Functional interpretation of non-coding sequence variation: concepts and challenges

UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV

BACKGROUND

Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs).

RESULTS

The Unified Variant Interpretation Platform (UniVar) is a free web server tool that offers an automated and comprehensive workflow on annotation, filtering and prioritization for SNV, INDEL, CNV and SV collectively to identify disease-causing variants for rare diseases in one interface, ensuring accessibility for users even without programming expertise. To filter common CNVs/SVs, a diverse SV catalogue has been generated, that enables robust filtering of common SVs based on population allele frequency. Through benchmarking our SV catalogue, we showed that it is more complete and accurate than the state-of-the-art SV catalogues. Furthermore, to cope with those patients without detailed clinical information, we have developed a novel computational method that enables variant prioritization from gene panels. Our analysis shows that our approach could prioritize pathogenic variants as effective as using HPO terms assigned by clinicians, which adds value for cases without specific clinically assigned HPO terms. Lastly, through a practical case study of disease-causing compound heterozygous variants across SNV and SV, we demonstrated the uniqueness and effectiveness in variant interpretation of UniVar, edging over any existing interpretation tools.

CONCLUSIONS

UniVar is a unified and versatile platform that empowers researchers and clinicians to identify and interpret disease-causing variants in rare diseases efficiently through a single holistic interface and without a prerequisite for HPO terms. It is freely available without login and installation at https://univar.live/.

Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.

Diversification and Functional Evolution of HOX Proteins

Gene duplication and divergence is a major contributor to the generation of morphological diversity and the emergence of novel features in vertebrates during evolution. The availability of sequenced genomes has facilitated our understanding of the evolution of genes and regulatory elements. However, progress in understanding conservation and divergence in the function of proteins has been slow and mainly assessed by comparing protein sequences in combination with in vitro analyses. These approaches help to classify proteins into different families and sub-families, such as distinct types of transcription factors, but how protein function varies within a gene family is less well understood. Some studies have explored the functional evolution of closely related proteins and important insights have begun to emerge. In this review, we will provide a general overview of gene duplication and functional divergence and then focus on the functional evolution of HOX proteins to illustrate evolutionary changes underlying diversification and their role in animal evolution.

Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants' effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes.

Functional characterization of the C7ORF76 genomic region, a prominent GWAS signal for osteoporosis in 7q21.3

Genome-wide association studies (GWAS) have repeatedly identified genetic variants associated with bone mineral density (BMD) and osteoporotic fracture in non-coding regions of C7ORF76, a poorly studied gene of unknown function. The aim of the present study was to elucidate the causality and molecular mechanisms underlying the association. We re-sequenced the genomic region in two extreme BMD groups from the BARCOS cohort of postmenopausal women to search for functionally relevant variants. Eight selected variants were tested for association in the complete cohort and 2 of them (rs4342521 and rs10085588) were found significantly associated with lumbar spine BMD and nominally associated with osteoporotic fracture. cis-eQTL analyses of these 2 SNPs, together with SNP rs4727338 (GWAS lead SNP in Estrada et al., Nat Genet. 44:491-501, 2012), performed in human primary osteoblasts, disclosed a statistically significant influence on the expression of the proximal neighbouring gene SLC25A13 and a tendency on the distal SHFM1. We then studied the functionality of a putative upstream regulatory element (UPE), containing rs10085588. Luciferase reporter assays showed transactivation capability with a strong allele-dependent effect. Finally, 4C-seq experiments in osteoblastic cell lines showed that the UPE interacted with different tissue-specific enhancers and a lncRNA (LOC100506136) in the region. In summary, this study is the first one to analyse in depth the functionality of C7ORF76 genomic region. We provide functional regulatory evidence for the rs10085588, which may be a causal SNP within the 7q21.3 GWAS signal for osteoporosis.

Postmortem brain tissue as an underutilized resource to study the molecular pathology of neuropsychiatric disorders across different ethnic populations

In recent years, large scale meta-analysis of genome-wide association studies (GWAS) have reliably identified genetic polymorphisms associated with neuropsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BPD) and major depressive disorder (MDD). However, the majority of disease-associated single nucleotide polymorphisms (SNPs) appear within functionally ambiguous non-coding genomic regions. Recently, increased emphasis has been placed on identifying the functional relevance of disease-associated variants via correlating risk polymorphisms with gene expression levels in etiologically relevant tissues. For neuropsychiatric disorders, the etiologically relevant tissue is brain, which requires robust postmortem sample sizes from varying genetic backgrounds. While small sample sizes are of decreasing concern, postmortem brain databases are composed almost exclusively of Caucasian samples, which significantly limits study design and result interpretation. In this review, we highlight the importance of gene expression and expression quantitative loci (eQTL) studies in clinically relevant postmortem tissue while addressing the current limitations of existing postmortem brain databases. Finally, we introduce future collaborations to develop postmortem brain databases for neuropsychiatric disorders from Chinese and Asian subpopulations.

Coagulopathy in Type 2 Diabetes Mellitus: Pathological Mechanisms and the Role of Factor XIII-A Single Nucleotide Polymorphisms

The prevalence of type 2 diabetes mellitus (T2DM) has quadrupled within three decades since 1980, affecting 422 million adults in 2016. It remains one of the most common noncommunicable chronic diseases and the underlying risk factor for cardiovascular diseases worldwide. There are different underlying mechanisms that play a role in the development of pathologies associated with the disease such as hyperglycaemia, oxidative stress, obesity, inflammation and hypercoagulation; each of which are interlinked. Hyperglycaemia, oxidative stress and obesity play a huge role in the activation of inflammation and coagulation. Activation of inflammatory pathways increases the production of thrombin which predisposes the development of thrombotic related diseases. One of the factors that contribute to the increase of thrombin is the impairment of the fibrinolysis process due to decreased expression of tissue-plasminogen activator (tPA) by increased levels of plasminogen activator inhibitor-1 (PAI-1). Coagulation factor XIII (FXIII), a transglutaminase that is composed of subunits A and B (FXIII-A2B2), is essential for the last step of fibrin clot formation in the coagulation pathway. Genetic variation of FXIII-A in the form of single nucleotide polymorphisms (SNPs) alters the activity of FXIII, altering clot properties which influence disease outcomes. This review discusses the link between underlying mechanisms of T2DM, well known FXIII-A variants and coagulation.

Chromatin accessibility prediction via a hybrid deep convolutional neural network

Motivation

A majority of known genetic variants associated with human-inherited diseases lie in non-coding regions that lack adequate interpretation, making it indispensable to systematically discover functional sites at the whole genome level and precisely decipher their implications in a comprehensive manner. Although computational approaches have been complementing high-throughput biological experiments towards the annotation of the human genome, it still remains a big challenge to accurately annotate regulatory elements in the context of a specific cell type via automatic learning of the DNA sequence code from large-scale sequencing data. Indeed, the development of an accurate and interpretable model to learn the DNA sequence signature and further enable the identification of causative genetic variants has become essential in both genomic and genetic studies.

Results

We proposed Deopen, a hybrid framework mainly based on a deep convolutional neural network, to automatically learn the regulatory code of DNA sequences and predict chromatin accessibility. In a series of comparison with existing methods, we show the superior performance of our model in not only the classification of accessible regions against background sequences sampled at random, but also the regression of DNase-seq signals. Besides, we further visualize the convolutional kernels and show the match of identified sequence signatures and known motifs. We finally demonstrate the sensitivity of our model in finding causative noncoding variants in the analysis of a breast cancer dataset. We expect to see wide applications of Deopen with either public or in-house chromatin accessibility data in the annotation of the human genome and the identification of non-coding variants associated with diseases.

Availability and implementation

Deopen is freely available at https://github.com/kimmo1019/Deopen.

Supplementary information

Supplementary data are available at Bioinformatics online.

Epistatic interaction of apolipoprotein E and lipolysis-stimulated lipoprotein receptor genetic variants is associated with Alzheimer's disease

The ε4 allele of the apolipoprotein E (APOE) gene common polymorphism is the strongest genetic risk factor for Alzheimer's disease (AD). Human APOE gene is located on chromosome 19q13.1, a region linked to AD that also includes the LSR gene, which encodes the lipolysis-stimulated lipoprotein receptor (LSR). As an APOE receptor, LSR is involved in the regulation of lipid homeostasis in both periphery and brain. This study aimed to determine the potential interactions between 2 LSR genetic variants, rs34259399 and rs916147, and the APOE common polymorphism in 142 AD subjects (mean age: 73.16 ± 8.50 years) and 63 controls (mean age: 70.41 ± 8.49 years). A significant epistatic interaction was observed between APOE and both LSR variants, rs34259399 (beta = -0.95; p = 2 × 10^-5) and rs916147 (beta = -0.83; p = 6.8 × 10^-3). Interestingly, the interaction of LSR polymorphisms with APOE non-ε4 alleles increased AD risk. This indicates the existence of complex molecular interactions between these 2 neighboring genes involved in the pathogenesis of AD, which merits further investigation.

eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data

Epigenome-wide association studies (EWAS) provide an alternative approach for studying human disease through consideration of non-genetic variants such as altered DNA methylation. To advance the complex interpretation of EWAS, we developed eFORGE (http://eforge.cs.ucl.ac.uk/), a new standalone and web-based tool for the analysis and interpretation of EWAS data. eFORGE determines the cell type-specific regulatory component of a set of EWAS-identified differentially methylated positions. This is achieved by detecting enrichment of overlap with DNase I hypersensitive sites across 454 samples (tissues, primary cell types, and cell lines) from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of eFORGE to 20 publicly available EWAS datasets identified disease-relevant cell types for several common diseases, a stem cell-like signature in cancer, and demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. Our approach bridges the gap between large-scale epigenomics data and EWAS-derived target selection to yield insight into disease etiology.

Open Chromatin Profiling in hiPSC-Derived Neurons Prioritizes Functional Noncoding Psychiatric Risk Variants and Highlights Neurodevelopmental Loci

Most disease variants lie within noncoding genomic regions, making their functional interpretation challenging. Because chromatin openness strongly influences transcriptional activity, we hypothesized that cell-type-specific open chromatin regions (OCRs) might highlight disease-relevant noncoding sequences. To investigate, we mapped global OCRs in neurons differentiating from hiPSCs, a cellular model for studying neurodevelopmental disorders such as schizophrenia (SZ). We found that the OCRs are highly dynamic and can stratify GWAS-implicated SZ risk variants. Of the more than 3,500 SZ-associated variants analyzed, we prioritized ∼100 putatively functional ones located in neuronal OCRs, including rs1198588, at a leading risk locus flanking MIR137. Excitatory neurons derived from hiPSCs with CRISPR/Cas9-edited rs1198588 or a rare proximally located SZ risk variant showed altered MIR137 expression, dendrite arborization, and synapse maturation. Our study shows that noncoding disease variants in OCRs can affect neurodevelopment, and that analysis of open chromatin regions can help prioritize functionally relevant noncoding variants identified by GWAS.

Brain imaging genetics in ADHD and beyond - Mapping pathways from gene to disorder at different levels of complexity

Attention-deficit/hyperactivity disorder (ADHD) is a common and often persistent neurodevelopmental disorder. Beyond gene-finding, neurobiological parameters, such as brain structure, connectivity, and function, have been used to link genetic variation to ADHD symptomatology. We performed a systematic review of brain imaging genetics studies involving 62 ADHD candidate genes in childhood and adult ADHD cohorts. Fifty-one eligible research articles described studies of 13 ADHD candidate genes. Almost exclusively, single genetic variants were studied, mostly focussing on dopamine-related genes. While promising results have been reported, imaging genetics studies are thus far hampered by methodological differences in study design and analysis methodology, as well as limited sample sizes. Beyond reviewing imaging genetics studies, we also discuss the need for complementary approaches at multiple levels of biological complexity and emphasize the importance of combining and integrating findings across levels for a better understanding of biological pathways from gene to disease. These may include multi-modal imaging genetics studies, bioinformatic analyses, and functional analyses of cell and animal models.

Identification of breast cancer associated variants that modulate transcription factor binding

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Bromodomain protein 4 discriminates tissue-specific super-enhancers containing disease-specific susceptibility loci in prostate and breast cancer

BACKGROUND

Epigenetic information can be used to identify clinically relevant genomic variants single nucleotide polymorphisms (SNPs) of functional importance in cancer development. Super-enhancers are cell-specific DNA elements, acting to determine tissue or cell identity and driving tumor progression. Although previous approaches have been tried to explain risk associated with SNPs in regulatory DNA elements, so far epigenetic readers such as bromodomain containing protein 4 (BRD4) and super-enhancers have not been used to annotate SNPs. In prostate cancer (PC), androgen receptor (AR) binding sites to chromatin have been used to inform functional annotations of SNPs.

RESULTS

Here we establish criteria for enhancer mapping which are applicable to other diseases and traits to achieve the optimal tissue-specific enrichment of PC risk SNPs. We used stratified Q-Q plots and Fisher test to assess the differential enrichment of SNPs mapping to specific categories of enhancers. We find that BRD4 is the key discriminant of tissue-specific enhancers, showing that it is more powerful than AR binding information to capture PC specific risk loci, and can be used with similar effect in breast cancer (BC) and applied to other diseases such as schizophrenia.

CONCLUSIONS

This is the first study to evaluate the enrichment of epigenetic readers in genome-wide associations studies for SNPs within enhancers, and provides a powerful tool for enriching and prioritizing PC and BC genetic risk loci. Our study represents a proof of principle applicable to other diseases and traits that can be used to redefine molecular mechanisms of human phenotypic variation.

Polymorphisms of the coagulation system and risk of cancer

Hypercoagulability is a frequently finding in patients with cancer, and is associated with an increased risk of venous thrombosis (VT). Cancer-associated VT is associated with poor prognosis and represents the leading non-cancer cause of death among these patients. Conversely, patients experiencing VT are at increased risk of subsequent cancer, suggesting an epidemiological bidirectional link between cancer and hemostasis, and indicating a role of the hemostatic system in cancer development. How the coagulation system relates to cancer etiology at the genetic level is largely unexplored. Data on the association of polymorphisms in genes involved in coagulation with cancer development is important to clarify the role of the coagulation system in cancer pathogenesis. Effects of coagulation-related gene polymorphisms on cancer risk may possibly be translated into novel treatment- and prevention strategies of cancer-associated thrombosis and the cancer itself. This article reviews the current knowledge of the relation between polymorphisms in genes involved in coagulation and cancer risk in solid tumors.

Genome editing: the road of CRISPR/Cas9 from bench to clinic

Molecular scissors engineered for site-specific modification of the genome hold great promise for effective functional analyses of genes, genomes and epigenomes and could improve our understanding of the molecular underpinnings of disease states and facilitate novel therapeutic applications. Several platforms for molecular scissors that enable targeted genome engineering have been developed, including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and, most recently, clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated-9 (Cas9). The CRISPR/Cas9 system's simplicity, facile engineering and amenability to multiplexing make it the system of choice for many applications. CRISPR/Cas9 has been used to generate disease models to study genetic diseases. Improvements are urgently needed for various aspects of the CRISPR/Cas9 system, including the system's precision, delivery and control over the outcome of the repair process. Here, we discuss the current status of genome engineering and its implications for the future of biological research and gene therapy.

Genome-wide association studies of autoimmune vitiligo identify 23 new risk loci and highlight key pathways and regulatory variants

Vitiligo is an autoimmune disease in which depigmented skin results from destruction of melanocytes¹, with epidemiologic association with other autoimmune diseases². In previous linkage and genome-wide association studies (GWAS1, GWAS2), we identified 27 vitiligo susceptibility loci in patients of European (EUR) ancestry. We carried out a third GWAS (GWAS3) in EUR subjects, with augmented GWAS1 and GWAS2 controls, genome-wide imputation, and meta-analysis of all three GWAS, followed by an independent replication. The combined analyses, with 4,680 cases and 39,586 controls, identified 23 new loci and 7 suggestive loci, most encoding immune and apoptotic regulators, some also associated with other autoimmune diseases, as well as several melanocyte regulators. Bioinformatic analyses indicate a predominance of causal regulatory variation, some corresponding to eQTL at these loci. Together, the identified genes provide a framework for vitiligo genetic architecture and pathobiology, highlight relationships to other autoimmune diseases and melanoma, and offer potential targets for treatment.

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Population and family-based genetic studies typically result in the identification of genetic variants that are statistically associated with a clinical disease or phenotype. For many diseases and traits, most variants are non-coding, and are thus likely to act by impacting subtle, comparatively hard to predict mechanisms controlling gene expression. Here, we describe a general strategic approach to prioritize non-coding variants, and screen them for their function. This approach involves computational prioritization using functional genomic databases followed by experimental analysis of differential binding of transcription factors (TFs) to risk and non-risk alleles. For both electrophoretic mobility shift assay (EMSA) and DNA affinity precipitation assay (DAPA) analysis of genetic variants, a synthetic DNA oligonucleotide (oligo) is used to identify factors in the nuclear lysate of disease or phenotype-relevant cells. For EMSA, the oligonucleotides with or without bound nuclear factors (often TFs) are analyzed by non-denaturing electrophoresis on a tris-borate-EDTA (TBE) polyacrylamide gel. For DAPA, the oligonucleotides are bound to a magnetic column and the nuclear factors that specifically bind the DNA sequence are eluted and analyzed through mass spectrometry or with a reducing sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) followed by Western blot analysis. This general approach can be widely used to study the function of non-coding genetic variants associated with any disease, trait, or phenotype.

Long noncoding RNA variations in cardiometabolic diseases

Cardiometabolic diseases are characterized as a combination of multiple risk factors for cardiovascular disease (CVD) and metabolic diseases including diabetes mellitus, dyslipidemia, hypertension and abdominal obesity. This cluster of abnormalities individually and interdependently leads to atherosclerosis and CVD morbidity and mortality. In the past decade, genome-wide association studies (GWASs) have identified a series of cardiometabolic disease-associated variants that can collectively explain a small proportion of the variability. Intriguingly, the susceptibility variants imputed from GWASs usually do not reside in the coding regions, suggesting a crucial role of the noncoding elements of the genome. In recent years, emerging evidence suggests that noncoding RNA (ncRNA) is functional for physiology and pathophysiology of human diseases. These include microRNAs and long noncoding RNAs (lncRNAs) that are now implicated in human diseases. The ncRNAs can interact with each other and with proteins, to interfere gene expressions, leading to the development of many human disorders. Although evidence suggests the functional role of lncRNAs in cardiometabolic traits, the molecular mechanisms of gene regulation underlying cardiometabolic diseases remain to be better defined. Here, we summarize the recent discoveries of lncRNA variations in the context of cardiometabolic diseases.

A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer

BACKGROUND

Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants have also been proven to significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions.

METHODS

We captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk. Custom oligonucleotide baits spanning the complete coding, non-coding, and intergenic regions 10 kb up- and downstream of ATM, BRCA1, BRCA2, CDH1, CHEK2, PALB2, and TP53 were synthesized for solution hybridization enrichment. Unique and divergent repetitive sequences were sequenced in 102 high-risk, anonymized patients without identified mutations in BRCA1/2. Aside from protein coding and copy number changes, IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. This approach was supplemented by in silico and laboratory analysis of UTR structure.

RESULTS

15,311 unique variants were identified, of which 245 occurred in coding regions. With the unified IT-framework, 132 variants were identified and 87 functionally significant VUS were further prioritized. An intragenic 32.1 kb interval in BRCA2 that was likely hemizygous was detected in one patient. We also identified 4 stop-gain variants and 3 reading-frame altering exonic insertions/deletions (indels).

CONCLUSIONS

We have presented a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression. This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.

Erosion of Conserved Binding Sites in Personal Genomes Points to Medical Histories

Although many human diseases have a genetic component involving many loci, the majority of studies are statistically underpowered to isolate the many contributing variants, raising the question of the existence of alternate processes to identify disease mutations. To address this question, we collect ancestral transcription factor binding sites disrupted by an individual's variants and then look for their most significant congregation next to a group of functionally related genes. Strikingly, when the method is applied to five different full human genomes, the top enriched function for each is invariably reflective of their very different medical histories. For example, our method implicates "abnormal cardiac output" for a patient with a longstanding family history of heart disease, "decreased circulating sodium level" for an individual with hypertension, and other biologically appealing links for medical histories spanning narcolepsy to axonal neuropathy. Our results suggest that erosion of gene regulation by mutation load significantly contributes to observed heritable phenotypes that manifest in the medical history. The test we developed exposes a hitherto hidden layer of personal variants that promise to shed new light on human disease penetrance, expressivity and the sensitivity with which we can detect them.

Genome-Wide Epigenetic Studies in Human Disease: A Primer on -Omic Technologies

Epigenetic information encoded in covalent modifications of DNA and histone proteins regulates fundamental biological processes through the action of chromatin regulators, transcription factors, and noncoding RNA species. Epigenetic plasticity enables an organism to respond to developmental and environmental signals without genetic changes. However, aberrant epigenetic control plays a key role in pathogenesis of disease. Normal epigenetic states could be disrupted by detrimental mutations and expression alteration of chromatin regulators or by environmental factors. In this primer, we briefly review the epigenetic basis of human disease and discuss how recent discoveries in this field could be translated into clinical diagnosis, prevention, and treatment. We introduce platforms for mapping genome-wide chromatin accessibility, nucleosome occupancy, DNA-binding proteins, and DNA methylation, primarily focusing on the integration of DNA methylation and chromatin immunoprecipitation-sequencing technologies into disease association studies. We highlight practical considerations in applying high-throughput epigenetic assays and formulating analytical strategies. Finally, we summarize current challenges in sample acquisition, experimental procedures, data analysis, and interpretation and make recommendations on further refinement in these areas. Incorporating epigenomic testing into the clinical research arsenal will greatly facilitate our understanding of the epigenetic basis of disease and help identify novel therapeutic targets.

HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease

More than 90% of common variants associated with complex traits do not affect proteins directly, but instead the circuits that control gene expression. This has increased the urgency of understanding the regulatory genome as a key component for translating genetic results into mechanistic insights and ultimately therapeutics. To address this challenge, we developed HaploReg (http://compbio.mit.edu/HaploReg) to aid the functional dissection of genome-wide association study (GWAS) results, the prediction of putative causal variants in haplotype blocks, the prediction of likely cell types of action, and the prediction of candidate target genes by systematic mining of comparative, epigenomic and regulatory annotations. Since first launching the website in 2011, we have greatly expanded HaploReg, increasing the number of chromatin state maps to 127 reference epigenomes from ENCODE 2012 and Roadmap Epigenomics, incorporating regulator binding data, expanding regulatory motif disruption annotations, and integrating expression quantitative trait locus (eQTL) variants and their tissue-specific target genes from GTEx, Geuvadis, and other recent studies. We present these updates as HaploReg v4, and illustrate a use case of HaploReg for attention deficit hyperactivity disorder (ADHD)-associated SNPs with putative brain regulatory mechanisms.

Understanding Language from a Genomic Perspective

Language is a defining characteristic of the human species, but its foundations remain mysterious. Heritable disorders offer a gateway into biological underpinnings, as illustrated by the discovery that FOXP2 disruptions cause a rare form of speech and language impairment. The genetic architecture underlying language-related disorders is complex, and although some progress has been made, it has proved challenging to pinpoint additional relevant genes with confidence. Next-generation sequencing and genome-wide association studies are revolutionizing understanding of the genetic bases of other neurodevelopmental disorders, like autism and schizophrenia, and providing fundamental insights into the molecular networks crucial for typical brain development. We discuss how a similar genomic perspective, brought to the investigation of language-related phenotypes, promises to yield equally informative discoveries. Moreover, we outline how follow-up studies of genetic findings using cellular systems and animal models can help to elucidate the biological mechanisms involved in the development of brain circuits supporting language.

Roles of imprinted genes in neural stem cells

Imprinted genes and neural stem cells (NSC) play an important role in the developing and mature brain. A central theme of imprinted gene function in NSCs is cell survival and G1 arrest to control cell division, cell-cycle exit, migration and differentiation. Moreover, genomic imprinting can be epigenetically switched off at some genes to ensure stem cell quiescence and differentiation. At the genome scale, imprinted genes are organized in dynamic networks formed by interchromosomal interactions and transcriptional coregulation of imprinted and nonimprinted genes. Such multilayered networks may synchronize NSC activity with the demand from the niche resembling their roles in adjusting fetal size.

Long-range regulatory interactions at the 4q25 atrial fibrillation risk locus involve PITX2c and ENPEP

BACKGROUND

Recent genome-wide association studies have uncovered genomic loci that underlie an increased risk for atrial fibrillation, the major cardiac arrhythmia in humans. The most significant locus is located in a gene desert at 4q25, approximately 170 kilobases upstream of PITX2, which codes for a transcription factor involved in embryonic left-right asymmetry and cardiac development. However, how this genomic region functionally and structurally relates to PITX2 and atrial fibrillation is unknown.

RESULTS

To characterise its function, we tested genomic fragments from 4q25 for transcriptional activity in a mouse atrial cardiomyocyte cell line and in transgenic mouse embryos, identifying a non-tissue-specific potentiator regulatory element. Chromosome conformation capture revealed that this region physically interacts with the promoter of the cardiac specific isoform of Pitx2. Surprisingly, this regulatory region also interacts with the promoter of the next neighbouring gene, Enpep, which we show to be expressed in regions of the developing mouse heart essential for cardiac electrical activity.

CONCLUSIONS

Our data suggest that de-regulation of both PITX2 and ENPEP could contribute to an increased risk of atrial fibrillation in carriers of disease-associated variants, and show the challenges that we face in the functional analysis of genome-wide disease associations.

Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites

Background

Psoriasis is a cytokine-mediated skin disease that can be treated effectively with immunosuppressive biologic agents. These medications, however, are not equally effective in all patients and are poorly suited for treating mild psoriasis. To develop more targeted therapies, interfering with transcription factor (TF) activity is a promising strategy.

Methods

Meta-analysis was used to identify differentially expressed genes (DEGs) in the lesional skin from psoriasis patients (n = 237). We compiled a dictionary of 2935 binding sites representing empirically-determined binding affinities of TFs and unconventional DNA-binding proteins (uDBPs). This dictionary was screened to identify “psoriasis response elements” (PREs) overrepresented in sequences upstream of psoriasis DEGs.

Results

PREs are recognized by IRF1, ISGF3, NF-kappaB and multiple TFs with helix-turn-helix (homeo) or other all-alpha-helical (high-mobility group) DNA-binding domains. We identified a limited set of DEGs that encode proteins interacting with PRE motifs, including TFs (GATA3, EHF, FOXM1, SOX5) and uDBPs (AVEN, RBM8A, GPAM, WISP2). PREs were prominent within enhancer regions near cytokine-encoding DEGs (IL17A, IL19 and IL1B), suggesting that PREs might be incorporated into complex decoy oligonucleotides (cdODNs). To illustrate this idea, we designed a cdODN to concomitantly target psoriasis-activated TFs (i.e., FOXM1, ISGF3, IRF1 and NF-kappaB). Finally, we screened psoriasis-associated SNPs to identify risk alleles that disrupt or engender PRE motifs. This identified possible sites of allele-specific TF/uDBP binding and showed that PREs are disproportionately disrupted by psoriasis risk alleles.

Conclusions

We identified new TF/uDBP candidates and developed an approach that (i) connects transcriptome informatics to cdODN drug development and (ii) enhances our ability to interpret GWAS findings. Disruption of PRE motifs by psoriasis risk alleles may contribute to disease susceptibility.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0054-5) contains supplementary material, which is available to authorized users.

In-silico analysis of inflammatory bowel disease (IBD) GWAS loci to novel connections

Genome-wide association studies (GWASs) for many complex diseases, including inflammatory bowel disease (IBD), produced hundreds of disease-associated loci—the majority of which are noncoding. The number of GWAS loci is increasing very rapidly, but the process of translating single nucleotide polymorphisms (SNPs) from these loci to genomic medicine is lagging. In this study, we investigated 4,734 variants from 152 IBD associated GWAS loci (IBD associated 152 lead noncoding SNPs identified from pooled GWAS results + 4,582 variants in strong linkage-disequilibrium (LD) (r² ≥0.8) for EUR population of 1K Genomes Project) using four publicly available bioinformatics tools, e.g. dbPSHP, CADD, GWAVA, and RegulomeDB, to annotate and prioritize putative regulatory variants. Of the 152 lead noncoding SNPs, around 11% are under strong negative selection (GERP++ RS ≥2); and ~30% are under balancing selection (Tajima’s D score >2) in CEU population (1K Genomes Project)—though these regions are positively selected (GERP++ RS <0) in mammalian evolution. The analysis of 4,734 variants using three integrative annotation tools produced 929 putative functional SNPs, of which 18 SNPs (from 15 GWAS loci) are in concordance with all three classifiers. These prioritized noncoding SNPs may contribute to IBD pathogenesis by dysregulating the expression of nearby genes. This study showed the usefulness of integrative annotation for prioritizing fewer functional variants from a large number of GWAS markers.

Path from schizophrenia genomics to biology: gene regulation and perturbation in neurons derived from induced pluripotent stem cells and genome editing

Schizophrenia (SZ) is a devastating mental disorder afflicting 1% of the population. Recent genome-wide association studies (GWASs) of SZ have identified >100 risk loci. However, the causal variants/genes and the causal mechanisms remain largely unknown, which hinders the translation of GWAS findings into disease biology and drug targets. Most risk variants are noncoding, thus likely regulate gene expression. A major mechanism of transcriptional regulation is chromatin remodeling, and open chromatin is a versatile predictor of regulatory sequences. MicroRNA-mediated post-transcriptional regulation plays an important role in SZ pathogenesis. Neurons differentiated from patient-specific induced pluripotent stem cells (iPSCs) provide an experimental model to characterize the genetic perturbation of regulatory variants that are often specific to cell type and/or developmental stage. The emerging genome-editing technology enables the creation of isogenic iPSCs and neurons to efficiently characterize the effects of SZ-associated regulatory variants on SZ-relevant molecular and cellular phenotypes involving dopaminergic, glutamatergic, and GABAergic neurotransmissions. SZ GWAS findings equipped with the emerging functional genomics approaches provide an unprecedented opportunity for understanding new disease biology and identifying novel drug targets.

Associating disease-related genetic variants in intergenic regions to the genes they impact

We present a method to assist in interpretation of the functional impact of intergenic disease-associated SNPs that is not limited to search strategies proximal to the SNP. The method builds on two sources of external knowledge: the growing understanding of three-dimensional spatial relationships in the genome, and the substantial repository of information about relationships among genetic variants, genes, and diseases captured in the published biomedical literature. We integrate chromatin conformation capture data (HiC) with literature support to rank putative target genes of intergenic disease-associated SNPs. We demonstrate that this hybrid method outperforms a genomic distance baseline on a small test set of expression quantitative trait loci, as well as either method individually. In addition, we show the potential for this method to uncover relationships between intergenic SNPs and target genes across chromosomes. With more extensive chromatin conformation capture data becoming readily available, this method provides a way forward towards functional interpretation of SNPs in the context of the three dimensional structure of the genome in the nucleus.

Re-sequencing data for refining candidate genes and polymorphisms in QTL regions affecting adiposity in chicken

In this study, we propose an approach aiming at fine-mapping adiposity QTL in chicken, integrating whole genome re-sequencing data. First, two QTL regions for adiposity were identified by performing a classical linkage analysis on 1362 offspring in 11 sire families obtained by crossing two meat-type chicken lines divergently selected for abdominal fat weight. Those regions, located on chromosome 7 and 19, contained a total of 77 and 84 genes, respectively. Then, SNPs and indels in these regions were identified by re-sequencing sires. Considering issues related to polymorphism annotations for regulatory regions, we focused on the 120 and 104 polymorphisms having an impact on protein sequence, and located in coding regions of 35 and 42 genes situated in the two QTL regions. Subsequently, a filter was applied on SNPs considering their potential impact on the protein function based on conservation criteria. For the two regions, we identified 42 and 34 functional polymorphisms carried by 18 and 24 genes, and likely to deeply impact protein, including 3 coding indels and 4 nonsense SNPs. Finally, using gene functional annotation, a short list of 17 and 4 polymorphisms in 6 and 4 functional genes has been defined. Even if we cannot exclude that the causal polymorphisms may be located in regulatory regions, this strategy gives a complete overview of the candidate polymorphisms in coding regions and prioritize them on conservation- and functional-based arguments.

Epigenetic mechanisms underlying the pathogenesis of neurogenetic diseases

There have been considerable advances in uncovering the complex genetic mechanisms that underlie nervous system disease pathogenesis, particularly with the advent of exome and whole genome sequencing techniques. The emerging field of epigenetics is also providing further insights into these mechanisms. Here, we discuss our understanding of the interplay that exists between genetic and epigenetic mechanisms in these disorders, highlighting the nascent field of epigenetic epidemiology-which focuses on analyzing relationships between the epigenome and environmental exposures, development and aging, other health-related phenotypes, and disease states-and next-generation research tools (i.e., those leveraging synthetic and chemical biology and optogenetics) for examining precisely how epigenetic modifications at specific genomic sites affect disease processes.

How does the Mediterranean diet promote cardiovascular health? Current progress toward molecular mechanisms: gene-diet interactions at the genomic, transcriptomic, and epigenomic levels provide novel insights into new mechanisms

Epidemiological evidence supports a health-promoting effect of the Mediterranean Diet (MedDiet), especially in the prevention of cardiovascular diseases. These cardiovascular benefits have been attributed to a number of components of the MedDiet such as monounsaturated fatty acids, antioxidant vitamins and phytochemicals. However, the underlying mechanisms remain unknown. Likewise, little is known about the genes that define inter-individual variation in response to the MedDiet, although the TCF7L2 gene is emerging as an illustrative candidate for determining relative risk of cardiovascular events in response to the MedDiet. Moreover, omics technologies are providing evidence supporting potential mechanisms, some of them implicating epigenetics (i.e. microRNAs, methylation), and certain data suggest that some traditional foods could contribute via microRNAs possibly acting as exogenous regulators of gene expression. Future research should aim at increasing and consolidating the nutrigenetic and nutrigenomic knowledge of the MedDiet in order to provide sound, personalized and optimized nutritional recommendations.