Expression Quantitative Trait Loci Research Articles

Liver regulatory mechanisms of noncoding variants at lipid and metabolic trait loci

Genome-wide association studies (GWASs) have identified hundreds of risk loci for liver disease and lipid-related metabolic traits, although identifying their target genes and molecular mechanisms remains challenging. We predicted target genes at GWAS signals by integrating them with molecular quantitative trait loci for liver gene expression (eQTL) and liver chromatin accessibility QTL (caQTL). We predicted specific regulatory caQTL variants at four GWAS signals located near EFHD1, LITAF, ZNF329, and GPR180. Using transcriptional reporter assays, we determined that caQTL variants rs13395911, rs11644920, rs34003091, and rs9556404 exhibit allelic differences in regulatory activity. We also performed a protein binding assay for rs13395911 and found that FOXA2 differentially interacts with the alleles of rs13395911. For variants rs13395911 and rs11644920 in putative enhancer regulatory elements, we used CRISPRi to demonstrate that repression of the enhancers altered the expression of the predicted target and/or nearby genes. Repression of the element at rs13395911 reduced the expression of EFHD1, and repression of the element at rs11644920 reduced the expression of LITAF, SNN, and TXNDC11. Finally, we showed that EFHD1 is a metabolically active gene in HepG2 cells. Together, these results provide key steps to connect genetic variants with cellular mechanisms and help elucidate the causes of liver disease.

Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows.

Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.

Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem—when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes’ expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these ‘genetic confounders’, resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

Integration of expression QTLs with fine mapping via SuSiE.

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.

Functional genetic variants of the disulfidptosis-related INF2 gene predict survival of hepatitis B virus-related hepatocellular carcinoma.

Disulfidptosis is a novel form of programmed cell death involved in migration and invasion of cancer cells, but few studies investigated the roles of genetic variants in disulfidptosis-related genes in survival of patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We used Cox proportional hazards regression analyses, Kaplan-Meier curves and receiver operating characteristic curves to assess effects of genetic variants in 14 disulfidptosis-related genes on overall survival of 866 HBV-HCC patients. The Bayesian false discovery probability was used for multiple testing corrections. We also investigated biological mechanisms of the significant variants through expression quantitative trait loci analyses using the data from publicly available databases, luciferase reporter assays and differential expression analyses. As a result, we identified two independently functional single nucleotide polymorphisms (SNPs) (INF2 rs4072285 G > A and INF2 rs4444271 A > T) that predicted overall survival of HBV-HCC patients, with adjusted hazard ratios of 1.60 (95% CI = 1.22-2.11, P = 0.001) and 1.50 (95% CI = 1.80-1.90, P < 0.001), respectively, after multiple testing correction. Luciferase reporter assays indicated that both INF2 rs4072285 A and INF2 rs4444271 T alleles increased INF2 mRNA expression levels (P < 0.001) that were also higher in HCC tumor tissues than in adjacent normal tissues (P < 0.001); such elevated INF2 expression levels were associated with a poorer survival of HBV-HCC patients (P < 0.001) in the TCGA database. In summary, this study supported that INF2 rs4072285 and INF2 rs4444271 may be novel biomarkers for survival of HBV-HCC patients.

Exome-Wide Sequencing Study Identified Genetic Variants Associated With Sarcopenic Obesity.

Sarcopenic obesity (SO) is an age-related disease characterized by the coexistence of excessive adiposity and low muscle mass or function. Although obesity and sarcopenia are heritable conditions, the genetic determinants of SO have not been fully understood. We conducted a large-scale exome-wide association analysis of SO in a sequenced sample of 2887 cases and 113284 controls and an imputed sample of 4003 cases and 161990 controls in the UK Biobank cohort. Single-variant association analysis identified one locus 1q41 (lead SNP rs1417066, LYPLAL1-AS1, odds ratio [OR] = 1.15, 95% confidence interval [CI] = [1.11-1.19], p = 1.75 × 10-14) that was significantly associated with SO at the exome-wide significance level (p < 1 × 10-8). Colocalization analysis in the Genotype-Tissue Expression expression quantitative trait locus database showed that LYPLAL1-AS1 was colocalized with SO in multiple musculoskeletal-related tissues. Gene-based burden test of rare loss-of-function variants identified 5 genes at the gene-wise significance level (p < 4.3 × 10-6): PDE3B (OR = 2.48, p = 1.10 × 10-6), MYOZ3 (OR = 25.49, p = 1.41 × 10-7), SLC15A3 (OR = 4.75, p = 6.82 × 10-7), RNF130 (OR = 25.83, p = 4.07 × 10-6), and TNK2 (OR = 4.25, p = 8.75 × 10-8). Overall, our study uncovered the genetic effects of both common and rare variants on SO susceptibility, expanded existing knowledge of the genetic architecture of SO, and improved understanding of the genetic mechanisms underlying SO.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster.

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs.

Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC

Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.

Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance

BackgroundUncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia.MethodsWe performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes.ResultsThe single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues.ConclusionsVDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

Expression levels of GSDMB and ORMDL3 are associated with relapsing-remitting multiple sclerosis and IKZF3 rs12946510 variant

Multiple sclerosis (MS), a noncurable autoimmune neurodegenerative disease, requires constant research that could improve understanding of both environmental and genetic factors that lead to its occurrence and/or progression. Recognition of the genetic basis of MS further leads to an investigation of the regulatory role of genetic variants on gene expression. Among risk variants for MS, Ikaros zinc finger 3 (IKZF3) gene variant rs12946510 was identified as one of the top-ranked and the expression quantitative trait loci (eQTL) for genes residing in chromosomal locus 17q12-21. The study aimed to investigate the association of gene expression of the immunologically relevant genes, which map to indicated locus, ORMDL3, GSDMB, and IKZF3, with MS and rs12946510 genotype, taking into account disease phase, clinical parameters of disease progression, and severity and immunomodulatory therapy. We used TaqMan® technology for both allelic discrimination and gene expression determination in 67 relapsing MS patients and 50 healthy controls. Decreased ORMDL3 and GSDMB mRNA levels had significant associations with MS and rs12946510 TT rare homozygote among patients. Significant positive correlations between ORMDL3 and GSDMB mRNA expression were observed in both patients and controls. We detected the significant between-effect of sex and rs12946510 on the expression of ORMDL3 in the patient group and interferon β therapy and rs12946510 on GSDMB expression.Our results show the association of ORMDL3 and GSDMB mRNA expression with the clinical manifestation of MS and confirm that IKZF3 rs12946510 exerts the eQTL effect on both genes in multiple sclerosis. Besides providing novel insight related to MS phases and interferon β therapy, the study results confirm previous studies on regulatory genetic variants, autoimmunity, and MS.

Associations between lipid-lowering drugs and pregnancy and perinatal outcomes: a Mendelian randomization study.

Mounting evidence has indicated that maternal dyslipidemia is associated with adverse obstetric outcomes, and the actions of lipid-lowering drugs in pregnant women remain controversial. Hence, this study aimed to appraise the causal relationship of lipid-lowering drugs [hydroxymethylglutaryl-coenzyme reductase (HMGCR) inhibitors, PCSK9 inhibitors, and NPC1L1 inhibitors] with pregnancy and perinatal outcomes using drug-targeting Mendelian randomization analysis. As a proxy for lipid-lowering drug exposure, two genetic instruments were used: genetic variants within or near the gene linked to low-density lipoprotein cholesterol (LDL-C) and the expression of quantitative trait loci of the drug target gene. Effect estimates were calculated using the inverse variance weighting (IVW) method and summary data-based Mendelian randomization (SMR) method. Heterogeneity and pleiotropy were assessed by Mendelian randomization-Egger regression, the Cochran Q test, and MR-PRESSO analysis. HMGCR inhibitors were ascribed to a reduced risk of preeclampsia in both the IVW-MR method [odds ratio (OR) 0.583; 95% confidence interval (CI) 0.418-0.812; P = 0.001] and SMR analysis (OR 0.816; 95% CI 0.675-0.986; P = 0.036). The causal link between HMGCR inhibitors and offspring birthweight was statistically significant only in the analysis using the IVW method (OR, 0.879; 95% CI, 0.788-0.980; P = 0.020), and the combined results of the OR values supported the potential inhibitory effect of HMGCR inhibitors on offspring birthweight. Causal associations between lipid-lowering drugs and gestational diabetes, preterm birth, and congenital anomalies were not detected in either analysis. No causal associations were observed between lipid-lowering drugs and gestational diabetes, preterm birth or congenital anomalies, whereas genetically predicted HMGCR inhibition dramatically reduced the risk of preeclampsia but attenuated offspring birthweight.

Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data

Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.

Identification of potential crucial genes and therapeutic targets for epilepsy

BackgroundEpilepsy, a central neurological disorder, has a complex genetic architecture. There is some evidence suggesting that genetic factors play a role in both the occurrence of epilepsy and its treatment. However, the genetic determinants of epilepsy are largely unknown. This study aimed to identify potential therapeutic targets for epilepsy.MethodsDifferentially expressed genes (DEGs) were extracted from the expression profiles of GSE44031 and GSE1834. Gene co-expression analysis was used to confirm the regulatory relationship between newly discovered epilepsy candidate genes and known epilepsy genes. Expression quantitative trait loci analysis was conducted to determine if epilepsy risk single-nucleotide polymorphisms regulate DEGs’ expression in human brain tissue. Finally, protein–protein interaction analysis and drug–gene interaction analysis were performed to assess the role of DEGs in epilepsy treatment.ResultsThe study found that the protein tyrosine phosphatase receptor-type O gene (PTPRO) and the growth arrest and DNA damage inducible alpha gene (GADD45A) were significantly upregulated in epileptic rats compared to controls in both datasets. Gene co-expression analysis revealed that PTPRO was co-expressed with RBP4, NDN, PAK3, FOXG1, IDS, and IDS, and GADD45A was co-expressed with LRRK2 in human brain tissue. Expression quantitative trait loci analysis suggested that epilepsy risk single-nucleotide polymorphisms could be responsible for the altered PTPRO and GADD45A expression in human brain tissue. Moreover, the protein encoded by GADD45A had a direct interaction with approved antiepileptic drug targets, and GADD45A interacts with genistein and cisplatin.ConclusionsThe results of this study highlight PTPRO and GADD45A as potential genes for the diagnosis and treatment of epilepsy.

Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma

Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.

Multi-tissue transcriptome analysis to identify candidate genes associated with weight regulation in Hanwoo cattle.

While genetic markers related to meat production traits have been identified in many other cattle breeds, research on weight in Hanwoo cattle (Korean native cattle) is still insufficient. In this study, we performed expression quantitative trait loci (eQTL) analysis and differential gene expression analysis to detect candidate genes influencing the weight characteristics of 32 castrated Hanwoo cattle across 22 tissues and, we identified variants that affect gene expression levels. In total, we identified a total of 3,298 differentially expressed genes, among which we discovered key genes such as UBD, RGS2, FASN, and SCD that have functions related to adipogenesis, body weight, obesity, and lipid metabolism. Gene-set enrichment analysis revealed that candidate genes in adipose tissue are involved in metabolic pathways linked to obesity-related traits, adipose metabolism, and lipid metabolism. Additionally, we found that decreased expression of TRIM31 contributes to weight gain which can be explained by the associated candidate cis-eQTL genotypes for TRIM31 and their effect on differential gene expression between the lower and higher weight groups. Our findings revealed candidate genes associated with the weight of Hanwoo cattle and perhaps can provide comprehensive insights into the association of weight with various tissues beyond adipose tissue and muscle, indicating the potential for expanding the focus of livestock trait research.

Associations between genetic variants in sphingolipid metabolism pathway genes and hepatitis B virus-related hepatocellular carcinoma survival.

Although the sphingolipid metabolism pathway is known to play a significant role in tumor progression, there have been few studies on how genetic variants in the sphingolipid metabolism pathway genes affect the survival of patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). We utilized available genotyping data to conduct multivariate Cox proportional hazards regression model analysis, examining the associations of 12,188 single nucleotide polymorphisms (SNPs) in 86 sphingolipid metabolism pathway genes on the survival of 866 HBV-HCC patients, and the model was also used in additive interaction analysis. We used bioinformatics functional prediction and expression quantitative trait locus (eQTL) analysis to explore the potential functions of SNPs and to evaluate the association of SNPs with the corresponding mRNA expression, respectively. We also used the online database TIMER2.0 (http://timer.comp-genomics.org/) to analyze the relationship between the corresponding mRNA expression levels and immune cell infiltration. Our study found that GBA2 rs1570247 G>A was significantly associated with elevated survival of HBV-HCC patients [(hazards ratio (HR)=0.74, 95% confidence interval (CI)=0.64-0.86, P<0.001)]. And on an additive scale, a synergistic effect was observed between the GG genotype of rs1570247 and advanced BCLC stage. Among HBV-HCC patients with advanced BCLC stage, those carrying the GBA2 rs1570247 GG genotype exhibited a significantly elevated risk of mortality (HR=3.32, 95%CI=2.45-4.50). Further functional prediction and eQTL analysis revealed that rs1570247 were located in the 5' untranslated region of the GBA2, the A allele of SNP rs1570247 was associated with higher mRNA expression levels of GBA2 in normal liver tissues (P=0.009). Moreover, we observed a positive correlation between GBA2 mRNA expression and the infiltration level of B lymphocytes cell (R=0.331, P<0.001), while a negative correlation was noted between GBA2 mRNA expression and the infiltration level of macrophage M2 in HCC (R=-0.383, P<0.001). Our findings suggest that GBA2 rs1570247 G>A in sphingolipid metabolism pathway may be a key factor for survival of HBV-HCC patients by regulating the expression of corresponding genes and affecting the infiltration level of immune cells.

Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation, that develops in a small subset of people with food allergies. Genome-wide association studies (GWASs) have identified 9 independent EoE risk loci reaching genome-wide significance (p<5×10-8) and 27 additional loci of suggestive significance (5×10-8<p<1×10-5). In the current study, we perform linkage disequilibrium (LD) expansion of these loci to nominate a set of 531 variants that are potentially causal. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their genomic sequence context cloned into a GFP reporter library. Analysis of reporter gene expression in TE-7, HaCaT, and Jurkat cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants, representing 6 genome-wide significant EoE loci and 7 suggestive EoE loci, that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in individuals with EoE. Transcription factor enrichment analyses reveal possible roles for cell-type-specific regulators, including GATA3. Our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing functional insights into the effects of genetic variation in this disease.

Signatures of Co-evolution and Co-regulation in the CYP3A and CYP4F Genes in Humans.

Cytochromes P450 (CYP450) are hemoproteins generally involved in the detoxification of the body of xenobiotic molecules. They participate in the metabolism of many drugs and genetic polymorphisms in humans have been found to impact drug responses and metabolic functions. In this study, we investigate the genetic diversity of CYP450 genes. We found that two clusters, CYP3A and CYP4F, are notably differentiated across human populations with evidence for selective pressures acting on both clusters: we found signals of recent positive selection in CYP3A and CYP4F genes and signals of balancing selection in CYP4F genes. Furthermore, an extensive amount of unusual linkage disequilibrium is detected in this latter cluster, indicating co-evolution signatures among CYP4F genes. Several of the selective signals uncovered co-localize with expression quantitative trait loci (eQTL), which could suggest epistasis acting on co-regulation in these gene families. In particular, we detected a potential co-regulation event between CYP3A5 and CYP3A43, a gene whose function remains poorly characterized. We further identified a causal relationship between CYP3A5 expression and reticulocyte count through Mendelian randomization analyses, potentially involving a regulatory region displaying a selective signal specific to African populations. Our findings linking natural selection and gene expression in CYP3A and CYP4F subfamilies are of importance in understanding population differences in metabolism of nutrients and drugs.