Ensembl 2022

BackgroundSevere equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants of potential relevance to the pathogenesis of asthma.MethodsRNA-Seq data were generated from endobronchial biopsies collected from six asthmatic and seven non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of <0.01 predicted with Sorting Intolerant from Tolerant software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping 2 and screening for non-acceptable polymorphism 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson’s Chi-squared test with Yates’ continuity correction, and difference in genotype frequency was calculated using the Fisher’s exact test for count data.ResultsRNA-Seq variant calling and filtering correctly identified substitution variants in PACRG and RTTN. Sanger sequencing confirmed that the PACRG substitution was appropriately identified in all 26 samples while the RTTN substitution was identified correctly in 24 of 26 samples. These variants of uncertain significance had substitutions that were predicted to result in loss of function and to be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, and a change in both for RTTN. For PACRG, no difference in allele frequency between the two groups was detected but a higher proportion of asthmatic horses had the altered RTTN allele compared to non-asthmatic animals.DiscussionRNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (<10–20 counts per million) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.

Background. Severe equine asthma is a chronic inflammatory disease of the lung in horses similar to low-Th2 late-onset asthma in humans. This study aimed to determine the utility of RNA-Seq to call gene sequence variants, and to identify sequence variants or potential relevance to the pathogenesis of asthma. Methods. RNA-Seq data were generated from endobronchial biopsies collected from 6 asthmatic and 7 non-asthmatic horses before and after challenge (26 samples total). Sequences were aligned to the equine genome with Spliced Transcripts Alignment to Reference software. Read preparation for sequence variant calling was performed with Picard tools and Genome Analysis Toolkit (GATK). Sequence variants were called and filtered using GATK and Ensembl Variant Effect Predictor (VEP) tools, and two RNA-Seq predicted sequence variants were investigated with both PCR and Sanger sequencing. Supplementary analysis of novel sequence variant selection with VEP was based on a score of &lt;0.01 predicted with Sorting Intolerant From Tolerant (SIFT) software, missense nature, location within the protein coding sequence and presence in all asthmatic individuals. For select variants, effect on protein function was assessed with Polymorphism Phenotyping (PolyPhen) 2 and Screening for Non-Acceptable Polymorphism (SNAP) 2 software. Sequences were aligned and 3D protein structures predicted with Geneious software. Difference in allele frequency between the groups was assessed using a Pearson's Chi-squared test with Yates' continuity correction, and difference in genotype frequency was calculated using the Fisher's exact test for count data. Results. RNA-Seq variant calling and filtering correctly identified substitution variants in PACRG and RTTN. Sanger sequencing confirmed that the PACRG substitution was appropriately identified in all 26 samples while the RTTN substitution was identified correctly in 24 of 26 samples. These variants of uncertain significance had substitutions that were predicted to result in loss of function and to be non-neutral. Amino acid substitutions projected no change of hydrophobicity and isoelectric point in PACRG, and a change in both for RTTN. For PACRG, no difference in allele frequency between the two groups was detected but a higher proportion of asthmatic horses had the altered RTTN allele compared to non-asthmatic animals. Discussion. RNA-Seq was sensitive and specific for calling gene sequence variants in this disease model. Even moderate coverage (&lt;10-20 cpm) yielded correct identification in 92% of samples, suggesting RNA-Seq may be suitable to detect sequence variants in low coverage samples. The impact of amino acid alterations in PACRG and RTTN proteins, and possible association of the sequence variants with asthma, is of uncertain significance, but their role in ciliary function may be of future interest.

Base editing enables the high-throughput screening of genetic variants for phenotypic effects. Base editing screens require the design of single guide RNA (sgRNA) libraries to enable either gene- or variant-centric approaches. While computational tools supporting the design of sgRNAs exist, no solution offers versatile and scalable library design enabling all major use cases. Here, we introduce BEscreen, a comprehensive base editing guide design tool provided as a web server (bescreen.ostendorflab.org) and as a command line tool. BEscreen provides variant-, gene-, and region-centric modes to accommodate various screening approaches. The variant mode accepts genomic coordinates, amino acid changes, or rsIDs as input. The gene mode designs near-saturation libraries covering the entire coding sequence of given genes or transcripts, and the region mode designs all possible guides for given genomic regions. BEscreen enables selection of guides by biological consequence, it features comprehensive customization of base editor characteristics, and it offers optional annotation using Ensembl's Variant Effect Predictor. In sum, BEscreen is a highly versatile tool to design base editing screens for a wide range of use cases with seamless scalability from individual variants to large, near-saturation libraries.

The interpretation of variants in cancer is frequently focused on direct protein coding alterations. However, most somatic mutations are in noncoding regions of the genome, and even exonic mutations may have unidentified noncoding consequences. Here we present Regtools, a software package designed to efficiently identify variants that may cause aberrant splicing in tumors. Our tool integrates variant calls from genomic data with junctions extracted from transcriptomic data in order to examine potential cis alterations to splicing near a somatic variant. Based on user-defined parameters and position relative to known exons, variants are first annotated as splicing relevant or not. Splicing junctions are inferred from transcriptomic sequencing data, and comparison of junctions to a reference transcriptome allows for identification and annotation of novel junctions and nearby regulatory or splicing motifs. From there, mutations are associated with junctions that overlap with a flanking region. In order to evaluate Regtools, we used it to analyze the transcriptional output of tumor-sequencing data from two cohorts of cancer patients, one of hepatocellular carcinoma and one of small cell lung cancer with 28 and 21 samples, respectively. We performed whole-exome and RNA sequencing on each sample. Somatic variants were called on whole-exome alignment data. For each cohort, we compared the junctional profiles between tumors and identified numerous examples of variants for which there are elevated levels of proximal novel or known junctions. Moreover, out of 754 (153 in HCC; 601 in SCLC) variants identified as splicing relevant by our approach, only 165 (20 in HCC; 145 in SCLC) were annotated as splicing relevant by Ensembl's Variant Effect Predictor, using the recommended “per_gene” option. This preliminary analysis illustrates the importance of an efficient, user-friendly computational tool for identifying important noncoding variants that would otherwise be undervalued or perhaps even completely ignored by traditional methods and annotators. Regtools is freely available and open source (https://github.com/griffithlab/regtools). Citation Format: Yang-Yang Feng, Avinash Ramu, Zachary L. Skidmore, Jason Kunisaki, Kelsy C. Cotto, Obi L. Griffith, Malachi Griffith. Regtools: Integrated analysis of genomic and transcriptomic data for discovery of mutations associated with aberrant splicing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2285.

BackgroundTo date, mutations in the MAPT (i.e., tau) gene within Alzheimer’s disease (AD) patients are either benign or have an unclear effect on pathogenicity. Significant prior literature and evidence from other tauopathies support the hypothesis that mutations can alter tau’s structure and function. Approximately one‐third of neuropathological autopsies identify individuals with comparable AD neuropathology (i.e., extracellular amyloid‐beta (Aβ) plaques and intracellular tau‐containing neurofibrillary tangles (NFTs)) without similar clinical manifestations or cognitive deficits (herein referred to as non‐demented with Alzheimer’s neuropathology or NDAN). The comparable neuropathological burden with divergent clinical manifestations between AD patients and NDAN individuals led us to hypothesize that a mutation in the NDAN MAPT gene could explain, at least in part, this dichotomous presentation.MethodRNA was isolated from the frontal cortices of seven NDAN individuals (4M, 3F) using a commercial RNA kit. Primers to amplify the tau coding sequence were found in the literature and confirmed to amplify the tau coding sequence. With assistance from UTMB’s Next Generation Sequencing core, the RNA quality was first assessed, and subsequently, library generation and sequencing were performed.ResultTwo novel somatic mutations were identified in all seven individuals with frequencies consistent with mosaic mutant frequencies. The first, an aspartic acid to alanine mutation, occurred at a position where no prior mutations have been documented. The second occurs at the serine immediately preceding tau’s amyloid motif, VQIVYK; in this mutation, the phosphoresidue is replaced by a charged and bulky arginine. Analysis using the Ensembl Variant Effect Predictor (VEP) predicted both mutations to be, at a minimum, moderately damaging to tau’s function, which is logical considering the amino acid substitutions occurring. The severity of each mutation’s effect on the tau structure and function was isoform specific.ConclusionWe identified, to the best of our knowledge, two completely novel tau mutations uniquely associated with individuals that remain cognitively intact despite abundant AD neuropathology. These mutations may help contextualize the unique phenotype observed in NDAN individuals, such as cognitive resilience and spreading with minimal toxicity.Funding Support: R01422460 and R0142690 to GT. Attendance at this conference was supported by an AAIC 2024 Conference Fellowship.

Genetic Susceptibility in Sinusoidal Obstruction Syndrome/Veno-Occlusive Disease

The global trend toward the reduction of human spermatogenic function observed in many countries, including Russia, raised the problem of extensive screening and monitoring of male fertility and elucidation of its genetic and ethnic mechanisms. Recently, whole-exome sequencing (WES) was developed as a powerful tool for genetic analysis of complex traits. We present here the first Russian WES study for identification of new genes associated with semen quality. The experimental 3 × 2 design of the WES study was based on the analysis of 157 samples including three ethnic groups—Slavs (59), Buryats (n = 49), and Yakuts (n = 49), and two different semen quality groups—pathozoospermia (n = 95) and normospermia (n = 62). Additionally, our WES study group was negative for complete AZF microdeletions of the Y-chromosome. The normospermia group included men with normal sperm parameters in accordance with the WHO-recommended reference limit. The pathozoospermia group included men with impaired semen quality, namely, with any combined parameters of sperm concentration <15 × 106/ml, and/or progressive motility <32%, and/or normal morphology <4%. The WES was performed for all 157 samples. Subsequent calling and filtering of variants were carried out according to the GATK Best Practices recommendations. On the genotyping stage, the samples were combined into four cohorts: three sets corresponded to three ethnic groups, and the fourth set contained all the 157 whole-exome samples. Association of the obtained polymorphisms with semen quality parameters was investigated using the χ2 test. To prioritize the obtained variants associated with pathozoospermia, their effects were determined using Ensembl Variant Effect Predictor. Moreover, polymorphisms located in genes expressed in the testis were revealed based on the genomic annotation. As a result, the nine potential SNP markers rs6971091, rs557806, rs610308, rs556052, rs1289658, rs278981, rs1129172, rs12268007, and rs17228441 were selected for subsequent verification on our previously collected population sample (about 1,500 males). The selected variants located in seven genes FAM71F1, PPP1R15A, TRIM45, PRAME, RBM47, WDFY4, and FSIP2 that are expressed in the testis and play an important role in cell proliferation, meiosis, and apoptosis.

Background: Small bowel carcinoids are insidious tumors that are often metastatic when diagnosed. Limited mutation landscape studies of carcinoids indicate that these tumors have a relatively low mutational burden. The development of targeted therapies will depend upon the identification of mutations that drive the pathogenesis and metastasis of carcinoid tumors. Methods: Whole exome and RNA sequencing of 5 matched sets of normal tissue, primary small intestine carcinoid tumors, and liver metastases were investigated. Germline and somatic variants included: single nucleotide variants (SNVs), insertions/deletions (indels), structural variants, and copy number alterations (CNAs). The functional impact of mutations was predicted using Ensembl Variant Effect Predictor. Results: Large-scale CNAs were observed including the loss of chromosome 18 in all 5 metastases and 3/5 primary tumors. Certain somatic SNVs were metastasis-specific; including mutations in ATRX, CDKN1B, MXRA5 (leading to the activation of a cryptic splice site and loss of mRNA), SMARCA2, and the loss of UBE4B. Additional mutations in ATRX, and splice site loss of PYGL, leading to intron retention observed in primary and metastatic tumors. Conclusions: We observed novel mutations in primary/metastatic carcinoid tumor pairs, and some have been observed in other types of neuroendocrine tumors. We confirmed a previously observed loss of chromosome 18 and CDKN1B. Transcriptome sequencing added relevant information that would not have been appreciated with DNA sequencing alone. The detection of several splicing mutations on the DNA level and their consequences at the RNA level suggests that RNA splicing aberrations may be an important mechanism underlying carcinoid tumors.

BackgroundExperimental data from functional assays have a critical role in interpreting the impact of genetic variants. Assay data must be unambiguously mapped to a reference genome to make it accessible, but it is often reported relative to assay-specific sequences, complicating downstream use and integration of variant data across resources. To make multiplexed assays of variant effect (MAVE) data more broadly available to the research and clinical communities, the Atlas of Variant Effects Alliance mapped MAVE data from the MaveDB community database to human reference sequences, creating an extensive set of machine-readable homology mappings that are incorporated into widely used human genomics applications.ResultsHere, we map approximately 9.0 million individual protein and nucleotide variants in MaveDB to the human genome, describing the examined variants with respect to human reference sequences while preserving the data provenance of the original MAVE sequences. We then disseminate the results to major genomic resources including the Genomics 2 Proteins Portal, UCSC Genome Browser, Ensembl Variant Effect Predictor, and DECIPHER platform. Within these applications, MAVE variants can now be visualized and integrated with other relevant clinical and biological data, making additional knowledge available when performing variant interpretation and conducting other research activities.ConclusionsMapping MAVE variants to human reference sequences and sharing the mapped dataset with several key human genomics applications enables a new and diverse set of applications for MAVE data. This study provides increased access to functional data that can assist in clinical variant interpretation pipelines and enable biomedical research and discovery.

BackgroundSplicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies.MethodsIt has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants.ResultsWe integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance.ConclusionsWhile splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

P575: Scalable, routine, long read genome sequencing of undiagnosed rare disease samples*

The deleterious genetic variants contributing to certain diseases may differ in terms of number and allele frequency from population to population depending on their evolutionary background. Here, we prioritize the deleterious variants from Pakistani population in manually curated gene list already reported to be associated with common, Mendelian, and congenital cardiovascular diseases (CVDs) using the genome/exome sequencing data of Pakistani individuals publically available in 1000 Genomes Project (PJL), and Exome Aggregation Consortium (ExAC) South Asia. By applying a set of tools such as Combined Annotation Dependent Depletion (CADD), ANNOVAR, and Variant Effect Predictor (VEP), we highlighted 561 potentially detrimental variants from PJL data, and 7374 variants from ExAC South Asian data. Likewise, filtration from ClinVar for CVDs revealed 03 pathogenic and 02 likely pathogenic variants from PJL and 112 pathogenic and 42 likely pathogenic variants from ExAC South Asians. The comparison of derived allele frequencies (DAF) revealed many of these prioritized variants having two fold and higher DAF in Pakistani individuals than in other populations. The highest number of deleterious variants contributing to common CVDs in descending order includes hypertension, atherosclerosis, heart failure, aneurysm, and coronary heart disease, and for Mendelian and congenital CVDs cardiomyopathies, cardiac arrhythmias, and atrioventricular septal defects.

The clinical spectrum of human infection by HEV ranges from asymptomatic to severe acute hepatitis. Furthermore, HEV can cause diverse neurological manifestations, especially Parsonage-Turner syndrome. Here, we used a large-scale human genomic approach to search for genetic determinants of severe clinical presentations of HEV infection. We performed whole genome sequencing in 3 groups of study participants with PCR-proven acute HEV infection: (1) 24 patients with symptomatic acute hepatitis E; (2) 12 patients with HEV-associated Parsonage-Turner syndrome; and (3) 16 asymptomatic blood donors (controls). For variant calling and annotation, we used GATK4 best practices followed by Variant Effect Predictor (VEP) and Annovar. For variant classification, we implemented the American College of Medical Genetics and Genomics/Association for Molecular Pathology Bayesian classification framework in R. Variants with a probability of pathogenicity >0.9 were considered damaging. We used all genes with at least 1 damaging variant as input for pathway enrichment analyses.We observed a significant enrichment of type I interferon response pathways in the symptomatic hepatitis group: 10 out of 24 patients carried a damaging variant in one of 9 genes encoding either intracellular viral sensors ( IFIH1 , DDX58 , TLR3 , POLR3B , POLR3C ) or other molecules involved in type I interferon response [interferon regulatory factor 7 ( IRF7 ), MYD88 , OAS3 , GAPDH ]. We did not find any enriched pathway in the Parsonage-Turner syndrome group or in the controls. Our results highlight the essential role of type I interferon in preventing symptomatic acute hepatitis E.

Accurate identification of germline variants from whole-exome sequencing (WES) data is foundational to population genetics, disease association studies, and clinical genomics. However, variant calling across cohorts poses challenges in scalability, consistency, and reproducibility. We present GermVarX, a fully automated, modular workflow for joint germline variant discovery and exploration in WES cohort studies. A key feature of GermVarX is its implementation of joint variant calling, enabling simultaneous genotyping of multiple samples to produce a single, high-confidence multi-sample VCF, optimized for downstream analyses. Developed with Nextflow DSL2, GermVarX ensures reproducibility, portability, and efficient parallelization across diverse computing environments, including workstations, HPC clusters, and cloud platforms. The workflow integrates two state-of-the-art variant callers—GATK HaplotypeCaller and DeepVariant—with joint genotyping performed via GATK or GLnexus. To increase reliability, GermVarX supports consensus generation between callers, coupled with sample- and cohort-level quality control, functional annotation using the Variant Effect Predictor (VEP), and unified reporting through MultiQC. In addition, it provides PLINK-compatible outputs, facilitating seamless integration with statistical and association analyses. GermVarX delivers a scalable, reproducible, and comprehensive solution for germline variant analysis in large WES studies, supporting consistent and interpretable results for both research and clinical genomics. The source code and usage instructions are available at https://github.com/thaontp711/GermVarX.

Characterization of Germline Variants in Multiple Myeloma