them have been targeted by recent positive selection. Our study suggested that natural selection shaped our genomic diversity to face pathogen exposure including M. leprae infection.

Age-Dependent Association of TNFSF15/TNFSF8 Variants and Leprosy Type 1 Reaction.

A current major challenge in leprosy control is the prevention of permanent disabilities. Host pathological inflammatory responses termed type 1 reaction (T1R) are a leading cause of nerve damage for leprosy patients. The environmental or inherited factors that predispose leprosy cases to undergo T1R are not known. However, studies have shown an important contribution of host genetics for susceptibility to T1R. We have previously identified variants encompassing the TNFSF15/TNFSF8 genes as T1R risk factors in a Vietnamese sample and replicated this association in a Brazilian sample. However, we failed to validate in Brazilian patients the strong association of TNFSF15/TNFSF8 markers rs6478108 and rs7863183 with T1R that we had observed in Vietnamese patients. Here, we investigated if the lack of validation of these variants was due to age-dependent effects on association using four independent population samples, two from Brazil and two from Vietnam. In the combined analysis across the four samples, we observed a strong association of the TNFSF15/TNFSF8 variants rs6478108, rs7863183, and rs3181348 with T1R (pcombined = 1.5E−05, pcombined = 1.8E−05, and pcombined = 6.5E−06, respectively). However, the association of rs6478108 with T1R was more pronounced in leprosy cases under 30 years of age compared to the global sample [odds ratio (OR) = 1.95, 95% confidence interval (CI) = 1.54–2.46, pcombined = 2.5E−08 versus OR = 1.46, 95% CI = 1.23–1.73, pcombined = 1.5E−05]. A multivariable analysis indicated that the association of rs6478108 with T1R was independent of either rs7863183 or rs3181348. These three variants are known regulators of the TNFSF8 gene transcription level in multiple tissues. The age dependency of association of rs6478108 and T1R suggests that the genetic control of gene expression varies across the human life span.

Autosomal genetic control of human gene expression does not differ across the sexes.

BackgroundDespite their nearly identical genomes, males and females differ in risk, incidence, prevalence, severity and age-at-onset of many diseases. Sexual dimorphism is also seen in human autosomal gene expression, and has largely been explored by examining the contribution of genotype-by-sex interactions to variation in gene expression.ResultsIn this study, we use data from a mixture of pedigree and unrelated individuals with verified European ancestry to investigate the sex-specific genetic architecture of gene expression measured in whole blood across n=1048 males and n=1005 females by treating gene expression intensities in the sexes as two distinct traits and estimating the genetic correlation (r G) between them. These correlations measure the similarity of the combined additive genetic effects of all single-nucleotide polymorphisms across the autosomal chromosomes, and thus the level of common genetic control of gene expression across the sexes. Genetic correlations are estimated across the sexes for the expression levels of 12,528 autosomal gene expression probes using bivariate GREML, and tested for differences in autosomal genetic control of gene expression across the sexes. Overall, no deviation of the distribution of test statistics is observed from that expected under the null hypothesis of a common autosomal genetic architecture for gene expression across the sexes.ConclusionsThese results suggest that males and females share the same common genetic control of gene expression.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1111-0) contains supplementary material, which is available to authorized users.

Evidence for mitochondrial genetic control of autosomal gene expression.

The mitochondrial and nuclear genomes coordinate and co-evolve in eukaryotes in order to adapt to environmental changes. Variation in the mitochondrial genome is capable of affecting expression of genes on the nuclear genome. Sex-specific mitochondrial genetic control of gene expression has been demonstrated in Drosophila melanogaster, where males were found to drive most of the total variation in gene expression. This has potential implications for male-related health and disease resulting from variation in mtDNA solely inherited from the mother. We used a family-based study comprised of 47,323 gene expression probes and 78 mitochondrial SNPs (mtSNPs) from n = 846 individuals to examine the extent of mitochondrial genetic control of gene expression in humans. This identified 15 significant probe-mtSNP associations (P<10-8) corresponding to 5 unique genes on the mitochondrial and nuclear genomes, with three of these genes corresponding to mitochondrial genetic control of gene expression in the nuclear genome. The associated mtSNPs for three genes (one cis and two trans associations) were replicated (P < 0.05) in an independent dataset of n = 452 unrelated individuals. There was no evidence for sexual dimorphic gene expression in any of these five probes. Sex-specific effects were examined by applying our analysis to males and females separately and testing for differences in effect size. The MEST gene was identified as having the most significantly different effect sizes across the sexes (P≈10-7). MEST was similarly expressed in males and females with the G allele; however, males with the C allele are highly expressed for MEST, while females show no expression of the gene. This study provides evidence for the mitochondrial genetic control of expression of several genes in humans, with little evidence found for sex-specific effects.

A hidden Markov approach for ascertaining cSNP genotypes from RNA sequence data in the presence of allelic imbalance by exploiting linkage disequilibrium.

BackgroundAllelic specific expression (ASE) increases our understanding of the genetic control of gene expression and its links to phenotypic variation. ASE testing is implemented through binomial or beta-binomial tests of sequence read counts of alternative alleles at a cSNP of interest in heterozygous individuals. This requires prior ascertainment of the cSNP genotypes for all individuals. To meet the needs, we propose hidden Markov methods to call SNPs from next generation RNA sequence data when ASE possibly exists.ResultsWe propose two hidden Markov models (HMMs), HMM-ASE and HMM-NASE that consider or do not consider ASE, respectively, in order to improve genotyping accuracy. Both HMMs have the advantages of calling the genotypes of several SNPs simultaneously and allow mapping error which, respectively, utilize the dependence among SNPs and correct the bias due to mapping error. In addition, HMM-ASE exploits ASE information to further improve genotype accuracy when the ASE is likely to be present.Simulation results indicate that the HMMs proposed demonstrate a very good prediction accuracy in terms of controlling both the false discovery rate (FDR) and the false negative rate (FNR). When ASE is present, the HMM-ASE had a lower FNR than HMM-NASE, while both can control the false discovery rate (FDR) at a similar level. By exploiting linkage disequilibrium (LD), a real data application demonstrate that the proposed methods have better sensitivity and similar FDR in calling heterozygous SNPs than the VarScan method. Sensitivity and FDR are similar to that of the BCFtools and Beagle methods. The resulting genotypes show good properties for the estimation of the genetic parameters and ASE ratios.ConclusionsWe introduce HMMs, which are able to exploit LD and account for the ASE and mapping errors, to simultaneously call SNPs from the next generation RNA sequence data. The method introduced can reliably call for cSNP genotypes even in the presence of ASE and under low sequencing coverage. As a byproduct, the proposed method is able to provide predictions of ASE ratios for the heterozygous genotypes, which can then be used for ASE testing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0479-2) contains supplementary material, which is available to authorized users.

Variation and genetic control of gene expression in primary immunocytes across inbred mouse strains.

To determine the breadth and underpinning of changes in immunocyte gene expression due to genetic variation in mice, we performed, as part of the Immunological Genome Project, gene expression profiling for CD4(+) T cells and neutrophils purified from 39 inbred strains of the Mouse Phenome Database. Considering both cell types, a large number of transcripts showed significant variation across the inbred strains, with 22% of the transcriptome varying by 2-fold or more. These included 119 loci with apparent complete loss of function, where the corresponding transcript was not expressed in some of the strains, representing a useful resource of "natural knockouts." We identified 1222 cis-expression quantitative trait loci (cis-eQTL) that control some of this variation. Most (60%) cis-eQTLs were shared between T cells and neutrophils, but a significant portion uniquely impacted one of the cell types, suggesting cell type-specific regulatory mechanisms. Using a conditional regression algorithm, we predicted regulatory interactions between transcription factors and potential targets, and we demonstrated that these predictions overlap with regulatory interactions inferred from transcriptional changes during immunocyte differentiation. Finally, comparison of these and parallel data from CD4(+) T cells of healthy humans demonstrated intriguing similarities in variability of a gene's expression: the most variable genes tended to be the same in both species, and there was an overlap in genes subject to strong cis-acting genetic variants. We speculate that this "conservation of variation" reflects a differential constraint on intraspecies variation in expression levels of different genes, either through lower pressure for some genes, or by favoring variability for others.

Genetic control of gene expression at novel and established chronic obstructive pulmonary disease loci.

Genetic risk loci have been identified for a wide range of diseases through genome-wide association studies (GWAS), but the relevant functional mechanisms have been identified for only a small proportion of these GWAS-identified loci. By integrating results from the largest current GWAS of chronic obstructive disease (COPD) with expression quantitative trait locus (eQTL) analysis in whole blood and sputum from 121 subjects with COPD from the ECLIPSE Study, this analysis identifies loci that are simultaneously associated with COPD and the expression of nearby genes (COPD eQTLs). After integrative analysis, 19 COPD eQTLs were identified, including all four previously identified genome-wide significant loci near HHIP, FAM13A, and the 15q25 and 19q13 loci. For each COPD eQTL, fine mapping and colocalization analysis to identify causal shared eQTL and GWAS variants identified a subset of sites with moderate-to-strong evidence of harboring at least one shared variant responsible for both the eQTL and GWAS signals. Transcription factor binding site (TFBS) analysis confirms that multiple COPD eQTL lead SNPs disrupt TFBS, and enhancer enrichment analysis for loci with the strongest colocalization signals showed enrichment for blood-related cell types (CD3 and CD4+ T cells, lymphoblastoid cell lines). In summary, integrative eQTL and GWAS analysis confirms that genetic control of gene expression plays a key role in the genetic architecture of COPD and identifies specific blood-related cell types as likely participants in the functional pathway from GWAS-associated variant to disease phenotype.

Mapping eQTL networks with mixed graphical Markov models.

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene-gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.

Genetic variability in the regulation of gene expression in ten regions of the human brain.

Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

The One Hour Yeast Proteome

We describe the comprehensive analysis of the yeast proteome in just over one hour of optimized analysis. We achieve this expedited proteome characterization with improved sample preparation, chromatographic separations, and by using a new Orbitrap hybrid mass spectrometer equipped with a mass filter, a collision cell, a high-field Orbitrap analyzer, and, finally, a dual cell linear ion trap analyzer (Q-OT-qIT, Orbitrap Fusion). This system offers high MS2 acquisition speed of 20 Hz and detects up to 19 peptide sequences within a single second of operation. Over a 1.3 h chromatographic method, the Q-OT-qIT hybrid collected an average of 13,447 MS1 and 80,460 MS2 scans (per run) to produce 43,400 (x̄) peptide spectral matches and 34,255 (x̄) peptides with unique amino acid sequences (1% false discovery rate (FDR)). On average, each one hour analysis achieved detection of 3,977 proteins (1% FDR). We conclude that further improvements in mass spectrometer scan rate could render comprehensive analysis of the human proteome within a few hours.

Genetic dissection of quantitative epigenomic variation in yeast

Advances in epigenomics are opening the exciting perspective to associate chromatin variation to the variation of physiological traits in natural populations. Yet, inter-individual epigenomic differences may or may not result from regulatory variation encoded in the DNA. If they do, association then resumes to mapping underlying genetic variants, as for any genetic study. If they don’t, association requires a framework where the epigenetic nature of the variation is taken into account, in particular its potential reversibility. The development of population epigenomics therefore requires an assessment of the nature of epigenomic variation. As a model experimental system, we have traced quantitative variation of acetylation at Lysine 14 of Histone H3 at all nucleosomes in a segregating population of S. cerevisiae. Treating acetylation of every nucleosome as a quantitative trait allowed the identification of hundreds of genetic loci underlying epigenomic divergence either locally (in cis) or distantly (in trans), and this genetic control overlapped only partially with the genetic control of gene expression. We also estimated the stability of epigenetic differences across environmentally induced reprogramming. Strikingly, ‘labile’ and ‘persistent’ nucleosomal variations were associated with poor and strong genetic control, respectively. The study reveals the dual nature of natural chromatin epi-polymorphisms (DNA-encoded vs. not), providing a basis for the development of population epigenomics. Supported by grant SiGHT no281359 from the E.U.

Genetic Modifiers of Chromatin Acetylation Antagonize the Reprogramming of Epi-Polymorphisms

Natural populations are known to differ not only in DNA but also in their chromatin-associated epigenetic marks. When such inter-individual epigenomic differences (or “epi-polymorphisms”) are observed, their stability is usually not known: they may or may not be reprogrammed over time or upon environmental changes. In addition, their origin may be purely epigenetic, or they may result from regulatory variation encoded in the DNA. Studying epi-polymorphisms requires, therefore, an assessment of their nature and stability. Here we estimate the stability of yeast epi-polymorphisms of chromatin acetylation, and we provide a genome-by-epigenome map of their genetic control. A transient epi-drug treatment was able to reprogram acetylation variation at more than one thousand nucleosomes, whereas a similar amount of variation persisted, distinguishing “labile” from “persistent” epi-polymorphisms. Hundreds of genetic loci underlied acetylation variation at 2,418 nucleosomes either locally (in cis) or distantly (in trans), and this genetic control overlapped only partially with the genetic control of gene expression. Trans-acting regulators were not necessarily associated with genes coding for chromatin modifying enzymes. Strikingly, “labile” and “persistent” epi-polymorphisms were associated with poor and strong genetic control, respectively, showing that genetic modifiers contribute to persistence. These results estimate the amount of natural epigenomic variation that can be lost after transient environmental exposures, and they reveal the complex genetic architecture of the DNA–encoded determinism of chromatin epi-polymorphisms. Our observations provide a basis for the development of population epigenetics.