dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs

Integrative analysis of KCNQ1 variants reveals molecular mechanisms of type 1 long QT syndrome pathogenesis

Loss-of-function (LOF) pathogenic variants in KCNQ1 encoding a cardiac potassium channel predispose to sudden cardiac death in type 1 congenital long QT syndrome (LQT1). To determine the spectrum of molecular mechanisms responsible for this life-threatening condition, we used an integrative approach to determine the biophysical, functional, and trafficking properties of 61 KCNQ1 variants distributed throughout all domains of the channel. Impaired trafficking to the plasma membrane was the most common cause of LOF across all channel domains, often but not always coinciding with protein instability. However, many LOF variants, particularly in transmembrane domains, trafficked normally, but when coexpressed with KCNE1 exhibited impaired conductance, altered voltage dependence, or abnormal gating kinetics, highlighting diverse pathogenic mechanisms. This indicates a need for personalized treatment approaches for LQT1. Use of our data to benchmark variant pathogenicity prediction methods demonstrated that prediction accuracy depends on the exact mechanism of pathogenicity associated with a given variant.

Whole-genome sequencing reveals the impact of lipid pathway and APOE genotype on brain amyloidosis

Amyloid-PET imaging tracks the accumulation of amyloid beta (Aβ) deposits in the brain. Amyloid plaques accumulation may begin 10 to 20 years before the individual experiences clinical symptoms associated with Alzheimer's diseases (ad). Recent large-scale genome-wide association studies reported common risk factors associated with brain amyloidosis, suggesting that this endophenotype is driven by genetic variants. However, these loci pinpoint to large genomic regions and the functional variants remain to be identified. To identify new risk factors associated with brain amyloid deposition, we performed whole-genome sequencing on a large cohort of European descent individuals with amyloid PET imaging data (n = 1,888). Gene-based analysis for coding variants was performed using SKAT-O for amyloid PET as a quantitative endophenotype that identified genome-wide significant association for APOE (P = 2.45 × 10-10), and 26 new candidate genes with suggestive significance association (P < 5. 0 × 10-03) including SCN7A (P = 7.31 × 10-05), SH3GL1 (P = 7.56 × 10-04), and MFSD12 (P = 8.51 × 10-04). Enrichment analysis highlighted the lipid binding pathways as associated with Aβ deposition in brain driven by PITPNM3 (P = 4.27 × 10-03), APOE (P = 2.45 × 10-10), AP2A2 (P = 1.06 × 10-03), and SH3GL1 (P = 7.56 × 10-04). Overall, our data strongly support a connection between lipid metabolism and the deposition of Aβ in the brain. Our study illuminates promising avenues for therapeutic interventions targeting lipid metabolism to address brain amyloidosis.

Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole-genome analysis by short-read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare-disease cohort of 98 samples from 41 families, using nanopore sequencing, achieving per sample ∼36× average coverage and 32-kb read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease-associated genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including structural variants (SVs) and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants and could be used to distinguish postzygotic mosaic variants from prezygotic de novos. Diagnostic variants were established by LRS in 11 probands, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models

Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs. The representations derived from protein language models (PLMs) from the structural neighbourhood of mutated sites were utilized to train ZFP-CanPred for differentiating between cancer-causing and neutral mutations. ZFP-CanPred, achieved a superior performance with an accuracy of 0.72, F1-score of 0.79, and area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.74, on an independent test set. In a comparative analysis against 11 existing prediction tools using a curated dataset of 331 mutations, ZFP-CanPred demonstrated the highest AU-ROC of 0.74, outperforming both generic and cancer-specific methods. The model's balanced performance across specificity and sensitivity addresses a significant limitation of current methodologies. The source code and other related files are available on GitHub at https://github.com/amitphogat/ZFP-CanPred.git. We envisage that the present study contributes to understand the oncogenic processes and developing targeted therapeutic strategies.

Meta-analysis reveals transcription factors and DNA binding domain variants associated with congenital heart defect and orofacial cleft

Many structural birth defect patients lack genetic diagnoses because there are many disease genes as yet to be discovered. We applied a gene burden test incorporating de novo predicted-loss-of-function (pLoF) and likely damaging missense variants together with inherited pLoF variants to a collection of congenital heart defect (CHD) and orofacial cleft (OC) parent-offspring trio cohorts (n = 3,835 and 1,844, respectively). We identified 17 novel candidate CHD genes and 10 novel candidate OC genes, of which many were known developmental disorder genes. Shorter genes were more powered in a " de novo only" analysis as compared to analysis including inherited pLoF variants. TFs were enriched among the significant genes; 14 and 8 transcription factor (TF) genes showed significant variant burden for CHD and OC, respectively. In total, 30 affected children had a de novo missense variant in a DNA binding domain of a known CHD, OC, and other developmental disorder TF genes. Our results suggest candidate pathogenic variants in CHD and OC and their potentially pleiotropic effects in other developmental disorders.

The membrane transporter SLC25A48 enables transport of choline into human mitochondria

Choline has important physiological functions as a precursor for essential cell components, signaling molecules, phospholipids, and the neurotransmitter acetylcholine. Choline is a water-soluble charged molecule requiring transport proteins to cross biological membranes. Although transporters continue to be identified, membrane transport of choline is incompletely understood and knowledge about choline transport into intracellular organelles such as mitochondria remains limited. Here we show that SLC25A48 imports choline into human mitochondria. Human loss-of-function mutations in SLC25A48 show impaired choline transport into mitochondria and are associated with elevated urine and plasma choline levels. Thus, our studies may have implications for understanding and treating conditions related to choline metabolism.

Interneuron loss and microglia activation by transcriptome analyses in the basal ganglia of Tourette disorder

BACKGROUND

Tourette disorder is characterized by motor hyperactivity and tics that are believed to originate in basal ganglia. Postmortem immunocytochemical analyses previously revealed decreases in cholinergic, parvalbumin, and somatostatin interneurons (IN) within the caudate/putamen of individuals with TS.

METHODS

We obtained transcriptome and open chromatin datasets by snRNAseq and snATAC-seq, respectively, from caudate/putamen postmortem specimens of 6 adult TS and 6 matched normal control (NC). Differential gene expression and differential chromatin accessibility analyses were performed in identified cell types.

RESULTS

The data reproduced the known cellular composition of the human striatum, including a majority of medium spiny neurons (MSN) and small populations of GABAergic and cholinergic IN. IN were decreased by ∼50% in TS brains, with no difference in other cell types. Differential gene expression analysis suggested that mitochondrial oxidative metabolism in MSN and synaptic adhesion and function in IN were both decreased in TS subjects, while there was activation of immune response in microglia. Gene expression changes correlated with changes in activity of cis-regulatory elements, suggesting a relationship of transcriptomic and regulatory abnormalities in MSN, OL and AST of TS brains.

CONCLUSIONS

This initial analysis of the TS basal ganglia transcriptome at the single cell level confirms the loss and synaptic dysfunction of basal ganglia IN, consistent with in vivo basal ganglia hyperactivity. In parallel, oxidative metabolism was decreased in MSN and correlated with activation of microglia cells, attributable at least in part to dysregulated activity of putative enhancers, implicating altered epigenomic regulation in TS.

An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

BACKGROUND AND OBJECTIVE

Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).

METHODS

The study used a dataset from 502 HNSC patients, classifying mutations based on 299 known high-confidence cancer driver genes. Missense somatic mutations in driver genes were treated as driver mutations, while non-driver mutations were randomly selected from other genes. Each mutation was annotated with 41 PCSAs. Three machine learning algorithms-logistic regression, random forest, and support vector machine-along with recursive feature elimination, were used to rank these PCSAs. The final ranking of the PCSAs was determined using rank-average-sort and rank-sum-sort methods.

RESULTS

The random forest algorithm emerged as the top performer among the three tested ML algorithms, with an AUC-ROC of 0.89, compared to 0.83 for the other two, in distinguishing pathogenic driver mutations from benign passenger mutations using all 41 PCSAs. The top 11 PCSAs were selected based on the first quintile cut-off from the final rank-sum distribution. Classifiers built using these top 11 PCSAs (DEOGEN2, Integrated_fitCons, MVP, etc.) demonstrated significantly higher performance (p-value < 2.22e-16) compared to those using the remaining 30 PCSAs across all three ML algorithms, in separating pathogenic driver from benign passenger mutations. The top PCSAs demonstrated strong performance on a validation cohort including independent HNSC and other cancer types: breast, lung, and colorectal - reflecting its consistency, robustness and generalizability.

CONCLUSIONS

The ensemble machine learning approach effectively evaluates the performance of PCSAs based on their ability to differentiate pathogenic drivers from benign passenger mutations in HNSC and other cancer types. Notably, some well-known PCSAs performed poorly, underscoring the importance of data-driven selection over relying solely on popularity.

Potentially actionable molecular alterations in particular related to poor oncologic outcomes in salivary gland carcinomas

AIM

The study was designed to evaluate molecular alterations, relevant to the prognosis and personalized therapy of salivary gland cancers (SGCs).

MATERIALS AND METHODS

DNA was extracted from archival tissue of 40 patients with various SGCs subtypes. A targeted next-generation sequencing (NGS) panel was used for the identification of small-scale mutations, focal and chromosomal arm-level copy number changes. The final analysis included selected genes with potential actionable aberrations for targeted therapies and outcome predictions in 37 tumours' samples.

RESULTS

The follow-up of the SGCs study cohort revealed disease recurrence or metastasis in 19 patients and indicated poor individual outcomes. The mean disease-free survival (DFS) within the poor outcome group was 2.4 years, and the overall survival (OS) was 5.4 years. The DFS and OS of the remaining 18 patients with favourable outcomes were 8.3 years. The genes most frequently affected with aberrations were NF1 (n = 9, 24%) and TP53 (n = 8, 22%), with increased occurrence observed in the poor outcome group: NF1 (n = 6, 32%) and TP53 (n = 6, 32%). CDKN2A biallelic deletion was the most common copy number variation (n = 5), and was detected in 4 cases with identified disease relapse. TERT promoter mutation and amplification were found in myoepithelial carcinoma. A p.Ile35Thr mutation was discovered in CTNNB1 in two cases of adenoid cystic carcinoma. ERBB2 alterations were remarkable for SDC ex PA. Furthermore, TP53 mutation was established as a relevant negative prognostic factor for overall survival (p = 0,04). The analysis revealed potentially actionable genes in detected alterations in: MECA 100% (1/1), SDC 100% (7/7), AD 92% (11/12), Ca ex PA 82% (18/22), MECA 65% (20/31), AdCC 64% (9/14) and AcCC 0% (0/1).

CONCLUSIONS

SGCs are a heterogeneous group of malignancies with distinct molecular landscape that characterized by poor prognosis and inadequate treatment options. Nonstandard strategies might be beneficial for patients who suffer from salivary gland cancers. Wider utilization of NGS analysis may increase the opportunity for patients with those rare cancers to receive more precise, personalized therapy.

PGxDB: an interactive web-platform for pharmacogenomics research

Pharmacogenomics, the study of how an individual's genetic makeup influences their response to medications, is a rapidly evolving field with significant implications for personalized medicine. As researchers and healthcare professionals face challenges in exploring the intricate relationships between genetic profiles and therapeutic outcomes, the demand for effective and user-friendly tools to access and analyze genetic data related to drug responses continues to grow. To address these challenges, we have developed PGxDB, an interactive, web-based platform specifically designed for comprehensive pharmacogenomics research. PGxDB enables the analysis across a wide range of genetic and drug response data types - informing cell-based validations and translational treatment strategies. We developed a pipeline that uniquely combines the relationship between medications indexed with Anatomical Therapeutic Chemical (ATC) codes with molecular target profiles with their genetic variability and predicted variant effects. This enables scientists from diverse backgrounds - including molecular scientists and clinicians - to link genetic variability to curated drug response variability and investigate indication or treatment associations in a single resource. With PGxDB, we aim to catalyze innovations in pharmacogenomics research, empower drug discovery, support clinical decision-making, and pave the way for more effective treatment regimens. PGxDB is a freely accessible database available at https://pgx-db.org/.

stSNV: a comprehensive resource of SNVs in spatial transcriptome

Single nucleotide variants (SNVs), as important components of genetic variation, affect gene expression, function and phenotype. Mining and summarizing the spatial distribution of SNVs in diseased and normal tissues for a better understanding of their characteristics and potential roles in cell-lineage determination, aging, or disease occurrence is significant. Herein, we have developed a comprehensive spatial mutation resource stSNV (http://bio-bigdata.hrbmu.edu.cn/stSNV/index.jsp), which provides an atlas of spatial SNVs in major diseased and normal tissues of human and mouse. stSNV documents 42 202 spatial mutated genes involving 898 908 SNVs called from 730 067 spots within 450 slices from 19 diseased and 28 normal tissues. Importantly, potential characteristics of SNVs are explored and provided by analyzing the perturbation of the SNVs to gene expression, spatial communication, biological function, region-specific mutated genes, spatial mutant signatures, SNV-cell co-localization and mutation core region. All these spatial mutation data and in-depth analyses have been integrated into a user-friendly interface, visualized through intuitive tables and various image formats. Flexible tools are developed to explore co-localization among clusters, genes, cell types and SNVs in the same slice. In summary, stSNV as a valuable resource helps to dissect intra-tissue genetic heterogeneity and lays the groundwork for understanding the SNVs' biological regulatory mechanisms.

Meta-EA: a gene-specific combination of available computational tools for predicting missense variant effects

Computational methods for estimating missense variant impact suffer from inconsistent performance across genes, which poses a major challenge for their reliable use in clinical practice. While ensemble scores leverage multiple prediction methods to enhance consistency, the overrepresentation of certain genes in the training data can bias their outcomes. To address this critical limitation, we propose a gene-specific ensemble framework trained on reference computational annotations rather than on clinical or experimental data. Accordingly, we generate Meta-EA ensemble scores that achieve comparable performance to the top individual predicting method for each gene set. Incorporating the effects of splicing and the allele frequency of human polymorphisms further enhances the performance of Meta-EA, achieving an area under the receiver operating characteristic curve of 0.97 for both gene-balanced and imbalanced clinical assessments. In conclusion, this work leverages the wealth of existing variant impact prediction approaches to generate improved estimations for clinical interpretation.

Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma

Pediatric low-grade gliomas (pLGGs) exhibit heterogeneous prognoses and variable responses to treatment, leading to tumor progression and adverse outcomes in cases where complete resection is unachievable. Early prediction of treatment responsiveness and suitability for immunotherapy has the potential to improve clinical management and outcomes. Here, we present a radiogenomic analysis of pLGGs, integrating MRI and RNA sequencing data. We identify three immunologically distinct clusters, with one group characterized by increased immune activity and poorer prognosis, indicating potential benefit from immunotherapies. We develop a radiomic signature that predicts these immune profiles with over 80% accuracy. Furthermore, our clinicoradiomic model predicts progression-free survival and correlates with treatment response. We also identify genetic variants and transcriptomic pathways associated with progression risk, highlighting links to tumor growth and immune response. This radiogenomic study in pLGGs provides a framework for the identification of high-risk patients who may benefit from targeted therapies.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases.

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review)

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer‑specific biomarkers. The availability of high‑throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation‑based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease‑free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo‑ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease‑free survival of patients with breast cancer through efficient medicine.

Cross-Sectional Analysis of Outer Retinal Tubulation in Inherited Retinal Diseases: A Multicenter Study

PURPOSE

This study aims to explore genetic variants that potentially lead to outer retinal tubulation (ORT), estimate the prevalence of ORT in these candidate genes, and investigate the clinical etiology of ORT in patients with inherited retinal diseases (IRDs), with respect to each gene.

DESIGN

Retrospective cohort study.

METHODS

A retrospective cross-sectional review was conducted on 565 patients with molecular diagnoses of IRD, confirming the presence of ORT as noted in each patient's respective spectral-domain optical coherence tomography (SD-OCT) imaging. Using SD-OCT imaging, the presence of ORT was analyzed in relation to specific genetic variants and phenotypic characteristics. Outcomes included the observed ORT frequencies across 2 gene-specific cohorts: non-retinal pigment epithelium (RPE)-specific genes, and RPE-specific genes; and to investigate the analogous characteristics caused by variants in these genes.

RESULTS

Among the 565 patients included in this study, 104 exhibited ORT on SD-OCT. We observed ORT frequencies among the following genes from our patient cohort: 100% (23/23) for CHM, 100% (2/2) for PNPLA6, 100% (4/4) for RCBTB1, 100% for mtDNA [100% (4/4) for MT-TL1 and 100% (1/1) for mtDNA deletion], 100% (1/1) for OAT, 95.2% (20/21) for CYP4V2, 72.7% (8/11) for CHM female carriers, 66.7% (2/3) for C1QTNF5, 57.1% (8/14) for PROM1, 53.8% (7/13) for PRPH2, 42.9% (3/7) for CERKL, 28.6% (2/7) for CDHR1, 20% (1/5) for RPE65, 4% (18/445) for ABCA4. In contrast, ORT was not observed in any patients with photoreceptor-specific gene variants, such as RHO (n = 13), USH2A (n = 118), EYS (n = 70), PDE6B (n = 10), PDE6A (n = 4), and others.

CONCLUSIONS

These results illustrate a compelling association between the presence of ORT and IRDs caused by variants in RPE-specific genes, as well as non-RPE-specific genes. In contrast, IRDs caused by photoreceptor-specific genes are typically not associated with ORT occurrence. Further analysis revealed that ORT tends to manifest in IRDs with milder intraretinal pigment migration (IPM), a finding that is typically associated with RPE-specific genes. These findings regarding ORT, genetic factors, atrophic patterns in the fundus, and IPM provide valuable insight into the complex etiology of IRDs. Future prospective studies are needed to further explore the association and underlying mechanisms of ORT in these contexts.

VAREANT: a bioinformatics application for gene variant reduction and annotation

Motivation

The analysis of high-quality genomic variant data may offer a more complete understanding of the human genome, enabling researchers to identify novel biomarkers, stratify patients based on disease risk factors, and decipher underlying biological pathways. Although the availability of genomic data has sharply increased in recent years, the accessibility of bioinformatic tools to aid in its preparation is still lacking. Limitations with processing genomic data primarily include its large volume, associated computational and storage costs, and difficulty in identifying targeted and relevant information.

Results

We present VAREANT, an accessible and configurable bioinformatic application to support the preparation of variant data into a usable analysis-ready format. VAREANT is comprised of three standalone modules: (i) Pre-processing, (ii) Variant Annotation, (iii) AI/ML Data Preparation. Pre-processing supports the fine-grained filtering of complex variant datasets to eliminate extraneous data. Variant Annotation allows for the addition of variant metadata from the latest public annotation databases for subsequent analysis and interpretation. AI/ML Data Preparation supports the user in creating AI/ML-ready datasets suitable for immediate analysis with minimal pre-processing required. We have successfully tested and validated our tool on numerous variable-sized datasets and implemented VAREANT in two case studies involving patients with cardiovascular diseases.

Availability and implementation

The open-source code of VAREANT is available at GitHub: https://github.com/drzeeshanahmed/Gene_VAREANT.

Quantitative natural history modeling of HPDL-related disease based on cross-sectional data reveals genotype-phenotype correlations

PURPOSE

Biallelic HPDL variants have been identified as the cause of a progressive childhood-onset movement disorder, with a broad clinical spectrum from severe neurodevelopmental disorder to juvenile-onset pure hereditary spastic paraplegia type 83. This study aims at delineating the geno- and phenotypic spectra of patients with HPDL-related disease, quantitatively modeling the natural history, and uncovering genotype-phenotype associations.

METHODS

A cross-sectional analysis of 90 published and 1 novel case was performed, using a Human-Phenotype-Ontology-based approach. Unsupervised phenotypic clustering was used alongside in silico analyses to identify distinct patient subgroups.

RESULTS

The study models the natural history of the HPDL-related disease in a global cohort, clarifying the molecular and phenotypic spectrum and identifying 3 distinct subgroups characterized by differences in onset, clinical trajectories, and survival. It establishes genotype-phenotype associations, showing that the presence of moderately pathogenic missense variants in 1 allele leads to a milder, spastic paraplegic phenotype with later disease onset, whereas biallelic, highly pathogenic missense or truncating variants are associated with a more severe phenotype and reduced life span.

CONCLUSION

Quantitative and unbiased natural history modeling in HPDL-related disease reveals significant genotype-phenotype associations, providing a foundation for variant interpretation, anticipatory guidance, and choice of outcome measures in future prospective and functional studies.

Integrating rare pathogenic variant prioritization with gene-based association analysis to identify novel genes and relevant multimodal traits for Alzheimer's disease

INTRODUCTION

Increasing evidence has highlighted rare variants in Alzheimer's disease (AD). However, insufficient sample sizes, especially in underrepresented ethnic groups, hinder their investigation. Additionally, their impact on endophenotypes remains largely unexplored.

METHODS

We prioritized rare likely-deleterious variants based on whole-genome sequencing data from a Chinese AD cohort (n = 988). Gene-based optimal sequence kernel association tests were conducted between AD cases and normal controls to identify AD-related genes. Network clustering, endophenotype association, and cellular experiments were conducted to evaluate their functional consequences.

RESULTS

We identified 11 novel AD candidate genes, which captured AD-related pathways and enhanced AD risk prediction performance. Key genes (RABEP1, VIPR1, RPL3L, and CABIN1) were linked to cognitive decline and brain atrophy. Experiments showed RABEP1 p.R845W inducing endocytosis dysregulation and exacerbating toxic amyloid β accumulation, underscoring its therapeutic potential.

DISCUSSION

Our findings highlighted the contributions of rare variants to AD and provided novel insights into AD therapeutics.

HIGHLIGHTS

Identified 11 novel AD candidate genes in a Chinese AD cohort. Correlated candidate genes with AD-related cognitive and brain imaging traits. Indicated RABEP1 p.R845W as a critical AD contributor in the endocytic pathway.

GPTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in G Protein-Coupled Receptors

Accurately predicting mutations in G protein-coupled receptors (GPCRs) is critical for advancing disease diagnosis and drug discovery. In response to this imperative, GPTrans has emerged as a highly accurate predictor of disease-related mutations in GPCRs. The core innovation of GPTrans resides in the design of a novel feature extraction network, that is capable of integrating features from both wildtype and mutant protein variant sites, utilizing multifeature connections within a transformer framework to ensure comprehensive feature extraction. A key aspect of GPTrans's effectiveness is our introduction of an innovative deep feature integration strategy, which merges embeddings and class tokens from multiple protein language models, including evolutionary scale modeling and ProtTrans, thus shedding light on the biochemical properties of proteins. Leveraging transformer components and a self-attention mechanism, GPTrans captures higher-level representations of protein features. Employing both wildtype and mutation site information for feature fusion not only enriches the predictive feature set but also avoids the common issue of overestimation associated with sequence-based predictions. This approach distinguishes GPTrans, enabling it to significantly outperform existing methods. Our evaluations across diverse GPCR data sets, including ClinVar and MutHTP, demonstrate GPTrans's superior performance, with average AUC values of 0.874 and 0.590 in 10-fold cross-validation. Notably, compared to the AlphaMissense method, GPTrans exhibited a remarkable 38.03% improvement in accuracy when predicting disease-associated mutations in the MutHTP data set. A thorough analysis of the predicted results further validates the model's effectiveness. The source code, data sets, and prediction results for GPTrans are available for academic use at https://github.com/EduardWang/GPTrans.

Next-generation sequencing analysis with a population-specific human reference genome

Next-generation sequencing (NGS) has become widely available and is routinely used in basic research and clinical practice. The reference genome sequence is an essential resource for NGS analysis, and several population-specific reference genomes have recently been constructed to provide a choice to deal with the vast genetic diversity of human samples. However, resources supporting population-specific references are insufficient, and it is burdensome to perform analysis using these reference genomes. Here, we constructed a set of resources to support NGS analysis using the Japanese reference genome, JG. We created resources for variant calling, variant effect prediction, gene and repeat element annotations, read mappability and RNA-seq analysis. We also provide a resource for reference coordinate conversion for further annotation enrichment. We then provide a variant calling protocol with JG. Our resources provide a guide to prepare sufficient resources for the use of population-specific reference genomes and can facilitate the migration of reference genomes.

A structure-based in silico analysis of the Kell blood group system

Kell is one of the most complex blood group systems, with a highly polymorphic genetic background. Extensive allelic variations in the KEL gene affect the encoded erythrocyte surface protein Kell. Genetic variants causing aberrant splicing, premature termination of protein translation, or specific amino acid exchanges lead to a variety of different phenotypes with altered Kell expression levels or changes in the antigenic properties of the Kell protein. Using an in silico structural model of the Kell protein, we analyzed the biophysical and structural context of all full-length Kell variants of known phenotype. The results provided insights regarding the 3D co-localization of antigenic Kell variants and led us to suggest several conformational epitopes on the Kell protein surface. We found a number of correlations between the properties of individual genetic variants in the Kell protein and their respective serological phenotypes, which we used as a search filter to predict potentially new immunogenic Kell variants from an in-house whole exome sequencing dataset of 19,772 exomes. Our analysis workflow and results aid blood group serologists in predicting whether a newly identified Kell genetic variant may result in a specific phenotype.

The genetic landscape of autism spectrum disorder in an ancestrally diverse cohort

Autism spectrum disorder (ASD) comprises neurodevelopmental disorders with wide variability in genetic causes and phenotypes, making it challenging to pinpoint causal genes. We performed whole exome sequencing on a modest, ancestrally diverse cohort of 195 families, including 754 individuals (222 with ASD), and identified 38,834 novel private variants. In 68 individuals with ASD (~30%), we identified 92 potentially pathogenic variants in 73 known genes, including BCORL1, CDKL5, CHAMP1, KAT6A, MECP2, and SETD1B. Additionally, we identified 158 potentially pathogenic variants in 120 candidate genes, including DLG3, GABRQ, KALRN, KCTD16, and SLC8A3. We also found 34 copy number variants in 31 individuals overlapping known ASD loci. Our work expands the catalog of ASD genetics by identifying hundreds of variants across diverse ancestral backgrounds, highlighting convergence on nervous system development and signal transduction. These findings provide insights into the genetic underpinnings of ASD and inform molecular diagnosis and potential therapeutic targets.

Using AI-predicted protein structures as a reference to predict loss-of-function activity in tumor suppressor breast cancer genes

Background

The loss-of-function (LOF) classification of most missense variants in tumor suppressor breast cancer genes BRCA1, BRCA2, PALB2, and RAD51C remains unclassified and confounds clinical actionability. Classifying these variants is challenging due to their rarity, leading clinicians to rely on in silico predictive methods. Protein stability changes are associated with function, making stability predictors valuable. Stability predictions upon missense variant perturbations require high-resolution protein structures. However, the availability of these high-resolution structures is lacking. This study explores using generative AI to predict high-resolution protein structures, which can then be analyzed with in silico protein stability prediction methods to assess LOF activity in ordered regions of the protein. This study also determines the appropriate in silico protein stability and dedicated in silico missense prediction methods in dbNSFP v4.7 database to predict LOF activity in ordered regions of these four genes. Functional classifications from homology recombination DNA repair (HDR) assays and variant classifications from the ClinVar database provide a reliable dataset for evaluating the performance of these in silico prediction methods.

Results

Complex AlphaFold2 structures of the BRCA1-C terminal (BRCT) domain and the DNA-binding (DB) domain of BRCA2, analyzed using protein stability tool FoldX predicts LOF activity from missense variants significantly better than experimentally-derived structures in ordered regions. The BRCT domain achieved an Area Under the Curve (AUC)= 0.861 (95 % CI:0.858-0.863) and AUC= 0.842 (95 % CI:0.840-0.845), while the DB domain achieved an AUC= 0.836 (95 % CI:0.8322-0.841), compared to AUC= 0.847 (95 % CI:0.844-0.850) and AUC= 0.835 (95 % CI:0.832-0.837) from the BRCT domain, and AUC= 0.830 (95 % CI:0.821-0.8320) from the DB domain from experimentally-derived structures. Protein stability does not predict LOF activity from missense variants better than dedicated in silico missense predictors. Overall, we find that AlphaMissense ranks highly, with an average AUC= 0.890 (95 % CI 0.886-0.895) from ordered regions across these four cancer genes, compared to all other in silico missense predictors present in the dbNSFP database.

Conclusions

The study reveals that generative AI protein predicted structures can outperform experimentally-derived structures in evaluating LOF activity from predicted protein stability in ordered regions of genes BRCA1, BRCA2, PALB2 and RAD51C. The study also highlights the predictive performance of AlphaMissense as the premier in silico missense prediction method to predict LOF activity from missense variants in these four tumor suppressor breast cancer genes. The code for this study can be downloaded for free on GitHub (https://github.com/rohandavidg/CarePred).

Discovery of myosin light chain kinase gene variant in a patient with tetralogy of Fallot suffering aortic dissection: Implications for pathogenesis and the role of family and population screening

Background

Thoracic aortic dissection (TAD) is an uncommon complication in patients with Tetralogy of Fallot (TOF). Information concerning risk factors for TAD in patients with TOF is very limited.

Methods

We report a case of Stanford type A TAD in a female patient with previously repaired TOF. Whole exome sequencing (WES); Novogene UK, Agilent V6 capture kit, Illumina HiSeq 100x depth) was performed to identify genetic variants in genes known to be associated with TAD. A systematic literature review was performed in the NCBI PubMed database to identify case reports of TAD in patients with TOF.

Results

The patient was a 31-year-old female who developed Stanford type A aortic dissection having had TOF repair at the age of four years. The thoracic aorta was only minimally dilated (sinus of Valsalva 43 mm) on clinical review 16 months prior to TAD. Of note the patient had completed pregnancy 5 months prior to the dissection. There were no other high-risk features predisposing to TAD. WES identified rare genetic variant in a gene previously associated with TAD: MYLK (p.Arg1405His). The literature review identified nine other case reports of TAD in patients with TOF. The reported patients, had no clinical characteristics that distinguished them from the wider population of patients with TOF.

Conclusions

The presence of a rare genetic variant in MYLK is a plausible explanation for the clinical presentation. The variant will need further verification to confirm pathogenicity. Pathogenic MYLK variants have been previously reported in context of dissection with minimally dilated aortas.

Insights into the role of JAK2-I724T variant in myeloproliferative neoplasms from a unique cohort of New Zealand patients

OBJECTIVES

This study aimed to compile bioinformatic and experimental information for JAK2 missense variants previously reported in myeloproliferative neoplasms (MPN) and determine if germline JAK2-I724T, recently found to be common in New Zealand Polynesians, associates with MPN.

METHODS

For all JAK2 variants found in the literature, gnomAD_exome allele frequencies were extracted and REVEL scores were calculated using the dbNSFP database. We investigated the prevalence of JAK2-I724T in a cohort of 111 New Zealand MPN patients using a TaqMan assay, examined its allelic co-occurrence with JAK2-V617F using Oxford Nanopore sequencing, and modelled the impact of I724T on JAK2 using I-Mutant and ChimeraX software.

RESULTS

Several non-V617F JAK2 variants previously reported in MPN had REVEL scores greater than 0.5, suggesting pathogenicity. JAK2-I724T (REVEL score 0.753) was more common in New Zealand Polynesian MPN patients (n = 2/27; 7.4%) than in other New Zealand patients (n = 0/84; 0%) but less common than expected for healthy Polynesians (n = 56/377; 14.9%). Patients carrying I724T (n = 2), one with polycythaemia vera and one with essential thrombocythaemia, had high-risk MPN. Both patients with JAK2-I724T were also positive for JAK2-V617F, found on the same allele as I724T, as well as separately. In silico modelling did not identify noticeable structural changes that would give JAK2-I724T a gain-of-function.

CONCLUSION

Several non-canonical JAK2 variants with high REVEL scores have been reported in MPN, highlighting the need to further understand their relationship with disease. The JAK2-I724T variant does not drive MPN, but additional investigations are required to exclude any potential modulatory effect on the MPN phenotype.

Diagnosing Monogenic Stroke at Younger Age

BACKGROUND

An increasing number of monogenic conditions underlying stroke are being identified. We explored the possibilities of increasing the diagnostic yield of monogenic stroke in a population under 56 years of age.

METHODS

Fifty probands ≤55 years at their first stroke episode were characterized clinically and investigated by whole genome sequencing. Probands had one or more of: (1) one or more first to second degree relatives with stroke under 60 years or same stroke-causing condition/disease; (2) no hypertension, hypercholesterolemia, diabetes, heart disease, or smoking; or (3) either multiple stroke episodes or multiple arterial dissections. Variants with minor allele frequency under 0.01, identified by using our stroke gene panels, were assessed. The stroke subtypes, including large artery atherosclerotic, large artery nonatherosclerotic (tortuosity, dolichoectasia, aneurysm, nonatherosclerotic dissection, or occlusion), cerebral small vessel disease, cardioembolic (arrhythmia, heart defect, or cardiomyopathy), coagulation dysfunctions (venous thrombosis, arterial thrombosis, or bleeding tendency), intracerebral hemorrhage, vascular malformations (cavernoma or arteriovenous malformations), metabolic disorders, or cryptogenic embolic, were used for genotype-phenotype correlation. In a final step, we combined genetic and clinical information to determine if the genetic variant likely was the cause of stroke in the patients.

RESULTS

Whole genome sequencing of younger patients with stroke identified 17 clinically matching genetic variants in 15 of 50 (30%) patients, while a stronger clinical correlation with stroke was established in only 6 (12%) of them. Stroke-related genetic variants were identified in 4 of 5 (80%) patients with cardioembolic stroke subtype, 3 of 4 (75%) with intracerebral hemorrhage, 7 of 18 (39%) with cryptogenic embolic stroke, 1 of 6 (17%) with small vessel disease, and 3 of 15 (20%) of patients with nonatherosclerotic large artery stroke, including 1 of 11 (9%) with cervical dissection stroke.

CONCLUSIONS

Careful clinical interpretation of whole genome data using stroke gene panels can detect monogenic causes of early stroke, allowing individualized follow-up and opening new possibilities for potential treatment.

Assessing the efficacy of an innovative diagnostic method for identifying 5 % variants in somatic ctDNA

BACKGROUND

Liquid biopsy is considered a complementary and recently also an alternative method to surgical biopsy. It allows for the acquisition of valuable information regarding the potential presence of tumors, particularly through the analysis of circulating tumor DNA (ctDNA). CtDNA is a fraction of circulating free DNA (cfDNA) that can be extracted from various tissues, with blood being the most readily available.

RESULTS

To maximize the yield of plasma separation, specific Streck tubes are recommended for blood collection. The MagPurix CFC DNA Extraction Kit can be used for cfDNA extraction, and the TWIST Library Preparation protocol can be optimized for further analysis. Next-generation sequencing (NGS) can be employed to compare somatic and germline lineages, enabling the identification of somatic variants with a Variant Allele Frequency (VAF) of 5 % or higher, which are absent in the germline lineage.

CONCLUSION

This analysis helps in the assessment of recurrence, analysis, and monitoring of cancer tissue.

Implementing genomic medicine in clinical practice for adults with undiagnosed rare diseases

The global burden of undiagnosed diseases, particularly in adults, is rising due to their significant socioeconomic impact. To address this, we enrolled 232 adult probands with undiagnosed conditions, utilizing bioinformatics tools for genetic analysis. Alongside exome and genome sequencing, repeat-primed PCR and Cas9-mediated nanopore sequencing were applied to suspected short tandem repeat disorders. Probands were classified into probable genetic (n = 128) or uncertain (n = 104) origins. The study found genetic causes in 66 individuals (28.4%) and non-genetic causes in 12 (5.2%), with a longer diagnostic journey for those in the probable genetic group or with pediatric symptom onset, emphasizing the need for increased efforts in these populations. Genetic diagnoses facilitated effective surveillance, cascade screening, drug repurposing, and pregnancy planning. This study demonstrates that integrating sequencing technologies improves diagnostic accuracy, may shorten the time to diagnosis, and enhances personalized management for adults with undiagnosed diseases.

Functional analysis of G6PD variants associated with low G6PD activity in the All of Us Research Program

The glucose-6-phosphate dehydrogenase (G6PD) enzyme protects red blood cells against oxidative damage. Individuals with G6PD-impairing polymorphisms are at risk of hemolytic anemia from oxidative stressors. Prevention of G6PD deficiency-related hemolytic anemia is achievable by identifying affected individuals through G6PD genetic testing. However, accurately predicting the clinical consequence of G6PD variants is limited by over 800 G6PD variants which remain of uncertain significance (VUS). There also remains inconsistency in which deficiency-causing variants are included in genetic testing arrays: many institutions only test c.202G > A, though dozens of other variants can cause G6PD deficiency. Here, we improve G6PD genotype interpretations using the All of Us Research Program data and a yeast functional assay. We confirm that G6PD coding variants are the main contributor to decreased G6PD activity and that 13% of individuals in the All of Us data with deficiency-causing variants would be missed by only genotyping for c.202G > A. We expand clinical interpretation for G6PD VUS, reporting that c.595A > G ("Dagua" or "Açores") and the novel variant c.430C > G reduce activity sufficiently to lead to G6PD deficiency. We also provide evidence that 5 missense VUS are unlikely to lead to G6PD deficiency, and we applied the new World Health Organization (WHO) guidelines to recommend classifying 2 synonymous variants as WHO Class C. In total, we provide new or updated clinical interpretations for 9 G6PD variants. We anticipate these results will improve the accuracy, and prompt increased use, of G6PD genetic tests through a more complete clinical interpretation of G6PD variants.

Assessment of the functionality and usability of open-source rare variant analysis pipelines

Sequencing of increasingly larger cohorts has revealed many rare variants, presenting an opportunity to further unravel the genetic basis of complex traits. Compared with common variants, rare variants are more complex to analyze. Specialized computational tools for these analyses should be both flexible and user-friendly. However, an overview of the available rare variant analysis pipelines and their functionalities is currently lacking. Here, we provide a systematic review of the currently available rare variant analysis pipelines. We searched MEDLINE and Google Scholar until 27 November 2023, and included open-source rare variant pipelines that accepted genotype data from cohort and case-control studies and group variants into testing units. Eligible pipelines were assessed based on functionality and usability criteria. We identified 17 rare variant pipelines that collectively support various trait types, association tests, testing units, and variant weighting schemes. Currently, no single pipeline can handle all data types in a scalable and flexible manner. We recommend different tools to meet diverse analysis needs. STAARpipeline is suitable for newcomers and common applications owing to its built-in definitions for the testing units. REGENIE is highly scalable, actively maintained, regularly updated, and well documented. Ravages is suitable for analyzing multinomial variables, and OrdinalGWAS is tailored for analyzing ordinal variables. Opportunities remain for developing a user-friendly pipeline that provides high degrees of flexibility and scalability. Such a pipeline would enable researchers to exploit the potential of rare variant analyses to uncover the genetic basis of complex traits.

Molecular and In Silico Analysis of the CHEK2 Gene in Individuals with High Risk of Cancer Predisposition from Türkiye

Background and Objectives:Checkpoint kinase 2 (CHEK2) is a tumor suppressor gene involved in DNA repair and cell cycle regulation. Pathogenic or likely pathogenic (P/LP) variants in CHEK2 are associated with increased cancer risk. Conversely, recent large cohort studies have identified certain variants that, despite being classified as P/LP by in silico analysis, are considered low risk. Thus, the genotype-phenotype correlations of CHEK2 require a better understanding. In this study, we aimed to characterize germline CHEK2 variants from a group of individuals who applied to cancer genetic clinics in the Marmara Region of Türkiye. We also aimed to assess the phenotypic impacts of these variants by using a new score of statistically significant in silico predictors (SSIPs). Methods: We analyzed 1707 individuals with high risk cancer predisposition, focusing on germline CHEK2 variants, using SSIP scores and population-specific data. Results:CHEK2 variants appeared in approximately 8% of cases. The SSIP scores indicated that the missense mutation, p.Arg117Gly, significantly impairs DNA repair. Almost half of the variants had higher allele frequencies than the variants listed in the Genome Aggregation Database (gnomAD), and three variants had significantly higher frequencies compared to the variants listed on the Turkish Variome database (p.Thr476Met, p.Arg137Gln, c.592+3A>T), emphasizing the importance of population-specific data. Conclusions: This comprehensive analysis of CHEK2 variants in the Turkish population provides crucial insights for cancer geneticists and oncologists. Our findings will help to enhance the evaluation and management of cancer predisposition associated with CHEK2 in Türkiye and other regions that have significant Turkish populations.

Genomic and transcriptomic analyses identify distinctive features of triple-negative inflammatory breast cancer

Triple-negative inflammatory breast cancer (TN-IBC) is the most aggressive type of breast cancer, yet its defining genomic, molecular, and immunological features remain largely unknown. In this study, we performed the largest and most comprehensive genomic and transcriptomic analyses of prospectively collected TN-IBC patient samples from a phase II clinical trial (ClinicalTrials.gov, NCT02876107, registered on August 22, 2016) and compared them to similarly analyzed stage III TN-non-IBC patient samples (ClinicalTrials.gov, NCT02276443, registered on October 21, 2014). We found that TN-IBC tumors have distinctive genomic, molecular, and immunological characteristics, including a lower tumor mutation load than TN-non-IBC, and an association of immunosuppressive tumor-infiltrating immune components with an unfavorable response to neoadjuvant chemotherapy. To our knowledge, this is the only study in which TN-IBC and TN-non-IBC samples were collected prospectively. Our analysis improves the understanding of the molecular landscape of the most aggressive subtype of breast cancer. Further studies are needed to discover novel prognostic biomarkers and druggable targets for TN-IBC.

Mapping rare protein-coding variants on multi-organ imaging traits

Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown. Here we present an exome-wide association study that evaluates 596 multi-organ MRI traits across over 50,000 individuals from the UK Biobank. We identified 107 variant-level associations and 224 gene-based burden associations (67 unique gene-trait pairs) across all MRI modalities, including PTEN with total brain volume, TTN with regional peak circumferential strain in the heart left ventricle, and TNFRSF13B with spleen volume. The singleton burden model and AlphaMissense annotations contributed 8 unique gene-trait pairs including the association between an approved drug target gene of KCNA5 and brain functional activity. The identified rare coding signals elucidate some shared genetic regulation across organs, prioritize previously identified GWAS loci, and are enriched for drug targets. Overall, we demonstrate how rare variants enhance our understanding of genetic effects on human organ morphology and function and their connections to complex diseases.

BRCAIndica: a resource for ACMG/AMP classified BRCA1 and BRCA2 variants

As genetic testing becomes increasingly accessible and affordable, the uniform and accurate interpretation of genetic variants becomes essential. The ACMG/AMP joint guidelines provide the basis for systematic and uniform interpretation of pathogenicity of genetic variants. However, the application of these in routine clinical interpretation at-scale has largely been limited by the lack of resources providing harmonized data especially at a population-scale. Here we describe BRCAIndica, a resource for BRCA1 and BRCA2 variants conforming to the ACMG & AMP joint guidelines to aid uniform clinical interpretation of genetic tests with a specific focus on variants reported in the Indian population. We collected and harmonized variants from across several resources including population-scale datasets, literature survey and other variant datasets. We then classified them according to the ACMG/AMP guidelines.We have collected a total of 10,490 unique variants, of which 2261 Pathogenic and 43 Likely Pathogenic variants belong to BRCA1 and 2694 Pathogenic and 20 Likely Pathogenic variants to BRCA2 respectively. BRCAIndica can be accessed at:https://clingen.igib.res.in/brcaindica/ . In conclusion, BRCAIndica is a powerful resource that offers researchers and clinicians with ACMG/AMP annotated BRCA variants.

Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

Gene-based burden tests of rare germline variants identify six cancer susceptibility genes

Discovery of cancer risk variants in the sequence of the germline genome can shed light on carcinogenesis. Here we describe gene burden association analyses, aggregating rare missense and loss of function variants, at 22 cancer sites, including 130,991 cancer cases and 733,486 controls from Iceland, Norway and the United Kingdom. We identified four genes associated with increased cancer risk; the pro-apoptotic BIK for prostate cancer, the autophagy involved ATG12 for colorectal cancer, TG for thyroid cancer and CMTR2 for both lung cancer and cutaneous melanoma. Further, we found genes with rare variants that associate with decreased risk of cancer; AURKB for any cancer, irrespective of site, and PPP1R15A for breast cancer, suggesting that inhibition of PPP1R15A may be a preventive strategy for breast cancer. Our findings pinpoint several new cancer risk genes and emphasize autophagy, apoptosis and cell stress response as a focus point for developing new therapeutics.

Assessment of the evidence yield for the calibrated PP3/BP4 computational recommendations

PURPOSE

To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the ClinGen-calibrated PP3/BP4 computational recommendations.

METHODS

Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools that were able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide.

RESULTS

From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of variants reaching PP3_Strong score thresholds increased approximately 2.6-fold compared with disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting.

CONCLUSION

A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3424 disease-associated genes. Although not the intended use of the recommendations, this was also observed genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as pathogenic or likely pathogenic by ACMG/AMP rules.

Yield of genetic association signals from genomes, exomes and imputation in the UK Biobank

Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies.

VCAT: an integrated variant function annotation tools

The development of sequencing technology has promoted discovery of variants in the human genome. Identifying functions of these variants is important for us to link genotype to phenotype, and to diagnose diseases. However, it usually requires researchers to visit multiple databases. Here, we presented a one-stop webserver for variant function annotation tools (VCAT, https://biomed.nscc-gz.cn/zhaolab/VCAT/ ) that is the first one connecting variant to functions via the epigenome, protein, drug and RNA. VCAT is also the first one to make all annotations visualized in interactive charts or molecular structures. VCAT allows users to upload data in VCF format, and download results via a URL. Moreover, VCAT has annotated a huge number (1,262,041,068) of variants collected from dbSNP, 1000 Genomes projects, gnomAD, ICGC, TCGA, and HPRC Pangenome project. For these variants, users are able to searcher their functions, related diseases and drugs from VCAT. In summary, VCAT provides a one-stop webserver to explore the potential functions of human genomic variants including their relationship with diseases and drugs.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

In silico and biological analyses of missense variants of the human biliary efflux transporter ABCC2: effects of novel rare missense variants

BACKGROUND AND PURPOSE

The ATP-dependent biliary efflux transporter ABCC2, also known as multidrug resistance protein 2 (MRP2), is essential for the cellular disposition and detoxification of various xenobiotics including drugs as well as endogenous metabolites. Common functionally relevant ABCC2 genetic variants significantly alter drug responses and contribute to side effects. The aim of this study was to determine functional consequences of rare variants identified in subjects with European ancestry using in silico tools and in vitro analyses.

EXPERIMENTAL APPROACH

Targeted next-generation sequencing of the ABCC2 gene was used to identify novel variants in European subjects (n = 143). Twenty-six in silico tools were used to predict functional consequences. For biological validation, transport assays were carried out with membrane vesicles prepared from cell lines overexpressing the newly identified ABCC2 variants and estradiol β-glucuronide and carboxydichlorofluorescein as the substrates.

KEY RESULTS

Three novel rare ABCC2 missense variants were identified (W227R, K402T, V489F). Twenty-five in silico tools predicted W227R as damaging and one as potentially damaging. Prediction of functional consequences was not possible for K402T and V489F and for the common linked variants V1188E/C1515Y. Characterisation in vitro showed increased function of W227R, V489F and V1188E/C1515Y for both substrates, whereas K402T function was only increased for carboxydichlorofluorescein.

CONCLUSION AND IMPLICATIONS

In silico tools were unable to accurately predict the substrate-dependent increase in function of ABCC2 missense variants. In vitro biological studies are required to accurately determine functional activity to avoid misleading consequences for drug therapy.

Assessment of ability of AlphaMissense to identify variants affecting susceptibility to common disease

An important issue in the analysis of rare variant association studies is the ability to annotate nonsynonymous variants in terms of their likely importance as affecting protein function. To address this, AlphaMissense was recently released and was shown to have good performance using benchmarks based on variants causing severe disease and on functional assays. Here, we assess the performance of AlphaMissense across 18 genes which had previously demonstrated association between rare coding variants and hyperlipidaemia, hypertension or type 2 diabetes. The strength of evidence in favour of association, expressed as the signed log p value (SLP), was compared between AlphaMissense and 43 other annotation methods. The results demonstrated marked variability between genes regarding the extent to which nonsynonymous variants contributed to evidence for association and also between the performance of different methods of annotating the nonsynonymous variants. Although AlphaMissense produced the highest SLP on average across genes, it produced the maximum SLP for only 4 genes. For some genes, other methods produced a considerably higher SLP and there were examples of genes where AlphaMissense produced no evidence for association while another method performed well. The marked inconsistency across genes means that it is difficult to decide on an optimal method of analysis of sequence data. The fact that different methods perform well for different genes suggests that if one wished to use sequence data for individual risk prediction then gene-specific annotation methods should be used.

Understanding the heterogeneous performance of variant effect predictors across human protein-coding genes

Variant effect predictors (VEPs) are computational tools developed to assess the impacts of genetic mutations, often in terms of likely pathogenicity, employing diverse algorithms and training data. Here, we investigate the performance of 35 VEPs in the discrimination between pathogenic and putatively benign missense variants across 963 human protein-coding genes. We observe considerable gene-level heterogeneity as measured by the widely used area under the receiver operating characteristic curve (AUROC) metric. To investigate the origins of this heterogeneity and the extent to which gene-level VEP performance is predictable, for each VEP, we train random forest models to predict the gene-level AUROC. We find that performance as measured by AUROC is related to factors such as gene function, protein structure, and evolutionary conservation. Notably, intrinsic disorder in proteins emerged as a significant factor influencing apparent VEP performance, often leading to inflated AUROC values due to their enrichment in weakly conserved putatively benign variants. Our results suggest that gene-level features may be useful for identifying genes where VEP predictions are likely to be more or less reliable. However, our work also shows that AUROC, despite being independent of class balance, still has crucial limitations when used for comparing VEP performance across different genes.

Splicing Dysregulation of Non-Canonical GC-5' Splice Sites of Breast Cancer Susceptibility Genes ATM and PALB2

Background/Objectives: The non-canonical GC-5' splice sites (5'ss) are the most common exception (~1%) to the classical GT/AG splicing rule. They constitute weak 5'ss and can be regulated by splicing factors, so they are especially sensitive to genetic variations inducing the misrecognition of their respective exons. We aimed to investigate the GC-5'ss of the breast/ovarian cancer susceptibility genes, ATM (exon 50), BRIP1 (exon 1), and PALB2 (exon 12), and their dysregulation induced by DNA variants. Methods: Splicing assays of the minigenes, mgATM_49-52, mgBRIP1_1-2, and mgPALB2_5-12, were conducted to study the regulation of the indicated GC-5'ss. Results: A functional map of the splicing regulatory elements (SRE) formed by overlapping exonic microdeletions revealed three essential intervals, ATM c.7335_7344del, PALB2 c.3229_3258del, and c.3293_3322del, which are likely targets for spliceogenic SRE-variants. We then selected 14 ATM and 9 PALB2 variants (Hexplorer score < -40) located at these intervals that were assayed in MCF-7 cells. Nine ATM and three PALB2 variants affected splicing, impairing the recognition of exons 50 and 12, respectively. Therefore, these variants likely disrupt the active SREs involved in the inclusion of both exons in the mature mRNA. DeepCLIP predictions suggested the participation of several splicing factors in exon recognition, including SRSF1, SRSF2, and SRSF7, involved in the recognition of other GC sites. The ATM spliceogenic variants c.7336G>T (p.(Glu2446Ter)) and c.7340T>A (p.(Leu2447Ter)) produced significant amounts of full-length transcripts (55-59%), which include premature termination stop codons, so they would inactivate ATM through both splicing disruption and protein truncation mechanisms. Conclusions: ATM exon 50 and PALB2 exon 12 require specific sequences for efficient recognition by the splicing machinery. The mapping of SRE-rich intervals in minigenes is a valuable approach for the identification of spliceogenic variants that outperforms any prediction software. Indeed, 12 spliceogenic SRE-variants were identified in the critical intervals.

Cross-ancestry analysis identifies genes associated with obesity risk and protection

Gene discoveries in obesity have largely been based on European cohorts, leading to an ancestral bias, that limits their generalizability across populations. We performed a gene-based rare variant association study of 721,941 individuals and identified 116 novel BMI-associated genes with consistent effects across ancestries, including 50 risk-conferring and 66 protective genes against obesity. Protective genes such as DCUN1D3 and NEUROD6 had effect sizes comparable to high-risk genes such as MC4R and BSN, and nearly twice that of known protective genes such as GPR75, which, along with five other genes, showed strong European bias. Notably, 82 of the 116 genes showed functional relevance to obesity including adiposity, energy homeostasis, and glucose metabolism. While polygenic risks or an obesogenic lifestyle amplified the effect of 15 genes on BMI, including the combination of low physical activity and MACROD1, 23 genes including VIRMA, AQP3, and PML retained protective effects even at high polygenic scores. Our findings provide further insights into the genetic basis of obesity that is conserved across ancestries and their interactions with obesogenic factors.

Identification of Novel Genetic Risk Variants Associated with Hidradenitis Suppurativa in an Exome Sequencing Cohort of 92,455 Individuals

INTRODUCTION

Hidradenitis suppurativa (HS) is a prevalent and persistent inflammatory skin disorder, lacking a known cure or effective biomarkers for early diagnosis at present. The genetic determinants of HS have not been fully documented, but it is believed to result from a combination of genetic and environmental factors.

METHODS

To identify relevant HS gene variants in sporadic HS patients, this study utilized longitudinal electronic health records (EHRs) and whole-exome sequencing. DNA exome sequencing data from 92,455 participant samples in the MyCode biobank, linked to Geisinger's EHR, were analyzed. This cohort included 1,092 HS cases and 91,363 healthy controls. The MyCode EHR has a median longitudinal follow-up of 15 years per participant, with an average of 87 clinical encounters, 687 laboratory tests, and 7 procedures.

RESULTS

There were 1,092 (901 females and 191 males) participants aged 14-89 years (median 47 years) with HS (L73.2), indicating a 1.18% prevalence and accounting for a 4.7:1 female-to-male ratio among the individuals presenting for clinical care. γ-secretase complex, syndromic, and autoinflammatory gene variants were assessed. Potential pathogenic variants were identified among 66 individuals in the HS genes studied. Molecularly, the estimated HS variant prevalence was 1:1,400 in the cohort, 12.3% of variant carriers had HS diagnosis in EHR.

CONCLUSIONS

Using longitudinal EHR data, genomic screening identified HS-associated gene variants in a defined group of sporadic HS patients to augment the clinical diagnosis, particularly in cases of ambiguity. Based on this study, the field of skin disorders can benefit from a personalized approach to HS diagnosis using large-scale sequencing.

Dominantly acting variants in ATP6V1C1 and ATP6V1B2 cause a multisystem phenotypic spectrum by altering lysosomal and/or autophagosome function

The vacuolar H+-ATPase (V-ATPase) is a functionally conserved multimeric complex localized at the membranes of many organelles where its proton-pumping action is required for proper lumen acidification. The V-ATPase complex is composed of several subunits, some of which have been linked to human disease. We and others previously reported pathogenic dominantly acting variants in ATP6V1B2, the gene encoding the V1B2 subunit, as underlying a clinically variable phenotypic spectrum including dominant deafness-onychodystrophy (DDOD) syndrome, Zimmermann-Laband syndrome (ZLS), and deafness, onychodystrophy, osteodystrophy, intellectual disability, and seizures (DOORS) syndrome. Here, we report on an individual with features fitting DOORS syndrome caused by dysregulated ATP6V1C1 function, expand the clinical features associated with ATP6V1B2 pathogenic variants, and provide evidence that these ATP6V1C1/ATP6V1B2 amino acid substitutions result in a gain-of-function mechanism upregulating V-ATPase function that drives increased lysosomal acidification. We demonstrate a disruptive effect of these ATP6V1B2/ATP6V1C1 variants on lysosomal morphology, localization, and function, resulting in a defective autophagic flux and accumulation of lysosomal substrates. We also show that the upregulated V-ATPase function affects cilium biogenesis, further documenting pleiotropy. This work identifies ATP6V1C1 as a new gene associated with a neurodevelopmental phenotype resembling DOORS syndrome, documents the occurrence of a phenotypic continuum between ZLS, and DDOD and DOORS syndromes, and classify these conditions as lysosomal disorders.

Polygenic risk and rare variant gene clustering enhance cancer risk stratification for breast and prostate cancers

Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.