Integrative annotation of variants from 1092 humans: application to cancer genomics

RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then, it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, which cannot be found by most current prioritization methods, for example, variants affecting splicing.

Unified inference of missense variant effects and gene constraints in the human genome

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows improved performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe a low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.

Numerous statistical methods have been developed to predict deleterious missense variants or constrained genes in the human genome, but unified prioritization methods that utilize both variant- and gene-level information are underdeveloped. Here we present UNEECON, an evolution-based deep learning framework for unified variant and gene prioritization. By integrating variant-level predictors and gene-level selective constraints, UNEECON outperforms existing methods in predicting missense variants and protein-coding genes associated with dominant disorders. Based on UNEECON, we show that disordered proteins are tolerant to missense mutations but not to loss-of-function mutations. In addition, we find that genes under strong selective constraints at both missense and loss-of-function levels are strongly associated with the central nervous system and the autism spectrum disorders, highlighting the need to investigate the function of these highly constrained genes in future studies.

Optimizing Genetic Diagnosis of Neurodevelopmental Disorders in the Clinical Setting

Progress in medical genetics has changed the practice of medicine in general and child neurology in particular. A genetic diagnosis has become critically important in determining optimal management of many neurodevelopmental disorders, making genetic testing a routine consideration of patient care in outpatient and inpatient settings. Today's child neurologists should be familiar with various genetic testing modalities and their appropriate use. Molecular genetic testing of children with unexplained developmental delays and/or congenital anomalies has a 20% to 30% chance of identifying a causative etiology. Newer methods have made genetic testing more widely available and sensitive but also more likely to produce ambiguous results.

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline1 to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

Mapping and characterization of structural variation in 17,795 human genomes

A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline¹ to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

RegulationSpotter: annotation and interpretation of extratranscriptic DNA variants

RegulationSpotter is a web-based tool for the user-friendly annotation and interpretation of DNA variants located outside of protein-coding transcripts (extratranscriptic variants). It is designed for clinicians and researchers who wish to assess the potential impact of the considerable number of non-coding variants found in Whole Genome Sequencing runs. It annotates individual variants with underlying regulatory features in an intuitive way by assessing over 100 genome-wide annotations. Additionally, it calculates a score, which reflects the regulatory potential of the variant region. Its dichotomous classifications, ‘functional’ or ‘non-functional’, and a human-readable presentation of the underlying evidence allow a biologically meaningful interpretation of the score. The output shows key aspects of every variant and allows rapid access to more detailed information about its possible role in gene regulation. RegulationSpotter can either analyse single variants or complete VCF files. Variants located within protein-coding transcripts are automatically assessed by MutationTaster as well as by RegulationSpotter to account for possible intragenic regulatory effects. RegulationSpotter offers the possibility of using phenotypic data to focus on known disease genes or genomic elements interacting with them. RegulationSpotter is freely available at https://www.regulationspotter.org.

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

Colorectal Adenomas-Genetics and Searching for New Molecular Screening Biomarkers

Colorectal cancer (CRC) is a malignant disease with an incidence of over 1.8 million new cases per year worldwide. CRC outcome is closely related to the respective stage of CRC and is more favorable at less advanced stages. Detection of early colorectal adenomas is the key to survival. In spite of implemented screening programs showing efficiency in the detection of early precancerous lesions and CRC in asymptomatic patients, a significant number of patients are still diagnosed in advanced stages. Research on CRC accomplished during the last decade has improved our understanding of the etiology and development of colorectal adenomas and revealed weaknesses in the general approach to their detection and elimination. Recent studies seek to find a reliable non-invasive biomarker detectable even in the blood. New candidate biomarkers could be selected on the basis of so-called liquid biopsy, such as long non-coding RNA, microRNA, circulating cell-free DNA, circulating tumor cells, and inflammatory factors released from the adenoma into circulation. In this work, we focused on both genetic and epigenetic changes associated with the development of colorectal adenomas into colorectal carcinoma and we also discuss new possible biomarkers that are detectable even in adenomas prior to cancer development.

Loss-of-function tolerance of enhancers in the human genome

Previous studies have surveyed the potential impact of loss-of-function (LoF) variants and identified LoF-tolerant protein-coding genes. However, the tolerance of human genomes to losing enhancers has not yet been evaluated. Here we present the catalog of LoF-tolerant enhancers using structural variants from whole-genome sequences. Using a conservative approach, we estimate that individual human genomes possess at least 28 LoF-tolerant enhancers on average. We assessed the properties of LoF-tolerant enhancers in a unified regulatory network constructed by integrating tissue-specific enhancers and gene-gene interactions. We find that LoF-tolerant enhancers tend to be more tissue-specific and regulate fewer and more dispensable genes relative to other enhancers. They are enriched in immune-related cells while enhancers with low LoF-tolerance are enriched in kidney and brain/neuronal stem cells. We developed a supervised learning approach to predict the LoF-tolerance of all enhancers, which achieved an area under the receiver operating characteristics curve (AUROC) of 98%. We predict 3,519 more enhancers would be likely tolerant to LoF and 129 enhancers that would have low LoF-tolerance. Our predictions are supported by a known set of disease enhancers and novel deletions from PacBio sequencing. The LoF-tolerance scores provided here will serve as an important reference for disease studies.

Enhancers are elements where transcription factors bind and regulate the expression of protein-coding genes. Although multiple previous studies have focused on which genes can tolerate loss-of-function (LoF), none has systematically evaluated the tolerance of all enhancers in the human genome to LoF. Individual studies have shown a broad range of phenotypic effects of enhancer LoF. The phenotypic effects of enhancer LoF likely fall into a spectrum where deletion of LoF-tolerant enhancers would not elicit substantial phenotypic impact, while some enhancers are likely to cause fitness defects when deleted. Here we report a systematic computational approach that uses machine learning and properties of enhancers in a unified human regulatory network with tissue-specific annotations to predict the LoF-tolerance of all enhancers identified in the human genome. The LoF-tolerance scores of enhancers provided in this study can significantly facilitate the interpretation and prioritization of non-coding sequence variants for disease and functional studies.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma

Alternative polyadenylation (APA) is a gene regulatory process that dictates mRNA 3'-UTR length, resulting in changes in mRNA stability and localization. APA is frequently disrupted in cancer and promotes tumorigenesis through altered expression of oncogenes and tumor suppressors. Pan-cancer analyses have revealed common APA events across the tumor landscape; however, little is known about tumor type-specific alterations that may uncover novel events and vulnerabilities. Here, we integrate RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project and The Cancer Genome Atlas (TCGA) to comprehensively analyze APA events in 148 pancreatic ductal adenocarcinomas (PDACs). We report widespread, recurrent, and functionally relevant 3'-UTR alterations associated with gene expression changes of known and newly identified PDAC growth-promoting genes and experimentally validate the effects of these APA events on protein expression. We find enrichment for APA events in genes associated with known PDAC pathways, loss of tumor-suppressive miRNA binding sites, and increased heterogeneity in 3'-UTR forms of metabolic genes. Survival analyses reveal a subset of 3'-UTR alterations that independently characterize a poor prognostic cohort among PDAC patients. Finally, we identify and validate the casein kinase CSNK1A1 (also known as CK1alpha or CK1a) as an APA-regulated therapeutic target in PDAC. Knockdown or pharmacological inhibition of CSNK1A1 attenuates PDAC cell proliferation and clonogenic growth. Our single-cancer analysis reveals APA as an underappreciated driver of protumorigenic gene expression in PDAC via the loss of miRNA regulation.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR < 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

Single-nucleotide polymorphism rs13426236 contributes to an increased prostate cancer risk via regulating MLPH splicing variant 4

A prostate cancer risk single-nucleotide polymorphism (SNP), rs13426236, is significantly associated with melanophilin (MLPH) expression. To functionally characterize role of the rs13426236 in prostate cancer, we first performed splicing-specific expression quantitative trait loci analysis and refined the significant association of rs13426236 allele G with an increased expression of MLPH splicing transcript variant 4 (V4) (P = 7.61E-5) but not other protein-coding variants (V1-V3) (P > .05). We then performed an allele-specific reporter assay to determine if SNP-containing sequences functioned as an active enhancer. Compared to allele A, allele G of rs13426236 showed significantly higher luciferase activity on the promoter of the splicing transcript V4 (P < .03) but not on the promoter of transcript V1 (P > .05) in two prostate cancer cell lines (DU145 and 22Rv1). Cell transfection assays showed stronger effect of transcript V4 than V1 on promoting cell proliferation, invasion, and antiapoptotic activities. RNA profiling analysis demonstrated that transcript V4 overexpression caused significant expression changes in glycosylation/glycoprotein and metal-binding gene ontology pathways (FDR < 0.01). We also found that both transcripts V4 and V1 were significantly upregulated in prostate adenocarcinoma (P ≤ 2.49E-6) but only transcript V4 upregulation was associated with poor recurrence-free survival (P = .028, hazard ratio = 1.63, 95% confidence interval = 1.05-2.42) in The Cancer Genome Atlas data. This study provides strong evidence showing that prostate cancer risk SNP rs13426236 upregulates expression of MLPH transcript V4, which may function as a candidate oncogene in prostate cancer.

Development and Characterization of Near-Isogenic Lines Revealing Candidate Genes for a Major 7AL QTL Responsible for Heat Tolerance in Wheat

Wheat is one of the most important food crops in the world, but as a cool-season crop, it is more prone to heat stress, which severely affects crop production and grain quality. Heat tolerance in wheat is a quantitative trait, and the genes underlying reported quantitative trait loci (QTL) have rarely been identified. Near-isogenic lines (NILs) with a common genetic background but differing at a particular locus could turn quantitative traits into a Mendelian factor; therefore, they are suitable material for identifying candidate genes for targeted locus/loci. In this study, we developed and characterized NILs from two populations Cascades × Tevere and Cascades × W156 targeting a major 7AL QTL responsible for heat tolerance. Molecular marker screening and phenotyping for SPAD chlorophyll content and grain-yield-related traits confirmed four pairs of wheat NILs that contrasted for heat-stress responses. Genotyping the NILs using a 90K Infinium iSelect SNP array revealed five single nucleotide polymorphism (SNP) markers within the QTL interval that were distinguishable between the isolines. Seven candidate genes linked to the SNPs were identified as related to heat tolerance, and involved in important processes and pathways in response to heat stress. The confirmed multiple pairs of NILs and identified candidate genes in this study are valuable resources and information for further fine-mapping to clone major genes for heat tolerance.

Transcription Factors Involved in Tumorigenesis Are Over-Represented in Mutated Active DNA-Binding Sites in Neuroblastoma

The contribution of coding mutations to oncogenesis has been largely clarified, whereas little is known about somatic mutations in noncoding DNA and their role in driving tumors remains controversial. Here, we used an alternative approach to interpret the functional significance of noncoding somatic mutations in promoting tumorigenesis. Noncoding somatic mutations of 151 neuroblastomas were integrated with ENCODE data to locate somatic mutations in regulatory elements specifically active in neuroblastoma cells, nonspecifically active in neuroblastoma cells, and nonactive. Within these types of elements, transcription factors (TF) were identified whose binding sites were enriched or depleted in mutations. For these TFs, a gene expression signature was built to assess their implication in neuroblastoma. DNA- and RNA-sequencing data were integrated to assess the effects of those mutations on mRNA levels. The pathogenicity of mutations was significantly higher in transcription factor binding site (TFBS) of regulatory elements specifically active in neuroblastoma cells, as compared with the others. Within these elements, there were 18 over-represented TFs involved mainly in cell-cycle phase transitions and 15 under-represented TFs primarily regulating cell differentiation. A gene expression signature based on over-represented TFs correlated with poor survival and unfavorable prognostic markers. Moreover, recurrent mutations in TFBS of over-represented TFs such as EZH2 affected MCF2L and ADP-ribosylhydrolase like 1 expression, among the others. We propose a novel approach to study the involvement of regulatory variants in neuroblastoma that could be extended to other cancers and provide further evidence that alterations of gene expression may have relevant effects in neuroblastoma development. SIGNIFICANCE: These findings propose a novel approach to study regulatory variants in neuroblastoma and suggest that noncoding somatic mutations have relevant implications in neuroblastoma development.

Germline variants in cancer genes in high-risk non-BRCA patients from Puerto Rico

Inherited pathogenic variants in genes that confer moderate to high risk of breast cancer may explain up to 50% of familial breast cancer. This study aimed at identifying inherited pathogenic variants in breast cancer cases from Puerto Rico that were not linked to BRCA1 or BRCA2. Forty-eight breast cancer patients that met the clinical criteria for BRCA testing but had received a negative BRCA1/2 result were recruited. Fifty-three genes previously implicated in hereditary cancer predisposition were captured using the BROCA Agilent cancer risk panel followed by massively parallel sequencing. Missense variants of uncertain clinical significance in CHEK2 were evaluated using an in vitro kinase assays to determine their impact on function. Pathogenic variants were identified in CHEK2, MUTYH, and RAD51B in four breast cancer patients, which represented 8.3% of the cohort. We identified three rare missense variants of uncertain significance in CHEK2 and two variants (p.Pro484Leu and p.Glu239Lys) showed markedly decreased kinase activity in vitro comparable to a known pathogenic variant. Interestingly, the local ancestry at the RAD51B locus in the carrier of p.Arg47* was predicted to be of African origin. In this cohort, 12.5% of the BRCA-negative breast cancer patients were found to carry a known pathogenic variant or a variant affecting protein activity. This study reveals an unmet clinical need of genetic testing that could benefit a significant proportion of at-risk Latinas. It also highlights the complexity of Hispanic populations as pathogenic factors may originate from any of the ancestral populations that make up their genetic backgrounds.

An Integrated Three-Long Non-coding RNA Signature Predicts Prognosis in Colorectal Cancer Patients

Colorectal cancer (CRC) is one of the most common cancers worldwide, whose morbidity and mortality gradually increased. Here, we aimed to identify and access prognostic long non-coding RNAs (lncRNAs) associated with overall survival (OS) in CRC. Firstly, RNA expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and 439 CRC patients were enrolled as a training set. Univariate Cox analysis and the least absolute shrinkage and selection operator analysis (LASSO) were performed to identify the prognostic lncRNAs. Multivariable Cox regression analysis was used to establish a prognostic risk formula including three lncRNAs (AP003555.2, AP006284.1, and LINC01602). The low-risk group had a better OS than the high-risk group (P < 0.0001), and the areas under the receiver operating characteristic curve (AUCs) of 3- and 5-year OS were 0.712 and 0.674, respectively. Then, we evaluated the signature in a clinical validation set which were collected from the Affiliated Hospital of Jiangnan University. Compared with the low-risk group, patients' OS were found to be significantly worse in the high-risk group (P = 0.0057). The AUCs of 3- and 5-year OS were 0.701 and 0.694, respectively. Finally, we constructed an lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network to explore the potential function of three differentially expressed lncRNAs (DElncRNAs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that these DElncRNAs were involved with several cancer-related pathways. In summary, our data provide evidence that the three-lncRNA signature could serve as an independent biomarker to predict prognosis in CRC. This study will also suggest that these three lncRNAs potentially participate in the progression of CRC.

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.

Long Noncoding RNAs and Repetitive Elements: Junk or Intimate Evolutionary Partners?

Our recent ability to sequence entire genomes, along with all of their transcribed RNAs, has led to the surprising finding that only ∼1% of the human genome is used to encode proteins. This finding has led to vigorous debate over the functional importance of the transcribed but untranslated portions of the genome. Currently, scientists tend to assume coding genes are functional until proven not to be, while the opposite is true for noncoding genes. This review takes a new look at the evidence for and against widespread noncoding gene functionality. We focus in particular on long noncoding RNA (noncoding RNAs longer than 200 nucleotides) genes and their 'junk' associates, transposable elements, and satellite repeats. Taken together, the suggestion put forward is that more of this junk DNA may be functional than nonfunctional and that noncoding RNAs and transposable elements act symbiotically to drive evolution.

Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner

There has been much effort to prioritize genomic variants with respect to their impact on "function". However, function is often not precisely defined: sometimes it is the disease association of a variant; on other occasions, it reflects a molecular effect on transcription or epigenetics. Here, we coupled multiple genomic predictors to build GRAM, a GeneRAlized Model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant on its associated gene, in a transferable, cell-specific manner. Firstly, we performed feature engineering: using LASSO, a regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other variant-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating only SELEX features and expression profiles; thus, the program combines a universal regulatory score with an easily obtainable modifier reflecting the particular cell type. We benchmarked GRAM on large-scale MPRA datasets, achieving AUROC scores of 0.72 in GM12878 and 0.66 in a multi-cell line dataset. We then evaluated the performance of GRAM on targeted regions using luciferase assays in the MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gave very different results, highlighting the importance of carefully defining the exact prediction target of the model. Finally, we illustrated the utility of GRAM in fine-mapping causal variants and developed a practical software pipeline to carry this out. In particular, we demonstrated in specific examples how the pipeline could pinpoint variants that directly modulate gene expression within a larger linkage-disequilibrium block associated with a phenotype of interest (e.g., for an eQTL).

Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions

A key issue in drug design is how population variation affects drug efficacy by altering binding affinity (BA) in different individuals, an essential consideration for government regulators. Ideally, we would like to evaluate the BA perturbations of millions of single-nucleotide variants (SNVs). However, only hundreds of protein-drug complexes with SNVs have experimentally characterized BAs, constituting too small a gold standard for straightforward statistical model training. Thus, we take a hybrid approach: using physically based calculations to bootstrap the parameterization of a full model. In particular, we do 3D structure-based docking on ∼10,000 SNVs modifying known protein-drug complexes to construct a pseudo gold standard. Then we use this augmented set of BAs to train a statistical model combining structure, ligand and sequence features and illustrate how it can be applied to millions of SNVs. Finally, we show that our model has good cross-validated performance (97% AUROC) and can also be validated by orthogonal ligand-binding data.

QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction

Motivation

In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants.

Results

We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution.

Availability and implementation

Source code and binaries of the QuantumClone R package are freely available for download at https://CRAN.R-project.org/package=QuantumClone.

Contact

gudrun.schleiermacher@curie.fr or valentina.boeva@inserm.fr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evidence that RNA Viruses Drove Adaptive Introgression between Neanderthals and Modern Humans

Neanderthals and modern humans interbred at least twice in the past 100,000 years. While there is evidence that most introgressed DNA segments from Neanderthals to modern humans were removed by purifying selection, less is known about the adaptive nature of introgressed sequences that were retained. We hypothesized that interbreeding between Neanderthals and modern humans led to (1) the exposure of each species to novel viruses and (2) the exchange of adaptive alleles that provided resistance against these viruses. Here, we find that long, frequent-and more likely adaptive-segments of Neanderthal ancestry in modern humans are enriched for proteins that interact with viruses (VIPs). We found that VIPs that interact specifically with RNA viruses were more likely to belong to introgressed segments in modern Europeans. Our results show that retained segments of Neanderthal ancestry can be used to detect ancient epidemics.

Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers

The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology.

Identification of Cancer Drivers at CTCF Insulators in 1,962 Whole Genomes

Recent studies have shown that mutations at non-coding elements, such as promoters and enhancers, can act as cancer drivers. However, an important class of non-coding elements, namely CTCF insulators, has been overlooked in the previous driver analyses. We used insulator annotations from CTCF and cohesin ChIA-PET and analyzed somatic mutations in 1,962 whole genomes from 21 cancer types. Using the heterogeneous patterns of transcription-factor-motif disruption, functional impact, and recurrence of mutations, we developed a computational method that revealed 21 insulators showing signals of positive selection. In particular, mutations in an insulator in multiple cancer types, including 16% of melanoma samples, are associated with TGFB1 up-regulation. Using CRISPR-Cas9, we find that alterations at two of the most frequently mutated regions in this insulator increase cell growth by 40%-50%, supporting the role of this boundary element as a cancer driver. Thus, our study reveals several CTCF insulators as putative cancer drivers.

A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph

BACKGROUND

Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task.

RESULTS

Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types.

CONCLUSIONS

The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment.

AVAILABILITY

The source code can be obtained at https://github.com/weiba/Subdyquency .

Heterogeneity in the extent of linkage disequilibrium among exonic, intronic, non-coding RNA and intergenic chromosome regions

Whole-genome sequence data enable construction of high-resolution linkage disequilibrium (LD) maps revealing the LD structure of functional elements within genic and subgenic sequences. The Malecot-Morton model defines LD map distances in linkage disequilibrium units (LDUs), analogous to the centimorgan scale of linkage maps. For whole-genome sequence-derived LD maps, we introduce the ratio of corresponding map lengths kilobases/LDU to describe the extent of LD within genome components. The extent of LD is highly variable across the genome ranging from ~38 kb for intergenic sequences to ~858 kb for centromeric regions. LD is ~16% more extensive in genic, compared with intergenic sequences, reflecting relatively increased selection and/or reduced recombination in genes. The LD profile across 18,268 autosomal genes reveals reduced extent of LD, consistent with elevated recombination, in exonic regions near the 5' end of genes but more extensive LD, compared with intronic sequences, across more centrally located exons. Genes classified as essential and genes linked to Mendelian phenotypes show more extensive LD compared with genes associated with complex traits, perhaps reflecting differences in selective pressure. Significant differences between exonic, intronic and intergenic components demonstrate that fine-scale LD structure provides important insights into genome function, which cannot be revealed by LD analysis of much lower resolution array-based genotyping and conventional linkage maps.

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.

Long Noncoding RNA and Epithelial Mesenchymal Transition in Cancer

Epithelial-mesenchymal transition (EMT) is a multistep process that allows epithelial cells to acquire mesenchymal properties. Fundamental in the early stages of embryonic development, this process is aberrantly activated in aggressive cancerous cells to gain motility and invasion capacity, thus promoting metastatic phenotypes. For this reason, EMT is a central topic in cancer research and its regulation by a plethora of mechanisms has been reported. Recently, genomic sequencing and functional genomic studies deepened our knowledge on the fundamental regulatory role of noncoding DNA. A large part of the genome is transcribed in an impressive number of noncoding RNAs. Among these, long noncoding RNAs (lncRNAs) have been reported to control several biological processes affecting gene expression at multiple levels from transcription to protein localization and stability. Up to now, more than 8000 lncRNAs were discovered as selectively expressed in cancer cells. Their elevated number and high expression specificity candidate these molecules as a valuable source of biomarkers and potential therapeutic targets. Rising evidence currently highlights a relevant function of lncRNAs on EMT regulation defining a new layer of involvement of these molecules in cancer biology. In this review we aim to summarize the findings on the role of lncRNAs on EMT regulation and to discuss their prospective potential value as biomarkers and therapeutic targets in cancer.

FunSPU: A versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data

Despite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on the ENCODE, Epigenomics Roadmap and GTEx projects, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n = 1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n = 1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package "FunSPU".

Shaping the nebulous enhancer in the era of high-throughput assays and genome editing

Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.

Positive selection in Europeans and East-Asians at the ABCA12 gene

Natural selection acts on genetic variants by increasing the frequency of alleles responsible for a cellular function that is favorable in a certain environment. In a previous genome-wide scan for positive selection in contemporary humans, we identified a signal of positive selection in European and Asians at the genetic variant rs10180970. The variant is located in the second intron of the ABCA12 gene, which is implicated in the lipid barrier formation and down-regulated by UVB radiation. We studied the signal of selection in the genomic region surrounding rs10180970 in a larger dataset that includes DNA sequences from ancient samples. We also investigated the functional consequences of gene expression of the alleles of rs10180970 and another genetic variant in its proximity in healthy volunteers exposed to similar UV radiation. We confirmed the selection signal and refine its location that extends over 35 kb and includes the first intron, the first two exons and the transcription starting site of ABCA12. We found no obvious effect of rs10180970 alleles on ABCA12 gene expression. We reconstructed the trajectory of the T allele over the last 80,000 years to discover that it was specific to H. sapiens and present in non-Africans 45,000 years ago.

Intronic CNVs and gene expression variation in human populations

Introns can be extraordinarily large and they account for the majority of the DNA sequence in human genes. However, little is known about their population patterns of structural variation and their functional implication. By combining the most extensive maps of CNVs in human populations, we have found that intronic losses are the most frequent copy number variants (CNVs) in protein-coding genes in human, with 12,986 intronic deletions, affecting 4,147 genes (including 1,154 essential genes and 1,638 disease-related genes). This intronic length variation results in dozens of genes showing extreme population variability in size, with 40 genes with 10 or more different sizes and up to 150 allelic sizes. Intronic losses are frequent in evolutionarily ancient genes that are highly conserved at the protein sequence level. This result contrasts with losses overlapping exons, which are observed less often than expected by chance and almost exclusively affect primate-specific genes. An integrated analysis of CNVs and RNA-seq data showed that intronic loss can be associated with significant differences in gene expression levels in the population (CNV-eQTLs). These intronic CNV-eQTLs regions are enriched for intronic enhancers and can be associated with expression differences of other genes showing long distance intron-promoter 3D interactions. Our data suggests that intronic structural variation of protein-coding genes makes an important contribution to the variability of gene expression and splicing in human populations.

HMGCLL1 is a predictive biomarker for deep molecular response to imatinib therapy in chronic myeloid leukemia

Achieving a deep molecular response (DMR) to tyrosine kinase inhibitor (TKI) therapy for chronic myeloid leukemia (CML) remains challenging and at present, there is no biomarker to predict DMR in this setting. Herein, we report that an HMGCLL1 genetic variant located in 6p12.1 can be used as a predictive genetic biomarker for intrinsic sensitivity to imatinib (IM) therapy. We measured DMR rate according to HMGCLL1 variant in a discovery set of CML patients (n = 201) and successfully replicated it in a validation set (n = 270). We also investigated the functional relevance of HMGCLL1 blockade with respect to response to TKI therapy and showed that small interfering RNA mediated blockade of HMGCLL1 isoform 3 results in significant decrease in viability of BCR-ABL1-positive cells including K562, CML-T1 or BaF3 cell lines with or without ABL1 kinase domain mutations such as T315I mutation. Decreased cell viability was also demonstrated in murine CML stem cells and human hematopoietic progenitor cells. RNA sequencing showed that blockade of HMGCLL1 was associated with G0/G1 arrest and the cell cycle. In summary, the HMGCLL1 gene polymorphism is a novel genetic biomarker for intrinsic sensitivity to IM therapy in CML patients that predicts DMR in this setting.

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

Background

The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods.

Results

Here, we present CNVcaller, a read-depth method for discovering CNVs in population sequencing data. The computational speed of CNVcaller was 1-2 orders of magnitude faster than CNVnator and Genome STRiP for complex genomes with thousands of unmapped scaffolds. CNV detection of 232 goats required only 1.4 days on a single compute node. Additionally, the Mendelian consistency of sheep trios indicated that CNVcaller mitigated the influence of high proportions of gaps and misassembled duplications in the nonhuman reference genome assembly. Furthermore, multiple evaluations using real sheep and human data indicated that CNVcaller achieved the best accuracy and sensitivity for detecting duplications.

Conclusions

The fast generalized detection algorithms included in CNVcaller overcome prior computational barriers for detecting CNVs in large-scale sequencing data with complex genomic structures. Therefore, CNVcaller promotes population genetic analyses of functional CNVs in more species.

A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in ‘Tissue Invasion and Metastasis’ in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy.

Positional integration of lung adenocarcinoma susceptibility loci with primary human alveolar epithelial cell epigenomes

AIM

To identify functional lung adenocarcinoma (LUAD) risk SNPs.

MATERIALS & METHODS

Eighteen validated LUAD risk SNPs (p ≤ 5 × 10-8) and 930 SNPs in high linkage disequilibrium (r2 > 0.5) were integrated with epigenomic information from primary human alveolar epithelial cells. Enhancer-associated SNPs likely affecting transcription factor-binding sites were predicted. Three SNPs were functionally investigated using luciferase assays, expression quantitative trait loci and cancer-specific expression.

RESULTS

Forty-seven SNPs mapped to putative enhancers; 11 located to open chromatin. Of these, seven altered predicted transcription factor-binding motifs. Rs6942067 showed allele-specific luciferase expression and expression quantitative trait loci analysis indicates that it influences expression of DCBLD1, a gene that encodes an unknown membrane protein and is overexpressed in LUAD.

CONCLUSION

Integration of candidate LUAD risk SNPS with epigenomic marks from normal alveolar epithelium identified numerous candidate functional LUAD risk SNPs including rs6942067, which appears to affect DCBLD1 expression. Data deposition: Data are provided in GEO record GSE84273.

Network Analysis as a Grand Unifier in Biomedical Data Science

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

ETS transcription factors induce a unique UV damage signature that drives recurrent mutagenesis in melanoma

Recurrent mutations are frequently associated with transcription factor (TF) binding sites (TFBS) in melanoma, but the mechanism driving mutagenesis at TFBS is unclear. Here, we use a method called CPD-seq to map the distribution of UV-induced cyclobutane pyrimidine dimers (CPDs) across the human genome at single nucleotide resolution. Our results indicate that CPD lesions are elevated at active TFBS, an effect that is primarily due to E26 transformation-specific (ETS) TFs. We show that ETS TFs induce a unique signature of CPD hotspots that are highly correlated with recurrent mutations in melanomas, despite high repair activity at these sites. ETS1 protein renders its DNA binding targets extremely susceptible to UV damage in vitro, due to binding-induced perturbations in the DNA structure that favor CPD formation. These findings define a mechanism responsible for recurrent mutations in melanoma and reveal that DNA binding by ETS TFs is inherently mutagenic in UV-exposed cells.

Many factors contribute to mutation hotspots in cancer cells. Here the authors map UV damage at single-nucleotide resolution across the human genome and find that binding sites of ETS transcription factors are especially prone to forming UV lesions, leading to mutation hotspots in melanoma.

Paternally inherited cis-regulatory structural variants are associated with autism

The genetic basis of autism spectrum disorder (ASD) is known to consist of contributions from de novo mutations in variant-intolerant genes. We hypothesize that rare inherited structural variants in cis-regulatory elements (CRE-SVs) of these genes also contribute to ASD. We investigated this by assessing the evidence for natural selection and transmission distortion of CRE-SVs in whole genomes of 9274 subjects from 2600 families affected by ASD. In a discovery cohort of 829 families, structural variants were depleted within promoters and untranslated regions, and paternally inherited CRE-SVs were preferentially transmitted to affected offspring and not to their unaffected siblings. The association of paternal CRE-SVs was replicated in an independent sample of 1771 families. Our results suggest that rare inherited noncoding variants predispose children to ASD, with differing contributions from each parent.

Gene co-opening network deciphers gene functional relationships

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

High-throughput screening of prostate cancer risk loci by single nucleotide polymorphisms sequencing

Functional characterization of disease-causing variants at risk loci has been a significant challenge. Here we report a high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) technology to simultaneously screen hundreds to thousands of SNPs for their allele-dependent protein-binding differences. This technology takes advantage of higher retention rate of protein-bound DNA oligos in protein purification column to quantitatively sequence these SNP-containing oligos. We apply this technology to test prostate cancer-risk loci and observe differential allelic protein binding in a significant number of selected SNPs. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq) in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci (RGS17 and ASCL2). Together, we introduce a powerful high-throughput pipeline for large-scale screening of functional SNPs at disease risk loci.

Functional characterization of disease-causing variants at risk loci in cancer is challenging. Here, in prostate cancer the authors report a pipeline for high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) for large scale screening of functional SNPs at disease risk loci.

The MDM2 rs937283 A > G variant significantly increases the risk of lung and gastric cancer in Chinese population

BACKGROUND

Currently, the MDM2 promoter rs937283 A > G variant that is able to alter MDM2 gene expression has been widely studied to explore the association of MDM2 with cancer risk. In this report, we investigate the association of MDM2 rs937283 A > G variant with risk of lung cancer (LC) and gastric cancer (GC) in a Chinese population of Hubei province, which was followed by a meta-analysis.

METHODS

The genotyping of rs937283 was performed by polymerase chain reaction-restriction fragment length polymorphism and confirmed by sequencing.

RESULTS

The results of the present study showed that rs937283 was significantly associated with the risk of LC, and the factors of age, gender, smoking status and drinking status would affect such association. However, rs937283 was only associated with the risk of GC in male, smoking and drinking subgroups. The following meta-analysis demonstrated that rs937283 was associated with the overall cancer risk particularly in Chinese population, which reinforced our present finding. Moreover, the meta-analysis according to cancer types revealed that rs937283 was associated with retinoblastoma risk, but not squamous cell carcinoma risk.

CONCLUSION

Collectively, the MDM2 rs937283 A > G variant may be a valuable risk factor or diagnostic biomarker for Chinese cancer patients.

Evaluating the Genetics of Common Variable Immunodeficiency: Monogenetic Model and Beyond

Common variable immunodeficiency (CVID) is the most frequent symptomatic primary immunodeficiency characterized by recurrent infections, hypogammaglobulinemia and poor response to vaccines. Its diagnosis is made based on clinical and immunological criteria, after exclusion of other diseases that can cause similar phenotypes. Currently, less than 20% of cases of CVID have a known underlying genetic cause. We have analyzed whole-exome sequencing and copy number variants data of 36 children and adolescents diagnosed with CVID and healthy relatives to estimate the proportion of monogenic cases. We have replicated an association of CVID to p.C104R in TNFRSF13B and reported the second case of homozygous patient to date. Our results also identify five causative genetic variants in LRBA, CTLA4, NFKB1, and PIK3R1, as well as other very likely causative variants in PRKCD, MAPK8, or DOCK8 among others. We experimentally validate the effect of the LRBA stop-gain mutation which abolishes protein production and downregulates the expression of CTLA4, and of the frameshift indel in CTLA4 producing expression downregulation of the protein. Our results indicate a monogenic origin of at least 15–24% of the CVID cases included in the study. The proportion of monogenic patients seems to be lower in CVID than in other PID that have also been analyzed by whole exome or targeted gene panels sequencing. Regardless of the exact proportion of CVID monogenic cases, other genetic models have to be considered for CVID. We propose that because of its prevalence and other features as intermediate penetrancies and phenotypic variation within families, CVID could fit with other more complex genetic scenarios. In particular, in this work, we explore the possibility of CVID being originated by an oligogenic model with the presence of heterozygous mutations in interacting proteins or by the accumulation of detrimental variants in particular immunological pathways, as well as perform association tests to detect association with rare genetic functional variation in the CVID cohort compared to healthy controls.

Towards pan-genome read alignment to improve variation calling

Background

Typical human genome differs from the reference genome at 4-5 million sites. This diversity is increasingly catalogued in repositories such as ExAC/gnomAD, consisting of >15,000 whole-genomes and >126,000 exome sequences from different individuals. Despite this enormous diversity, resequencing data workflows are still based on a single human reference genome. Identification and genotyping of genetic variants is typically carried out on short-read data aligned to a single reference, disregarding the underlying variation.

Results

We propose a new unified framework for variant calling with short-read data utilizing a representation of human genetic variation – a pan-genomic reference. We provide a modular pipeline that can be seamlessly incorporated into existing sequencing data analysis workflows. Our tool is open source and available online: https://gitlab.com/dvalenzu/PanVC.

Conclusions

Our experiments show that by replacing a standard human reference with a pan-genomic one we achieve an improvement in single-nucleotide variant calling accuracy and in short indel calling accuracy over the widely adopted Genome Analysis Toolkit (GATK) in difficult genomic regions.

Cancer driver mutation prediction through Bayesian integration of multi-omic data

Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.