Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Pleiotropic modifiers of age-related diabetes and neonatal intestinal obstruction in cystic fibrosis.

Individuals with cystic fibrosis (CF) develop complications of the gastrointestinal tract influenced by genetic variants outside of CFTR. Cystic fibrosis-related diabetes (CFRD) is a distinct form of diabetes with a variable age of onset that occurs frequently in individuals with CF, while meconium ileus (MI) is a severe neonatal intestinal obstruction affecting ∼20% of newborns with CF. CFRD and MI are slightly correlated traits with previous evidence of overlap in their genetic architectures. To better understand the genetic commonality between CFRD and MI, we used whole-genome-sequencing data from the CF Genome Project to perform genome-wide association. These analyses revealed variants at 11 loci (6 not previously identified) that associated with MI and at 12 loci (5 not previously identified) that associated with CFRD. Of these, variants at SLC26A9, CEBPB, and PRSS1 associated with both traits; variants at SLC26A9 and CEBPB increased risk for both traits, while variants at PRSS1, the higher-risk alleles for CFRD, conferred lower risk for MI. Furthermore, common and rare variants within the SLC26A9 locus associated with MI only or CFRD only. As expected, different loci modify risk of CFRD and MI; however, a subset exhibit pleiotropic effects indicating etiologic and mechanistic overlap between these two otherwise distinct complications of CF.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies.

Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD.

Genetic variation in severe cystic fibrosis liver disease is associated with novel mechanisms for disease pathogenesis.

BACKGROUND AND AIMS: It is not known why severe cystic fibrosis (CF) liver disease (CFLD) with portal hypertension occurs in only ~7% of people with CF. We aimed to identify genetic modifiers for severe CFLD to improve understanding of disease mechanisms. APPROACH AND RESULTS: Whole-genome sequencing was available in 4082 people with CF with pancreatic insufficiency (n = 516 with severe CFLD; n = 3566 without CFLD). We tested ~15.9 million single nucleotide polymorphisms (SNPs) for association with severe CFLD versus no-CFLD, using pre-modulator clinical phenotypes including (1) genetic variant ( SERPINA1 ; Z allele) previously associated with severe CFLD; (2) candidate SNPs (n = 205) associated with non-CF liver diseases; (3) genome-wide association study of common/rare SNPs; (4) transcriptome-wide association; and (5) gene-level and pathway analyses. The Z allele was significantly associated with severe CFLD ( p = 1.1 × 10 -4 ). No significant candidate SNPs were identified. A genome-wide association study identified genome-wide significant SNPs in 2 loci and 2 suggestive loci. These 4 loci contained genes [significant, PKD1 ( p = 8.05 × 10 -10 ) and FNBP1 ( p = 4.74 × 10 -9 ); suggestive, DUSP6 ( p = 1.51 × 10 -7 ) and ANKUB1 ( p = 4.69 × 10 -7 )] relevant to severe CFLD pathophysiology. The transcriptome-wide association identified 3 genes [ CXCR1 ( p = 1.01 × 10 -6 ) , AAMP ( p = 1.07 × 10 -6 ), and TRBV24 ( p = 1.23 × 10 -5 )] involved in hepatic inflammation and innate immunity. Gene-ranked analyses identified pathways enriched in genes linked to multiple liver pathologies. CONCLUSION: These results identify loci/genes associated with severe CFLD that point to disease mechanisms involving hepatic fibrosis, inflammation, innate immune function, vascular pathology, intracellular signaling, actin cytoskeleton and tight junction integrity and mechanisms of hepatic steatosis and insulin resistance. These discoveries will facilitate mechanistic studies and the development of therapeutics for severe CFLD.

Characterizing PFAS hazards and risks: a human population-based in vitro cardiotoxicity assessment strategy.

Per- and poly-fluoroalkyl substances (PFAS) are emerging contaminants of concern because of their wide use, persistence, and potential to be hazardous to both humans and the environment. Several PFAS have been designated as substances of concern; however, most PFAS in commerce lack toxicology and exposure data to evaluate their potential hazards and risks. Cardiotoxicity has been identified as a likely human health concern, and cell-based assays are the most sensible approach for screening and prioritization of PFAS. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a widely used method to test for cardiotoxicity, and recent studies showed that many PFAS affect these cells. Because iPSC-derived cardiomyocytes are available from different donors, they also can be used to quantify human variability in responses to PFAS. The primary objective of this study was to characterize potential human cardiotoxic hazard, risk, and inter-individual variability in responses to PFAS. A total of 56 PFAS from different subclasses were tested in concentration-response using human iPSC-derived cardiomyocytes from 16 donors without known heart disease. Kinetic calcium flux and high-content imaging were used to evaluate biologically-relevant phenotypes such as beat frequency, repolarization, and cytotoxicity. Of the tested PFAS, 46 showed concentration-response effects in at least one phenotype and donor; however, a wide range of sensitivities were observed across donors. Inter-individual variability in the effects could be quantified for 19 PFAS, and risk characterization could be performed for 20 PFAS based on available exposure information. For most tested PFAS, toxicodynamic variability was within a factor of 10 and the margins of exposure were above 100. This study identified PFAS that may pose cardiotoxicity risk and have high inter-individual variability. It also demonstrated the feasibility of using a population-based human in vitro method to quantify population variability and identify cardiotoxicity risks of emerging contaminants.

Control of false discoveries in grouped hypothesis testing for eQTL data.

BACKGROUND: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. RESULTS: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. CONCLUSION: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes.

Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

Genetic Modifiers of Cystic Fibrosis Lung Disease Severity: Whole-Genome Analysis of 7,840 Patients.

Rationale: Lung disease is the major cause of morbidity and mortality in persons with cystic fibrosis (pwCF). Variability in CF lung disease has substantial non-CFTR (CF transmembrane conductance regulator) genetic influence. Identification of genetic modifiers has prognostic and therapeutic importance. Objectives: Identify genetic modifier loci and genes/pathways associated with pulmonary disease severity. Methods: Whole-genome sequencing data on 4,248 unique pwCF with pancreatic insufficiency and lung function measures were combined with imputed genotypes from an additional 3,592 patients with pancreatic insufficiency from the United States, Canada, and France. This report describes association of approximately 15.9 million SNPs using the quantitative Kulich normal residual mortality-adjusted (KNoRMA) lung disease phenotype in 7,840 pwCF using premodulator lung function data. Measurements and Main Results: Testing included common and rare SNPs, transcriptome-wide association, gene-level, and pathway analyses. Pathway analyses identified novel associations with genes that have key roles in organ development, and we hypothesize that these genes may relate to dysanapsis and/or variability in lung repair. Results confirmed and extended previous genome-wide association study findings. These whole-genome sequencing data provide finely mapped genetic information to support mechanistic studies. No novel primary associations with common single variants or rare variants were found. Multilocus effects at chr5p13 (SLC9A3/CEP72) and chr11p13 (EHF/APIP) were identified. Variant effect size estimates at associated loci were consistently ordered across the cohorts, indicating possible age or birth cohort effects. Conclusions: This premodulator genomic, transcriptomic, and pathway association study of 7,840 pwCF will facilitate mechanistic and postmodulator genetic studies and the development of novel therapeutics for CF lung disease.

Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood.

Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multiple traits.

Model systems and organisms for addressing inter- and intra-species variability in risk assessment.

Addressing inter- and intra-species differences in potential hazardous effects of chemicals remains a long-standing challenge in human health risk assessment that is typically addressed heuristically through use of 10-fold default "uncertainty" or "safety" factors. Although it has long been recognized that chemical-specific data would be preferable to replace the "defaults," only recently have there emerged experimental model systems and organisms with the potential to experimentally quantify the population variability in both toxicokinetics and toxicodynamics for specific chemicals. Progress is most evident in the use of population in vitro human cell-based models and population in vivo mouse models. Multiple case studies were published in the past 10-15 years that clearly demonstrate the utility of such models to derive data with direct application to quantifying variability at hazard identification, exposure-response assessment, and mechanistic understanding of toxicity steps of traditional risk assessments. Here, we review recent efforts to develop fit-for-purpose approaches utilizing these novel population-based in vitro and in vivo models in the context of risk assessment. We also describe key challenges and opportunities to broadening application of population-based experimental approaches. We conclude that population-based models are now beginning to realize their potential to address long-standing data gaps in inter- and intra-species variability.

Characterization of population variability of 1,3-butadiene derived protein adducts in humans and mice.

1,3-butadiene is a known human carcinogen and a chemical to which humans are exposed occupationally and through environmental pollution. Inhalation risk assessment of 1,3-butadiene was completed several decades ago before data on molecular biomarkers of exposure and effect have been reported from both human studies of workers and experimental studies in mice. To improve risk assessment of 1,3-butadiene, the quantitative characterization of uncertainty in estimations of inter-individual variability in cancer-related effects is needed. For this, we ought to take advantage of the availability of the data on 1,3-butadiene hemoglobin adducts, well established biomarkers of the internal dose of the reactive epoxides, from several large-scale human studies and from a study in a Collaborative Cross mouse population. We found that in humans, toxicokinetic uncertainty factor for 99th percentile of the population ranged from 3.27 to 7.9, depending on the hemoglobin adduct. For mice, these values ranged from less than 2 to 7.51, depending on the dose and the adduct. Quantitative estimated from this study can be used to reduce uncertainties in the parameter estimates used in the models to derive the inhalation unit risk, as well as to address possible differences in variability in 1,3-butadiene metabolism that may be dose-related.

Characterization of Compositional Variability in Petroleum Substances.

In the process of registration of substances of Unknown or Variable Composition, Complex Reaction Products or Biological Materials (UVCBs), information sufficient to enable substance identification must be provided. Substance identification for UVCBs formed through petroleum refining is particularly challenging due to their chemical complexity, as well as variability in refining process conditions and composition of the feedstocks. This study aimed to characterize compositional variability of petroleum UVCBs both within and across product categories. We utilized ion mobility spectrometry (IMS)-MS as a technique to evaluate detailed chemical composition of independent production cycle-derived samples of 6 petroleum products from 3 manufacturing categories (heavy aromatic, hydrotreated light paraffinic, and hydrotreated heavy paraffinic). Atmospheric pressure photoionization and drift tube IMS-MS were used to identify structurally related compounds and quantified between- and within-product variability. In addition, we determined both individual molecules and hydrocarbon blocks that were most variable in samples from different production cycles. We found that detailed chemical compositional data on petroleum UVCBs obtained from IMS-MS can provide the information necessary for hazard and risk characterization in terms of quantifying the variability of the products in a manufacturing category, as well as in subsequent production cycles of the same product.

Intra- and Inter-Species Variability in Urinary N7-(1-Hydroxy-3-buten-2-yl)guanine Adducts Following Inhalation Exposure to 1,3-Butadiene.

1,3-Butadiene is a known carcinogen primarily targeting lymphoid tissues, lung, and liver. Cytochrome P450 activates butadiene to epoxides which form covalent DNA adducts that are thought to be a key mechanistic event in cancer. Previous studies suggested that inter-species, -tissue, and -individual susceptibility to adverse health effects of butadiene exposure may be due to differences in metabolism and other mechanisms. In this study, we aimed to examine the extent of inter-individual and inter-species variability in the urinary N7-(1-hydroxy-3-buten-2-yl)guanine (EB-GII) DNA adduct, a well-known biomarker of exposure to butadiene. For a population variability study in mice, we used the collaborative cross model. Female and male mice from five strains were exposed to filtered air or butadiene (590 ppm, 6 h/day, 5 days/week for 2 weeks) by inhalation. Urine samples were collected, and the metabolic activation of butadiene by DNA-reactive species was quantified as urinary EB-GII adducts. We quantified the degree of EB-GII variation across mouse strains and sexes; then, we compared this variation with the data from rats (exposed to 62.5 or 200 ppm butadiene) and humans (0.004-2.2 ppm butadiene). We show that sex and strain are significant contributors to the variability in urinary EB-GII levels in mice. In addition, we find that the degree of variability in urinary EB-GII in collaborative cross mice, when expressed as an uncertainty factor for the inter-individual variability (UFH), is relatively modest (≤threefold) possibly due to metabolic saturation. By contrast, the variability in urinary EB-GII (adjusted for exposure) observed in humans, while larger than the default value of 10-fold, is largely consistent with UFH estimates for other chemicals based on human data for non-cancer endpoints. Overall, these data demonstrate that urinary EB-GII levels, particularly from human studies, may be useful for quantitative characterization of human variability in cancer risks to butadiene.

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.

Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues.

Genetic variants that modulate gene expression levels play an important role in the etiology of human diseases and complex traits. Although large-scale eQTL mapping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variants regulate expression remain unclear, particularly for distal eQTLs, which these studies are not well powered to detect. Here, we leveraged all variants (not just those that pass stringent significance thresholds) to analyze the functional architecture of local and distal regulation of gene expression in 15 human tissues by employing an extension of stratified LD-score regression that produces robust results in simulations. The top enriched functional categories in local regulation of peripheral-blood gene expression included coding regions (11.41×), conserved regions (4.67×), and four histone marks (p < 5 × 10-5 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral-blood gene expression: coding regions (4.47×), conserved regions (4.51×), and two histone marks (p < 3 × 10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues confirmed that local regulation of gene expression is largely shared across tissues but that distal regulation is highly tissue specific. Our results elucidate the functional components of the genetic architecture of local and distal regulation of gene expression.

Low-Dose Silver Nanoparticle Surface Chemistry and Temporal Effects on Gene Expression in Human Liver Cells.

Silver nanoparticles (AgNPs) are widely incorporated into consumer and biomedical products for their antimicrobial and plasmonic properties with limited risk assessment of low-dose cumulative exposure in humans. To evaluate cellular responses to low-dose AgNP exposures across time, human liver cells (HepG2) are exposed to AgNPs with three different surface charges (1.2 µg mL-1 ) and complete gene expression is monitored across a 24 h period. Time and AgNP surface chemistry mediate gene expression. In addition, since cells are fed, time has marked effects on gene expression that should be considered. Surface chemistry of AgNPs alters gene transcription in a time-dependent manner, with the most dramatic effects in cationic AgNPs. Universal to all surface coatings, AgNP-treated cells responded by inactivating proliferation and enabling cell cycle checkpoints. Further analysis of these universal features of AgNP cellular response, as well as more detailed analysis of specific AgNP treatments, time points, or specific genes, is facilitated with an accompanying application. Taken together, these results provide a foundation for understanding hepatic response to low-dose AgNPs for future risk assessment.

Conditional eQTL analysis reveals allelic heterogeneity of gene expression.

In recent years, multiple eQTL (expression quantitative trait loci) catalogs have become available that can help understand the functionality of complex trait-related single nucleotide polymorphisms (SNPs). In eQTL catalogs, gene expression is often strongly associated with multiple SNPs, which may reflect either one or multiple independent associations. Conditional eQTL analysis allows a distinction between dependent and independent eQTLs. We performed conditional eQTL analysis in 4,896 peripheral blood microarray gene expression samples. Our analysis showed that 35% of genes with a cis eQTL have at least two independent cis eQTLs; for several genes up to 13 independent cis eQTLs were identified. Also, 12% (671) of the independent cis eQTLs identified in conditional analyses were not significant in unconditional analyses. The number of GWAS catalog SNPs identified as eQTL in the conditional analyses increases with 24% as compared to unconditional analyses. We provide an online conditional cis eQTL mapping catalog for whole blood (https://eqtl.onderzoek.io/), which can be used to lookup eQTLs more accurately than in standard unconditional whole blood eQTL databases.

An empirical Bayes approach for multiple tissue eQTL analysis.

Expression quantitative trait locus (eQTL) analyses identify genetic markers associated with the expression of a gene. Most up-to-date eQTL studies consider the connection between genetic variation and expression in a single tissue. Multi-tissue analyses have the potential to improve findings in a single tissue, and elucidate the genotypic basis of differences between tissues. In this article, we develop a hierarchical Bayesian model (MT-eQTL) for multi-tissue eQTL analysis. MT-eQTL explicitly captures patterns of variation in the presence or absence of eQTL, as well as the heterogeneity of effect sizes across tissues. We devise an efficient Expectation-Maximization (EM) algorithm for model fitting. Inferences concerning eQTL detection and the configuration of eQTL across tissues are derived from the adaptive thresholding of local false discovery rates, and maximum a posteriori estimation, respectively. We also provide theoretical justification of the adaptive procedure. We investigate the MT-eQTL model through an extensive analysis of a 9-tissue data set from the GTEx initiative.