Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Identification and Characterization of a Transcribed Distal Enhancer Involved in Cardiac Kcnh2 Regulation

The human ether-a-go-go-related gene KCNH2 encodes the voltage-gated potassium channel underlying I_Kr, a current critical for the repolarization phase of the cardiac action potential. Mutations in KCNH2 that cause a reduction of the repolarizing current can result in cardiac arrhythmias associated with long-QT syndrome. Here, we investigate the regulation of KCNH2 and identify multiple active enhancers. A transcribed enhancer ∼85 kbp downstream of Kcnh2 physically contacts the promoters of two Kcnh2 isoforms in a cardiac-specific manner in vivo. Knockdown of its ncRNA transcript results in reduced expression of Kcnh2b and two neighboring mRNAs, Nos3 and Abcb8, in vitro. Genomic deletion of the enhancer, including the ncRNA transcription start site, from the mouse genome causes a modest downregulation of both Kcnh2a and Kcnh2b in the ventricles. These findings establish that the regulation of Kcnh2a and Kcnh2b is governed by a complex regulatory landscape that involves multiple partially redundantly acting enhancers.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r²<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Many DNA variants affect common human traits, and distinct variants can have different effects on the function or expression level of the same gene. We identified variants associated with levels of adiponectin, a hormone involved in glucose regulation. Among these variants, we specifically studied the sets of variants located near two genes, ADIPOQ and CDH13, to determine how the variants affect gene expression or function. We focused on sets of variants that can be inherited together but are not always inherited together. Of the variants associated with adiponectin and located near ADIPOQ, one set were also associated with higher expression levels of the protein-coding ADIPOQ gene and a nearby non-coding gene, a second set of variants were associated with lower levels of the ADIPOQ-AS1 antisense gene, and additional variants changed the amino acid sequence or size of the adiponectin protein. Our examples show the benefits of identifying multiple sets of trait-associated variants in the same DNA region. These variants explain more trait variation, help identify genes that affect the trait, and guide studies of gene regulation and biological processes.

A mega-analysis of expression quantitative trait loci in retinal tissue

Significant association signals from genome-wide association studies (GWAS) point to genomic regions of interest. However, for most loci the causative genetic variant remains undefined. Determining expression quantitative trait loci (eQTL) in a disease relevant tissue is an excellent approach to zoom in on disease- or trait-associated association signals and hitherto on relevant disease mechanisms. To this end, we explored regulation of gene expression in healthy retina (n = 311) and generated the largest cis-eQTL data set available to date. Genotype- and RNA-Seq data underwent rigorous quality control protocols before FastQTL was applied to assess the influence of genetic markers on local (cis) gene expression. Our analysis identified 403,151 significant eQTL variants (eVariants) that regulate 3,007 genes (eGenes) (Q-Value < 0.05). A conditional analysis revealed 744 independent secondary eQTL signals for 598 of the 3,007 eGenes. Interestingly, 99,165 (24.71%) of all unique eVariants regulate the expression of more than one eGene. Filtering the dataset for eVariants regulating three or more eGenes revealed 96 potential regulatory clusters. Of these, 31 harbour 130 genes which are partially regulated by the same genetic signal. To correlate eQTL and association signals, GWAS data from twelve complex eye diseases or traits were included and resulted in identification of 80 eGenes with potential association. Remarkably, expression of 10 genes is regulated by eVariants associated with multiple eye diseases or traits. In conclusion, we generated a unique catalogue of gene expression regulation in healthy retinal tissue and applied this resource to identify potentially pleiotropic effects in highly prevalent human eye diseases. Our study provides an excellent basis to further explore mechanisms of various retinal disease etiologies.

The retina is a multilayered and highly specified neural tissue crucial for high-resolution visual perception and spatial orientation. Environmental and genetic insults to the retina result in many blinding diseases, such as age-related macular degeneration or glaucoma. Commonly, many of these diseases are age-related suggesting that minor changes are accumulating over a life-time, with little or no contribution of strong individual effects. Specifically, this is true for genetic factors known to underlie the etiology of complex diseases including the prevalent eye diseases. In our study, we searched for effects on gene expression due to genetic variation using 311 healthy post-mortem retinal tissue samples. We show that 3,007 of the 16,766 genes investigated are regulated in the retina by genetic variations. Of these, 80 genes are potentially associated to one or more of twelve complex eye diseases or retinal traits tested. Interestingly, 10 genes appear to be involved in the development of several eye traits suggesting that cellular mechanisms may act at a common point in the disease process. Consequently, our study provides the basis to further explore retinal disease pathways and is likely to highlight target molecules for future therapeutic applications.

Characterizing the Causal Pathway for Genetic Variants Associated with Neurological Phenotypes Using Human Brain-Derived Proteome Data

Leveraging high-dimensional molecular datasets can help us develop mechanistic insight into associations between genetic variants and complex traits. In this study, we integrated human proteome data derived from brain tissue to evaluate whether targeted proteins putatively mediate the effects of genetic variants on seven neurological phenotypes (Alzheimer disease, amyotrophic lateral sclerosis, depression, insomnia, intelligence, neuroticism, and schizophrenia). Applying the principles of Mendelian randomization (MR) systematically across the genome highlighted 43 effects between genetically predicted proteins derived from the dorsolateral prefrontal cortex and these outcomes. Furthermore, genetic colocalization provided evidence that the same causal variant at 12 of these loci was responsible for variation in both protein and neurological phenotype. This included genes such as DCC, which encodes the netrin-1 receptor and has an important role in the development of the nervous system (p = 4.29 × 10-11 with neuroticism), as well as SARM1, which has been previously implicated in axonal degeneration (p = 1.76 × 10-08 with amyotrophic lateral sclerosis). We additionally conducted a phenome-wide MR study for each of these 12 genes to assess potential pleiotropic effects on 700 complex traits and diseases. Our findings suggest that genes such as SNX32, which was initially associated with increased risk of Alzheimer disease, may potentially influence other complex traits in the opposite direction. In contrast, genes such as CTSH (which was also associated with Alzheimer disease) and SARM1 may make worthwhile therapeutic targets because they did not have genetically predicted effects on any of the other phenotypes after correcting for multiple testing.

A powerful and versatile colocalization test

Transcriptome-wide association studies (TWAS and PrediXcan) have been increasingly applied to detect associations between genetically predicted gene expressions and GWAS traits, which may suggest, however do not completely determine, causal genes for GWAS traits, due to the likely violation of their imposed strong assumptions for causal inference. Testing colocalization moves it closer to establishing causal relationships: if a GWAS trait and a gene's expression share the same associated SNP, it may suggest a regulatory (and thus putative causal) role of the SNP mediated through the gene on the GWAS trait. Accordingly, it is of interest to develop and apply various colocalization testing approaches. The existing approaches may each have some severe limitations. For instance, some methods test the null hypothesis that there is colocalization, which is not ideal because often the null hypothesis cannot be rejected simply due to limited statistical power (with too small sample sizes). Some other methods arbitrarily restrict the maximum number of causal SNPs in a locus, which may lead to loss of power in the presence of wide-spread allelic heterogeneity. Importantly, most methods cannot be applied to either GWAS/eQTL summary statistics or cases with more than two possibly correlated traits. Here we present a simple and general approach based on conditional analysis of a locus on multiple traits, overcoming the above and other shortcomings of the existing methods. We demonstrate that, compared with other methods, our new method can be applied to a wider range of scenarios and often perform better. We showcase its applications to both simulated and real data, including a large-scale Alzheimer's disease GWAS summary dataset and a gene expression dataset, and a large-scale blood lipid GWAS summary association dataset. An R package "jointsum" implementing the proposed method is publicly available at github.

Interpretation of risk loci from genome-wide association studies of Alzheimer's disease

BACKGROUND

Alzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry.

RECENT DEVELOPMENTS

Three new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.

FAM13A Represses AMPK Activity and Regulates Hepatic Glucose and Lipid Metabolism

Obesity commonly co-exists with fatty liver disease with increasing health burden worldwide. Family with Sequence Similarity 13, Member A (FAM13A) has been associated with lipid levels and fat mass by genome-wide association studies (GWAS). However, the function of FAM13A in maintaining metabolic homeostasis in vivo remains unclear. Here, we demonstrated that rs2276936 in this locus has allelic-enhancer activity in massively parallel reporter assays (MPRA) and reporter assay. The DNA region containing rs2276936 regulates expression of endogenous FAM13A in HepG2 cells. In vivo, Fam13a-/- mice are protected from high-fat diet (HFD)-induced fatty liver accompanied by increased insulin sensitivity and reduced glucose production in liver. Mechanistically, loss of Fam13a led to the activation of AMP-activated protein kinase (AMPK) and increased mitochondrial respiration in primary hepatocytes. These findings demonstrate that FAM13A mediates obesity-related dysregulation of lipid and glucose homeostasis. Targeting FAM13A might be a promising treatment of obesity and fatty liver disease.

Integrated GWAS and mRNA Microarray Analysis Identified IFNG and CD40L as the Central Upstream Regulators in Primary Biliary Cholangitis

Genome‐wide association studies (GWASs) in European and East Asian populations have identified more than 40 disease‐susceptibility genes in primary biliary cholangitis (PBC). The aim of this study is to computationally identify disease pathways, upstream regulators, and therapeutic targets in PBC through integrated GWAS and messenger RNA (mRNA) microarray analysis. Disease pathways and upstream regulators were analyzed with ingenuity pathway analysis in data set 1 for GWASs (1,920 patients with PBC and 1,770 controls), which included 261 annotated genes derived from 6,760 single‐nucleotide polymorphisms (P < 0.00001), and data set 2 for mRNA microarray analysis of liver biopsy specimens (36 patients with PBC and 5 normal controls), which included 1,574 genes with fold change >2 versus controls (P < 0.05). Hierarchical cluster analysis and categorization of cell type–specific genes were performed for data set 2. There were 27 genes, 10 pathways, and 149 upstream regulators that overlapped between data sets 1 and 2. All 10 pathways were immune‐related. The most significant common upstream regulators associated with PBC disease susceptibility identified were interferon‐gamma (IFNG) and CD40 ligand (CD40L). Hierarchical cluster analysis of data set 2 revealed two distinct groups of patients with PBC by disease activity. The most significant upstream regulators associated with disease activity were IFNG and CD40L. Several molecules expressed in B cells, T cells, Kupffer cells, and natural killer–like cells were identified as potential therapeutic targets in PBC with reference to a recently reported list of cell type–specific gene expression in the liver. Conclusion: Our integrated analysis using GWAS and mRNA microarray data sets predicted that IFNG and CD40L are the central upstream regulators in both disease susceptibility and activity of PBC and identified potential downstream therapeutic targets.

Profiling haplotype specific CpG and CpH methylation within a schizophrenia GWAS locus on chromosome 14 in schizophrenia and healthy subjects

Interrogating DNA methylation within schizophrenia risk loci holds promise to identify mechanisms by which genes influence the disease. Based on the hypothesis that allele specific methylation (ASM) of a single CpG, or perhaps CpH, might mediate or mark the effects of genetic variants on disease risk and phenotypes, we explored haplotype specific methylation levels of individual cytosines within a genomic region harbouring the BAG5, APOPT1 and KLC1 genes in peripheral blood of schizophrenia patients and healthy controls. Three DNA fragments located in promoter, intronic and intergenic areas were studied by single-molecule real-time bisulfite sequencing enabling the analysis of long reads of DNA with base-pair resolution and the determination of haplotypes directly from sequencing data. Among 1,012 cytosines studied, we did not find any site where methylation correlated with the disease or cognitive deficits after correction for multiple testing. At the same time, we determined the methylation profile associated with the schizophrenia risk haplotype within the KLC1 fourth intron and confirmed ASM for cytosines located in the vicinity of rs67899457. These genetically associated DNA methylation variations may be related to the pathophysiological mechanism differentiating the risk and non-risk haplotypes and merit further investigation.

Are You There? Genetic Variation Impacts Long-Distance Connections in Diabetes

A new study by Fasolino et al. defines how genetic variation in a mouse model of type 1 diabetes mellitus (T1DM) affects long-distance genomic interactions. The research has widespread implications for understanding how genetic diversity impacts disease susceptibility, and raises important concepts about mechanisms that can be influenced by genetic diversity between individuals.

Applications of Functional Genomics for Drug Discovery

Many diseases, such as diabetes, autoimmune diseases, cancer, and neurological disorders, are caused by a dysregulation of a complex interplay of genes. Genome-wide association studies have identified thousands of disease-linked polymorphisms in the human population. However, detailing the causative gene expression or functional changes underlying those associations has been elusive in many cases. Functional genomics is an emerging field of research that aims to deconvolute the link between genotype and phenotype by making use of large -omic data sets and next-generation gene and epigenome editing tools to perturb genes of interest. Here we review how functional genomic tools can be used to better understand the biological interplay between genes, improve disease modeling, and identify novel drug targets. Incorporation of functional genomic capabilities into conventional drug development pipelines is predicted to expedite the development of first-in-class therapeutics.

Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of vision loss. To better understand disease pathogenesis and identify causal genes in GWAS loci for AMD risk, we present a comprehensive database of human retina and retinal pigment epithelium (RPE). Our database comprises macular and non-macular RNA sequencing (RNA-seq) profiles from 129 donors, a genome-wide expression quantitative trait loci (eQTL) dataset that includes macula-specific retina and RPE/choroid, and single-nucleus RNA-seq (NucSeq) from human retina and RPE with subtype resolution from more than 100,000 cells. Using NucSeq, we find enriched expression of AMD candidate genes in RPE cells. We identify 15 putative causal genes for AMD on the basis of co-localization of genetic association signals for AMD risk and eye eQTL, including the genes TSPAN10 and TRPM1. These results demonstrate the value of our human eye database for elucidating genetic pathways and potential therapeutic targets for ocular diseases.

Trouble With Tribbles-1

Genome-wide association studies (GWAS) have identified hundreds of genomic loci in humans that are significantly associated with plasma cholesterol, triglycerides, and coronary artery disease. Although some loci contain genes with known regulatory roles in lipid metabolism and atherosclerosis, the majority were being implicated for the first time. The 8q24 locus, containing the gene TRIB1 ( Tribbles-1), is the only novel GWAS locus that associates with all 4 plasma lipid traits and coronary artery disease, an observation that has spurred immense interest in this locus. Subsequent in vivo loss and gain of function studies confirmed that Trib1 plays a role in hepatic lipid metabolism, validating the initial genetic observation. Yet, many challenges remain in discerning the nature of the association between the TRIB1 locus and cardiometabolic phenotypes. Is TRIB1 the causal gene at the 8q24 locus and what is the functional consequence of the associated noncoding variation? Is the relationship between TRIB1 and the transcription factor C/EBPα (CCAAT/enhancer-binding protein alpha) the primary molecular mechanism governing the genetic association or could it be an as yet unknown function for this interesting pseudokinase? Is hepatic TRIB1 the sole regulator of lipid metabolism or could extrahepatic TRIB1 play a role as well? The following review summarizes key findings related to these questions and highlights both the challenges and excitement in pursuing translational research of a novel gene in the post-GWAS era.

Genetic Predisposition to Colon and Rectal Adenocarcinoma Is Mediated by a Super-enhancer Polymorphism Coactivating CD9 and PLEKHG6

BACKGROUND

Genome-wide association studies (GWAS) have identified dozens of loci associated with colon and rectal adenocarcinoma risk. As tissue-specific super-enhancers (SE) play important roles in tumorigenesis, we systematically investigate SEs and inner variants in established GWAS loci to decipher the underlying biological mechanisms.

METHODS

Through a comprehensive bioinformatics analysis on multi-omics data, we screen potential single-nucleotide polymorphisms (SNP) in cancer-specific SEs, and then subject them to a two-stage case-control study containing 4,929 cases and 7,083 controls from the Chinese population. A series of functional assays, including reporter gene assays, electrophoretic mobility shift assays (EMSA), CRISPR-Cas9 genome editing, chromosome conformation capture (3C) assays, and cell proliferation experiments, are performed to characterize the variant's molecular consequence and target genes.

RESULTS

The SNP rs11064124 in 12p13.31 is found significantly associated with the risk of colon and rectal adenocarcinoma with an odds ratio (OR) of 0.87 [95% confidence interval (CI), 0.82-0.92, P = 8.67E-06]. The protective rs11064124-G weakens the binding affinity with vitamin D receptor (VDR) and increases the enhancer's activity and interactions with two target genes' promoters, thus coactivating the transcription of CD9 and PLEKHG6, which are both putative tumor suppressor genes for colon and rectal adenocarcinoma.

CONCLUSIONS

Our integrative study highlights an SE polymorphism rs11064124 and two susceptibility genes CD9 and PLEKHG6 in 12p13.31 for colon and rectal adenocarcinoma.

IMPACT

These findings suggest a novel insight for genetic pathogenesis of colon and rectal adenocarcinoma, involving transcriptional coactivation of diverse susceptibility genes via the SE element as a gene regulation hub.

DSNetwork: An Integrative Approach to Visualize Predictions of Variants' Deleteriousness

One of the most challenging tasks of the post-genome-wide association studies (GWAS) research era is the identification of functional variants among those associated with a trait for an observed GWAS signal. Several methods have been developed to evaluate the potential functional implications of genetic variants. Each of these tools has its own scoring system, which forces users to become acquainted with each approach to interpret their results. From an awareness of the amount of work needed to analyze and integrate results for a single locus, we proposed a flexible and versatile approach designed to help the prioritization of variants by aggregating the predictions of their potential functional implications. This approach has been made available through a graphical user interface called DSNetwork, which acts as a single point of entry to almost 60 reference predictors for both coding and non-coding variants and displays predictions in an easy-to-interpret visualization. We confirmed the usefulness of our methodology by successfully identifying functional variants in four breast cancer and nine schizophrenia susceptibility loci.

Obesity of G2e3 Knockout Mice Suggests That Obesity-Associated Variants Near Human G2E3 Decrease G2E3 Activity

PURPOSE

In humans, single nucleotide polymorphisms (SNPs) near the adjacent protein kinase D1 (PRKD1) and G2/M-phase-specific E3 ubiquitin protein ligase (G2E3) genes on chromosome 14 are associated with obesity. To date, no published evidence links inactivation of either gene to changes in body fat. These two genes are also adjacent on mouse chromosome 12. Because obesity genes are highly conserved between humans and mice, we analyzed body fat in adult G2e3 and Prkd1 knockout (KO) mice to determine whether inactivating either gene leads to obesity in mice and, by inference, probably in humans.

METHODS

The G2e3 and Prkd1 KO lines were generated by gene trapping and by homologous recombination methodologies, respectively. Body fat was measured by DEXA in adult mice fed chow from weaning and by QMR in a separate cohort of mice fed high-fat diet (HFD) from weaning. Glucose homeostasis was evaluated with oral glucose tolerance tests (OGTTs) performed on adult mice fed HFD from weaning.

RESULTS

Body fat was increased in multiple cohorts of G2e3 KO mice relative to their wild-type (WT) littermates. When data from all G2e3 KO (n=32) and WT (n=31) mice were compared, KO mice showed increases of 11% in body weight (P<0.01), 65% in body fat (P<0.001), 48% in % body fat (P<0.001), and an insignificant 3% decrease in lean body mass. G2e3 KO mice were also glucose intolerant during an OGTT (P<0.05). In contrast, Prkd1 KO and WT mice had comparable body fat levels and glucose tolerance.

CONCLUSION

Significant obesity and glucose intolerance were observed in G2e3, but not Prkd1, KO mice. The conservation of obesity genes between mice and humans strongly suggests that the obesity-associated SNPs located near the human G2E3 and PRKD1 genes are linked to variants that decrease the amount of functional human G2E3.

An omics perspective on drug target discovery platforms

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.

Network medicine-travelling with the insulin receptor: Encounter of the second type

Important progress has been made in understanding many aspects of insulin action in the last 10 years. Attention will be focused here on the physical protein interaction network of the internalized insulin receptor (IR) and its relationships with the genetic architecture of type 2 diabetes mellitus (T2D). The IR recognizes signals from the outside (circulating insulin) and engages the insulin signaling response. Within seconds, the IR is also involved in insulin internalization and its subsequent degradation in endosomes (physiological clearance of insulin). A T2D disease module sharing functional similarities with insulin secretion in pancreatic islets was recently identified in the close neighborhood of the internalized IR in liver. This module brought a new light on the apparent functional heterogeneity of numerous genes at risk to T2D by linking them to a few noncanonical layers of signaling feedback loops. These findings should be translated into a better understanding of the primary mechanisms of the disease and consequently a more precise sub-classification of T2D, ultimately leading to precision medicine and the development of new therapeutical drugs.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

Genetic Regulation of Enoyl-CoA Hydratase Domain-Containing 3 in Adipose Tissue Determines Insulin Sensitivity in African Americans and Europeans

Insulin resistance (IR) is a harbinger of type 2 diabetes (T2D) and partly determined by genetic factors. However, genetically regulated mechanisms of IR remain poorly understood. Using gene expression, genotype, and insulin sensitivity data from the African American Genetics of Metabolism and Expression (AAGMEx) cohort, we performed transcript-wide correlation and expression quantitative trait loci (eQTL) analyses to identify IR-correlated cis-regulated transcripts (cis-eGenes) in adipose tissue. These IR-correlated cis-eGenes were tested in the European ancestry individuals in the Metabolic Syndrome in Men (METSIM) cohort for trans-ethnic replication. Comparison of Matsuda index-correlated transcripts in AAGMEx with the METSIM study identified significant correlation of 3,849 transcripts, with concordant direction of effect for 97.5% of the transcripts. cis-eQTL for 587 Matsuda index-correlated genes were identified in both cohorts. Enoyl-CoA hydratase domain-containing 3 (ECHDC3) was the top-ranked Matsuda index-correlated cis-eGene. Expression levels of ECHDC3 were positively correlated with Matsuda index, and regulated by cis-eQTL, rs34844369 being the top cis-eSNP in AAGMEx. Silencing of ECHDC3 in adipocytes significantly reduced insulin-stimulated glucose uptake and Akt Ser⁴⁷³ phosphorylation. RNA sequencing analysis identified 691 differentially expressed genes in ECHDC3-knockdown adipocytes, which were enriched in γ-linolenate biosynthesis, and known IR genes. Thus, our studies elucidated genetic regulatory mechanisms of IR and identified genes and pathways in adipose tissue that are mechanistically involved in IR.

Novel idiopathic pulmonary fibrosis susceptibility variants revealed by deep sequencing

Background

Specific common and rare single nucleotide variants (SNVs) increase the likelihood of developing sporadic idiopathic pulmonary fibrosis (IPF). We performed target-enriched sequencing on three loci previously identified by a genome-wide association study to gain a deeper understanding of the full spectrum of IPF genetic risk and performed a two-stage case–control association study.

Methods

A total of 1.7 Mb of DNA from 181 IPF patients was deep sequenced (>100×) across 11p15.5, 14q21.3 and 17q21.31 loci. Comparisons were performed against 501 unrelated controls and replication studies were assessed in 3968 subjects.

Results

36 SNVs were associated with IPF susceptibility in the discovery stage (p<5.0×10⁻⁸). After meta-analysis, the strongest association corresponded to rs35705950 (p=9.27×10⁻⁵⁷) located upstream from the mucin 5B gene (MUC5B). Additionally, a novel association was found for two co-inherited low-frequency SNVs (<5%) in MUC5AC, predicting a missense amino acid change in mucin 5AC (lowest p=2.27×10⁻²²). Conditional and haplotype analyses in 11p15.5 supported the existence of an additional contribution of MUC5AC variants to IPF risk.

Conclusions

This study reinforces the significant IPF associations of these loci and implicates MUC5AC as another key player in IPF susceptibility.

Deep sequencing of genome-wide association study hits identified novel low-frequency variants associated with IPF susceptibility.http://bit.ly/2IF4AT8

Regulation of MFGE8 by the intergenic coronary artery disease locus on 15q26.1

BACKGROUND AND AIMS

A recently identified locus for coronary artery disease (CAD) tagged by rs8042271 is in a region of tight linkage disequilibrium (LD) between 2 genes (MFGE8, ABHD2) previously linked to atherosclerosis. Here we have explored the regulatory framework of this region to identify its functional relationship to CAD.

METHODS

The CAD Associated Region between MFGE8 and ABHD2 (CARMA) was investigated by bioinformatic approaches and transcriptional reporter assays to prioritize target genes and identify putative causal variants. Findings were integrated with publicly available gene expression datasets. MFGE8 silencing was performed in cell models relevant to CAD.

RESULTS

The regulatory potential of CARMA is disseminated sparsely over the entire region. CARMA contains multiple eQTL that regulate MFGE8 in coronary artery and coronary artery smooth muscle cell (CoSMC). SNPs that predict the expression of MFGE8 in artery are concordantly associated with higher risk of CAD (pval = 0.0014). Targeting CARMA by CRISPR/Cas9 in a cellular model increased MFGE8 expression. MFGE8 silencing was found to reduce CoSMC and monocyte (THP-1) but not endothelial cell proliferation.

CONCLUSIONS

These findings support a mechanistic link between a GWAS identified CAD risk locus and atherosclerosis. The intergenic locus CARMA regulates MFGE8 in a haplotype dependent manner. Individuals genetically susceptible to increased MFGE8 expression exhibit greater CAD risk. Suppressing MFGE8 expression reduced SMC and THP-1 proliferation. These data support an atherogenic contribution of CARMA/MFGE8 that may be linked to cell proliferation and/or improved survival of CAD relevant cell types.

Zebrafish as an Emerging Model for Osteoporosis: A Primary Testing Platform for Screening New Osteo-Active Compounds

Osteoporosis is metabolic bone disease caused by an altered balance between bone anabolism and catabolism. This dysregulated balance is responsible for fragile bones that fracture easily after minor falls. With an aging population, the incidence is rising and as yet pharmaceutical options to restore this imbalance is limited, especially stimulating osteoblast bone-building activity. Excitingly, output from large genetic studies on people with high bone mass (HBM) cases and genome wide association studies (GWAS) on the population, yielded new insights into pathways containing osteo-anabolic players that have potential for drug target development. However, a bottleneck in development of new treatments targeting these putative osteo-anabolic genes is the lack of animal models for rapid and affordable testing to generate functional data and that simultaneously can be used as a compound testing platform. Zebrafish, a small teleost fish, are increasingly used in functional genomics and drug screening assays which resulted in new treatments in the clinic for other diseases. In this review we outline the zebrafish as a powerful model for osteoporosis research to validate potential therapeutic candidates, describe the tools and assays that can be used to study bone homeostasis, and affordable (semi-)high-throughput compound testing.

Novel hypotheses emerging from GWAS in migraine?

Recent technical advances in genetics made large-scale genome-wide association studies (GWAS) in migraine feasible and have identified over 40 common DNA sequence variants that affect risk for migraine types. Most of the variants, which are all single nucleotide polymorphisms (SNPs), show robust association with migraine as evidenced by the fact that the vast majority replicate in subsequent independent studies. However, despite thorough bioinformatic efforts aimed at linking the migraine risk SNPs with genes and their molecular pathways, there remains quite some discussion as to how successful this endeavour has been, and their current practical use for the diagnosis and treatment of migraine patients. Although existing genetic information seems to favour involvement of vascular mechanisms, but also neuronal and other mechanisms such as metal ion homeostasis and neuronal migration, the complexity of the underlying genetic pathophysiology presents challenges to advancing genetic knowledge to clinical use. A major issue is to what extent one can rely on bioinformatics to pinpoint the actual disease genes, and from this the linked pathways. In this Commentary, we will provide an overview of findings from GWAS in migraine, current hypotheses of the disease pathways that emerged from these findings, and some of the major drawbacks of the approaches used to identify the genes and pathways. We argue that more functional research is urgently needed to turn the hypotheses that emerge from GWAS in migraine to clinically useful information.