Variant Annotation Research Articles

Novel Variants Linked to the Prodromal Stage of Parkinson's Disease (PD) Patients.

The symptoms of most neurodegenerative diseases, including Parkinson's disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Data were obtained from the Parkinson's Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Genetic biomarkers are crucial for the early detection of Parkinson's disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.

Gene polymorphisms of TACR1 serve as the potential pharmacogenetic predictors of response to the neurokinin-1 receptor antagonist-based antiemetic regimens: a candidate-gene association study in breast cancer patients.

The current candidate gene association study aims to investigate tag SNPs from the TACR1 gene as pharmacogenetic predictors of response to the antiemetic guidelines-recommended, NK-1 receptor antagonist-based, triple antiemetic regimens. A set of eighteen tag SNPs of TACR1 were genotyped in breast cancer patients receiving anthracycline and cyclophosphamide (with/without docetaxel) applying real-time PCR-HRMA. Data analysis for 121 ultimately enrolled patients was initiated by defining haplotype blocks using PHASE v.2.1. The association of each tag SNP and haplotype alleles with failure to achieve the defined antiemetic regimen efficacy endpoints was tested using PLINK (v.1.9 and v.1.07, respectively) based on the logistic regression, adjusting for the previously known chemotherapy-induced nausea and vomiting (CINV) prognostic factors. All reported p-values were corrected using the permutation test (n = 100,000). Four variants of rs881, rs17010730, rs727156, and rs3755462, as well as haplotypes containing the mentioned variants, were significantly associated with failure to achieve at least one of the defined efficacy endpoints. Variant annotation via in-silico studies revealed that the non-seed sequence variant, rs881, is located in the miRNA (hsa-miR-613) binding site. The other three variants or a variant in complete linkage disequilibrium with them overlap a region of high H3K9ac-promoter-like signature or regions of high enhancer-like signature in the brain or gastrointestinal tissue. Playing an essential role in regulating TACR1 expression, gene polymorphisms of TACR1 serve as the potential pharmacogenetic predictors of response to the NK-1 receptor antagonist-based, triple antiemetic regimens. If clinically approved, modifying the NK-1 receptor antagonist dose leads to better management of CINV in risk-allele carriers.

A novel framework for functional annotation of variants of uncertain significance in ID/ASD risk gene CC2D1A.

Intellectual disability (ID) and autism spectrum disorder (ASD) are genetically heterogeneous with hundreds of identified risk genes, most affecting only a few patients. Novel missense variants in these genes are being discovered as clinical exome sequencing is now routinely integrated into diagnosis, yet most of them are annotated as variants of uncertain significance (VUS). VUSs are a major roadblock in using patient genetics to inform clinical action. We developed a framework to characterize VUSs in Coiled-coil and C2 domain containing 1A (CC2D1A), a gene causing autosomal recessive ID with comorbid ASD in 40% of cases. We analyzed seven VUSs (p.Pro319Leu, p.Ser327Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, p.Arg886His and p.Glu910Lys) from four cases of individuals with ID and ASD. Variants were cloned and overexpressed in HEK293 individually and in their respective heterozygous combination. CC2D1A is a signaling scaffold that positively regulates PKA-CREB signaling by repressing phosphodiesterase 4D (PDE4D) to prevent cAMP degradation. After testing multiple parameters including direct interaction between PDE4D and CC2D1A, cAMP levels and CREB activation, we found that the most sensitive readout was CREB transcriptional activity using a luciferase assay. Compared to WT CC2D1A, five VUSs (p.Pro319Leu, p.Gly441Val, p.Val449Met, p.Thr580Ile, and p.Arg886His) led to significantly blunted response to forskolin induced CREB activation. This luciferase assay approach can be scaled up to annotate ~150 CC2D1A VUSs that are currently listed in ClinVar. Since CREB activation is a common denominator for multiple ASD/ID genes, our paradigm can also be adapted for their VUSs.

Genome-wide characterization of the mutational landscape of proliferative verrucous leukoplakia

ObjectiveProliferative verrucous leukoplakia (PVL) is a rare but highly aggressive variant of oral leukoplakia that almost inevitably progresses to oral squamous cell carcinoma (OSCC). The aims of this study were to perform whole exome sequencing of a cohort of patients diagnosed with PVL and identify potential mutational profiles and pathways in this disorder. Study DesignA total of 12 oral cavity mucosal biopsies from 6 patients with oral lesions clinically compatible with PVL were used. Of these, 9 were diagnosed as dysplasia, 1 OSCC and 2 hyperkeratosis/hyperplasia. Exome sequencing used the Ion AmpliSeq Exome platform. Ion Reporter software was used for variant calling, annotation and filtering. Analysis and visualization of somatic mutations was carried out using the MAFtools R package. ResultsFollowing exome sequencing and mutational profiling we analyzed the profiles for cancer associated genes and signatures. Genes previously associated with OSCC including HYDIN, MUC16, MAML3, CDKN2A, FAT1 and CASP8 were mutated in multiple samples. Several DNA damage repair genes including PARP1 were mutated in PVL samples. NOTCH and Hippo pathways were the most frequently impacted by mutation. ConclusionThis genome wide characterization of premalignant PVL identifies both known and potentially novel oncogenic mechanisms in this disorder.

Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery.

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

Variant effect predictors: a systematic review and practical guide.

Large-scale association analyses using whole-genome sequence data have become feasible, but understanding the functional impacts of these associations remains challenging. Although many tools are available to predict the functional impacts of genetic variants, it is unclear which tool should be used in practice. This work provides a practical guide to assist in selecting appropriate tools for variant annotation. We conducted a MEDLINE search up to November 10, 2023, and included tools that are applicable to a broad range of phenotypes, can be used locally, and have been recently updated. Tools were categorized based on the types of variants they accept and the functional impacts they predict. Sequence Ontology terms were used for standardization. We identified 118 databases and software packages, encompassing 36 variant types and 161 functional impacts. Combining only three tools, namely SnpEff, FAVOR, and SparkINFERNO, allows predicting 99 (61%) distinct functional impacts. Thirty-seven tools predict 89 functional impacts that are not supported by any other tool, while 75 tools predict pathogenicity and can be used within the ACMG/AMP guidelines in a clinical context. We launched a website allowing researchers to select tools based on desired variants and impacts. In summary, more than 100 tools are already available to predict approximately 160 functional impacts. About 60% of the functional impacts can be predicted by the combination of three tools. Unexpectedly, recent tools do not predict more impacts than older ones. Future research should allow predicting the functionality of so far unsupported variant types, such as gene fusions.URL: https://cardio-care.shinyapps.io/VEP_Finder/ .Registration: OSF Registries on November 10, 2023, https://osf.io/s2gct .

Pharmacogenomics-based systematic review of coronary artery disease based on personalized medicine procedure

BackgroundCoronary artery disease (CAD) is the most common reason for mortality and disability-adjusted life years (DALYs) lost globally. This study aimed to suggest a new gene list for the treatment of CAD by a systematic review of bioinformatics analyses of pharmacogenomics impacts of potential genes and variants. MethodsPubMed search was filtered by the title including Coronary Artery Disease during 2020–2023. To find the genes with pharmacogenetic impact on the CAD, additional filtrations were considered according to the variant annotations. Protein-Protein Interactions (PPIs), Gene-miRNA Interactions (GMIs), Protein-Drug Interactions (PDIs), and variant annotation assessments (VAAs) performed by STRING-MODEL (ver. 12), Cytoscape (ver. 3.10), miRTargetLink.2., NetworkAnalyst (ver 0.3.0), and PharmGKB. ResultsResults revealed 5618 publications, 1290 papers were qualified, and finally, 650 papers were included. 4608 protein-coding genes were extracted, among them, 1432 unique genes were distinguished and 530 evidence-based repeated genes remained. 71 genes showed a pharmacogenetics-related variant annotation in at least (entirely 6331 annotations). Variant annotation assessment (VAA) showed 532 potential variants for the final report, and finally, the concluding PGs list represented 175 variants. Based on the function and MAF, 57 nonsynonymous variants of 29 Pharmacogenomics-related genes were associated with CAD. ConclusionConclusively, evaluating circulating miR33a in individuals’ plasma with CAD, and genotyping of rs2230806, rs2230808, rs2487032, rs12003906, rs2472507, rs2515629, and rs4149297 (ABCA1 variants) lead to precisely prescribing of well-known drugs. Also, the findings of this review can be used in both whole-genome sequencing (WGS) and whole-exome sequencing (WES) analysis in the prognosis and diagnosis of CAD.

A cloud-based precision oncology framework for whole genome sequence analysis

Cancer is one of the wide-ranging diseases which have a high mortality rate impacting globally. This scenario can be switched by early detection and correct precision treatment, a major concern for cancer patients. Clinicians can figure out the best-suited treatments for cancer patients by analyzing the patient's genome, which will treat the patient well and minimize the chances of side effects as well. Therefore, we have developed a fast, robust, and efficient solution as our precision oncology framework based on the whole genome sequencing of the individual's DNA. This platform can perform the entire genomic analysis, starting from the quality assessment of the input file to the variant annotation and functional prediction, followed by a certain level of interpretation. This analysis helps in the molecular profiling of the tumors for the identification of the targetable alterations. It takes in FASTQ or BAM file as an input and provides us with two output reports: a primary report, which consists of the patients' details, a summary of the analysis, and a secondary report, which is an elaborated report comprised of numerous results obtained from the analysis such as base changes, codon changes, amino acid changes, TMB analysis, MSI analysis, the variant frequency with its effects and impacts, affected biomarkers, etc. This framework can be effectively utilized for cancer treatment guidance, identification and validation of novel biomarkers, oncology research & development, genomic analysis, and gene manipulation.

Abstract 4884: Evaluating the utility of in silico variant annotation tools for cancer driver detection

Abstract In cancer genomics, precise variant annotation is crucial for clinical decisions, drug development, and research. The burgeoning genomic data offers an opportunity to use data-driven approaches to generate knowledge that supports clinical decisions. Particularly machine learning (ML) and Deep learning (DL), are becoming essential, as their application is fast, scalable, simple to implement, and generates reproducible results. The methods ranging from simple sequence-based alignment scoring to advanced algorithms like Logistic regression, Support vector machine, and Recurrent neural networks, have been employed by multiple variant annotation tools. This study compares in-silico methods available in Ensembl's Variant Effect Predictor (VEP) using a test dataset with COSMIC annotations. ML/DL success relies on robust training sets with comprehensive genomic variants features, including effects on transcription/translation, genomic context, annotation resources, in silico pathogenicity predictions, and population allele frequency. The training set, composed of known benign or pathogenic variants, serves as a reference for these algorithms to classify new and unseen variants. Our analysis reveals a limited concordance between the prediction algorithms. Despite comparable true/false positives/negatives, discrepancies persist in variant classification. Certain algorithms exhibit a propensity to over or under-call deleterious mutations. Some demonstrate a tendency to classify random variants in non-cancer genes as deleterious. Challenges include the absence of consensus on informative features, diverse training datasets, and restriction to well annotated proteins/transcripts. Balancing the sensitivity and false positives in detecting cancer drivers is crucial. Integrating individual prediction scores with ML algorithms enhances tool performance but comes with risk of error propagation, and limited accuracy. The study emphasises the need for context-specific variant classification tools, as many variants' impacts are cancer-type specific, and some may drive disease synergistically. Existing tools, designed for a "one variant - one score approach," struggle to capture complex associations, especially those dependent on changes in the tumour microenvironment. Highlighting areas for improvement, the study addresses the "black box" problem in decision processes. While limited interpretability might not hinder practical applications, tools should evolve to assess more complex associations guided by biology. Formal consensus, reference training datasets, and standards are deemed essential for developing next-generation tools. The envisioned context-dependent tools aim to streamline feature complexity, thereby mitigating the black box problem and advancing the accuracy and interpretability of cancer variant annotation. Citation Format: Madhumita, Zbyslaw Sondka, Jon Teague. Evaluating the utility of in silico variant annotation tools for cancer driver detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4884.

Abstract 3886: Analysis of clonal heterogeneity within paired primary and metastatic tumor samples of patients with solid tumors and implications for neoantigen-based personalized cancer vaccines

Abstract Background: Neoantigen-based personalized cancer vaccines carry significant promise in treating solid malignancies. However, there are uncertainties regarding the choice between the primary or the metastatic tumor for neoantigen prediction in individual patients. Here, we conducted a thorough examination of somatic variations in 676 patients who had paired primary and metastatic solid tumors. Methods: Patients were enrolled in the Total Cancer Care protocol (NCT03977402) to which patients provided an IRB-approved written informed consent within the Oncology Research Information Exchange Network (ORIEN). Whole-exome sequencing of 756 primary and metastatic tumor pairs was performed (N = 676 patients). These included Genitourinary (n=83), Gynecological (n=97), Gastrointestinal (n=213), Thoracic (n=33), Cutaneous (n=24), Breast (n=108), Endometrial (n=49), Sarcoma (n=35), Head-and-Neck (n=106) and others (n=8). The data was analyzed through the ORIEN AVATAR Molecular Analysis Pipeline for somatic mutation variant detection and variant annotation. In this analysis, we focused on somatic events that result in an in-frame alteration (such as missense, in-frame deletion and in-frame insertion) and out-of-frame protein-altering mutations (such as frameshifts, de novo start, out-of-frame, and nonstop gain). Clonal population structure was determined based on pyclone-vi. Results: For in-frame events, bladder cancer, melanoma, and gynecological cancers shared close to 75% of the mutations between paired primary and metastatic cases. In contrast, sarcoma and thyroid cancer had a low overlap (~ 25%) of variants. For out-of-frame events, these events tend to have a lower proportion of shared somatic variants between primary and metastasis than in-frame variants. Oncogenic drivers (e.g., BRAF V600E, KRAS G12A, and TP53 loss-of-function) were highly likely to be present in both paired primary and metastatic tumors. Next, we performed additional analysis on evolutionary selection of protein-coding variants via dN/dS calculation. We found no significant global shift in dN/dS ratio between paired primary and metastatic tumors across malignancies. However, we found increased selection of protein-coding variants in brain and liver metastatic sites, which correlated with increased homologous recombination deficiency within these sites. Conclusions: Our analysis demonstrates genetic variations that exist when comparing paired primary and metastatic tumors that appear to vary by histology. Variants are potentially undergoing negative selection supported by the preferential loss of out-of-frame events in metastatic tumors and positive selection in specific metastatic sites. Overall, understanding the clonal structure will be key to neoantigen prediction for effective neoantigen-based vaccines. Citation Format: Alyssa Obermayer, Timothy Shaw, Darwin Chang, Joshua Davis, Jamie K. Teer, Xiaoqing Yu, Xuefeng Wang, Dale Hedges, Aik Choon Tan, Robert Rounbehler, Abdul Rafeh Naqash, Margaret Gatti-Mays, Aakrosh Ratan, Martin McCarter, Howard Colman, Igor Puzanov, Susanne Arnold, Michelle Churchman, Patrick Hwu, William Dalton, George Weiner, Jose Conejo-Garcia, Paulo C. Rodriguez, Bodour Salhia, Ahmad A. Tarhini. Analysis of clonal heterogeneity within paired primary and metastatic tumor samples of patients with solid tumors and implications for neoantigen-based personalized cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3886.

Abstract 5664: Genetic alterations and pharmacological response profiles from 10 cervical cancer cell lines

Abstract Background: There are significant parallels between CC cell lines (CCCL) and newly diagnosed tumors, the most prominent being HPV infection/integrations and PI3K as well as epigenetic pathway alterations. Objectives: Identify genomic alterations associated with resistance to specific drug families in pharmacological profiles in Cell Lines (CL) and in Primary Tumors. Material and Methods: 16 public and 4 newly patient-derived CL have been extensively characterized by HPV type, genetic (WES) and protein (RPPA) profiles. Pharmacological profiling against 32 drugs was carried out in triplicate samples and repeated on 10 selected CL. Variant calling of small-scale alterations (SNVs, InDels) was performed using GATK HaplotypeCaller (v4.0.2.1). Variants annotation was performed with Annovar (v 2017/07/06) using respectively gene (refGene), known polymorphims (avsnp v147, 1000G v2015/08, ESP6500v2, ExAC) and cancer databases (COSMIC v70, ICGC v21) as well as deleterious impact predictors (dbNSFP v33). Copy number alterations were assessed using Facets (v0.5.11) with 4 blood samples from the RAIDs tumor project used to generate a “baseline” copy number profiles for CLs. Results: All 10 cell lines had strikingly similar DSS (drug sensitivity scores) to a family of four different microtubule targeting agents (MTAs). Bioinformatics subtraction of genetic alterations present in resistant (CRL 10302, CRL 7920, CRL 1550) and absent in sensitive CLs: (IC1, IC3, IC5), revealed a consistent LoF (loss of function) alteration in one single gene: PABPC3 ‘Poly A tail binding protein, cytoplasmic C3’, a gene involved in messenger RNA stability and translational initiation. Additionally, 50 gene alterations from the pathway “POST TRANSLATIONAL MODIFICATIONS OF TUBULIN” were variably detected in resistant CLs. In primary tumors from the RAIDS database, alterations in PABPC3 were present in 10/301 (3%), half of which documented by at least one database: (Cosmic, ICGC, Cancer Hotspot). All CLs were sensitive to Bortezomib, Omipalisib and Palbociclib. A large spectrum activity to Vorinostat, (HDAC inhibitor) in line with frequent (sensitizing) LoF alterations in epigenetic suppressor genes, contrasted with a selective response to other well-known CC drugs: Methotrexate, Gemcitabine, Sorafenib. Three out of CL were highly sensitive to APR246 (Eprenetapopt) as single agent. Bioinformatics gene subtraction identified OSBPL1A, a marker of synthetic lethality in the ferroptosis pathway, in all three CLs. Discussion and Conclusion: A string of LoF alterations (CSMD2/CSMD3, ZNF717, CDC27) had been previously associated with poor response to radiation or to chemo-radiation, in tumors and are present in most CLs which are derived from resistant tumors. Future objectives are to validate the present biomarker findings in the context of a clinical platform trial. Citation Format: Suzy Scholl, Pierre Gestraud, Elodie Girard, Lolita Lecompte, Maral Halladjian, Leanne de Koning, Nicolas Servant, Maud Kamal, Elaine Del Nery. Genetic alterations and pharmacological response profiles from 10 cervical cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5664.

Abstract 5060: Role of molecular driver mutations in recurrence of early stage laryngeal cancer following narrow field definitive radiotherapy

Abstract PURPOSE/OBJECTIVES Recurrent early-stage laryngeal cancer poses profound clinical and prognostic dilemmas. The primary endpoint of this study was to identify patient exome differences between early-stage laryngeal cancer patients who responded or rapidly progressed following definitive, narrow field radiotherapy. Our study offers the first exploration of mutation patterns in early-stage laryngeal head and neck squamous cell carcinoma (HNSCC) patients receiving curative radiation therapy. MATERIALS/METHODS We identified 36 early-stage laryngeal (T1 = 8, T1a = 4, T1b = 2, T1c = 1, T2 = 20, NA =1) HNSCC patients from the University of Kansas Medical Center treated with definitive narrow field radiotherapy to 63-70 Gray (gy) dose. Whole exome sequencing was performed in all patients. Patients were stratified into “responders” (never failed, N=14) and “non-responders” (recurrence <12 months, N=21). Most non-responder patients with local recurrence underwent total salvage laryngectomy (TSL, N=18). All patients underwent whole exome sequencing. The Genome Analysis Toolkit (GATK) was used for pre-processing and functional annotation of variants. Cancer driver genes were detected for each group using the OncodriveCLUST algorithm. Differentially mutated genes were assessed using Fisher’s exact or exact McNemar’s test at P < 0.05. Tumor mutational burden (TMB) comparisons employed the Wilcoxon rank-sum test with the same threshold. For pathway analysis in Reactome and Ingenuity, a P < 0.1 was used for exploratory significance. RESULTS Mutation analysis identified distinct cancer driver genes between responders, (N=3), non-responders (N=36), and post-TSL (N=4). Among 75 genes with significant pre-treatment mutation differences between responders and non-responders, KCNT2 and AGAP6 were exclusively mutated in non-responders (OR=0, P=0.005 and OR=0, P=0.027, respectively), ADAMTS7 was solely mutated in responders (OR=inf, P=0.019), and PLEC showed more mutations in responders (OR=11.6, P=0.006). Pathway analysis showed these genes were involved in the RND2 GTPase cycle, protein O-glycosylation related diseases, and apoptotic pathways. Post-treatment analysis indicated an enrichment of mutations in apoptosis regulation pathways, with genes like PLEC being more frequently mutated in responders than in non-responders or TSL patients. In addition, we observed in non-responders that they trended towards higher median pre-treatment TMB compared to responders (7.98 vs 6.16, P=0.072). There was no significant difference in TMB between TSL patients and responders or non-responders pre-treatment. CONCLUSION Our analysis reveals distinct mutation patterns in early-stage laryngeal cancer patients by response to radiotherapy and the potential role of these mutated genes in apoptosis related pathways. Citation Format: Aaron Segura, Emily Nissen, Chris Lominska, Ryan Morse, Deri Morgan, Prakash Neupane, Dong Pei, Devin Koestler, Randall Kimple, Umamaheswar Duvvuri, Kevin Sykes, Sufi Thomas, Rohit Nallani, Lisa Shnayder, Kiran Kakarala, Emrullah Yilmaz, Andres Bur. Role of molecular driver mutations in recurrence of early stage laryngeal cancer following narrow field definitive radiotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5060.

Abstract 336: Genome integrity assessment by optical genome mapping for cell manufacturing/bioprocessing applications

Abstract Cells and cell lines are used for multiple applications such as bioprocessing, therapy, and research. To ensure quality of cells and downstream applications, appropriate quality control (QC) methods are critical. Historically, karyotyping has been employed, but it is limited by its very low resolution and tedious laboratory workflow. Other methods such as PCR and targeted sequencing can characterize only small genomic variants at specific loci. Whole genome sequencing can detect small variants genome-wide but has limited sensitivity in detecting structural variants (SVs). Optical genome mapping (OGM) is a novel genome analysis technique that can fill many of the gaps in current capabilities for assessing genome integrity. To find clonal variants, 400 Gbp of data is collected from the parental/control and test samples. For both samples, a de novo assembly is constructed, and homozygous or heterozygous SVs are assessed. Subsequently, the dual variant annotation pipeline identifies unique SVs in the test sample compared to the parental sample. To discover subclonal SVs down to 5% variant allele fraction (VAF), the workflow leverages the generation of 1.5 Tbp of data, requiring a simple adjustment to longer data collection time. Comparison of test sample to parental sample enables easy determination of acquired variants. Finally, generation of ~5 Tbp of data, requiring 2-3 flowcells to be run, enables the detection of SVs at ultra-low VAF down to ~1%. In this study, several dilutions and simulations were performed to examine OGM’s limit of detection. Targeting a coverage of 5 Tbp and analysis using the somatic SV-analysis workflow revealed that OGM has the sensitivity to detect deletions >50kbp, insertions >20kbp, duplications >100 kbp, and translocations at ~1% VAF. We have applied the clonal and somatic workflows to verify genomes’ integrity after cell immortalization, induced pluripotency, transgene-integration, and gene-editing. The data using the robust and sensitive workflows demonstrate that the OGM platform is a cost-effective solution for cell manufacturing/bioprocessing QC applications. Citation Format: Alex Hastie, Andy W. Pang, Alka Chaubey. Genome integrity assessment by optical genome mapping for cell manufacturing/bioprocessing applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 336.

MVAR: A Mouse Variation Registry

The Mouse Variation Registry (MVAR) resource is a scalable registry of mouse single nucleotide variants and small indels and variant annotation. The resource accepts data in standard Variant Call Format (VCF) and assesses the uniqueness of the submitted variants via a canonicalization process. Novel variants are assigned a unique, persistent MVAR identifier; variants that are equivalent to an existing variant in the resource are associated with the existing identifier. Annotations for variant type, molecular consequence, impact, and genomic region in the context of specific transcripts and protein sequences are generated using Ensembl’s Variant Effect Predictor (VEP) and Jannovar. Access to the data and annotations in MVAR are supported via an Application Programming Interface (API) and web application. Researchers can search the resource by gene symbol, genomic region, variant (expressed in Human Genome Variation Society syntax), refSNP identifiers, or MVAR identifiers. Tabular search results can be filtered by variant annotations (variant type, molecular consequence, impact, variant region) and viewed according to variant distribution across mouse strains. The registry currently comprises more than 99 million canonical single nucleotide variants for 581 strains of mice. MVAR is accessible from https://mvar.jax.org.

DVA: predicting the functional impact of single nucleotide missense variants

BackgroundIn the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants.ResultsWe present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein–protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods.ConclusionsDVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Comparing Classifications from Multiple Variant Annotation Software Solutions Using Real-World Next Generation Sequencing Data from Oncology Testing

Variant annotation is an important step in deciphering the functional impact of genomic variants and their association with diseases. In this study, we analyzed 80 pan-cancer cases that underwent comprehensive genomic testing and compared the auto-classified variant tiers among four globally-available software solutions for variant interpretation from Roche, SOPHiA GENETICS, QIAGEN, and Genoox. The results revealed striking differences in tier classifications, which are believed to be a result of several factors, including subjectivity in the AMP/ASCO/CAP guidelines, threshold settings for variant allele frequencies and population allele frequencies, as well as variation in disease ontologies. Although the software tools described here provide a time-saving and repeatable process for interpretation of genomic data, it is crucial to understand the nuances and various settings for these solutions, as they can strongly influence variant tier classifications and downstream management.

Preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology.

In precision oncology (PO), clinicians aim to find the best treatment for any patient based on their molecular characterization. A major bottleneck is the manual annotation and evaluation of individual variants, for which usually a range of knowledge bases are screened. To incorporate and integrate the vast information of different databases, fast and accurate methods for harmonizing databases with different types of information are necessary. An essential step for harmonization in PO includes the normalization of tumor entities as well as therapy options for patients. preon is a fast and accurate library for the normalization of drug names and cancer types in large-scale data integration. preon is implemented in Python and freely available via the PyPI repository. Source code and the data underlying this article are available in GitHub at https://github.com/ermshaua/preon/.

Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

BackgroundStreptococcus agalactiae, commonly known as Group B Streptococcus (GBS), exhibits a broad host range, manifesting as both a beneficial commensal and an opportunistic pathogen across various species. In humans, it poses significant risks, causing neonatal sepsis and meningitis, along with severe infections in adults. Additionally, it impacts livestock by inducing mastitis in bovines and contributing to epidemic mortality in fish populations. Despite its wide host spectrum, the mechanisms enabling GBS to adapt to specific hosts remain inadequately elucidated. Therefore, the development of a rapid and accurate method differentiates GBS strains associated with particular animal hosts based on genome-wide information holds immense potential. Such a tool would not only bolster the identification and containment efforts during GBS outbreaks but also deepen our comprehension of the bacteria's host adaptations spanning humans, livestock, and other natural animal reservoirs. Methods and resultsHere, we developed three machine learning models—random forest (RF), logistic regression (LR), and support vector machine (SVM) based on genome-wide mutation data. These models enabled precise prediction of the host origin of GBS, accurately distinguishing between human, bovine, fish, and pig hosts. Moreover, we conducted an interpretable machine learning using SHapley Additive exPlanations (SHAP) and variant annotation to uncover the most influential genomic features and associated genes for each host. Additionally, by meticulously examining misclassified samples, we gained valuable insights into the dynamics of host transmission and the potential for zoonotic infections. ConclusionsOur study underscores the effectiveness of random forest (RF) and logistic regression (LR) models based on mutation data for accurately predicting GBS host origins. Additionally, we identify the key features associated with each GBS host, thereby enhancing our understanding of the bacteria's host-specific adaptations.

Genomic prediction based on preselected single-nucleotide polymorphisms from genome-wide association study and imputed whole-genome sequence data annotation for growth traits in Duroc pigs.

The use of whole-genome sequence (WGS) data is expected to improve genomic prediction (GP) power of complex traits because it may contain mutations that in strong linkage disequilibrium pattern with causal mutations. However, a few previous studies have shown no or small improvement in prediction accuracy using WGS data. Incorporating prior biological information into GP seems to be an attractive strategy that might improve prediction accuracy. In this study, a total of 6334 pigs were genotyped using 50K chips and subsequently imputed to the WGS level. This cohort includes two prior discovery populations that comprise 294 Landrace pigs and 186 Duroc pigs, as well as two validation populations that consist of 3770 American Duroc pigs and 2084 Canadian Duroc pigs. Then we used annotation information and genome-wide association study (GWAS) from the WGS data to make GP for six growth traits in two Duroc pig populations. Based on variant annotation, we partitioned different genomic classes, such as intron, intergenic, and untranslated regions, for imputed WGS data. Based on GWAS results of WGS data, we obtained trait-associated single-nucleotide polymorphisms (SNPs). We then applied the genomic feature best linear unbiased prediction (GFBLUP) and genomic best linear unbiased prediction (GBLUP) models to estimate the genomic estimated breeding values for growth traits with these different variant panels, including six genomic classes and trait-associated SNPs. Compared with 50K chip data, GBLUP with imputed WGS data had no increase in prediction accuracy. Using only annotations resulted in no increase in prediction accuracy compared to GBLUP with 50K, but adding annotation information into the GFBLUP model with imputed WGS data could improve the prediction accuracy with increases of 0.00%-2.82%. In conclusion, a GFBLUP model that incorporated prior biological information might increase the advantage of using imputed WGS data for GP.

Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis

Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduces a novel metric, the Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant explaining the patient’s clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared to a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77/82) of the family-based exomes and in the first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28/35) of the cases. The SAP score, which is independent of detected variants, demonstrates comparable performance to Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be utilized in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.