FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer

Impact of 5' Near Gene Variants of Mannose Binding Lectin (MBL2) on Breast Cancer Risk

The immune system plays a bifaceted role in tumour development through modulation of inflammation. MBL binds to damage-associated molecular patterns and induces inflammation through the activation of complement pathway. Dysregulated inflammation plays a major role in breast cancer pathogenesis, thereby suggesting its contribution towards breast cancer risk. Literature asserts single-nucleotide polymorphisms (SNPs) modulating serum MBL levels. Therefore, studying MBL2 SNPs in breast cancer might provide valuable insight in the disease pathogenesis. The present case-control association study aimed to elucidate the association between MBL2 5' near gene SNPs and breast cancer risk. Breast cancer patients were recruited from Government Medical College, G.N.D. Hospital, Amritsar. The age- and gender-matched genetically unrelated healthy individuals, from adjoining regions, with no history of malignancy up to three generations were recruited as controls. The SNPs of MBL2 from the 5' near gene region with putative functional significance were selected based upon the in silico analysis and literature review. The genotypic, allelic and haplotype frequencies for the studied variants were assessed and compared in the study participants by ARMS-PCR and PCR-RFLP. No difference in allelic, genotypic and haplotype frequencies was reported for rs7096206, rs7084554 and rs11003125 in both the participant groups. rs7084554 (CC) was found to confer risk towards hormone receptor-positive breast cancer. An intermediate LD was observed between rs7084554 and rs11003125. The study reports association between MBL2 variant (rs7084554) and hormone receptor-positive breast cancer risk. Further research in this direction might validate the findings.

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Disease risk prediction based on genomic sequence and transcriptional profile can improve disease screening and prevention. Despite identifying many disease-associated DNA variants, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. In this study, we designed in vitro experiments to uncover the significance of occupancy and competitive binding between P53 and cMYC on common target genes. Analyzing publicly available ChIP-seq data for P53 and cMYC in embryonic stem cells showed that ~344-366 regions are co-occupied, and on average, two cis-overlapping motifs (CisOMs) per region were identified, suggesting that co-occupancy is evolutionarily conserved. Using U2OS and Raji cells untreated and treated with doxorubicin to increase P53 protein level while potentially reducing cMYC level, ChIP-seq analysis illustrated that around 16 to 922 genomic regions were co-occupied by P53 and cMYC, and substitutions of cMYC signals by P53 were detected post doxorubicin treatment. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data analysis. We utilized a computational motif-matching approach to illustrate that changes in predicted P53 binding affinity in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data, and expression of target genes from GTEx portal. We found significant correlation between change in cMYC-motif binding affinity in CisOMs and altered expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with common diseases.

Exploring non-coding genetic variability in ACE2: Functional annotation and in vitro validation of regulatory variants

The surge in human whole-genome sequencing data has facilitated the study of non-coding region variations, yet understanding their biological significance remains a challenge. We used a computational workflow to assess the regulatory potential of non-coding variants, with a particular focus on the Angiotensin Converting Enzyme 2 (ACE2) gene. This gene is crucial in physiological processes and serves as the entry point for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 19 (COVID-19). In our analysis, using data from the gnomAD population database and functional annotation, we identified 17 significant Single Nucleotide Variants (SNVs) in ACE2, particularly in its enhancers, promoters, and 3' untranslated regions (UTRs). We found preliminary evidence supporting the regulatory impact of some of these variants on ACE2 expression. Our detailed examination of two SNVs, rs147718775 and rs140394675, in the ACE2 promoter revealed that these co-occurring SNVs, when mutated, significantly enhance promoter activity, suggesting a possible increase in specific ACE2 isoform expression. This method proves effective in identifying and interpreting impactful non-coding variants, aiding in further studies and enhancing understanding of molecular bases of monogenic and complex traits.

In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder

Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. Massively parallel reporter assays (MPRAs), which are an in vitro high-throughput method, can simultaneously test thousands of variants by evaluating the existence of allele specific regulatory activity. Nevertheless, the identified labelled variants by MPRAs, which shows differential allelic regulatory effects on the gene expression are usually limited to the scale of hundreds, limiting their potential to be used as the training set for achieving a robust genome-wide prediction. To address the limitation, we propose a deep generative model, MpraVAE, to in silico generate and augment the training sample size of labelled variants. By benchmarking on several MPRA datasets, we demonstrate that MpraVAE significantly improves the prediction performance for MPRA regulatory variants compared to the baseline method, conventional data augmentation approaches as well as existing variant scoring methods. Taking autoimmune diseases as one example, we apply MpraVAE to perform a genome-wide prediction of regulatory variants and find that predicted regulatory variants are more enriched than background variants in enhancers, active histone marks, open chromatin regions in immune-related cell types, and chromatin states associated with promoter, enhancer activity and binding sites of cMyC and Pol II that regulate gene expression. Importantly, predicted regulatory variants are found to link immune-related genes by leveraging chromatin loop and accessible chromatin, demonstrating the importance of MpraVAE in genetic and gene discovery for complex traits.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

TAD hierarchy restricts poised LTR activation and loss of TAD hierarchy promotes LTR co-option in cancer

Transposable elements (TEs) are abundant in the human genome, and they provide the sources for genetic and functional diversity. The regulation of TEs expression and their functional consequences in physiological conditions and cancer development remain to be fully elucidated. Previous studies suggested TEs are repressed by DNA methylation and chromatin modifications. The effect of 3D chromatin topology on TE regulation remains elusive. Here, by integrating transcriptome and 3D genome architecture studies, we showed that haploinsufficient loss of NIPBL selectively activates alternative promoters at the long terminal repeats (LTRs) of the TE subclasses. This activation occurs through the reorganization of topologically associating domain (TAD) hierarchical structures and recruitment of proximal enhancers. These observations indicate that TAD hierarchy restricts transcriptional activation of LTRs that already possess open chromatin features. In cancer, perturbation of the hierarchical chromatin topology can lead to co-option of LTRs as functional alternative promoters in a context-dependent manner and drive aberrant transcriptional activation of novel oncogenes and other divergent transcripts. These data uncovered a new layer of regulatory mechanism of TE expression beyond DNA and chromatin modification in human genome. They also posit the TAD hierarchy dysregulation as a novel mechanism for alternative promoter-mediated oncogene activation and transcriptional diversity in cancer, which may be exploited therapeutically.

CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data

Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.

Predicting functional UTR variants by integrating region-specific features

The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.

An Integrated Framework for Analysis and Prediction of Impact of Single Nucleotide Polymorphism Associated with Human Diseases

Single nucleotide polymorphisms are most common type of genetic variation in human genome. Analyzing genetic variants can help us better understand the genetic basis of diseases and develop predictive models which are useful to identify individuals who are at increased risk for certain diseases. Several SNP analysis tools have already been developed. For running these tools, the user needs to collect data from various databases. Secondly, often researchers have to use multiple variant analysis tools for cross validating their results and increase confidence in their findings. Extracting data from multiple databases and running multiple tools at a time, increases complexity and time required for analysis. There are some web-based tools that integrate multiple genetic variant databases and provide variant annotations for a few tools. These approaches have some limitations such as retrieving annotation information, filtering common pathogenic variants. The proposed web-based tool, namely IPSNP: An Integrated Platform for Predicting Impact of SNPs is written in Django which is a python-based framework. It uses RESTful API of MyVariant.info to extract annotation information of variants associated with a given gene, rsID, HGVS format variants specified in a VCF file for 29 tools. The results are in the form of a CSV file of predictions (1) derived from the consensus decision, (2) a file having annotations for the variants associated with the given gene, (3) a file showing variants declared as pathogenic commonly by the selected tools, and (4) a CSV file containing chromosome coordinates based on GRCh37 and GRCh38 genome assemblies, rsIDs and proteomic data, so that users may use tools of their choice and avoiding manual parameter collection for each tool. IPSNP is a valuable resource for researchers and clinicians and it can help to save time and effort in discovering the novel disease-associated variants and the development of personalized treatments.

Potential new cancer biomarkers revealed by quantum chemistry associated with bioinformatics in the study of selectin polymorphisms

Understanding the complex mechanisms involved in diseases caused by or related to important genetic variants has led to the development of clinically useful biomarkers. However, the increasing number of described variants makes it difficult to identify variants worthy of investigation, and poses challenges to their validation. We combined publicly available datasets and open source robust bioinformatics tools with molecular quantum chemistry methods to investigate the involvement of selectins, important molecules in the cell adhesion process that play a fundamental role in the cancer metastasis process. We applied this strategy to investigate single nucleotide variants (SNPs) in the intronic and UTR regions and missense SNPs with amino acid changes in the SELL, SELP, SELE, and SELPLG genes. We then focused on thyroid cancer, seeking these SNPs potential to identify biomarkers for susceptibility, diagnosis, prognosis, and therapeutic targets. We demonstrated that SELL gene polymorphisms rs2229569, rs1131498, rs4987360, rs4987301 and rs2205849; SELE gene polymorphisms rs1534904 and rs5368; rs3917777, rs2205894 and rs2205893 of SELP gene; and rs7138370, rs7300972 and rs2228315 variants of SELPLG gene may produce important alterations in the DNA structure and consequent changes in the morphology and function of the corresponding proteins. In conclusion, we developed a strategy that may save valuable time and resources in future investigations, as we were able to provide a solid foundation for the selection of selectin gene variants that may become important biomarkers and deserve further investigation in cancer patients. Large-scale clinical studies in different ethnic populations and laboratory experiments are needed to validate our results.

CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data

Variants in cis-regulatory elements link the noncoding genome to human brain pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS) employs both whole-genome sequencing and user-provided functional data to enhance noncoding variant analysis, with a faster and more efficient execution of the CWAS workflow. Here, we used single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type specific enhancers and promoters. Examining autism spectrum disorder whole-genome sequencing data (n = 7,280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer's disease whole-genome sequencing data (n = 1,087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus's utility in genomic disorders and scalability for processing large-scale whole-genome sequencing data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants

Massively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results.

Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk

Whole-genome sequencing (WGS) studies of autism spectrum disorder (ASD) have demonstrated the roles of rare promoter de novo variants (DNVs). However, most promoter DNVs in ASD are not located immediately upstream of known ASD genes. In this study analyzing WGS data of 5,044 ASD probands, 4,095 unaffected siblings, and their parents, we show that promoter DNVs within topologically associating domains (TADs) containing ASD genes are significantly and specifically associated with ASD. An analysis considering TADs as functional units identified specific TADs enriched for promoter DNVs in ASD and indicated that common variants in these regions also confer ASD heritability. Experimental validation using human induced pluripotent stem cells (iPSCs) showed that likely deleterious promoter DNVs in ASD can influence multiple genes within the same TAD, resulting in overall dysregulation of ASD-associated genes. These results highlight the importance of TADs and gene-regulatory mechanisms in better understanding the genetic architecture of ASD.

Three linked variants have opposing regulatory effects on isovaleryl-CoA dehydrogenase gene expression

While genome-wide association studies (GWAS) and positive selection scans identify genomic loci driving human phenotypic diversity, functional validation is required to discover the variant(s) responsible. We dissected the IVD gene locus-which encodes the isovaleryl-CoA dehydrogenase enzyme-implicated by selection statistics, multiple GWAS, and clinical genetics as important to function and fitness. We combined luciferase assays, CRISPR/Cas9 genome-editing, massively parallel reporter assays (MPRA), and a deletion tiling MPRA strategy across regulatory loci. We identified three regulatory variants, including an indel, that may underpin GWAS signals for pulmonary fibrosis and testosterone, and that are linked on a positively selected haplotype in the Japanese population. These regulatory variants exhibit synergistic and opposing effects on IVD expression experimentally. Alleles at these variants lie on a haplotype tagged by the variant most strongly associated with IVD expression and metabolites, but with no functional evidence itself. This work demonstrates how comprehensive functional investigation and multiple technologies are needed to discover the true genetic drivers of phenotypic diversity.

VarCards2: an integrated genetic and clinical database for ACMG-AMP variant-interpretation guidelines in the human whole genome

VarCards, an online database, combines comprehensive variant- and gene-level annotation data to streamline genetic counselling for coding variants. Recognising the increasing clinical relevance of non-coding variations, there has been an accelerated development of bioinformatics tools dedicated to interpreting non-coding variations, including single-nucleotide variants and copy number variations. Regrettably, most tools remain as either locally installed databases or command-line tools dispersed across diverse online platforms. Such a landscape poses inconveniences and challenges for genetic counsellors seeking to utilise these resources without advanced bioinformatics expertise. Consequently, we developed VarCards2, which incorporates nearly nine billion artificially generated single-nucleotide variants (including those from mitochondrial DNA) and compiles vital annotation information for genetic counselling based on ACMG-AMP variant-interpretation guidelines. These annotations include (I) functional effects; (II) minor allele frequencies; (III) comprehensive function and pathogenicity predictions covering all potential variants, such as non-synonymous substitutions, non-canonical splicing variants, and non-coding variations and (IV) gene-level information. Furthermore, VarCards2 incorporates 368 820 266 documented short insertions and deletions and 2 773 555 documented copy number variations, complemented by their corresponding annotation and prediction tools. In conclusion, VarCards2, by integrating over 150 variant- and gene-level annotation sources, significantly enhances the efficiency of genetic counselling and can be freely accessed at http://www.genemed.tech/varcards2/.

JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

Leveraging Tissue-Specific Enhancer-Target Gene Regulatory Networks Identifies Enhancer Somatic Mutations That Functionally Impact Lung Cancer

Enhancers are noncoding regulatory DNA regions that modulate the transcription of target genes, often over large distances along with the genomic sequence. Enhancer alterations have been associated with various pathological conditions, including cancer. However, the identification and characterization of somatic mutations in noncoding regulatory regions with a functional effect on tumorigenesis and prognosis remain a major challenge. Here, we present a strategy for detecting and characterizing enhancer mutations in a genome-wide analysis of patient cohorts, across three lung cancer subtypes. Lung tissue-specific enhancers were defined by integrating experimental data and public epigenomic profiles, and the genome-wide enhancer-target gene regulatory network of lung cells was constructed by integrating chromatin three-dimensional architecture data. Lung cancers possessed a similar mutation burden at tissue-specific enhancers and exons but with differences in their mutation signatures. Functionally relevant alterations were prioritized on the basis of the pathway-level integration of the effect of a mutation and the frequency of mutations on individual enhancers. The genes enriched for mutated enhancers converged on the regulation of key biological processes and pathways relevant to tumor biology. Recurrent mutations in individual enhancers also affected the expression of target genes, with potential relevance for patient prognosis. Together, these findings show that noncoding regulatory mutations have a potential relevance for cancer pathogenesis and can be exploited for patient classification.

SIGNIFICANCE

Mapping enhancer-target gene regulatory interactions and analyzing enhancer mutations at the level of their target genes and pathways reveal convergence of recurrent enhancer mutations on biological processes involved in tumorigenesis and prognosis.

Functional analysis of recurrent CDC20 promoter variants in human melanoma

Small nucleotide variants in non-coding regions of the genome can alter transcriptional regulation, leading to changes in gene expression which can activate oncogenic gene regulatory networks. Melanoma is heavily burdened by non-coding variants, representing over 99% of total genetic variation, including the well-characterized TERT promoter mutation. However, the compendium of regulatory non-coding variants is likely still functionally under-characterized. We developed a pipeline to identify hotspots, i.e. recurrently mutated regions, in melanoma containing putatively functional non-coding somatic variants that are located within predicted melanoma-specific regulatory regions. We identified hundreds of statistically significant hotspots, including the hotspot containing the TERT promoter variants, and focused on a hotspot in the promoter of CDC20. We found that variants in the promoter of CDC20, which putatively disrupt an ETS motif, lead to lower transcriptional activity in reporter assays. Using CRISPR/Cas9, we generated an indel in the CDC20 promoter in human A375 melanoma cell lines and observed decreased expression of CDC20, changes in migration capabilities, increased growth of xenografts, and an altered transcriptional state previously associated with a more proliferative and less migratory state. Overall, our analysis prioritized several recurrent functional non-coding variants that, through downregulation of CDC20, led to perturbation of key melanoma phenotypes.

Genetic subtype-guided immunochemotherapy in diffuse large B cell lymphoma: The randomized GUIDANCE-01 trial

We report the results of GUIDANCE-01 (NCT04025593), a randomized, phase II trial of R-CHOP alone or combined with targeted agents (R-CHOP-X) guided by genetic subtyping of newly diagnosed, intermediate-risk, or high-risk diffuse large B cell lymphoma (DLBCL). A total of 128 patients were randomized 1:1 to receive R-CHOP-X or R-CHOP. The study achieved the primary endpoint, showing significantly higher complete response rate with R-CHOP-X than R-CHOP (88% vs. 66%, p = 0.003), with overall response rate of 92% vs. 73% (p = 0.005). Two-year progression-free survival rates were 88% vs. 63% (p < 0.001), and 2-year overall survival rates were 94% vs. 77% (p = 0.001). Meanwhile, post hoc RNA-sequencing validated our simplified genetic subtyping algorithm and previously established lymphoma microenvironment subtypes. Our findings highlight the efficacy and safety of R-CHOP-X, a mechanism-based tailored therapy, which dually targeted genetic and microenvironmental alterations in patients with newly diagnosed DLBCL.

Multimodal learning of noncoding variant effects using genome sequence and chromatin structure

MOTIVATION

A growing amount of noncoding genetic variants, including single-nucleotide polymorphisms, are found to be associated with complex human traits and diseases. Their mechanistic interpretation is relatively limited and can use the help from computational prediction of their effects on epigenetic profiles. However, current models often focus on local, 1D genome sequence determinants and disregard global, 3D chromatin structure that critically affects epigenetic events.

RESULTS

We find that noncoding variants of unexpected high similarity in epigenetic profiles, with regards to their relatively low similarity in local sequences, can be largely attributed to their proximity in chromatin structure. Accordingly, we have developed a multimodal deep learning scheme that incorporates both data of 1D genome sequence and 3D chromatin structure for predicting noncoding variant effects. Specifically, we have integrated convolutional and recurrent neural networks for sequence embedding and graph neural networks for structure embedding despite the resolution gap between the two types of data, while utilizing recent DNA language models. Numerical results show that our models outperform competing sequence-only models in predicting epigenetic profiles and their use of long-range interactions complement sequence-only models in extracting regulatory motifs. They prove to be excellent predictors for noncoding variant effects in gene expression and pathogenicity, whether in unsupervised "zero-shot" learning or supervised "few-shot" learning.

AVAILABILITY AND IMPLEMENTATION

Codes and data can be accessed at https://github.com/Shen-Lab/ncVarPred-1D3D and https://zenodo.org/record/7975777.

A Combination of BRAF and EZH1/SPOP/ZNF148 Three-Gene Mutational Classifier Improves Benign Call Rate in Indeterminate Thyroid Nodules

Reliable preoperative diagnosis of thyroid nodules remained challenging because of the inconclusiveness of fine-needle aspiration (FNA) cytology. In the present study, 583 formalin-fixed paraffin embedded (FFPE) thyroid nodule tissues and 161 FNA specimens were enrolled retrospectively. Then BRAF V600E, EZH1 Q571R, SPOP P94R, and ZNF148 mutations among these samples were identified using Sanger sequencing. Based on this four-gene genomic classifier, we proposed a two-step modality to diagnose thyroid nodules to differentiate benign and malignant thyroid nodules. In the FFPE group, thyroid cancers were effectively diagnosed in 37.7% (220/583) of neoplasms by the primary BRAF V600E testing, and 15.7% (57/363) of thyroid nodules could be further determined as benign by subsequent EZH1 Q571R, SPOP P94R, and ZNF148 (we called them "ESZ") mutation testing. In the FNA group, 161 BRAF wild-type specimens were classified according to The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC). A total of 7 mutated samples fell within Bethesda categories III-IV, and the mutation rate of "ESZ" in Bethesda III-IV categories was 8.4%. The two-step genomic classifier could further improve thyroid nodule diagnosis, which may inform more optimal patient management.

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Background and methods

Disease risk prediction based on DNA sequence and transcriptional profile can improve disease screening, prevention, and potential therapeutic approaches by revealing contributing genetic factors and altered regulatory networks. Despite identifying many disease-associated DNA variants through genome-wide association studies, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. We previously reported that non-coding variations disrupting cis-overlapping motifs (CisOMs) of opposing transcription factors significantly affect enhancer activity. We designed in vitro experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on common target gene expression.

Results

Analyzing publicly available ChIP-seq data for P53 and cMYC in human embryonic stem cells and mouse embryonic cells showed that ~ 344-366 genomic regions are co-occupied by P53 and cMYC. We identified, on average, two CisOMs per region, suggesting that co-occupancy is evolutionarily conserved in vertebrates. Our data showed that treating U2OS cells with doxorubicin increased P53 protein level while reducing cMYC level. In contrast, no change in protein levels was observed in Raji cells. ChIP-seq analysis illustrated that 16-922 genomic regions were co-occupied by P53 and cMYC before and after treatment, and substitutions of cMYC signals by P53 were detected after doxorubicin treatment in U2OS. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data. We utilized a computational motif-matching approach to determine that changes in predicted P53 binding affinity by DNA variations in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data in U2OS and Raji, and expression of target genes from the GTEx portal.

Conclusions

We found a significant correlation between change in motif-predicted cMYC binding affinity by SNPs in CisOMs and altered gene expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases.

Learning vector quantized representation for cancer subtypes identification

BACKGROUND AND OBJECTIVE

Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.

METHODS

This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input.

RESULTS

Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems.

CONCLUSION

Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481-0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984-0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837-0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766-0.5188). We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Novel correlative analysis identifies multiple genomic variations impacting ASD with macrocephaly

Autism spectrum disorders (ASD) display both phenotypic and genetic heterogeneity, impeding the understanding of ASD and development of effective means of diagnosis and potential treatments. Genes affected by genomic variations for ASD converge in dozens of gene ontologies (GOs), but the relationship between the variations at the GO level have not been well elucidated. In the current study, multiple types of genomic variations were mapped to GOs and correlations among GOs were measured in ASD and control samples. Several ASD-unique GO correlations were found, suggesting the importance of co-occurrence of genomic variations in genes from different functional categories in ASD etiology. Combined with experimental data, several variations related to WNT signaling, neuron development, synapse morphology/function and organ morphogenesis were found to be important for ASD with macrocephaly, and novel co-occurrence patterns of them in ASD patients were found. Furthermore, we applied this gene ontology correlation analysis method to find genomic variations that contribute to ASD etiology in combination with changes in gene expression and transcription factor binding, providing novel insights into ASD with macrocephaly and a new methodology for the analysis of genomic variation.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Jasmine and Iris: population-scale structural variant comparison and analysis

The availability of long reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine and Iris ( https://github.com/mkirsche/Jasmine/ ), for fast and accurate SV refinement, comparison and population analysis. Using an SV proximity graph, Jasmine outperforms six widely used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than fivefold, and reveals a set of high-confidence de novo SVs confirmed by multiple technologies. We also present a unified callset of 122,813 SVs and 82,379 indels from 31 samples of diverse ancestry sequenced with long reads. We genotype these variants in 1,317 samples from the 1000 Genomes Project and the Genotype-Tissue Expression project with DNA and RNA-sequencing data and assess their widespread impact on gene expression, including within medically relevant genes.

Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

A Clinician's Guide to Bioinformatics for Next-Generation Sequencing

Next-generation sequencing (NGS) technologies are high-throughput methods for DNA sequencing and have become a widely adopted tool in cancer research. The sheer amount and variety of data generated by NGS assays require sophisticated computational methods and bioinformatics expertise. In this review, we provide background details of NGS technology and basic bioinformatics concepts for the clinician investigator interested in cancer research applications, with a focus on DNA-based approaches. We introduce the general principles of presequencing library preparation, postsequencing alignment, and variant calling. We also highlight the common variant annotations and NGS applications for other molecular data types. Finally, we briefly discuss the revealed utility of NGS methods in NSCLC research and study design considerations for research studies that aim to leverage NGS technologies for clinical care.

A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.

FAVOR: functional annotation of variants online resource and annotator for variation across the human genome

Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.

Investigation of Rare Non-Coding Variants in Familial Multiple Myeloma

Multiple myeloma (MM) is a plasma cell malignancy whereby a single clone of plasma cells over-propagates in the bone marrow, resulting in the increased production of monoclonal immunoglobulin. While the complex genetic architecture of MM is well characterized, much less is known about germline variants predisposing to MM. Genome-wide sequencing approaches in MM families have started to identify rare high-penetrance coding risk alleles. In addition, genome-wide association studies have discovered several common low-penetrance risk alleles, which are mainly located in the non-coding genome. Here, we further explored the genetic basis in familial MM within the non-coding genome in whole-genome sequencing data. We prioritized and characterized 150 upstream, 5' untranslated region (UTR) and 3' UTR variants from 14 MM families, including 20 top-scoring variants. These variants confirmed previously implicated biological pathways in MM development. Most importantly, protein network and pathway enrichment analyses also identified 10 genes involved in mitogen-activated protein kinase (MAPK) signaling pathways, which have previously been established as important MM pathways.

Integrative Meta-Analysis of Huntington's Disease Transcriptome Landscape

Huntington's disease (HD) is a neurodegenerative disorder with autosomal dominant inheritance caused by glutamine expansion in the Huntingtin gene (HTT). Striatal projection neurons (SPNs) in HD are more vulnerable to cell death. The executive striatal population is directly connected with the Brodmann Area (BA9), which is mainly involved in motor functions. Analyzing the disease samples from BA9 from the SRA database provides insights related to neuron degeneration, which helps to identify a promising therapeutic strategy. Most gene expression studies examine the changes in expression and associated biological functions. In this study, we elucidate the relationship between variants and their effect on gene/downstream transcript expression. We computed gene and transcript abundance and identified variants from RNA-seq data using various pipelines. We predicted the effect of genome-wide association studies (GWAS)/novel variants on regulatory functions. We found that many variants affect the histone acetylation pattern in HD, thereby perturbing the transcription factor networks. Interestingly, some variants affect miRNA binding as well as their downstream gene expression. Tissue-specific network analysis showed that mitochondrial, neuroinflammation, vasculature, and angiogenesis-related genes are disrupted in HD. From this integrative omics analysis, we propose that abnormal neuroinflammation acts as a two-edged sword that indirectly affects the vasculature and associated energy metabolism. Rehabilitation of blood-brain barrier functionality and energy metabolism may secure the neuron from cell death.

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome

MOTIVATION

Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease.

RESULTS

By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lichen-lab/DeepPerVar.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.

GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

A database of 5305 healthy Korean individuals reveals genetic and clinical implications for an East Asian population

Despite substantial advances in disease genetics, studies to date have largely focused on individuals of European descent. This limits further discoveries of novel functional genetic variants in other ethnic groups. To alleviate the paucity of East Asian population genome resources, we established the Korean Variant Archive 2 (KOVA 2), which is composed of 1896 whole-genome sequences and 3409 whole-exome sequences from healthy individuals of Korean ethnicity. This is the largest genome database from the ethnic Korean population to date, surpassing the 1909 Korean individuals deposited in gnomAD. The variants in KOVA 2 displayed all the known genetic features of those from previous genome databases, and we compiled data from Korean-specific runs of homozygosity, positively selected intervals, and structural variants. In doing so, we found loci, such as the loci of ADH1A/1B and UHRF1BP1, that are strongly selected in the Korean population relative to other East Asian populations. Our analysis of allele ages revealed a correlation between variant functionality and evolutionary age. The data can be browsed and downloaded from a public website ( https://www.kobic.re.kr/kova/ ). We anticipate that KOVA 2 will serve as a valuable resource for genetic studies involving East Asian populations.

Prediction of Regulatory SNPs in Putative Minor Genes of the Neuro-Cardiovascular Variant in Fabry Reveals Insights into Autophagy/Apoptosis and Fibrosis

Even though a mutation in monogenic diseases leads to a “classic” manifestation, many disorders exhibit great clinical variability that could be due to modifying genes also called minor genes. Fabry disease (FD) is an X-linked inborn error resulting from the deficient or absent activity of alpha-galactosidase A (α-GAL) enzyme, that leads to deposits of globotriaosylceramide. With our proprietary software SNPclinic v.1.0, we analyzed 110 single nucleotide polymorphisms (SNPs) in the proximal promoter of 14 genes that could modify the FD phenotype FD. We found seven regulatory-SNP (rSNPs) in three genes (IL10, TGFB1 and EDN1) in five cell lines relevant to FD (Cardiac myocytes and fibroblasts, Astrocytes-cerebellar, endothelial cells and T helper cells 1-TH1). Each SNP was confirmed as a true rSNP in public eQTL databases, and additional software suggested the prediction of variants. The two proposed rSNPs in IL10, could explain components for the regulation of active B cells that influence the fibrosis process. The three predicted rSNPs in TGFB1, could act in apoptosis-autophagy regulation. The two putative rSNPs in EDN1, putatively regulate chronic inflammation. The seven rSNPs described here could act to modulate Fabry’s clinical phenotype so we propose that IL10, TGFB1 and EDN1 be considered minor genes in FD.

"Demystifying non-coding GWAS variants: an overview of computational tools and methods."

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

scEpiLock: A Weakly Supervised Learning Framework for cis-Regulatory Element Localization and Variant Impact Quantification for Single-Cell Epigenetic Data

Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock’s object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer’s disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies.

PTBP2 - a gene with relevance for both Anorexia nervosa and body weight regulation

Genetic factors are relevant for both eating disorders and body weight regulation. A recent genome-wide association study (GWAS) for anorexia nervosa (AN) detected eight genome-wide significant chromosomal loci. One of these loci, rs10747478, was also genome-wide and significantly associated with body mass index (BMI). The nearest coding gene is the Polypyrimidine Tract Binding Protein 2 gene (PTBP2). To detect mutations in PTBP2, Sanger sequencing of the coding region was performed in 192 female patients with AN (acute or recovered) and 191 children or adolescents with (extreme) obesity. Twenty-five variants were identified. Twenty-three of these were predicted to be pathogenic or functionally relevant in at least one in silico tool. Two novel synonymous variants (p.Ala77Ala and p.Asp195Asp), one intronic SNP (rs188987764), and the intronic deletion (rs561340981) located in the highly conserved region of PTBP2 may have functional consequences. Ten of 20 genes interacting with PTBP2 were studied for their impact on body weight regulation based on either previous functional studies or GWAS hits for body weight or BMI. In a GWAS for BMI (Pulit et al. 2018), the number of genome-wide significant associations at the PTBP2 locus was different between males (60 variants) and females (two variants, one of these also significant in males). More than 65% of these 61 variants showed differences in the effect size pertaining to BMI between sexes (absolute value of Z-score >2, two-sided p < 0.05). One LD block overlapping 5'UTR and all coding regions of PTBP2 comprises 56 significant variants in males. The analysis based on sex-stratified BMI GWAS summary statistics implies that PTBP2 may have a more pronounced effect on body weight regulation in males than in females.

Evaluation of cfDNA as an early detection assay for dense tissue breast cancer

A cell-free DNA (cfDNA) assay would be a promising approach to early cancer diagnosis, especially for patients with dense tissues. Consistent cfDNA signatures have been observed for many carcinogens. Recently, investigations of cfDNA as a reliable early detection bioassay have presented a powerful opportunity for detecting dense tissue screening complications early. We performed a prospective study to evaluate the potential of characterizing cfDNA as a central element in the early detection of dense tissue breast cancer (BC). Plasma samples were collected from 32 consenting subjects with dense tissue and positive mammograms, 20 with positive biopsies and 12 with negative biopsies. After screening and before biopsy, cfDNA was extracted, and whole-genome next-generation sequencing (NGS) was performed on all samples. Copy number alteration (CNA) and single nucleotide polymorphism (SNP)/insertion/deletion (Indel) analyses were performed to characterize cfDNA. In the positive-positive subjects (cases), a total of 5 CNAs overlapped with 5 previously reported BC-related oncogenes (KSR2, MAP2K4, MSI2, CANT1 and MSI2). In addition, 1 SNP was detected in KMT2C, a BC oncogene, and 9 others were detected in or near 10 genes (SERAC1, DAGLB, MACF1, NVL, FBXW4, FANK1, KCTD4, CAVIN1; ATP6V0A1 and ZBTB20-AS1) previously associated with non-BC cancers. For the positive–negative subjects (screening), 3 CNAs were detected in BC genes (ACVR2A, CUL3 and PIK3R1), and 5 SNPs were identified in 6 non-BC cancer genes (SNIP1, TBC1D10B, PANK1, PRKCA and RUNX2; SUPT3H). This study presents evidence of the potential of using cfDNA somatic variants as dense tissue BC biomarkers from a noninvasive liquid bioassay for early cancer detection.

MiRLog and dbmiR: prioritization and functional annotation tools to study human microRNA sequence variants

The recent identification of noncoding variants with pathogenic effects suggests that these variations could underlie a significant number of undiagnosed cases. Several computational methods have been developed to predict the functional impact of noncoding variants, but they exhibit only partial concordance and are not integrated with functional annotation resources, making the interpretation of these variants still challenging. MicroRNAs (miRNAs) are small noncoding RNA molecules that act as fine regulators of gene expression and play crucial functions in several biological processes, such as cell proliferation and differentiation. An increasing number of studies demonstrate a significant impact of miRNA single nucleotide variants (SNVs) both in Mendelian diseases and complex traits. To predict the functional effect of miRNA SNVs, we implemented a new meta‐predictor, MiRLog, and we integrated it into a comprehensive database, dbmiR, which includes a precompiled list of all possible miRNA allelic SNVs, providing their biological annotations at nucleotide and miRNA levels. MiRLog and dbmiR were used to explore the genetic variability of miRNAs in 15,708 human genomes included in the gnomAD project, finding several ultra‐rare SNVs with a potentially deleterious effect on miRNA biogenesis and function representing putative contributors to human phenotypes.

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotating biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpret non-coding regions. Here, we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only two self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of the unlabelled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against the fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based DNA language model. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.