Inferring CTCF-binding patterns and anchored loops across human tissues and cell types.

CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.

VannoPortal: multiscale functional annotation of human genetic variants for interrogating molecular mechanism of traits and diseases.

Interpreting the molecular mechanism of genomic variations and their causal relationship with diseases/traits are important and challenging problems in the human genetic study. To provide comprehensive and context-specific variant annotations for biologists and clinicians, here, by systematically integrating over 4TB genomic/epigenomic profiles and frequently-used annotation databases from various biological domains, we develop a variant annotation database, called VannoPortal. In general, the database has following major features: (i) systematically integrates 40 genome-wide variant annotations and prediction scores regarding allele frequency, linkage disequilibrium, evolutionary signature, disease/trait association, tissue/cell type-specific epigenome, base-wise functional prediction, allelic imbalance and pathogenicity; (ii) equips with our recent novel index system and parallel random-sweep searching algorithms for efficient management of backend databases and information extraction; (iii) greatly expands context-dependent variant annotation to incorporate large-scale epigenomic maps and regulatory profiles (such as EpiMap) across over 33 tissue/cell types; (iv) compiles many genome-scale base-wise prediction scores for regulatory/pathogenic variant classification beyond protein-coding region; (v) enables fast retrieval and direct comparison of functional evidence among linked variants using highly interactive web panel in addition to plain table; (vi) introduces many visualization functions for more efficient identification and interpretation of functional variants in single web page. VannoPortal is freely available at http://mulinlab.org/vportal.

Genome-wide association and functional interrogation identified a variant at 3p26.1 modulating ovarian cancer survival among Chinese women.

Ovarian cancer survival varies considerably among patients, to which germline variation may also contribute in addition to mutational signatures. To identify genetic markers modulating ovarian cancer outcome, we performed a genome-wide association study in 2130 Chinese ovarian cancer patients and found a hitherto unrecognized locus at 3p26.1 to be associated with the overall survival (Pcombined = 8.90 × 10-10). Subsequent statistical fine-mapping, functional annotation, and eQTL mapping prioritized a likely casual SNP rs9311399 in the non-coding regulatory region. Mechanistically, rs9311399 altered its enhancer activity through an allele-specific transcription factor binding and a long-range interaction with the promoter of a lncRNA BHLHE40-AS1. Deletion of the rs9311399-associated enhancer resulted in expression changes in several oncogenic signaling pathway genes and a decrease in tumor growth. Thus, we have identified a novel genetic locus that is associated with ovarian cancer survival possibly through a long-range gene regulation of oncogenic pathways.

vSampler: fast and annotation-based matched variant sampling tool.

SUMMARY: Sampling of control variants having matched properties with input variants is widely used in enrichment analysis of genome-wide association studies/quantitative trait loci and negative data construction for pathogenic/regulatory variant prediction methods. Spurious enrichment results because of confounding factors, such as minor allele frequency and linkage disequilibrium pattern, can be avoided by calibration of statistical significance based on matched controls. Here, we presented vSampler which can generate sets of randomly drawn variants with comprehensive choices of matching properties, such as tissue/cell type-specific epigenomic features. Importantly, the development of a novel data structure and sampling algorithms for vSampler makes it significantly fast than existing tools. AVAILABILITY AND IMPLEMENTATION: vSampler web server and local program are available at http://mulinlab.org/vsampler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ultrafast and scalable variant annotation and prioritization with big functional genomics data.

The advances of large-scale genomics studies have enabled compilation of cell type-specific, genome-wide DNA functional elements at high resolution. With the growing volume of functional annotation data and sequencing variants, existing variant annotation algorithms lack the efficiency and scalability to process big genomic data, particularly when annotating whole-genome sequencing variants against a huge database with billions of genomic features. Here, we develop VarNote to rapidly annotate genome-scale variants in large and complex functional annotation resources. Equipped with a novel index system and a parallel random-sweep searching algorithm, VarNote shows substantial performance improvements (two to three orders of magnitude) over existing algorithms at different scales. It supports both region-based and allele-specific annotations and introduces advanced functions for the flexible extraction of annotations. By integrating massive base-wise and context-dependent annotations in the VarNote framework, we introduce three efficient and accurate pipelines to prioritize the causal regulatory variants for common diseases, Mendelian disorders, and cancers.

The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs.

Cardiovascular dysfunction is one of the most common complications of long-term cancer treatment. Growing evidence has shown that antineoplastic drugs can increase cardiovascular risk during cancer therapy, seriously affecting patient survival. However, little is known about the genetic factors associated with the cardiovascular risk of antineoplastic drugs. We established a compendium of genetic evidence that supports cardiovascular risk induced by antineoplastic drugs. Most of this genetic evidence is attributed to causal alleles altering the expression of cardiovascular disease genes. We found that antineoplastic drugs predicted to induce cardiovascular risk are significantly enriched in drugs associated with cardiovascular adverse reactions, including many first-line cancer treatments. Functional experiments validated that retinoid X receptor agonists can reduce triglyceride lipolysis, thus modulating cardiovascular risk. Our results establish a link between the causal allele of cardiovascular disease genes and the direction of pharmacological modulation, which could facilitate cancer drug discovery and clinical trial design.

Methods and resources to access mutation-dependent effects on cancer drug treatment.

In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.

QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes.

Recent advances in genome sequencing and functional genomic profiling have promoted many large-scale quantitative trait locus (QTL) studies, which connect genotypes with tissue/cell type-specific cellular functions from transcriptional to post-translational level. However, no comprehensive resource can perform QTL lookup across multiple molecular phenotypes and investigate the potential cascade effect of functional variants. We developed a versatile resource, named QTLbase, for interpreting the possible molecular functions of genetic variants, as well as their tissue/cell-type specificity. Overall, QTLbase has five key functions: (i) curating and compiling genome-wide QTL summary statistics for 13 human molecular traits from 233 independent studies; (ii) mapping QTL-relevant tissue/cell types to 78 unified terms according to a standard anatomogram; (iii) normalizing variant and trait information uniformly, yielding >170 million significant QTLs; (iv) providing a rich web client that enables phenome- and tissue-wise visualization; and (v) integrating the most comprehensive genomic features and functional predictions to annotate the potential QTL mechanisms. QTLbase provides a one-stop shop for QTL retrieval and comparison across multiple tissues and multiple layers of molecular complexity, and will greatly help researchers interrogate the biological mechanism of causal variants and guide the direction of functional validation. QTLbase is freely available at http://mulinlab.org/qtlbase.

CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies.

Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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.

mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers.

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.

cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes.

It remains challenging to predict regulatory variants in particular tissues or cell types due to highly context-specific gene regulation. By connecting large-scale epigenomic profiles to expression quantitative trait loci (eQTLs) in a wide range of human tissues/cell types, we identify critical chromatin features that predict variant regulatory potential. We present cepip, a joint likelihood framework, for estimating a variant's regulatory probability in a context-dependent manner. Our method exhibits significant GWAS signal enrichment and is superior to existing cell type-specific methods. Furthermore, using phenotypically relevant epigenomes to weight the GWAS single-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.