Cis-expression Quantitative Trait Loci Research Articles

Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing

AbstractGenome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

Inflammation status modulates the effect of host genetic variation on intestinal gene expression in inflammatory bowel disease

More than 240 genetic risk loci have been associated with inflammatory bowel disease (IBD), but little is known about how they contribute to disease development in involved tissue. Here, we hypothesized that host genetic variation affects gene expression in an inflammation-dependent way, and investigated 299 snap-frozen intestinal biopsies from inflamed and non-inflamed mucosa from 171 IBD patients. RNA-sequencing was performed, and genotypes were determined using whole exome sequencing and genome wide genotyping. In total, 28,746 genes and 6,894,979 SNPs were included. Linear mixed models identified 8,881 independent intestinal cis-expression quantitative trait loci (cis-eQTLs) (FDR < 0.05) and interaction analysis revealed 190 inflammation-dependent intestinal cis-eQTLs (FDR < 0.05), including known IBD-risk genes and genes encoding immune-cell receptors and antibodies. The inflammation-dependent cis-eQTL SNPs (eSNPs) mainly interact with prevalence of immune cell types. Inflammation-dependent intestinal cis-eQTLs reveal genetic susceptibility under inflammatory conditions that can help identify the cell types involved in and the pathways underlying inflammation, knowledge that may guide future drug development and profile patients for precision medicine in IBD.

Genome-wide genetic links between amyotrophic lateral sclerosis and autoimmune diseases

BackgroundEpidemiological and clinical studies have suggested comorbidity between amyotrophic lateral sclerosis (ALS) and autoimmune disorders. However, little is known about their shared genetic architecture.MethodsTo examine the relation between ALS and 10 autoimmune diseases, including asthma, celiac disease (CeD), Crohn’s disease (CD), inflammatory bowel disease (IBD), multiple sclerosis (MS), psoriasis, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type 1 diabetes (T1D), and ulcerative colitis (UC), and identify shared risk loci, we first estimated the genetic correlation using summary statistics from genome-wide association studies, and then analyzed the genetic enrichment leveraging the conditional false discovery rate statistical method.ResultsWe identified a significant positive genetic correlation between ALS and CeD, MS, RA, and SLE, as well as a significant negative genetic correlation between ALS and IBD, UC, and CD. Robust genetic enrichment was observed between ALS and CeD and MS, and moderate enrichment was found between ALS and UC and T1D. Thirteen shared genetic loci were identified, among which five were suggestively significant in another ALS GWAS, namely rs3828599 (GPX3), rs3849943 (C9orf72), rs7154847 (G2E3), rs6571361 (SCFD1), and rs9903355 (GGNBP2). By integrating cis-expression quantitative trait loci analyses in Braineac and GTEx, we further identified GGNBP2, ATXN3, and SLC9A8 as novel ALS risk genes. Functional enrichment analysis indicated that the shared risk genes were involved in four pathways including membrane trafficking, vesicle-mediated transport, ER to Golgi anterograde transport, and transport to the Golgi and subsequent modification.ConclusionsOur findings demonstrate a specific genetic correlation between ALS and autoimmune diseases and identify shared risk loci, including three novel ALS risk genes. These results provide a better understanding for the pleiotropy of ALS and have implications for future therapeutic trials.

Abstract PR003: Multi-tissue transcriptome-wide association study identifies genetic mechanisms underlying endometrial cancer susceptibility

Abstract To date, genome-wide association studies (GWAS) of endometrial cancer have identified 16 genetic risk regions; however, candidate susceptibility genes at these regions are yet to be confirmed. We performed a transcriptome-wide association study (TWAS) to identify candidate endometrial cancer susceptibility genes and provide evidence to support previously identified candidate genes. To maximise statistical power, we used a multi-tissue TWAS approach, integrating the largest endometrial cancer GWAS (12,906 cases and 108,979 controls of European ancestry) with cis-expression quantitative trait loci (eQTLs) from 48 tissues. Identified genes were prioritised by colocalisation analysis to reduce false positive findings. We found seven candidate endometrial cancer susceptibility genes, six of which had previously been identified as candidate susceptibility genes by parallel functional analyses. The remaining identified gene was a lncRNA located at a potentially novel endometrial cancer genetic risk region. Using phenome-wide association approaches, we found these genes associate with traits related to endometrial cancer risk factors, including cardiovascular phenotypes, diabetes, and hormone levels. This analysis additionally identified associations with traits related to bone health, haematopoiesis and liver function, providing avenues for future study. Comparing genetically predicted endometrial cancer gene expression with drug-induced gene expression profiles from the Connectivity Map database, we identified 14 drug repurposing candidates including tubulin inhibitors, a drug class already in use for treatment of advanced endometrial cancer. In summary, using TWAS we have identified candidate endometrial cancer susceptibility genes and provided evidence to prioritise existing candidates. Follow-up analyses have revealed further potential risk factors for endometrial cancer and compounds that could be repurposed for endometrial cancer treatment. Citation Format: Tracy A. O'Mara, Pik Fang Kho, Gabriel Cuellar Partida, Amanda B. Spurdle, Dylan M. Glubb. Multi-tissue transcriptome-wide association study identifies genetic mechanisms underlying endometrial cancer susceptibility [abstract]. In: Proceedings of the AACR Virtual Special Conference: Endometrial Cancer: New Biology Driving Research and Treatment; 2020 Nov 9-10. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(3_Suppl):Abstract nr PR003.

A systems genetics approach to deciphering the effect of dosage variation on leaf morphology in Populus.

Gene copy number variation is frequent in plant genomes of various species, but the impact of such gene dosage variation on morphological traits is poorly understood. We used a large population of Populus carrying genomically characterized insertions and deletions across the genome to systematically assay the effect of gene dosage variation on a suite of leaf morphology traits. A systems genetics approach was used to integrate insertion and deletion locations, leaf morphology phenotypes, gene expression, and transcriptional network data, to provide an overview of how gene dosage influences morphology. Dosage-sensitive genomic regions were identified that influenced individual or pleiotropic morphological traits. We also identified cis-expression quantitative trait loci (QTL) within these dosage QTL regions, a subset of which modulated trans-expression QTL as well. Integration of data types within a gene co-expression framework identified co-expressed gene modules that are dosage sensitive, enriched for dosage expression QTL, and associated with morphological traits. Functional description of these modules linked dosage-sensitive morphological variation to specific cellular processes, as well as candidate regulatory genes. Together, these results show that gene dosage variation can influence morphological variation through complex changes in gene expression, and suggest that frequently occurring gene dosage variation has the potential to likewise influence quantitative traits in nature.

Integration of genetic variants and gene network for drug repurposing in colorectal cancer

Even though many genetic risk loci for human diseases have been identified and comprehensively cataloged, strategies to guide clinical research by integrating the extensive results of genetic studies and biological resources are still limited. Moreover, integrative analyses that provide novel insights into disease biology are expected to be especially useful for drug discovery. Herein, we used text mining of genetic studies on colorectal cancer (CRC) and assigned biological annotations to identified risk genes in order to discover novel drug targets and potential drugs for repurposing. Risk genes for CRC were obtained from PubMed text mining, and for each gene, six functional and bioinformatic annotations were analyzed. The annotations include missense mutations, cis-expression quantitative trait loci (cis-eQTL), molecular pathway analyses, protein-protein interactions (PPIs), a genetic overlap with knockout mouse phenotypes, and primary immunodeficiency (PID). We then prioritized the biological risk candidate genes according to a scoring system of the six functional annotations. Each functional annotation was assigned one point, and those genes with a score ≥2 were designated “biological CRC risk genes”. Using this method, we revealed 82 biological CRC risk genes, which were mapped to 128 genes in an expanded PPI network. Further utilizing DrugBank and the Therapeutic Target Database, we found 21 genes in our list that are targeted by 166 candidate drugs. Based on data from ClinicalTrials.gov and literature review, we found four known target genes with six drugs for clinical treatment in CRC, and three target genes with nine drugs supported by previous preclinical results in CRC. Additionally, 12 genes are targeted by 32 drugs approved for other indications, which can possibly be repurposed for CRC treatment. Finally, analysis from Connectivity Map (CMap) showed that 18 drugs have a high potential for CRC.

Evaluation of relationship between DNA methyltransferase 3 β gene and the risk of hip osteoarthritis: A case-control study based on a Han Chinese population.

Osteoarthritis (OA) is a chronic degenerative joint disease. Early studies have indicated that genetic and environmental factors contribute to the risk of OA. However, the etiology of OA remains unknown. Our study aimed to evaluate the association of DNMT3B gene with the risk of hip OA in Han Chinese individuals. A total of 2070 subjects were recruited into the study, including 658 patients with hip OA and 1412 healthy controls. Twelve tag single nucleotide polymorphisms (SNPs) were selected and genotyped in our samples. Genetic associations between DNMT3B gene and the risk of hip OA were examined at both the single marker and haplotype levels. Cis-expression quantitative trait loci signals that achieve genome-wide significance of targeted SNPs from multiple types of human tissues were extracted from the GTEx database. Significant signals were identified for SNP rs2424905 in 4 genetic models. The T allele was significantly associated with an increased risk of hip OA (odds ratio=1.53; 95% CI=1.28-1.83). The T allele was also significantly associated with higher Kellgren-Lawrence grade in the patients with hip OA (χ2 =32.70, P=1.37×10-6 ). Moreover, SNP rs2424905 was significantly associated with the gene expression level of multiple genes, including DNMT3B, C20orf203, COMMD7, EFCAB8, MAPRE1, and RP5-1085F17.3, from several types of human tissues. Our results indicated that rs2424905 of DNMT3B gene contributed to the risk of hip OA and its clinical severity in a Han Chinese population. These findings suggested that rs2424905 of DNMT3B could be a promising genetic marker to assess susceptibility to hip OA in Han Chinese populations.

Genome-wide transcriptome analysis identifies novel dysregulated genes implicated in Alzheimer's pathology.

Abnormal gene expression patterns may contribute to the onset and progression of late-onset Alzheimer's disease (LOAD). We performed transcriptome-wide meta-analysis (N = 1440) of blood-based microarray gene expression profiles as well as neuroimaging and cerebrospinal fluid (CSF) endophenotype analysis. We identified and replicated five genes (CREB5, CD46, TMBIM6, IRAK3, and RPAIN) as significantly dysregulated in LOAD. The most significantly altered gene, CREB5, was also associated with brain atrophy and increased amyloid beta (Aβ) accumulation, especially in the entorhinal cortex region. cis-expression quantitative trait loci mapping analysis of CREB5 detected five significant associations (P < 5 × 10-8 ), where rs56388170 (most significant) was also significantly associated with global cortical Aβ deposition measured by [18 F]Florbetapir positron emission tomography and CSF Aβ1-42 . RNA from peripheral blood indicated a differential gene expression pattern in LOAD. Genes identified have been implicated in biological processes relevant to Alzheimer's disease. CREB, in particular, plays a key role in nervous system development, cell survival, plasticity, and learning and memory.

Multiple sclerosis risk variants regulate gene expression in innate and adaptive immune cells.

At least 200 single-nucleotide polymorphisms (SNPs) are associated with multiple sclerosis (MS) risk. A key function that could mediate SNP-encoded MS risk is their regulatory effects on gene expression. We performed microarrays using RNA extracted from purified immune cell types from 73 untreated MS cases and 97 healthy controls and then performed Cis expression quantitative trait loci mapping studies using additive linear models. We describe MS risk expression quantitative trait loci associations for 129 distinct genes. By extending these models to include an interaction term between genotype and phenotype, we identify MS risk SNPs with opposing effects on gene expression in cases compared with controls, namely, rs2256814 MYT1 in CD4 cells (q = 0.05) and rs12087340 RF00136 in monocyte cells (q = 0.04). The rs703842 SNP was also associated with a differential effect size on the expression of the METTL21B gene in CD8 cells of MS cases relative to controls (q = 0.03). Our study provides a detailed map of MS risk loci that function by regulating gene expression in cell types relevant to MS.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

The evolving role of TonEBP as an immunometabolic stress protein.

Tonicity-responsive enhancer-binding protein (TonEBP), which is also known as nuclear factor of activated T cells 5 (NFAT5), was discovered 20 years ago as a transcriptional regulator of the cellular response to hypertonic (hyperosmotic salinity) stress in the renal medulla. Numerous studies since then have revealed that TonEBP is a pleiotropic stress protein that is involved in a range of immunometabolic diseases. Some of the single-nucleotide polymorphisms (SNPs) in TONEBP introns are cis-expression quantitative trait loci that affect TONEBP transcription. These SNPs are associated with increased risk of type 2 diabetes mellitus, diabetic nephropathy, inflammation, high blood pressure and abnormal plasma osmolality, indicating that variation in TONEBP expression might contribute to these phenotypes. In addition, functional studies have shown that TonEBP is involved in the pathogenesis of rheumatoid arthritis, atherosclerosis, diabetic nephropathy, acute kidney injury, hyperlipidaemia and insulin resistance, autoimmune diseases (including type 1 diabetes mellitus and multiple sclerosis), salt-sensitive hypertension and hepatocellular carcinoma. These pathological activities of TonEBP are in contrast to the protective actions of TonEBP in response to hypertonicity, bacterial infection and DNA damage induced by genotoxins. An emerging theme is that TonEBP is a stress protein that mediates the cellular response to a range of pathological insults, including excess caloric intake, inflammation and oxidative stress.

Enhancer Domains Predict Gene Pathogenicity and Inform Gene Discovery in Complex Disease

Non-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the sizes and number of enhancers linked to a gene reflect its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and they are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved a robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This solves a puzzle about why genes associated with human disease are depleted of cis-eQTLs (cis-expression quantitative trait loci), suggesting that this relationship might complicate gene identification in causal genome-wide association studies (GWASs) using eQTL information, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

Genomic dissection of 43 serum urate-associated loci provides multiple insights into molecular mechanisms of urate control.

High serum urate is a prerequisite for gout and associated with metabolic disease. Genome-wide association studies (GWAS) have reported dozens of loci associated with serum urate control; however, there has been little progress in understanding the molecular basis of the associated loci. Here, we employed trans-ancestral meta-analysis using data from European and East Asian populations to identify 10 new loci for serum urate levels. Genome-wide colocalization with cis-expression quantitative trait loci (eQTL) identified a further five new candidate loci. By cis- and trans-eQTL colocalization analysis, we identified 34 and 20 genes, respectively, where the causal eQTL variant has a high likelihood that it is shared with the serum urate-associated locus. One new locus identified was SLC22A9 that encodes organic anion transporter 7 (OAT7). We demonstrate that OAT7 is a very weak urate-butyrate exchanger. Newly implicated genes identified in the eQTL analysis include those encoding proteins that make up the dystrophin complex, a scaffold for signaling proteins and transporters at the cell membrane; MLXIP that, with the previously identified MLXIPL, is a transcription factor that may regulate serum urate via the pentose-phosphate pathway and MRPS7 and IDH2 that encode proteins necessary for mitochondrial function. Functional fine mapping identified six loci (RREB1, INHBC, HLF, UBE2Q2, SFMBT1 and HNF4G) with colocalized eQTL containing putative causal SNPs. This systematic analysis of serum urate GWAS loci identified candidate causal genes at 24 loci and a network of previously unidentified genes likely involved in control of serum urate levels, further illuminating the molecular mechanisms of urate control.

Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits.

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and transcriptomic data to showcase their improved statistical power of identifying gene-trait associations while, importantly, offering further biological insights. TWAS have thus far focused on common variants as available from GWAS. Compared with common variants, the findings for or even applications to low-frequency variants are limited and their underlying role in regulating gene expression is less clear. To fill this gap, we extend TWAS to integrating whole genome sequencing data with transcriptomic data for low-frequency variants. Using the data from the Framingham Heart Study, we demonstrate that low-frequency variants play an important and universal role in predicting gene expression, which is not completely due to linkage disequilibrium with the nearby common variants. By including low-frequency variants, in addition to common variants, we increase the predictivity of gene expression for 79% of the examined genes. Incorporating this piece of functional genomic information, we perform association testing for five lipid traits in two UK10K whole genome sequencing cohorts, hypothesizing that cis-expression quantitative trait loci, including low-frequency variants, are more likely to be trait-associated. We discover that two genes, LDLR and TTC22, are genome-wide significantly associated with low-density lipoprotein cholesterol based on 3203 subjects and that the association signals are largely independent of common variants. We further demonstrate that a joint analysis of both common and low-frequency variants identifies association signals that would be missed by testing on either common variants or low-frequency variants alone.

Genome-wide association study in Chinese cohort reveals a new susceptibility locus within the 12q13.13 region for hypospadias

Hypospadias risk–associated gene variants have been reported in populations of European descent using genome-wide association studies (GWAS). There is little known at present about any possible hypospadias risk associations in Han Chinese populations. To systematically investigate hypospadias risk–associated gene variants in Chinese patients, we performed the first GWAS in a Han Chinese cohort consisting of 200 moderate-severe hypospadias cases and 1008 unaffected controls. Suggestive loci (p<1×10−4) were replicated in 118 cases and 383 controls, as well as in a second independent validation population of 137 cases and 190 controls. Regulatory were then conducted for the functional analyses of candidate variants. We identified rs11170516 with the risk allele G within the SP1/SP7 region that was independently associated with moderate-severe Hypospadias [SP1/SP7, rs11170516, Pcombine = 3.5×10-9, odds ratio (OR) = 1.96 (1.59-2.44)]. Results also suggested that rs11170516 is associated with the expression of SP1 as a cis-expression quantitative trait loci (cis-eQTL). We performed the first GWAS of moderate-severe hypospadias in a Han Chinese cohort, and identified one novel susceptibility cis-acting regulatory locus at 12q13.13, which may regulate a variety of hypospadias-related pathways by affecting proximal SP1 gene expression. This study complements known common hypospadias risk-associated variants and provides the possible role of cis-acting regulatory variant in causing hypospadias.

Genome-wide association study in Chinese cohort identifies one novel hypospadias risk associated locus at 12q13.13

BackgroundHypospadias risk–associated gene variants have been reported in populations of European descent using genome-wide association studies (GWASs). There is little known at present about any possible hypospadias risk associations in Han Chinese populations.MethodsTo systematically investigate hypospadias risk–associated gene variants in Chinese patients, we performed the first GWAS in a Han Chinese cohort consisting of 197 moderate-severe hypospadias cases and 933 unaffected controls. Suggestive loci (p < 1 × 10− 4) were replicated in 118 cases and 383 controls, as well as in a second independent validation population of 137 cases and 190 controls. Regulatory and protein-protein interactions (PPIs) were then conducted for the functional analyses of candidate variants.ResultsWe identified rs11170516 with the risk allele G within the SP1/SP7 region that was independently associated with moderate-severe hypospadias [SP1/SP7, rs11170516, Pcombine = 3.5 × 10− 9, odds ratio (OR) = 1.96 (1.59–2.44)]. Results also suggested that rs11170516 is associated with the expression of SP1 as a cis-expression quantitative trait locus (cis-eQTL). Protein SP1 could affect the risk of hypospadias via PPIs.ConclusionsWe performed the first GWAS of moderate-severe hypospadias in a Han Chinese cohort, and identified one novel susceptibility cis-acting regulatory locus at 12q13.13, which may regulate a variety of hypospadias-related pathways by affecting proximal SP1 gene expression and subsequent PPIs. This study complements known common hypospadias risk-associated variants and provides the possible role of cis-acting regulatory variant in causing hypospadias.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Smoking Modifies the Genetic Risk for Early-Onset Periodontitis

Periodontitis has low-prevalence, highly severe disease manifestations with an early onset and rapid progression. The diagnosis is based on severe destruction of the alveolar bone in adolescents and young adults. Genetic susceptibility variants and smoking are well-established risk factors, but their interactions in modifying disease susceptibility have not been studied. We aimed to identify genetic risk variants of early-onset periodontitis that unmask their effects on tobacco smoke exposure. To this end, we analyzed 79,780,573 common variants in 741 northwest Europeans diagnosed to have >30% bone loss at >2 teeth before 35 y of age, using imputed genotypes of the OmniExpress BeadChip. Never versus ever smokers were compared in a logistic regression analysis via a case-only approach. To explore the effect of tobacco smoke on the expression of the G×S-associated genes, cultures of primary gingival fibroblasts (n = 9) were exposed to cigarette smoke extract, and transcripts were quantified by reverse transcription polymerase chain reaction. We identified 16 loci for which our analysis suggested an association with G×S increased disease risk (P < 5 × 10−5). Nine loci had previously been reported to be associated with spirometric measures of pulmonary function by an earlier G×S genome-wide association study. Genome-wide significant cis expression quantitative trait loci were reported for G×S-associated single-nucleotide polymorphisms at ST8SIA1 and SOST, indicating a causal role of these genes in tobacco-related etiopathology. Notably, SOST is a negative regulator of bone growth, and ST8SIA1 has a role in tissue remodeling. Cigarette smoke extract significantly altered the expression of 2 associated genes: SSH1 (P = 5 × 10−07), which is required for NF-κB activation and innate immune responses to bacterial invasion, and ST8SIA1 (P = 0.0048). We conclude that the genetic predisposition to early-onset periodontitis is in part triggered by smoking and that tobacco smoke directly affects the expression of genes involved in bone homeostasis, tissue repair, and immune response.

Genetic determinants controlling maize rubisco activase gene expression and a comparison with rice counterparts

BackgroundRubisco activase (RCA) regulates the activity of Rubisco and is a key enzyme of photosynthesis. RCA expression was widely reported to affect plant photosynthesis and crop yield, but the molecular basis of natural variation in RCA expression in a wide range of maize materials has not been fully elucidated.ResultsIn this study, correlation analysis in approximately 200 maize inbred lines revealed a significantly positive correlation between the expression of maize RCA gene ZmRCAβ and grain yield. A genome-wide association study revealed both cis-expression quantitative trait loci (cis-eQTLs) and trans-eQTLs underlying the expression of ZmRCAβ, with the latter playing a more important role. Further allele mining and genetic transformation analysis showed that a 2-bp insertion and a 14-bp insertion in the promoter of ZmRCAβ conferred increased gene expression. Because rice is reported to have higher RCA gene expression than does maize, we subsequently compared the genetic factors underlying RCA gene expression between maize and rice. The promoter activity of the rice RCA gene was shown to be stronger than that of the maize RCA gene, suggesting that replacing the maize RCA gene promoter with that of the rice RCA gene would improve the expression of RCA in maize.ConclusionOur results revealed two DNA polymorphisms regulating maize RCA gene ZmRCAβ expression, and the RCA gene promoter activity of rice was stronger than that of maize. This work increased understanding of the genetic mechanism that underlies RCA gene expression and identify new targets for both genetic engineering and selection for maize yield improvement.

Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers

Genome-wide association studies (GWASs) have identified hundreds of genetic risk variants for human cancers. However, target genes for the majority of risk loci remain largely unexplored. It is also unclear whether GWAS risk-loci-associated genes contribute to mutational signatures and tumor mutational burden (TMB) in cancer tissues. We systematically conducted cis-expression quantitative trait loci (cis-eQTL) analyses for 294 GWAS-identified variants for six major types of cancer-colorectal, lung, ovary, prostate, pancreas, and melanoma-by using transcriptome data from the Genotype-Tissue Expression (GTEx) Project, the Cancer Genome Atlas (TCGA), and other public data sources. By using integrative analysis strategies, we identified 270 candidate target genes, including 99 with previously unreported associations, for six cancer types. By analyzing functional genomic data, our results indicate that 180 genes (66.7% of 270) had evidence of cis-regulation by putative functional variants via proximal promoter or distal enhancer-promoter interactions. Together with our previously reported associations for breast cancer risk, our results show that 24 genes are shared by at least two cancer types, including four genes for both breast and ovarian cancer. By integrating mutation data from TCGA, we found that expression levels of 33 and 66 putative susceptibility genes were associated with specific mutational signatures and TMB of cancer-driver genes, respectively, at a Bonferroni-corrected p < 0.05. Together, these findings provide further insight into our understanding of how genetic risk variants might contribute to carcinogenesis through the regulation of susceptibility genes that are related to the biogenesis of somatic mutations.