Comprehensive variant effect predictions of single nucleotide variants in model organisms

AbstractThe effect of single nucleotide variants (SNVs) in coding and non-coding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this we compiled and benchmarked sequence and structure-based variant effect predictors and we analyzed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of H. sapiens, S. cerevisiae and E. coli. Studied mechanisms include protein stability, interaction interfaces, post-translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc, a tool by which users can query precomputed predictions by providing amino acid or nucleotide-level variants.

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Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Nature Communications ◽

10.1038/s41467-019-11959-3 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 12

Author(s):

Robert Fragoza ◽

Jishnu Das ◽

Shayne D. Wierbowski ◽

Jin Liang ◽

Tina N. Tran ◽

...

Keyword(s):

Allele Frequency ◽

Protein Interactions ◽

Human Populations ◽

Protein Protein Interactions ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Allele Frequency Spectrum ◽

Disease Mutations ◽

Coding Variants ◽

The Impact

Abstract Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual’s genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

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A resource of variant effect predictions of single nucleotide variants in model organisms

Molecular Systems Biology ◽

10.15252/msb.20188430 ◽

2018 ◽

Vol 14 (12) ◽

Cited By ~ 30

Author(s):

Omar Wagih ◽

Marco Galardini ◽

Bede P Busby ◽

Danish Memon ◽

Athanasios Typas ◽

...

Keyword(s):

Model Organisms ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Variant Effect

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Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

Scientific Reports ◽

10.1038/s41598-020-74793-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sebastian Carrasco Pro ◽

Katia Bulekova ◽

Brian Gregor ◽

Adam Labadorf ◽

Juan Ignacio Fuxman Bass

Keyword(s):

Binding Sites ◽

Cancer Type ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Regulatory Regions ◽

Genome Wide ◽

Transcriptional Regulatory ◽

Gene Regulatory ◽

The Impact ◽

The Relationship

Abstract Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

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Re-evaluation of single nucleotide variants and identification of structural variants in a cohort of 45 sudden unexplained death cases

International Journal of Legal Medicine ◽

10.1007/s00414-021-02580-5 ◽

2021 ◽

Author(s):

Jacqueline Neubauer ◽

Shouyu Wang ◽

Giancarlo Russo ◽

Cordula Haas

Keyword(s):

Sudden Death ◽

Cardiac Diseases ◽

Structural Variants ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Sudden Unexplained Death ◽

Unexplained Death ◽

Pathogenic Variants ◽

The Impact ◽

Death Cases

AbstractSudden unexplained death (SUD) takes up a considerable part in overall sudden death cases, especially in adolescents and young adults. During the past decade, many channelopathy- and cardiomyopathy-associated single nucleotide variants (SNVs) have been identified in SUD studies by means of postmortem molecular autopsy, yet the number of cases that remain inconclusive is still high. Recent studies had suggested that structural variants (SVs) might play an important role in SUD, but there is no consensus on the impact of SVs on inherited cardiac diseases. In this study, we searched for potentially pathogenic SVs in 244 genes associated with cardiac diseases. Whole-exome sequencing and appropriate data analysis were performed in 45 SUD cases. Re-analysis of the exome data according to the current ACMG guidelines identified 14 pathogenic or likely pathogenic variants in 10 (22.2%) out of the 45 SUD cases, whereof 2 (4.4%) individuals had variants with likely functional effects in the channelopathy-associated genes SCN5A and TRDN and 1 (2.2%) individual in the cardiomyopathy-associated gene DTNA. In addition, 18 structural variants (SVs) were identified in 15 out of the 45 individuals. Two SVs with likely functional impairment were found in the coding regions of PDSS2 and TRPM4 in 2 SUD cases (4.4%). Both were identified as heterozygous deletions, which were confirmed by multiplex ligation-dependent probe amplification. In conclusion, our findings support that SVs could contribute to the pathology of the sudden death event in some of the cases and therefore should be investigated on a routine basis in suspected SUD cases.

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CSIG-22. CANCER-ASSOCIATED MISSENSE SINGLE NUCLEOTIDE VARIANTS REGULATE THE STABILITY AND SUBCELLULAR LOCALIZATION OF NF2/MERLIN

Neuro-Oncology ◽

10.1093/neuonc/noaa215.134 ◽

2020 ◽

Vol 22 (Supplement_2) ◽

pp. ii32-ii32

Author(s):

Charlotte Eaton ◽

Paola Bisignano ◽

David Raleigh

Keyword(s):

Tumor Suppressor ◽

Subcellular Localization ◽

Function Analysis ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Ferm Domain ◽

Binding Partners ◽

Subcellular Compartments ◽

The Stability ◽

The Impact

Abstract BACKGROUND Alterations in the NF2 tumor suppressor gene lead to meningiomas and schwannomas, but the tumor suppressor functions of the NF2 gene product, Merlin, are incompletely understood. To address this problem, we performed a structure-function analysis of Merlin by expressing cancer-associated missense single-nucleotide variants (mSNVs) in primary cancer cells for biochemical and cell biology experiments. METHODS All NF2 mSNVs were assembled from cBioPortal and COSMIC, and modelled on the FERM, a-helical, and C-terminal domains of Merlin (PDB 4ZRJ) using comparative structure prediction on the Robetta server and visually inspected using Pymol. mSNV hotspots were defined from sliding windows with at least 10 mutations within 5 residues in either direction. mSNVs from hotspots in meningiomas, schwannomas, or both, were selected for in vitro mechanistic analyses using immunofluorescence and immunoblotting of whole cell, plasma membrane, cytoskeletal, cytoplasmic, nuclear, and chromatin subcellular fractions from M10G meningioma cells and HEI-193 schwannoma cells. RESULTS We identified the following cancer-associated hotspot mSNVs in NF2, which were over-expressed for mechanistic studies: L46R, S156N, W191R, A211D, V219M, R418C and R462K. Endogenous Merlin was detected in all subcellular compartments, but was enriched in the nucleus. L46R and A211D mapped to hydrophobic pockets in the FERM domain, destabilized Merlin, and excluded Merlin from all subcellular compartments except the cytoskeleton. S156N, W191R and V219M also mapped to the FERM domain, but did not affect Merlin stability, and V219M attenuated chromatin localization, suggesting this motif may be involved in binding events that regulate subcellular localization. R418C and R463K mapped to the a-helical domain, but only R418C destabilized Merlin. CONCLUSION Our results suggest that cancer-associated mSNVs inactive the tumor suppressor functions of NF2 by altering the stability, subcellular localization, or binding partners of Merlin. Further work is required to identify and understand the impact of binding partners and subcellular localization on Merlin function.

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MaveRegistry: a collaboration platform for multiplexed assays of variant effect

10.1101/2020.10.14.339499 ◽

2020 ◽

Author(s):

Da Kuang ◽

Jochen Weile ◽

Nishka Kishore ◽

Alan F. Rubin ◽

Stanley Fields ◽

...

Keyword(s):

Amino Acid ◽

International Community ◽

Human Variation ◽

Single Nucleotide ◽

Clinical Interpretation ◽

Link Type ◽

Variant Effect ◽

Genomic Regions ◽

Amino Acid Variants

AbstractSummaryMultiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating ‘variant effect maps’, which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets, and enable tracking and sharing of progress on ongoing MAVE projects.Availability and implementationhttps://[email protected]

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Twenty-Five Years of Propagation in Suspension Cell Culture Results in Substantial Alterations of the Arabidopsis Thaliana Genome

Genes ◽

10.3390/genes10090671 ◽

2019 ◽

Vol 10 (9) ◽

pp. 671 ◽

Cited By ~ 2

Author(s):

Pucker ◽

Rückert ◽

Stracke ◽

Viehöver ◽

Kalinowski ◽

...

Keyword(s):

Cell Culture ◽

Arabidopsis Thaliana ◽

Large Scale ◽

Suspension Cell ◽

Reference Sequence ◽

Model Organisms ◽

Suspension Cell Culture ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Haploid Genome Size

Arabidopsis thaliana is one of the best studied plant model organisms. Besides cultivation in greenhouses, cells of this plant can also be propagated in suspension cell culture. At7 is one such cell line that was established about 25 years ago. Here, we report the sequencing and the analysis of the At7 genome. Large scale duplications and deletions compared to the Columbia-0 (Col-0) reference sequence were detected. The number of deletions exceeds the number of insertions, thus indicating that a haploid genome size reduction is ongoing. Patterns of small sequence variants differ from the ones observed between A. thaliana accessions, e.g., the number of single nucleotide variants matches the number of insertions/deletions. RNA-Seq analysis reveals that disrupted alleles are less frequent in the transcriptome than the native ones.

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Annotation of Human Exome Gene Variants with Consensus Pathogenicity

Genes ◽

10.3390/genes11091076 ◽

2020 ◽

Vol 11 (9) ◽

pp. 1076

Author(s):

Victor Jaravine ◽

James Balmford ◽

Patrick Metzger ◽

Melanie Boerries ◽

Harald Binder ◽

...

Keyword(s):

Conservation Score ◽

Species Conservation ◽

Gradient Boosting ◽

Biological Applications ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Novel Approach ◽

Phenotypic Variant ◽

Variant Effect ◽

Direct Use

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.

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RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Genome Biology ◽

10.1186/s13059-019-1847-4 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 5

Author(s):

Hai Lin ◽

Katherine A. Hargreaves ◽

Rudong Li ◽

Jill L. Reiter ◽

Yue Wang ◽

...

Keyword(s):

Rna Splicing ◽

Evolutionary Conservation ◽

Random Forest Classifier ◽

Training Data ◽

Reporter Assay ◽

Single Nucleotide Variants ◽

Excellent Performance ◽

Computational Framework ◽

Single Nucleotide ◽

The Impact

AbstractSingle nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure, and evolutionary conservation features. RegSNPs-intron showed excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of RegSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

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Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

Nucleic Acids Research ◽

10.1093/nar/gkz420 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W136-W141 ◽

Cited By ~ 1

Author(s):

Emidio Capriotti ◽

Ludovica Montanucci ◽

Giuseppe Profiti ◽

Ivan Rossi ◽

Diana Giannuzzi ◽

...

Keyword(s):

Matthews Correlation Coefficient ◽

Genomic Variation ◽

Gradient Boosting ◽

Binary Classifier ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Coding Regions ◽

Variation Data ◽

Boosting Algorithm ◽

The Impact

Abstract As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

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