The effect of genetic variation on promoter usage and enhancer activity

Genome-wide mapping of DNase I hypersensitive sites revealed differential chromatin accessibility and regulatory DNA elements under drought stress in rice cultivars

Drought stress (DS) is one of the major constraints limiting yield in crop plants including rice. Gene regulation under DS is largely governed by accessibility of the transcription factors (TFs) to their cognate cis-regulatory elements (CREs). In this study, we used DNase I hypersensitive assays followed by sequencing to identify the accessible chromatin regions under DS in a drought-sensitive (IR64) and a drought-tolerant (N22) rice cultivar. Our results indicated that DNase I hypersensitive sites (DHSs) were highly enriched at transcription start sites (TSSs) and numerous DHSs were detected in the promoter regions. DHSs were concurrent with epigenetic marks and the genes harboring DHSs in their TSS and promoter regions were highly expressed. In addition, DS induced changes in DHSs (∆DHSs) in TSS and promoter regions were positively correlated with upregulation of several genes involved in drought/abiotic stress response, those encoding TFs and located within drought-associated quantitative trait loci, much preferentially in the drought-tolerant cultivar. The CREs representing the binding sites of TFs involved in DS response were detected within the ∆DHSs, suggesting differential accessibility of TFs to their cognate sites under DS in different rice cultivars, which may be further deployed for enhancing drought tolerance in rice.

Fundamentals for predicting transcriptional regulations from DNA sequence patterns

Cell-type-specific regulatory elements, cataloged through extensive experiments and bioinformatics in large-scale consortiums, have enabled enrichment analyses of genetic associations that primarily utilize positional information of the regulatory elements. These analyses have identified cell types and pathways genetically associated with human complex traits. However, our understanding of detailed allelic effects on these elements' activities and on-off states remains incomplete, hampering the interpretation of human genetic study results. This review introduces machine learning methods to learn sequence-dependent transcriptional regulation mechanisms from DNA sequences for predicting such allelic effects (not associations). We provide a concise history of machine-learning-based approaches, the requirements, and the key computational processes, focusing on primers in machine learning. Convolution and self-attention, pivotal in modern deep-learning models, are explained through geometrical interpretations using dot products. This facilitates understanding of the concept and why these have been used for machine learning for DNA sequences. These will inspire further research in this genetics and genomics field.

Deep Resequencing of the 1q22 Locus in Non-Lobar Intracerebral Hemorrhage

OBJECTIVE

Genome-wide association studies have identified 1q22 as a susceptibility locus for cerebral small vessel diseases, including non-lobar intracerebral hemorrhage (ICH) and lacunar stroke. In the present study, we performed targeted high-depth sequencing of 1q22 in ICH cases and controls to further characterize this locus and prioritize potential causal mechanisms, which remain unknown.

METHODS

A total of 95,000 base pairs spanning 1q22, including SEMA4A, SLC25A44, and PMF1/PMF1-BGLAP were sequenced in 1,055 spontaneous ICH cases (534 lobar and 521 non-lobar) and 1,078 controls. Firth regression and Rare Variant Influential Filtering Tool analysis were used to analyze common and rare variants, respectively. Chromatin interaction analyses were performed using Hi-C, chromatin immunoprecipitation followed by sequencing, and chromatin interaction analysis with paired-end tag databases. Multivariable Mendelian randomization assessed whether alterations in gene-specific expression relative to regionally co-expressed genes at 1q22 could be causally related to ICH risk.

RESULTS

Common and rare variant analyses prioritized variants in SEMA4A 5'-UTR and PMF1 intronic regions, overlapping with active promoter and enhancer regions based on ENCODE annotation. Hi-C data analysis determined that 1q22 is spatially organized in a single chromatin loop, and that the genes therein belong to the same topologically associating domain. Chromatin immunoprecipitation followed by sequencing and chromatin interaction analysis with paired-end tag data analysis highlighted the presence of long-range interactions between the SEMA4A-promoter and PMF1-enhancer regions prioritized by association testing. Multivariable Mendelian randomization analyses demonstrated that PMF1 overexpression could be causally related to non-lobar ICH risk.

INTERPRETATION

Altered promoter-enhancer interactions leading to PMF1 overexpression, potentially dysregulating polyamine catabolism, could explain demonstrated associations with non-lobar ICH risk at 1q22, offering a potential new target for prevention of ICH and cerebral small vessel disease. ANN NEUROL 2024;95:325-337.

Pan-cancer analysis of promoter activity quantitative trait loci

Altered promoter activity has been generally observed in diverse biological processes, including tumorigenesis. Accumulating evidence suggests that employing a quantitative trait locus mapping approach is effective in comprehending the genetic basis of promoter activity. By utilizing genotype data from The Cancer Genome Atlas and calculating corresponding promoter activity values using proActiv, we systematically evaluated the impact of genetic variants on promoter activity and identified >1.0 million promoter activity quantitative trait loci (paQTLs) as both cis- and trans-acting. Additionally, leveraging data from the genome-wide association study (GWAS) catalog, we discovered >1.3 million paQTLs that overlap with known GWAS linkage disequilibrium regions. Remarkably, ∼9324 paQTLs exhibited significant associations with patient prognosis. Moreover, investigating the impact of promoter activity on >1000 imputed antitumor therapy responses among pan-cancer patients revealed >43 000 million significant associations. Furthermore, ∼25 000 significant associations were identified between promoter activity and immune cell abundance. Finally, a user-friendly data portal, Pancan-paQTL (https://www.hbpding.com/PancanPaQTL/), was constructed for users to browse, search and download data of interest. Pancan-paQTL serves as a comprehensive multidimensional database, enabling functional and clinical investigations into genetic variants associated with promoter activity, drug responses and immune infiltration across multiple cancer types.

eRNA co-expression network uncovers TF dependency and convergent cooperativity

Enhancer RNAs (eRNAs) are non-coding RNAs produced by transcriptional enhancers that are highly correlated with their activity. Using a capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across 67 individuals, we identified inter-individual variation in the expression of over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers are associated with each other and with promoters. Mid- to long-range co-expression showed a distance-dependent decay that was modified by TF occupancy. In particular, we found a class of "bivalent" TFs, including Cohesin, that both facilitate and isolate the interaction between enhancers and/or promoters, depending on their topology. At short distances, we observed strand-specific correlations between nearby eRNAs in both convergent and divergent orientations. Our results support a cooperative model of convergent eRNAs, consistent with eRNAs facilitating adjacent enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions as a function of distance, orientation, and binding landscapes of TFs.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Deep resequencing of the 1q22 locus in non-lobar intracerebral hemorrhage

Objective

Genome-wide association studies have identified 1q22 as a susceptibility locus for cerebral small vessel diseases (CSVDs), including non-lobar intracerebral hemorrhage (ICH) and lacunar stroke. In the present study we performed targeted high-depth sequencing of 1q22 in ICH cases and controls to further characterize this locus and prioritize potential causal mechanisms, which remain unknown.

Methods

95,000 base pairs spanning 1q22 , including SEMA4A, SLC25A44 and PMF1 / PMF1-BGLAP were sequenced in 1,055 spontaneous ICH cases (534 lobar and 521 non-lobar) and 1,078 controls. Firth regression and RIFT analysis were used to analyze common and rare variants, respectively. Chromatin interaction analyses were performed using Hi-C, ChIP-Seq and ChIA-PET databases. Multivariable Mendelian randomization (MVMR) assessed whether alterations in gene-specific expression relative to regionally co-expressed genes at 1q22 could be causally related to ICH risk.

Results

Common and rare variant analyses prioritized variants in SEMA4A 5'-UTR and PMF1 intronic regions, overlapping with active promoter and enhancer regions based on ENCODE annotation. Hi-C data analysis determined that 1q22 is spatially organized in a single chromatin loop and that the genes therein belong to the same Topologically Associating Domain. ChIP-Seq and ChIA-PET data analysis highlighted the presence of long-range interactions between the SEMA4A -promoter and PMF1 -enhancer regions prioritized by association testing. MVMR analyses demonstrated that PMF1 overexpression could be causally related to non-lobar ICH risk.

Interpretation

Altered promoter-enhancer interactions leading to PMF1 overexpression, potentially dysregulating polyamine catabolism, could explain demonstrated associations with non-lobar ICH risk at 1q22 , offering a potential new target for prevention of ICH and CSVD.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping

Genetic sharing is extensively observed for autoimmune diseases, but the causal variants and their underlying molecular mechanisms remain largely unknown. Through systematic investigation of autoimmune disease pleiotropic loci, we found most of these shared genetic effects are transmitted from regulatory code. We used an evidence-based strategy to functionally prioritize causal pleiotropic variants and identify their target genes. A top-ranked pleiotropic variant, rs4728142, yielded many lines of evidence as being causal. Mechanistically, the rs4728142-containing region interacts with the IRF5 alternative promoter in an allele-specific manner and orchestrates its upstream enhancer to regulate IRF5 alternative promoter usage through chromatin looping. A putative structural regulator, ZBTB3, mediates the allele-specific loop to promote IRF5-short transcript expression at the rs4728142 risk allele, resulting in IRF5 overactivation and M1 macrophage polarization. Together, our findings establish a causal mechanism between the regulatory variant and fine-scale molecular phenotype underlying the dysfunction of pleiotropic genes in human autoimmunity.

Revealing eRNA interactions: TF dependency and convergent cooperativity

Enhancer RNAs (eRNAs) are non-coding RNAs produced from transcriptional enhancers that are highly correlated with their activities. Using capped nascent RNA sequencing (PRO-cap) dataset in human lymphoblastoid cell lines across individuals, we identified inter-individual variation of expression in over 80 thousand transcribed transcriptional regulatory elements (tTREs), in both enhancers and promoters. Co-expression analysis of eRNAs from tTREs across individuals revealed how enhancers interact with each other and with promoters. Mid-to-long range interactions showed distance-dependent decay, which was modified by TF occupancy. In particular, we found a class of 'bivalent' TFs, including Cohesin, which both facilitates and insulates the interaction between enhancers and/or promoters depending on the topology. In short ranges, we observed strand specific interactions between nearby eRNAs in both convergent or divergent orientations. Our finding supports a cooperative convergent eRNA model, which is compatible with eRNA remodeling neighboring enhancers rather than interfering with each other. Therefore, our approach to infer functional interactions from co-expression analyses provided novel insights into the principles of enhancer interactions depending on the distance, orientation, and the binding landscapes of TFs.

Prediction of the cell-type-specific transcription of non-coding RNAs from genome sequences via machine learning

Gene transcription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). As the transcription of ncRNAs, especially of enhancer RNAs, is often low and cell type specific, how the levels of RNA transcription depend on genotype remains largely unexplored. Here we report the development and utility of a machine-learning model (MENTR) that reliably links genome sequence and ncRNA expression at the cell type level. Effects on ncRNA transcription predicted by the model were concordant with estimates from published studies in a cell-type-dependent manner, regardless of allele frequency and genetic linkage. Among 41,223 variants from genome-wide association studies, the model identified 7,775 enhancer RNAs and 3,548 long ncRNAs causally associated with complex traits across 348 major human primary cells and tissues, such as rare variants plausibly altering the transcription of enhancer RNAs to influence the risks of Crohn's disease and asthma. The model may aid the discovery of causal variants and the generation of testable hypotheses for biological mechanisms driving complex traits.

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits

Genomic variations are associated with gene expression levels, which are called expression quantitative trait loci (eQTL). Most eQTL may affect the total gene expression levels by regulating transcriptional activities of a specific promoter. However, the direct exploration of genomic loci associated with promoter activities using RNA-seq data has been challenging because eQTL analyses treat the total expression levels estimated by summing those of all isoforms transcribed from distinct promoters. Here we propose a new method for identifying genomic loci associated with promoter activities, called promoter usage quantitative trait loci (puQTL), using conventional RNA-seq data. By leveraging public RNA-seq datasets from the lymphoblastoid cell lines of 438 individuals from the GEUVADIS project, we obtained promoter activity estimates and mapped 2,592 puQTL at the 10% FDR level. The results of puQTL mapping enabled us to interpret the manner in which genomic variations regulate gene expression. We found that 310 puQTL genes (16.1%) were not detected by eQTL analysis, suggesting that our pipeline can identify novel variant–gene associations. Furthermore, we identified genomic loci associated with the activity of “hidden” promoters, which the standard eQTL studies have ignored. We found that most puQTL signals were concordant with at least one genome-wide association study (GWAS) signal, enabling novel interpretations of the molecular mechanisms of complex traits. Our results emphasize the importance of the re-analysis of public RNA-seq datasets to obtain novel insights into gene regulation by genomic variations and their contributions to complex traits.

Many variations exist in the human genome, creating phenotypic diversity among individuals. It is well known that they are associated with the risk of disease development by affecting the expression levels of genes. Genes are transcribed from regulatory elements called promoters. Although some genes are transcribed from multiple promoters and translated into proteins with different functions, the relationship between genomic variations and promoter activities has not been investigated in depth compared to the relationship between genomic variations and gene expression levels. In this study, we proposed a new method to detect the association between genomic variations and promoter activities. Our method identified the associations between many variations and promoters using genomic and promoter activity data from blood cells of 438 individuals. This study allowed us to identify new functional associations between genomic variations and genes. Furthermore, we identified previously undiscovered variation-gene-disease associations. Our results will help to elucidate the molecular mechanisms of diseases in which genetic factors are involved.

A genome-wide association study for varicose veins

Background

The aim was to compare the genetic information of varicose vein patients with that of a healthy population attempting to identify certain significant genetic associations.

Method

Patients’ clinical characteristics and demographics were collected, and their genetic samples were examined. The results were compared to the genetic information of one thousand sex-matched healthy controls from Taiwan Biobank database. The Clinical-Etiology-Anatomy-Pathophysiology classification was applied for further subgroup analysis.

Results

After comparison of genetic information of ninety-six patients to that of healthy controls, two significant single nucleotide polymorphisms (SNPs) were identified. One was in DPYSL2 gene, and the other was in VSTM2L gene. A further comparison between C2-3 patient subgroup and C4-6 subgroup identified another four significant SNPs, which were located in ZNF664-FAM101A, PHF2, ACOT11, and TOM1L1 genes.

Conclusion

Our preliminary result identified six significant SNPs located in six different genes. All of them and their genetic products may warrant further investigations.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Polymorphisms of an oncogenic gene, mesothelin, predict the risk and prognosis of gastric cancer in a Chinese Han population

Mesothelin (MSLN) is a cell surface protein associated with tumor invasion and metastasis. This study aims to explore the biological function of MSLN in gastric cancer and to evaluate the association of MSLN polymorphism (rs3764247, rs3764246, rs12597489, rs1057147, rs3765319) with the risk and prognosis of gastric cancer. Small interfering RNA (siRNA) transfection and MSLN overexpression were performed in human gastric cancer cell lines, respectively. The proliferation of tumor cells was evaluated by Cell counting kit 8(CCK-8) and colony formation assay. Wound healing assay and transwell assay were used to elucidate gastric cancer cell migration and invasion rates. We conducted a case-control study involving 860 patients with gastric cancer and 870 controls. All mutation sites were genotyped by PCR-LDR sequencing. First, our study revealed the cancer-promoting role of MSLN in gastric cancer. Second, we also demonstrated that rs3764247 and rs3764246 were associated with a reduced risk of gastric cancer (OR = 0.83, p = 0.010; OR = 0.84, p = 0.011; respectively). The clinicopathological analysis further showed that rs3764247 was closely related to T stage, vascular infiltration, and HER2 expression. In addition, in the survival analysis of 392 patients with gastric cancer, patients with rs3764247 CC genotype had poorer survival than patients with AA + AC genotype after adjusting for age, sex, TNM stage, and Lauren classification (HR = 2.07, p = 0.029). Our findings indicated that MSLN could be an oncogene whose polymorphisms were closely related to the risk and prognosis of gastric cancer.

Genetic variants associated mRNA stability in lung

Background

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).

Results

Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08405-y.

Promoter sequence and architecture determine expression variability and confer robustness to genetic variants

Genetic and environmental exposures cause variability in gene expression. Although most genes are affected in a population, their effect sizes vary greatly, indicating the existence of regulatory mechanisms that could amplify or attenuate expression variability. Here, we investigate the relationship between the sequence and transcription start site architectures of promoters and their expression variability across human individuals. We find that expression variability can be largely explained by a promoter's DNA sequence and its binding sites for specific transcription factors. We show that promoter expression variability reflects the biological process of a gene, demonstrating a selective trade-off between stability for metabolic genes and plasticity for responsive genes and those involved in signaling. Promoters with a rigid transcription start site architecture are more prone to have variable expression and to be associated with genetic variants with large effect sizes, while a flexible usage of transcription start sites within a promoter attenuates expression variability and limits genotypic effects. Our work provides insights into the variable nature of responsive genes and reveals a novel mechanism for supplying transcriptional and mutational robustness to essential genes through multiple transcription start site regions within a promoter.

The functional variant in promoter of OVCA1 was associated with the risk of gastric cancer in the northeast Chinese Han population

We previously found allelic deletions on chromosomes 17 in primary gastric cancers (GC) using microsatellite markers for loss of heterozygosity (LOH). OVCA1 lies in one of these regions (17q21.33). The association between single nucleotide polymorphism (SNP) of OVCA1 gene and risk of gastric cancer is not yet clear. In this study, the peripheral blood of 505 gastric cancer patients and 544 healthy controls were genotyped for six SNPs (rs2273981, rs1131600, rs3752963, rs3803806, rs2236375, and rs1051322) of OVCA1, to evaluate the association of these SNPs with the risk of gastric cancer in the Han population in northeast China. The effect of rs2273981 located in the promoter region of OVCA1 on the transcription activity was determined using dual luciferase reporter assay. We found that the association between the AA + AG genotype of rs2273981 and the risk of gastric cancer was significant in smokers (AA + AG vs. GG, OR = 2.47, 95% CI = 1.04 - 5.87, P < 0.05). Stratified analysis of the clinicopathological parameters revealed that rs1131600 AG + GG genotype were significantly associated with increased gastric tumor volume (AG + GG vs. AA, OR = 1.81, 95% CI = 1.00 - 3.29, P < 0.05). The rs2236375 CT + TT genotype was also significantly associated with increased gastric tumor volume (CT + TT vs. CC, OR = 2.65, 95% CI = 1.38 - 5.10, P < 0.05). Additionally, by interacting with the transcription factor AP2A, the GG genotype the rs2273981 increased the transcription activity of OVCA1 compared with AA genotype, thus involved in gastric cancer development.

Inferred expression regulator activities suggest genes mediating cardiometabolic genetic signals

Expression QTL (eQTL) analyses have suggested many genes mediating genome-wide association study (GWAS) signals but most GWAS signals still lack compelling explanatory genes. We have leveraged an adipose-specific gene regulatory network to infer expression regulator activities and phenotypic master regulators (MRs), which were used to detect activity QTLs (aQTLs) at cardiometabolic trait GWAS loci. Regulator activities were inferred with the VIPER algorithm that integrates enrichment of expected expression changes among a regulator's target genes with confidence in their regulator-target network interactions and target overlap between different regulators (i.e., pleiotropy). Phenotypic MRs were identified as those regulators whose activities were most important in predicting their respective phenotypes using random forest modeling. While eQTLs were typically more significant than aQTLs in cis, the opposite was true among candidate MRs in trans. Several GWAS loci colocalized with MR trans-eQTLs/aQTLs in the absence of colocalized cis-QTLs. Intriguingly, at the 1p36.1 BMI GWAS locus the EPHB2 cis-aQTL was stronger than its cis-eQTL and colocalized with the GWAS signal and 35 BMI MR trans-aQTLs, suggesting the GWAS signal may be mediated by effects on EPHB2 activity and its downstream effects on a network of BMI MRs. These MR and aQTL analyses represent systems genetic methods that may be broadly applied to supplement standard eQTL analyses for suggesting molecular effects mediating GWAS signals.

Harnessing natural variation to identify cis regulators of sex-biased gene expression in a multi-strain mouse liver model

Sex differences in gene expression are widespread in the liver, where many autosomal factors act in tandem with growth hormone signaling to regulate individual variability of sex differences in liver metabolism and disease. Here, we compare hepatic transcriptomic and epigenetic profiles of mouse strains C57BL/6J and CAST/EiJ, representing two subspecies separated by 0.5-1 million years of evolution, to elucidate the actions of genetic factors regulating liver sex differences. We identify 144 protein coding genes and 78 lncRNAs showing strain-conserved sex bias; many have gene ontologies relevant to liver function, are more highly liver-specific and show greater sex bias, and are more proximally regulated than genes whose sex bias is strain-dependent. The strain-conserved genes include key growth hormone-dependent transcriptional regulators of liver sex bias; however, three other transcription factors, Trim24, Tox, and Zfp809, lose their sex-biased expression in CAST/EiJ mouse liver. To elucidate the observed strain specificities in expression, we characterized the strain-dependence of sex-biased chromatin opening and enhancer marks at cis regulatory elements (CREs) within expression quantitative trait loci (eQTL) regulating liver sex-biased genes. Strikingly, 208 of 286 eQTLs with strain-specific, sex-differential effects on expression were associated with a complete gain, loss, or reversal of the sex differences in expression between strains. Moreover, 166 of the 286 eQTLs were linked to the strain-dependent gain or loss of localized sex-biased CREs. Remarkably, a subset of these CREs apparently lacked strain-specific genetic variants yet showed coordinated, strain-dependent sex-biased epigenetic regulation. Thus, we directly link hundreds of strain-specific genetic variants to the high variability in CRE activity and expression of sex-biased genes and uncover underlying genetically-determined epigenetic states controlling liver sex bias in genetically diverse mouse populations.

Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer

BACKGROUND

Enhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity-however, most predicted enhancer regions remain to be functionally tested.

METHODS

We analyzed 132 epigenomic histone modification profiles of 18 primary gastric cancer (GC) samples, 18 normal gastric tissues, and 28 GC cell lines using Nano-ChIP-seq technology. We applied Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to assess functional enhancer activity. An Activity-by-contact (ABC) model was employed to explore the effects of histone acetylation and CapSTARR-seq levels on enhancer-promoter interactions.

RESULTS

We report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are GC-associated in vivo (> 50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying CapSTARR-seq to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers, even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. We identified cancer-relevant genes (ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4α as a master trans-acting factor associated with GC enhancer heterogeneity.

CONCLUSIONS

Our results indicate that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, providing insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.

Cap analysis of gene expression (CAGE) and noncoding regulatory elements

Cap analysis of gene expression (CAGE) was developed to detect the 5' end of RNA. Trapping of the RNA 5'-cap structure enables the enrichment and selective sequencing of complete transcripts. Upscaled high-throughput versions of CAGE have enabled the genome-wide identification of transcription start sites, including transcriptionally active promoters and enhancers. CAGE sequencing can be exploited to draw comprehensive maps of active genomic regulatory elements in a cell type- and activation-specific manner. The cells of the immune system are among the best candidates to be analyzed in humans, since they are easily accessible. In this review, we discuss how CAGE data are instrumental for integrative analyses with quantitative trait loci and omics data, and their usefulness in the mechanistic interpretation of the effects of genetic variations over the entire human genome. Integrating CAGE data with the currently available omics information will contribute to better understanding of the genome-wide association study variants that lie outside of annotated genes, deepening our knowledge on human diseases, and enabling the targeted design of more specific therapeutic interventions.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

Chromatin information content landscapes inform transcription factor and DNA interactions

Interactions between transcription factors and chromatin are fundamental to genome organization and regulation and, ultimately, cell state. Here, we use information theory to measure signatures of organized chromatin resulting from transcription factor-chromatin interactions encoded in the patterns of the accessible genome, which we term chromatin information enrichment (CIE). We calculate CIE for hundreds of transcription factor motifs across human samples and identify two classes: low and high CIE. The 10-20% of common and tissue-specific high CIE transcription factor motifs, associate with higher protein-DNA residence time, including different binding site subclasses of the same transcription factor, increased nucleosome phasing, specific protein domains, and the genetic control of both chromatin accessibility and gene expression. These results show that variations in the information encoded in chromatin architecture reflect functional biological variation, with implications for cell state dynamics and memory.

Functional genomics of autoimmune diseases

For more than a decade, genome-wide association studies have been applied to autoimmune diseases and have expanded our understanding on the pathogeneses. Genetic risk factors associated with diseases and traits are essentially causative. However, elucidation of the biological mechanism of disease from genetic factors is challenging. In fact, it is difficult to identify the causal variant among multiple variants located on the same haplotype or linkage disequilibrium block and thus the responsible biological genes remain elusive. Recently, multiple studies have revealed that the majority of risk variants locate in the non-coding region of the genome and they are the most likely to regulate gene expression such as quantitative trait loci. Enhancer, promoter and long non-coding RNA appear to be the main target mechanisms of the risk variants. In this review, we discuss functional genetics to challenge these puzzles.

Population-scale study of eRNA transcription reveals bipartite functional enhancer architecture

Enhancer RNAs (eRNA) are unstable non-coding RNAs, transcribed bidirectionally from active regulatory sequences, whose expression levels correlate with enhancer activity. We use capped-nascent-RNA sequencing to efficiently capture bidirectional transcription initiation across several human lymphoblastoid cell lines (Yoruba population) and detect ~75,000 eRNA transcription sites with high sensitivity and specificity. The use of nascent-RNA sequencing sidesteps the confounding effect of eRNA instability. We identify quantitative trait loci (QTLs) associated with the level and directionality of eRNA expression. High-resolution analyses of these two types of QTLs reveal distinct positions of enrichment at the central transcription factor (TF) binding regions and at the flanking eRNA initiation regions, both of which are associated with mRNA expression QTLs. These two regions-the central TF-binding footprint and the eRNA initiation cores-define a bipartite architecture of enhancers, inform enhancer function, and can be used as an indicator of the significance of non-coding regulatory variants.

Epigenetic regulation of ACE2, the receptor of the SARS-CoV-2 virus1

The angiotensin-converting enzyme 2 (ACE2) is the receptor for the three coronaviruses HCoV-NL63, SARS-CoV, and SARS-CoV-2. ACE2 is involved in the regulation of the renin-angiotensin system and blood pressure. ACE2 is also involved in the regulation of several signaling pathways, including integrin signaling. ACE2 expression is regulated transcriptionally and post-transcriptionally. The expression of the gene is regulated by two promoters, with usage varying among tissues. ACE2 expression is greatest in the small intestine, kidney, and heart and detectable in a variety of tissues and cell types. Herein we review the chemical and mechanical signal transduction pathways regulating the expression of the ACE2 gene and the epigenetic/chromatin features of the expressed gene.

AP-TSS: A New Method for the Analysis of RNA Expression from Particular and Challenging Transcription Start Sites

Alternative promoter usage involved in the regulation of transcription, splicing, and translation contributes to proteome diversity and is involved in a large number of diseases, in particular, cancer. Epigenetic mechanisms and cis regulatory elements are involved in alternative promoter activity. Multiple transcript isoforms can be produced from a gene, due to the initiation of transcription at different transcription start sites (TSS). These transcripts may not have regions that allow discrimination during RT-qPCR, making quantification technically challenging. This study presents a general method for the relative quantification of a transcript synthesized from a particular TSS that we called AP-TSS (analysis of particular TSS). AP-TSS is based on the specific elongation of the cDNA of interest, followed by its quantification by qPCR. As proof of principle, AP-TSS was applied to two non-coding RNA: telomeric repeat-containing RNAs (TERRA) from a particular subtelomeric TSS, and Alu transcripts. The treatment of cells with a DNA methylation inhibitor was associated with a global increase of the total TERRA level, but the TERRA expression from the TSS of interest did not change in HT1080 cells, and only modestly increased in HeLa cells. This result suggests that TERRA upregulation induced by global demethylation of the genome is mainly due to activation from sites other than this particular TSS. For Alu RNA, the signal obtained by AP-TSS is specific for the RNA Polymerase III-dependent Alu transcript. In summary, our method provides a tool to study regulation of gene expression from a given transcription start site, in different conditions that could be applied to many genes. In particular, AP-TSS can be used to investigate the epigenetic regulation of alternative TSS usage that is of importance for the development of epigenetic-targeted therapies.

Proteomic and interactomic insights into the molecular basis of cell functional diversity

The ability of living systems to adapt to changing conditions originates from their capacity to change their molecular constitution. This is achieved by multiple mechanisms that modulate the quantitative composition and the diversity of the molecular inventory. Molecular diversification is particularly pronounced on the proteome level, at which multiple proteoforms derived from the same gene can in turn combinatorially form different protein complexes, thus expanding the repertoire of functional modules in the cell. The study of molecular and modular diversity and their involvement in responses to changing conditions has only recently become possible through the development of new 'omics'-based screening technologies. This Review explores our current knowledge of the mechanisms regulating functional diversification along the axis of gene expression, with a focus on the proteome and interactome. We explore the interdependence between different molecular levels and how this contributes to functional diversity. Finally, we highlight several recent techniques for studying molecular diversity, with specific focus on mass spectrometry-based analysis of the proteome and its organization into functional modules, and examine future directions for this rapidly growing field.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Exploratory analysis of genetic variants influencing molecular traits in cerebral cortex of suicide completers

Genetic factors have been implicated in suicidal behavior. It has been suggested that one of the roles of genetic factors in suicide could be represented by the effect of genetic variants on gene expression regulation. Alteration in the expression of genes participating in multiple biological systems in the suicidal brain has been demonstrated, so it is imperative to identify genetic variants that could influence gene expression or its regulatory mechanisms. In this study, we integrated DNA methylation, gene expression, and genotype data from the prefrontal cortex of suicides to identify genetic variants that could be factors in the regulation of gene expression, generally called quantitative trait locus (xQTLs). We identify 6,224 methylation quantitative trait loci and 2,239 expression quantitative trait loci (eQTLs) in the prefrontal cortex of suicide completers. The xQTLs identified influence the expression of genes involved in neurodevelopment and cell organization. Two of the eQTLs identified (rs8065311 and rs1019238) were previously associated with cannabis dependence, highlighting a candidate genetic variant for the increased suicide risk in subjects with substance use disorders. Our findings suggest that genetic variants may regulate gene expression in the prefrontal cortex of suicides through the modulation of promoter and enhancer activity, and to a lesser extent, binding transcription factors.

A practical view of fine-mapping and gene prioritization in the post-genome-wide association era

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

Characterising the genetic basis of immune response variation to identify causal mechanisms underlying disease susceptibility

Over the last 10 years, genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for autoimmune diseases. However, despite increasing power for the detection of both common and rare coding variants affecting disease susceptibility, a large fraction of disease heritability has remained unexplained. In addition, a majority of the identified loci are located in noncoding regions, and translation of disease-associated loci into new biological insights on the etiology of immune disorders has been lagging. This highlights the need for a better understanding of noncoding variation and new strategies to identify causal genes at disease loci. In this review, I will first detail the molecular basis of gene expression and review the various mechanisms that contribute to alter gene activity at the transcriptional and post-transcriptional level. I will then review the findings from 10 years of functional genomics studies regarding the genetics on gene expression, in particular in the context of infection. Finally, I will discuss the extent to which genetic variants that modulate gene expression at transcriptional and post-transcriptional level contribute to disease susceptibility and present strategies to leverage this information for the identification of causal mechanisms at disease loci in the era of whole genome sequencing.

New subtypes of allele-specific epigenetic effects: implications for brain development, function and disease

Typically, it is assumed that the maternal and paternal alleles for most genes are equally expressed. Known exceptions include canonical imprinted genes, random X-chromosome inactivation, olfactory receptors and clustered protocadherins. Here, we highlight recent studies showing that allele-specific expression is frequent in the genome and involves subtypes of epigenetic allelic effects that differ in terms of heritability, clonality and stability over time. Different forms of epigenetic allele regulation could have different roles in brain development, function, and disease. An emerging area involves understanding allelic effects in a cell-type and developmental stage-specific manner and determining how these effects influence the impact of genetic variants and mutations on the brain. A deeper understanding of epigenetics at the allele and cellular level in the brain could help clarify the mechanisms underlying phenotypic variance.

Genetic effects on promoter usage are highly context-specific and contribute to complex traits

Genetic variants regulating RNA splicing and transcript usage have been implicated in both common and rare diseases. Although transcript usage quantitative trait loci (tuQTLs) have been mapped across multiple cell types and contexts, it is challenging to distinguish between the main molecular mechanisms controlling transcript usage: promoter choice, splicing and 3' end choice. Here, we analysed RNA-seq data from human macrophages exposed to three inflammatory and one metabolic stimulus. In addition to conventional gene-level and transcript-level analyses, we also directly quantified promoter usage, splicing and 3' end usage. We found that promoters, splicing and 3' ends were predominantly controlled by independent genetic variants enriched in distinct genomic features. Promoter usage QTLs were also 50% more likely to be context-specific than other tuQTLs and constituted 25% of the transcript-level colocalisations with complex traits. Thus, promoter usage might be an underappreciated molecular mechanism mediating complex trait associations in a context-specific manner.

Promoter analysis and prediction in the human genome using sequence-based deep learning models

Motivation

Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences.

Results

In this work, we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences. Instead of focusing on the classification accuracy, in this work we predict the exact positions of the transcription start site inside the genomic sequences testing every possible location. We studied human promoters to find effective regions for discrimination and built corresponding deep learning models. These models use adaptively constructed negative set, which iteratively improves the model’s discriminative ability. Our method significantly outperforms the previously developed promoter prediction programs by considerably reducing the number of false-positive predictions. We have achieved error-per-1000-bp rate of 0.02 and have 0.31 errors per correct prediction, which is significantly better than the results of other human promoter predictors.

Availability and implementation

The developed method is available as a web server at http://www.cbrc.kaust.edu.sa/PromID/.

Biophysical Psychiatry-How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a "biophysical psychiatry," an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.

Genetic Variants in STAT3 Promoter Regions and Their Application in Molecular Breeding for Body Size Traits in Qinchuan Cattle

Signal transducer and activator of transcription 3 (STAT3) plays a critical role in leptin-mediated regulation of energy metabolism. This study investigated genetic variation in STAT3 promoter regions and verified their contribution to bovine body size traits. We first estimated the degree of conservation in STAT3, followed by measurements of its mRNA expression during fetal and adult stages of Qinchuan cattle. We then sequenced the STAT3 promoter region to determine genetic variants and evaluate their association with body size traits. From fetus to adult, STAT3 expression increased significantly in muscle, fat, heart, liver, and spleen tissues (p < 0.01), but decreased in the intestine, lung, and rumen (p < 0.01). We identified and named five single nucleotide polymorphisms (SNPs): SNP1-304A>C, SNP2-285G>A, SNP3-209A>C, SNP4-203A>G, and SNP5-188T>C. These five mutations fell significantly outside the Hardy-Weinberg equilibrium (HWE) (Chi-squared test, p < 0.05) and significantly associated with body size traits (p < 0.05). Individuals with haplotype H3H3 (CC-GG-CC-GG-CC) were larger in body size than other haplotypes. Therefore, variations in the STAT3 gene promoter regions, most notably haplotype H3H3, may benefit marker-assisted breeding of Qinchuan cattle.