Linking disease associations with regulatory information in the human genome

A screen of 1,049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential

Recent genome-wide association studies (GWAS) identified numerous schizophrenia (SZ) and Alzheimer's disease (AD) associated loci, most outside protein-coding regions and hypothesized to affect gene transcription. We used a massively parallel reporter assay to screen, 1,049 SZ and 30 AD variants in 64 and nine loci, respectively for allele differences in driving reporter gene expression. A library of synthetic oligonucleotides assaying each allele five times was transfected into K562 chronic myelogenous leukemia lymphoblasts and SK-SY5Y human neuroblastoma cells. One hundred forty eight variants showed allelic differences in K562 and 53 in SK-SY5Y cells, on average 2.6 variants per locus. Nine showed significant differences in both lines, a modest overlap reflecting different regulatory landscapes of these lines that also differ significantly in chromatin marks. Eight of nine were in the same direction. We observe no preference for risk alleles to increase or decrease expression. We find a positive correlation between the number of SNPs in linkage disequilibrium and the proportion of functional SNPs supporting combinatorial effects that may lead to haplotype selection. Our results prioritize future functional follow up of disease associated SNPs to determine the driver GWAS variant(s), at each locus and enhance our understanding of gene regulation dynamics.

Understanding the genetics of neuropsychiatric disorders: the potential role of genomic regulatory blocks

Recent genome-wide association studies have identified numerous loci associated with neuropsychiatric disorders. The majority of these are in non-coding regions, and are commonly assigned to the nearest gene along the genome. However, this approach neglects the three-dimensional organisation of the genome, and the fact that the genome contains arrays of extremely conserved non-coding elements termed genomic regulatory blocks (GRBs), which can be utilized to detect genes under long-range developmental regulation. Here we review a GRB-based approach to assign loci in non-coding regions to potential target genes, and apply it to reanalyse the results of one of the largest schizophrenia GWAS (SWG PGC, 2014). We further apply this approach to GWAS data from two related neuropsychiatric disorders-autism spectrum disorder and bipolar disorder-to show that it is applicable to developmental disorders in general. We find that disease-associated SNPs are overrepresented in GRBs and that the GRB model is a powerful tool for linking these SNPs to their correct target genes under long-range regulation. Our analysis identifies novel genes not previously implicated in schizophrenia and corroborates a number of predicted targets from the original study. The results are available as an online resource in which the genomic context and the strength of enhancer-promoter associations can be browsed for each schizophrenia-associated SNP.

Multi-trait genome-wide association study identifies new loci associated with optic disc parameters

A new avenue of mining published genome-wide association studies includes the joint analysis of related traits. The power of this approach depends on the genetic correlation of traits, which reflects the number of pleiotropic loci, i.e. genetic loci influencing multiple traits. Here, we applied new meta-analyses of optic nerve head (ONH) related traits implicated in primary open-angle glaucoma (POAG); intraocular pressure and central corneal thickness using Haplotype reference consortium imputations. We performed a multi-trait analysis of ONH parameters cup area, disc area and vertical cup-disc ratio. We uncover new variants; rs11158547 in PPP1R36-PLEKHG3 and rs1028727 near SERPINE3 at genome-wide significance that replicate in independent Asian cohorts imputed to 1000 Genomes. At this point, validation of these variants in POAG cohorts is hampered by the high degree of heterogeneity. Our results show that multi-trait analysis is a valid approach to identify novel pleiotropic variants for ONH.

The Genetic Contribution to Type 1 Diabetes

PURPOSE OF REVIEW

To provide an updated summary of discoveries made to date resulting from genome-wide association study (GWAS) and sequencing studies, and to discuss the latest loci added to the growing repertoire of genetic signals predisposing to type 1 diabetes (T1D).

RECENT FINDINGS

Genetic studies have identified over 60 loci associated with T1D susceptibility. GWAS alone does not specifically inform on underlying mechanisms, but in combination with other sequencing and omics-data, advances are being made in our understanding of T1D genetic etiology and pathogenesis. Current knowledge indicates that genetic variation operating in both pancreatic β cells and in immune cells is central in mediating T1D risk. One of the main challenges is to determine how these recently discovered GWAS-implicated variants affect the expression and function of gene products. Once we understand the mechanism of action for disease-causing variants, we will be well placed to apply targeted genomic approaches to impede the premature activation of the immune system in an effort to ultimately prevent the onset of T1D.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

An integrative analysis of non-coding regulatory DNA variations associated with autism spectrum disorder

A number of genetic studies have identified rare protein-coding DNA variations associated with autism spectrum disorder (ASD), a neurodevelopmental disorder with significant genetic etiology and heterogeneity. In contrast, the contributions of functional, regulatory genetic variations that occur in the extensive non-protein-coding regions of the genome remain poorly understood. Here we developed a genome-wide analysis to identify the rare single nucleotide variants (SNVs) that occur in non-coding regions and determined the regulatory function and evolutionary conservation of these variants. Using publicly available datasets and computational predictions, we identified SNVs within putative regulatory regions in promoters, transcription factor binding sites, and microRNA genes and their target sites. Overall, we found that the regulatory variants in ASD cases were enriched in ASD-risk genes and genes involved in fetal neurodevelopment. As with previously reported coding mutations, we found an enrichment of the regulatory variants associated with dysregulation of neurodevelopmental and synaptic signaling pathways. Among these were several rare inherited SNVs found in the mature sequence of microRNAs predicted to affect the regulation of ASD-risk genes. We show a paternally inherited miR-873-5p variant with altered binding affinity for several risk-genes including NRXN2 and CNTNAP2 putatively overlay maternally inherited loss-of-function coding variations in NRXN1 and CNTNAP2 to likely increase the genetic liability in an idiopathic ASD case. Our analysis pipeline provides a new resource for identifying loss-of-function regulatory DNA variations that may contribute to the genetic etiology of complex disorders.

Epigenetic landscapes suggest that genetic risk for intracranial aneurysm operates on the endothelium

BACKGROUND

Genetics play an important role in intracranial aneurysm (IA) pathophysiology. Genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) that are linked to IA but how they affect disease pathobiology remains poorly understood. We used Encyclopedia of DNA Elements (ENCODE) data to investigate the epigenetic landscapes surrounding genetic risk loci to determine if IA-associated SNPs affect functional elements that regulate gene expression and if those SNPs are most likely to impact a specific type of cells.

METHODS

We mapped 16 highly significant IA-associated SNPs to linkage disequilibrium (LD) blocks within the human genome. Within these regions, we examined the presence of H3K4me1 and H3K27ac histone marks and CCCTC-binding factor (CTCF) and transcription-factor binding sites using chromatin immunoprecipitation-sequencing (ChIP-Seq) data. This analysis was conducted in several cell types relevant to endothelial (human umbilical vein endothelial cells [HUVECs]) and inflammatory (monocytes, neutrophils, and peripheral blood mononuclear cells [PBMCs]) biology. Gene ontology analysis was performed on genes within extended IA-risk regions to understand which biological processes could be affected by IA-risk SNPs. We also evaluated recently published data that showed differential methylation and differential ribonucleic acid (RNA) expression in IA to investigate the correlation between differentially regulated elements and the IA-risk LD blocks.

RESULTS

The IA-associated LD blocks were statistically significantly enriched for H3K4me1 and/or H3K27ac marks (markers of enhancer function) in endothelial cells but not in immune cells. The IA-associated LD blocks also contained more binding sites for CTCF in endothelial cells than monocytes, although not statistically significant. Differentially methylated regions of DNA identified in IA tissue were also present in several IA-risk LD blocks, suggesting SNPs could affect this epigenetic machinery. Gene ontology analysis supports that genes affected by IA-risk SNPs are associated with extracellular matrix reorganization and endopeptidase activity.

CONCLUSION

These findings suggest that known genetic alterations linked to IA risk act on endothelial cell function. These alterations do not correlate with IA-associated gene expression signatures of circulating blood cells, which suggests that such signatures are a secondary response reflecting the presence of IA rather than indicating risk for IA.

Influence of genetic polymorphism on transcriptional enhancer activity in the malaria vector Anopheles coluzzii

Enhancers are cis-regulatory elements that control most of the developmental and spatial gene expression in eukaryotes. Genetic variation of enhancer sequences is known to influence phenotypes, but the effect of enhancer variation upon enhancer functional activity and downstream phenotypes has barely been examined in any species. In the African malaria vector, Anopheles coluzzii, we identified candidate enhancers in the proximity of genes relevant for immunity, insecticide resistance, and development. The candidate enhancers were functionally validated using luciferase reporter assays, and their activity was found to be essentially independent of their physical orientation, a typical property of enhancers. All of the enhancers segregated genetically polymorphic alleles, which displayed significantly different levels of functional activity. Deletion mutagenesis and functional testing revealed a fine structure of positive and negative regulatory elements that modulate activity of the enhancer core. Enhancer polymorphisms segregate in wild A. coluzzii populations in West Africa. Thus, enhancer variants that modify target gene expression leading to likely phenotypic consequences are frequent in nature. These results demonstrate the existence of naturally polymorphic A. coluzzii enhancers, which may help explain important differences between individuals or populations for malaria transmission efficiency and vector adaptation to the environment.

Association of the Rheumatoid Arthritis Severity Variant rs26232 with the Invasive Activity of Synovial Fibroblasts

rs26232, located in intron one of C5orf30, is associated with the susceptibility to and severity of rheumatoid arthritis (RA). Here, we investigate the relationship between this variant and the biological activities of rheumatoid arthritis synovial fibroblasts (RASFs). RASFs were isolated from the knee joints of 33 RA patients. The rs26232 genotype was determined and cellular migration, invasion, and apoptosis were compared using in vitro techniques. The production of adhesion molecules, chemokines, and proteases was measured by ELISA or flow cytometry. Cohort genotypes were CC n = 16; CT n = 14; TT n = 3. In comparison with the RASFs of the CT genotype, the CC genotype showed a 1.48-fold greater invasiveness in vitro (p = 0.02), 1.6-fold higher expression intracellular adhesion molecule (ICAM)-1 (p = 0.001), and 5-fold IFN-γ inducible protein-10 (IP-10) (p = 0.01). There was no association of the rs26232 genotype with the expression levels of either total C5orf30 mRNA or any of the three transcript variants. The rs26232 C allele, which has previously been associated with both the risk and severity of RA, is associated with greater invasive activity of RASFs in vitro, and with higher expression of ICAM-1 and IP-10. In resting RASFs, rs26232 is not a quantitative trait locus for C5orf30 mRNA, indicating a more complex mechanism underlying the genotype‒phenotype relationship.

Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms

Tissue-specific regulatory regions harbor substantial genetic risk for disease. Because brain development is a critical epoch for neuropsychiatric disease susceptibility, we characterized the genetic control of the transcriptome in 201 mid-gestational human brains, identifying 7,962 expression quantitative trait loci (eQTL) and 4,635 spliceQTL (sQTL), including several thousand prenatal-specific regulatory regions. We show that significant genetic liability for neuropsychiatric disease lies within prenatal eQTL and sQTL. Integration of eQTL and sQTL with genome-wide association studies (GWAS) via transcriptome-wide association identified dozens of novel candidate risk genes, highlighting shared and stage-specific mechanisms in schizophrenia (SCZ). Gene network analysis revealed that SCZ and autism spectrum disorder (ASD) affect distinct developmental gene co-expression modules. Yet, in each disorder, common and rare genetic variation converges within modules, which in ASD implicates superficial cortical neurons. More broadly, these data, available as a web browser and our analyses, demonstrate the genetic mechanisms by which developmental events have a widespread influence on adult anatomical and behavioral phenotypes.

Epigenetics of scleroderma: Integrating genetic, ethnic, age, and environmental effects

Scleroderma or systemic sclerosis is thought to result from the interplay between environmental or non-genetic factors in a genetically susceptible individual. Epigenetic modifications are influenced by genetic variation and environmental exposures, and change with chronological age and between populations. Despite progress in identifying genetic, epigenetic, and environmental risk factors, the underlying mechanism of systemic sclerosis remains unclear. Since epigenetics provides the regulatory mechanism linking genetic and non-genetic factors to gene expression, understanding the role of epigenetic regulation in systemic sclerosis will elucidate how these factors interact to cause systemic sclerosis. Among the cell types under tight epigenetic control and susceptible to epigenetic dysregulation, immune cells are critically involved in early pathogenic events in the progression of fibrosis and systemic sclerosis. This review starts by summarizing the changes in DNA methylation, histone modification, and non-coding RNAs associated with systemic sclerosis. It then discusses the role of genetic, ethnic, age, and environmental effects on epigenetic regulation, with a focus on immune system dysregulation. Given the potential of epigenome editing technologies for cell reprogramming and as a therapeutic approach for durable gene regulation, this review concludes with a prospect on epigenetic editing. Although epigenomics in systemic sclerosis is in its infancy, future studies will help elucidate the regulatory mechanisms underpinning systemic sclerosis and inform the design of targeted epigenetic therapies to control its dysregulation.

Integrative analysis identifies genetic variant modulating MICA expression and altering susceptibility to persistent HBV infection

BACKGROUND & AIMS

Genome-wide association studies have identified multiple genetic signals associated with the risk of persistent hepatitis B virus (HBV) infection and HBV-related hepatocellular carcinoma. However, the majority of the associated variants may only be markers of functional variants and the underlying biological mechanisms remain elusive. We hypothesized that the functional variants with modulating transcription factor (TF) binding affinity in genome-wide association studies-identified loci may influence the risk of persistent HBV infection in Chinese people.

METHODS

A systematic bioinformatics approach was implemented to prioritize potential functional variants that may influence TF binding. A two-stage case-control study, including 1595 HBV-persistent carriers and 1590 subjects with HBV natural clearance, was conducted to examine the associations between candidate variants and susceptibility to persistent HBV infection. Biological assays were carried out to elucidate the underlying mechanism of the associated genetic variants.

RESULTS

Twelve candidate variants were identified, and rs2523454 G > A increased the risk of persistent HBV infection (dominant model: ORcombined  = 1.37, 95% CI = 1.19-1.58, P = 1.610 × 10-5 ). Functional assays indicated that the rs2523454 A allele significantly decreased transcriptional activity compared to the G allele by influencing TF-binding affinity. In addition, expression quantitative trait loci analyses revealed that the A allele was associated with the reduced expression of MICA (P < 0.01).

CONCLUSIONS

Our findings suggest that the germline G > A variation at rs2523454 may influence TF-DNA interaction, downregulate the expression of MICA and play an important role in the development of persistent HBV infection in the Chinese population.

Identification of C2CD4A as a human diabetes susceptibility gene with a role in β cell insulin secretion

Fine mapping and validation of genes causing β cell failure from susceptibility loci identified in type 2 diabetes genome-wide association studies (GWAS) poses a significant challenge. The VPS13C-C2CD4A-C2CD4B locus on chromosome 15 confers diabetes susceptibility in every ethnic group studied to date. However, the causative gene is unknown. FoxO1 is involved in the pathogenesis of β cell dysfunction, but its link to human diabetes GWAS has not been explored. Here we generated a genome-wide map of FoxO1 superenhancers in chemically identified β cells using 2-photon live-cell imaging to monitor FoxO1 localization. When parsed against human superenhancers and GWAS-derived diabetes susceptibility alleles, this map revealed a conserved superenhancer in C2CD4A, a gene encoding a β cell/stomach-enriched nuclear protein of unknown function. Genetic ablation of C2cd4a in β cells of mice phenocopied the metabolic abnormalities of human carriers of C2CD4A-linked polymorphisms, resulting in impaired insulin secretion during glucose tolerance tests as well as hyperglycemic clamps. C2CD4A regulates glycolytic genes, and notably represses key β cell "disallowed" genes, such as lactate dehydrogenase A We propose that C2CD4A is a transcriptional coregulator of the glycolytic pathway whose dysfunction accounts for the diabetes susceptibility associated with the chromosome 15 GWAS locus.

Genetic association of molecular traits: A help to identify causative variants in complex diseases

In the past 15 years, major progresses have been made in the understanding of the genetic basis of regulation of gene expression. These new insights have revolutionized our approach to resolve the genetic variation underlying complex diseases. Gene transcript levels were the first expression phenotypes that were studied. They are heritable and therefore amenable to genome-wide association studies. The genetic variants that modulate them are called expression quantitative trait loci. Their study has been extended to other molecular quantitative trait loci (molQTLs) that regulate gene expression at the various levels, from chromatin state to cellular responses. Altogether, these studies have generated a wealth of basic information on the genome-wide patterns of gene expression and their inter-individual variation. Most importantly, molQTLs have become an invaluable asset in the genetic study of complex diseases. Although the identification of the disease-causing variants on the basis of their overlap with molQTLs requires caution, molQTLs can help to prioritize the relevant candidate gene(s) in the disease-associated regions and bring a functional interpretation of the associated variants, therefore, bridging the gap between genotypes and clinical phenotypes.

Enhancer variants associated with Alzheimer's disease affect gene expression via chromatin looping

BACKGROUND

Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer's disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops.

METHODS

To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10- 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments.

RESULTS

We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes.

CONCLUSION

Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

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.

AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes

Motivation

Reliance on mapping to a single reference haplotype currently limits accurate estimation of allele or haplotype-specific expression using RNA-sequencing, notably in highly polymorphic regions such as the major histocompatibility complex.

Results

We present AltHapAlignR, a method incorporating alternate reference haplotypes to generate gene- and haplotype-level estimates of transcript abundance for any genomic region where such information is available. We validate using simulated and experimental data to quantify input allelic ratios for major histocompatibility complex haplotypes, demonstrating significantly improved correlation with ground truth estimates of gene counts compared to standard single reference mapping. We apply AltHapAlignR to RNA-seq data from 462 individuals, showing how significant underestimation of expression of the majority of classical human leukocyte antigen genes using conventional mapping can be corrected using AltHapAlignR to allow more accurate quantification of gene expression for individual alleles and haplotypes.

Availability and implementation

Source code freely available at https://github.com/jknightlab/AltHapAlignR.

Supplementary information

Supplementary data are available at Bioinformatics online.

Islet-immune interactions in type 1 diabetes: the nexus of beta cell destruction

Recent studies in Type 1 Diabetes (T1D) support an emerging model of disease pathogenesis that involves intrinsic β-cell fragility combined with defects in both innate and adaptive immune cell regulation. This combination of defects induces systematic changes leading to organ-level atrophy and dysfunction of both the endocrine and exocrine portions of the pancreas, ultimately culminating in insulin deficiency and β-cell destruction. In this review, we discuss the animal model data and human tissue studies that have informed our current understanding of the cross-talk that occurs between β-cells, the resident stroma, and immune cells that potentiate T1D. Specifically, we will review the cellular and molecular signatures emerging from studies on tissues derived from organ procurement programs, focusing on in situ defects occurring within the T1D islet microenvironment, many of which are not yet detectable by standard peripheral blood biomarkers. In addition to improved access to organ donor tissues, various methodological advances, including immune receptor repertoire sequencing and single-cell molecular profiling, are poised to improve our understanding of antigen-specific autoimmunity during disease development. Collectively, the knowledge gains from these studies at the islet-immune interface are enhancing our understanding of T1D heterogeneity, likely to be an essential component for instructing future efforts to develop targeted interventions to restore immune tolerance and preserve β-cell mass and function.

Functional genetic variants can mediate their regulatory effects through alteration of transcription factor binding

Functional variants in the genome are usually identified by their association with local gene expression, DNA methylation or chromatin states. DNA sequence motif analysis and chromatin immunoprecipitation studies have provided indirect support for the hypothesis that functional variants alter transcription factor binding to exert their effects. In this study, we provide direct evidence that functional variants can alter transcription factor binding. We identify a multifunctional variant within the TBC1D4 gene encoding a canonical NFκB binding site, and edited it using CRISPR-Cas9 to remove this site. We show that this editing reduces TBC1D4 expression, local chromatin accessibility and binding of the p65 component of NFκB. We then used CRISPR without genomic editing to guide p65 back to the edited locus, demonstrating that this re-targeting, occurring ~182 kb from the gene promoter, is enough to restore the function of the locus, supporting the central role of transcription factors mediating the effects of functional variants.

Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

OBJECTIVES

There is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.

METHODS

High confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.

RESULTS

Overall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.

CONCLUSION

Capture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

EnDisease: a manually curated database for enhancer-disease associations

Genome-wide association studies have successfully identified thousands of genomic loci potentially associated with hundreds of complex traits in the past decade. Nevertheless, the fact that more than 90% of such disease-associated variants lie in non-coding DNA with unknown functional implications has been appealing for advanced analysis of plenty of genetic variants. Toward this goal, recent studies focusing on individual non-coding variants have revealed that complex diseases are often the consequences of erroneous interactions between enhancers and their target genes. However, such enhancer-disease associations are dispersed in a variety of independent studies, and thus far it is still difficult to carry out comprehensive downstream analysis with these experimentally supported enhancer-disease associations. To fill in this gap, we collected experimentally supported associations between complex diseases and enhancers and then developed a manually curated database called EnDisease (http://bioinfo.au.tsinghua.edu.cn/endisease/). Concretely, EnDisease documents 535 associations between 133 diseases and 454 enhancers, extracted from 199 articles. Moreover, after annotating these enhancers using 649 human and 115 mouse DNase-seq experiments, we find that cancer-related enhancers tend to be open across a large number of cell types. This database provides a user-friendly interface for browsing and searching, and it also allows users to download data freely. EnDisease has the potential to become a helpful and important resource for researchers who aim to understand the molecular mechanisms of enhancers involved in complex diseases.

Addressing the Missing Heritability Problem With the Help of Regulatory Features

Genome-wide association studies (GWASs) have successfully identified thousands of susceptibility loci for human complex diseases. However, missing heritability is still a challenging problem. Considering most GWAS loci are located in regulatory elements, we recently developed a pipeline named functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP), to predict novel susceptibility loci for complex diseases based on the interpretation of regulatory features and published GWAS results with machine learning. When applied to type 2 diabetes and hypertension, the predicted susceptibility loci by FDSP were proved to be capable of explaining additional heritability. In addition, potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways. Our results suggested that taking regulatory features into consideration might be a useful way to address the missing heritability problem. We hope FDSP could offer help for the identification of novel susceptibility loci for complex diseases.

A screen for deeply conserved non-coding GWAS SNPs uncovers a MIR-9-2 functional mutation associated to retinal vasculature defects in human

Thousands of human disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding genome, but only a handful have been demonstrated to affect gene expression and human biology. We computationally identified risk-associated SNPs in deeply conserved non-exonic elements (CNEs) potentially contributing to 45 human diseases. We further demonstrated that human CNE1/rs17421627 associated with retinal vasculature defects showed transcriptional activity in the zebrafish retina, while introducing the risk-associated allele completely abolished CNE1 enhancer activity. Furthermore, deletion of CNE1 led to retinal vasculature defects and to a specific downregulation of microRNA-9, rather than MEF2C as predicted by the original genome-wide association studies. Consistent with these results, miR-9 depletion affects retinal vasculature formation, demonstrating MIR-9-2 as a critical gene underpinning the associated trait. Importantly, we validated that other CNEs act as transcriptional enhancers that can be disrupted by conserved non-coding SNPs. This study uncovers disease-associated non-coding mutations that are deeply conserved, providing a path for in vivo testing to reveal their cis-regulated genes and biological roles.

Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

The relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ∼500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

Integrating genome-wide association and eQTLs studies identifies the genes associated with age at menarche and age at natural menopause

Objective

An early onset of menarche and, later, menopause are well-established risk factors for the development of breast cancer and endometrial cancer. Although the largest GWASs have identified 389 independent signals for age at menarche (AAM) and 44 regions for age at menopause (ANM), GWAS can only identify the associations between variants and traits. The aim of this study was to identify genes whose expression levels were associated with AAM or ANM due to pleiotropy or causality by integrating GWAS data with genome-wide expression quantitative trait loci (eQTLs) data. We also aimed to identify the pleiotropic genes that influenced both phenotypes.

Method

We employed GWAS data of AAM and ANM and genome-wide eQTL data from whole blood. The summary data-based Mendelian randomization method was used to prioritize the associated genes for further study. The colocalization analysis was used to identify the pleiotropic genes associated with both phenotypes.

Results

We identified 31 genes whose expression was associated with AAM and 24 genes whose expression was associated with ANM due to pleiotropy or causality. Two pleiotropic genes were identified to be associated with both phenotypes.

Conclusion

The results point out the most possible genes which were responsible for the association. Our study prioritizes the associated genes for further functional mechanistic study of AAM and ANM and illustrates the benefit of integrating different omics data into the study of complex traits.

Disease classification: from phenotypic similarity to integrative genomics and beyond

A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).

The inherited variations of a p53-responsive enhancer in 13q12.12 confer lung cancer risk by attenuating TNFRSF19 expression

Background

Inherited factors contribute to lung cancer risk, but the mechanism is not well understood. Defining the biological consequence of GWAS hits in cancers is a promising strategy to elucidate the inherited mechanisms of cancers. The tag-SNP rs753955 (A>G) in 13q12.12 is highly associated with lung cancer risk in the Chinese population. Here, we systematically investigate the biological significance and the underlying mechanism behind 13q12.12 risk locus in vitro and in vivo.

Results

We characterize a novel p53-responsive enhancer with lung tissue cell specificity in a 49-kb high linkage disequilibrium block of rs753955. This enhancer harbors 3 highly linked common inherited variations (rs17336602, rs4770489, and rs34354770) and six p53 binding sequences either close to or located between the variations. The enhancer effectively protects normal lung cell lines against pulmonary carcinogen NNK-induced DNA damages and malignant transformation by upregulating TNFRSF19 through chromatin looping. These variations significantly weaken the enhancer activity by affecting its p53 response, especially when cells are exposed to NNK. The effect of the mutant enhancer alleles on TNFRSF19 target gene in vivo is supported by expression quantitative trait loci analysis of 117 Chinese NSCLC samples and GTEx data. Differentiated expression of TNFRSF19 and its statistical significant correlation with tumor TNM staging and patient survival indicate a suppressor role of TNFRSF19 in lung cancer.

Conclusion

This study provides evidence of how the inherited variations in 13q12.12 contribute to lung cancer risk, highlighting the protective roles of the p53-responsive enhancer-mediated TNFRSF19 activation in lung cells under carcinogen stress.

Electronic supplementary material

The online version of this article (10.1186/s13059-019-1696-1) contains supplementary material, which is available to authorized users.

Molecular Origins of Complex Heritability in Natural Genotype-to-Phenotype Relationships

The statistical complexity of heredity has long been evident, but its molecular origins remain elusive. To investigate, we charted 90 comprehensive genotype-to-phenotype maps in a large population of wild diploid yeast. In contrast to long-standing assumptions, all types of genetic variation contributed similarly to phenotype. Causal synonymous and regulatory variants exhibited distinct molecular signatures, as did nonlinearities in heterozygote fitness that likely contribute to hybrid vigor. Highly pleiotropic variants altered disordered sequences within signaling hubs, and their effects correlated across environments-even when antagonistic-suggesting that large fitness gains bring concomitant costs. Natural genetic networks defined by the causal loci differed from those determined by precise gene deletions or protein-protein interactions. Finally, we found that traits that would appear omnigenic in less powered studies do in fact have finite genetic determinants. Integrating these molecular principles will be crucial as genome reading and writing become routine in research, industry, and medicine.

Non-coding DNA in IBD: from sequence variation in DNA regulatory elements to novel therapeutic potential

Genome-wide association studies have identified over 200 loci associated with IBD. We and others have recently shown that, in addition to variants in protein-coding genes, the majority of the associated loci are related to DNA regulatory elements (DREs). These findings add a dimension to the already complex genetic background of IBD. In this review we summarise the existing evidence on the role of DREs in IBD. We discuss how epigenetic research can be used in candidate gene approaches that take non-coding variants into account and can help to pinpoint the essential pathways and cell types in the pathogenesis of IBD. Despite the increased level of genetic complexity, these findings can contribute to novel therapeutic options that target transcription factor binding and enhancer activity. Finally, we summarise the future directions and challenges of this emerging field.

Applications of ENCODE data to Systematic Analyses via Data Integration

Large-scale genomic data have been utilized to generate unprecedented biological findings and new hypotheses. To delineate functional elements in the human genome, the Encyclopedia of DNA Elements (ENCODE) project has generated an enormous amount of genomic data, yielding around 7,000 data profiles in different cell and tissue types. In this article, we reviewed the systematic analyses that have integrated ENCODE data with other data sources to reveal new biological insights, ranging from human genome annotation to the identification of new candidate drugs. These analyses demonstrate the critical impact of ENCODE data on basic biology and translational research.

Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

Background

Fertility is among the most important economic traits in dairy cattle. Genomic prediction for cow fertility has received much attention in the last decade, while bull fertility has been largely overlooked. The goal of this study was to assess genomic prediction of dairy bull fertility using markers with large effect and functional annotation data. Sire conception rate (SCR) was used as a measure of service sire fertility. Dataset consisted of 11.5 k U.S. Holstein bulls with SCR records and about 300 k single nucleotide polymorphism (SNP) markers. The analyses included the use of both single-kernel and multi-kernel predictive models fitting either all SNPs, markers with large effect, or markers with presumed functional roles, such as non-synonymous, synonymous, or non-coding regulatory variants.

Results

The entire set of SNPs yielded predictive correlations of 0.340. Five markers located on chromosomes BTA8, BTA9, BTA13, BTA17, and BTA27 showed marked dominance effects. Interestingly, the inclusion of these five major markers as fixed effects in the predictive models increased predictive correlations to 0.403, representing an increase in accuracy of about 19% compared with the standard model. Single-kernel models fitting functional SNP classes outperformed their counterparts using random sets of SNPs, suggesting that the predictive power of these functional variants is driven in part by their biological roles. Multi-kernel models fitting all the functional SNP classes together with the five major markers exhibited predictive correlations around 0.405.

Conclusions

The inclusion of markers with large effect markedly improved the prediction of dairy sire fertility. Functional variants exhibited higher predictive ability than random variants, but did not outperform the standard whole-genome approach. This research is the foundation for the development of novel strategies that could help the dairy industry make accurate genome-guided selection decisions on service sire fertility.

Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Flowering time is an important agronomic trait, attributed by multiple genes, gene-gene interactions and environmental factors. Population stratification and polygenic effects might confound genetic effects of the causal loci underlying this complex trait. We proposed a two-step approach for detecting epistasis interactions underlying rice flowering time by accounting population structure and polygenic effects. Simulation studies showed that the approach used in this study performs better than classical and PC-linear approaches in terms of powers and false discovery rates in the case of population stratification and polygenic effects. Whole genome epistasis analyses identified 589 putative genetic interactions for flowering time. Eighteen of these interactions are located within 10 kilobases of regions of known protein-protein interactions. Thirty-seven SNPs near to twenty-five genes involve in rice or/and Arabidopsis (orthologue) flowering pathway. Bioinformatics analysis showed that 66.55% pairwise genes of the identified interactions (392 out of the 589 interactions) have similarity in various genomic features. Moreover, significant numbers of detected epistatic genes have high expression in different floral tissues. Our findings highlight the importance of epistasis analysis by controlling population stratification and polygenic effect and provided novel insights into the genetic architecture of rice flowering which could assist breeding programmes.

Genomic and epigenomic mapping of leptin-responsive neuronal populations involved in body weight regulation

Genome wide association studies (GWAS) in obesity have identified a large number of noncoding loci located near genes expressed in the central nervous system. However, due to the difficulties in isolating and characterizing specific neuronal subpopulations, few obesity-associated SNPs have been functionally characterized. Leptin responsive neurons in the hypothalamus are essential in controlling energy homeostasis and body weight. Here, we combine FACS-sorting of leptin-responsive hypothalamic neuron nuclei with genomic and epigenomic approaches (RNA-seq, ChIP-seq, ATAC-seq) to generate a comprehensive map of leptin-response specific regulatory elements, several of which overlap obesity-associated GWAS variants. We demonstrate the usefulness of our leptin-response neuron regulome, by functionally characterizing a novel enhancer near Socs3, a leptin response-associated transcription factor. We envision our data to serve as a useful resource and a blueprint for functionally characterizing obesity-associated SNPs in the hypothalamus.

Super-enhancers in cancer

Cancer is fueled by the aberrant activity of oncogenic and tumor suppressive pathways. Transcriptional dysregulation of these pathways play a major role both in the genesis and development of cancer. Dysregulation of transcriptional programs can be mediated by genetic and epigenetic alterations targeting both protein coding genes and non-coding regulatory elements like enhancers and super-enhancers. Super-enhancers, characterized as large clusters of enhancers in close proximity, have been identified as essential oncogenic drivers required for the maintenance of cancer cell identity. As a result, cancer cells are often addicted to the super-enhancer driven transcriptional programs. Furthermore, pharmacological inhibitors targeting key components of super-enhancer assembly and activation have shown great promise in reducing tumor growth and proliferation in several pre-clinical tumor models. This article reviews the current understanding of super-enhancer assembly and activation, the different mechanisms by which cancer cells acquire oncogenic super-enhancers and, finally, the potential of targeting super-enhancers as future therapeutics.

Genetic regulation of methylation in human endometrium and blood and gene targets for reproductive diseases

Background

Major challenges in understanding the functional consequences of genetic risk factors for human disease are which tissues and cell types are affected and the limited availability of suitable tissue. The aim of this study was to evaluate tissue-specific genotype-epigenetic characteristics in DNA samples from both endometrium and blood collected from women at different stages of the menstrual cycle and relate results to genetic risk factors for reproductive traits and diseases.

Results

We analysed DNA methylation (DNAm) data from endometrium and blood samples from 66 European women. Methylation profiles were compared between stages of the menstrual cycle, and changes in methylation overlaid with changes in transcription and genotypes. We observed large changes in methylation (27,262 DNAm probes) across the menstrual cycle in endometrium that were not observed in blood. Individual genotype data was tested for association with methylation at 443,016 and 443,101 DNAm probes in endometrium and blood respectively to identify methylation quantitative trait loci (mQTLs). A total of 4546 sentinel cis-mQTLs (P < 1.13 × 10⁻¹⁰) and 434 sentinel trans-mQTLs (P < 2.29 × 10⁻¹²) were detected in endometrium and 6615 sentinel cis-mQTLs (P < 1.13 × 10⁻¹⁰) and 590 sentinel trans-mQTLs (P < 2.29 × 10⁻¹²) were detected in blood. Following secondary analyses, conducted to test for overlap between mQTLs in the two tissues, we found that 62% of endometrial cis-mQTLs were also observed in blood and the genetic effects between tissues were highly correlated. A number of mQTL SNPs were associated with reproductive traits and diseases, including one mQTL located in a known risk region for endometriosis (near GREB1).

Conclusions

We report novel findings characterising genetic regulation of methylation in endometrium and the association of endometrial mQTLs with endometriosis risk and other reproductive traits and diseases. The high correlation of genetic effects between tissues highlights the potential to exploit the power of large mQTL datasets in endometrial research and identify target genes for functional studies. However, tissue-specific methylation profiles and genetic effects also highlight the importance of also using disease-relevant tissues when investigating molecular mechanisms of disease risk.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0648-7) contains supplementary material, which is available to authorized users.

Effectiveness of imaging genetics analysis to explain degree of depression in Parkinson's disease

Depression is one of the most common and important neuropsychiatric symptoms in Parkinson's disease and often becomes worse as Parkinson's disease progresses. However, the underlying mechanisms of depression in Parkinson's disease are not clear. The aim of our study was to find genetic features related to depression in Parkinson's disease using an imaging genetics approach and to construct an analytical model for predicting the degree of depression in Parkinson's disease. The neuroimaging and genotyping data were obtained from an openly accessible database. We computed imaging features through connectivity analysis derived from tractography of diffusion tensor imaging. The imaging features were used as intermediate phenotypes to identify genetic variants according to the imaging genetics approach. We then constructed a linear regression model using the genetic features from imaging genetics approach to describe clinical scores indicating the degree of depression. As a comparison, we constructed other models using imaging features and genetic features based on references to demonstrate the effectiveness of our imaging genetics model. The models were trained and tested in a five-fold cross-validation. The imaging genetics approach identified several brain regions and genes known to be involved in depression, with the potential to be used as meaningful biomarkers. Our proposed model using imaging genetic features predicted and explained the degree of depression in Parkinson's disease appropriately (adjusted R2 larger than 0.6 over five training folds) and with a lower error and higher correlation than with other models over five test folds.

CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features

BACKGROUND

We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP "radius" as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures.

RESULTS

We expanded the set of reference SNPs to 39,083 (the OSU18 set) and extracted CERENKOV SNP feature data. We computed radius empirical likelihoods and likelihood densities for rSNPs and control SNPs, and found significant likelihood differences between rSNPs and control SNPs. We fit parametric models of likelihood distributions for five different distance measures to obtain ten log-likelihood features that we combined with the 248-dimensional CERENKOV feature matrix. On the OSU18 SNP set, we measured the classification accuracy of CERENKOV with and without the new distance-based features, and found that the addition of distance-based features significantly improves rSNP recognition performance as measured by AUPVR, AUROC, and AVGRANK. Along with feature data for the OSU18 set, the software code for extracting the base feature matrix, estimating ten distance-based likelihood ratio features, and scoring candidate causal SNPs, are released as open-source software CERENKOV2.

CONCLUSIONS

Accounting for the locus-specific geometry of SNPs in data-space significantly improved the accuracy with which noncoding rSNPs can be computationally identified.

Mechanical activation of noncoding-RNA-mediated regulation of disease-associated phenotypes in human cardiomyocytes

How common polymorphisms in noncoding genome regions can regulate cellular function remains largely unknown. Here we show that cardiac fibrosis, mimicked using a hydrogel with controllable stiffness, affects the regulation of the phenotypes of human cardiomyocytes by a portion of the long noncoding RNA ANRIL, the gene of which is located in the disease-associated 9p21 locus. In a physiological environment, cultured cardiomyocytes derived from induced pluripotent stem cells obtained from patients who are homozygous for cardiovascular-risk alleles (R/R cardiomyocytes) or from healthy individuals who are homozygous for nonrisk alleles contracted synchronously, independently of genotype. After hydrogel stiffening to mimic fibrosis, only the R/R cardiomyocytes exhibited asynchronous contractions. These effects were associated with increased expression of the short ANRIL isoform in R/R cardiomyocytes, which induced a c-Jun N-terminal kinase (JNK) phosphorylation-based mechanism that impaired gap junctions (particularly, loss of connexin-43 expression) following stiffening. Deletion of the risk locus or treatment with a JNK antagonist was sufficient to maintain gap junctions and prevent asynchronous contraction of cardiomyocytes. Our findings suggest that mechanical changes in the microenvironment of cardiomyocytes can activate the regulation of their function by noncoding loci.

Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains

Most variants associated with complex phenotypes in genome-wide association studies (GWAS) do not directly index coding changes affecting protein structure. Instead they are hypothesized to influence gene regulation, with common variants associated with disease being enriched in regulatory domains including enhancers and regions of open chromatin. There is interest, therefore, in using epigenomic annotation data to identify the specific regulatory mechanisms involved and prioritize risk variants. We quantified lysine H3K27 acetylation (H3K27ac) - a robust mark of active enhancers and promoters that is strongly correlated with gene expression and transcription factor binding - across the genome in entorhinal cortex samples using chromatin immunoprecipitation followed by highly parallel sequencing (ChIP-seq). H3K27ac peaks were called using high quality reads combined across all samples and formed the basis of partitioned heritability analysis using LD score regression along with publicly-available GWAS results for seven psychiatric and neurodegenerative traits. Heritability for all seven brain traits was significantly enriched in these H3K27ac peaks (enrichment ranging from 1.09-2.13) compared to regions of the genome containing other active regulatory and functional elements across multiple cell types and tissues. The strongest enrichments were for amyotrophic lateral sclerosis (ALS) (enrichment = 2.19; 95% CI = 2.12-2.27), autism (enrichment = 2.11; 95% CI = 2.05-2.16) and major depressive disorder (enrichment = 2.04; 95% CI = 1.92-2.16). Much lower enrichments were observed for 14 non-brain disorders, although we identified enrichment in cortical H3K27ac domains for body mass index (enrichment = 1.16; 95% CI = 1.13-1.19), ever smoked (enrichment = 2.07; 95% CI = 2.04-2.10), HDL (enrichment = 1.53; 95% CI = 1.45-1.62) and trigylcerides (enrichment = 1.33; 95% CI = 1.24-1.42). These results indicate that risk alleles for brain disorders are preferentially located in regions of regulatory/enhancer function in the cortex, further supporting the hypothesis that genetic variants for these phenotypes influence gene regulation in the brain.

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10-8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.

Polymorphisms in RAS/RAF/MEK/ERK Pathway Are Associated with Gastric Cancer

The RAS/RAF/MEK/ERK pathway regulates certain cellular functions, including cell proliferation, differentiation, survival, and apoptosis. Dysregulation of this pathway leads to the occurrence and progression of cancers mainly by somatic mutations. This study aimed to assess if polymorphisms of the RAS/RAF/MEK/ERK pathway are associated with gastric cancer. A case-control study of 242 gastric cancer patients and 242 controls was performed to assess the association of 27 single nucleotide polymorphisms (SNPs) in the RAS/RAF/MEK/ERK pathway genes with gastric cancer. Analyses performed under the additive model (allele) showed four significantly associated SNPs: RAF1 rs3729931 (Odds ratio (OR) = 1.54, 95%, confidence interval (CI): 1.20–1.98, p-value = 7.95 × 10⁻⁴), HRAS rs45604736 (OR = 1.60, 95% CI: 1.16–2.22, p-value = 4.68 × 10⁻³), MAPK1 rs2283792 (OR = 1.45, 95% CI: 1.12–1.87, p-value = 4.91 × 10⁻³), and MAPK1 rs9610417 (OR = 0.60, 95% CI: 0.42–0.87, p-value = 6.64 × 10⁻³). Functional annotation suggested that those variants or their proxy variants may have a functional effect. In conclusion, this study suggests that RAF1 rs3729931, HRAS rs45604736, MAPK1 rs2283792, and MAPK1 rs9610417 are associated with gastric cancer.

Exploring the genetic basis of human population differences in DNA methylation and their causal impact on immune gene regulation

BACKGROUND

DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.

RESULTS

We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.

CONCLUSION

Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.

Parkinson-Associated SNCA Enhancer Variants Revealed by Open Chromatin in Mouse Dopamine Neurons

The progressive loss of midbrain (MB) dopaminergic (DA) neurons defines the motor features of Parkinson disease (PD), and modulation of risk by common variants in PD has been well established through genome-wide association studies (GWASs). We acquired open chromatin signatures of purified embryonic mouse MB DA neurons because we anticipated that a fraction of PD-associated genetic variation might mediate the variants' effects within this neuronal population. Correlation with >2,300 putative enhancers assayed in mice revealed enrichment for MB cis-regulatory elements (CREs), and these data were reinforced by transgenic analyses of six additional sequences in zebrafish and mice. One CRE, within intron 4 of the familial PD gene SNCA, directed reporter expression in catecholaminergic neurons from transgenic mice and zebrafish. Sequencing of this CRE in 986 individuals with PD and 992 controls revealed two common variants associated with elevated PD risk. To assess potential mechanisms of action, we screened >16,000 proteins for DNA binding capacity and identified a subset whose binding is impacted by these enhancer variants. Additional genotyping across the SNCA locus identified a single PD-associated haplotype, containing the minor alleles of both of the aforementioned PD-risk variants. Our work posits a model for how common variation at SNCA might modulate PD risk and highlights the value of cell-context-dependent guided searches for functional non-coding variation.

WITHDRAWN: Single nucleotide polymorphism, a putative driver for the role of long intergeneric non-coding RNA

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Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap

Functional annotation of genomes is a prerequisite for contemporary basic and applied genomic research, yet farmed animal genomics is deficient in such annotation. To address this, the FAANG (Functional Annotation of Animal Genomes) Consortium is producing genome-wide data sets on RNA expression, DNA methylation, and chromatin modification, as well as chromatin accessibility and interactions. In addition to informing our understanding of genome function, including comparative approaches to elucidate constrained sequence or epigenetic elements, these annotation maps will improve the precision and sensitivity of genomic selection for animal improvement. A scientific community-driven effort has already created a coordinated data collection and analysis enterprise crucial for the success of this global effort. Although it is early in this continuing process, functional data have already been produced and application to genetic improvement reported. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.

Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.