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High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format (VCF) file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank (PDB), if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to enable researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP.

Background: With a global prevalence of 25%, more non-obese individuals are developing non-alcoholic fatty liver disease (NAFLD) especially in India, despite its association with obesity. This highlights the role of genetics in NAFLD pathogenesis. Therefore, we performed the first GWAS to identify genetic risk factors associated with NAFLD in the South Asian population.
Method: 536 cases and 397 controls were included from the Trivandrum NAFLD cohort. Genome-wide association analysis was performed using PLINK2.0 following quality control and data imputation. Trans-ancestral meta-analysis was carried out across our cohort and datasets of individuals from European ancestry.
Results: Multiple genetic variants with suggestive evidence of association were identified in the Trivandrum NAFLD cohort, including rs1256113(MTHFD1), rs2069923(MAP3K2), rs10877190(LRIG3) and rs10467865(RIN3). Meta-analysis confirmed associations in PNPLA3 gene(rs2294915). More importantly, a novel variant, rs2980888 in TRIB1, was discovered to be associated with NAFLD at genome-wide significance for the first time.
Conclusion: Results from our study suggest a potential divergence in genetic risk profile in the South Asian population as compared to other populations. In addition to identification of association between novel variant rs2980888 in TRIB1 and NAFLD, this novel study creates a multitude of possibilities for further research into genetic risk factors and pathogenesis of NAFLD.

Tumour cell lines serve as valuable model systems for cancer research. Molecular characterisation of cell lines supports researchers to identify the model system most suitable for their specific research question. Recently, 100 publicly available human leukemia and lymphoma cell lines have been sequenced and transcriptome data has been made accessible on DSMZCellDive (https://celldive.dsmz.de). Beside STR (short tandem repeats) profile search and provision of HLA (human leukocyte antigen) typing, gene expression profiles can be visualised as heatmaps or barplots.
In order to include short genetic variation of these cell lines on this web resource for cancer research, small nucleotide variants and indels are called by GATK MuTect2 on RNA-seq and whole-exome sequencing data. Panels of normal (PON), dbSNP and Gnomad served as filters during SNV identification. Functional analysis was performed by annotation via VEP (variant effect predictor). Variants are validated with benchmarking COSMIC mutant and gene census data available for common cell lines. Despite several filtering steps the mutation calling pipeline still needs further improvements.
In future, small variant information will be available for the given cell lines in DSMZCellDive which will significantly enrich the data resource for cancer research.

Corticosteroids are effective therapy for asthma management but can lead to hypothalamic-pituitary-adrenal suppression (HPAS). There is no single biochemical parameter that can be used for assessing risk of HPAS. Therefore, this study aimed to identify single nucleotide polymorphisms (SNPs) as markers for HPAS.

SNP data of ninety-five asthmatic children on inhaled corticosteroids and nasal steroids were studied. The participants underwent an overnight metyrapone test. Baseline adrenocorticotropic hormone and cortisol were measured as well as post-metyrapone adrenocorticotropic hormone (PMACTH), 11-deoxycortisol (11DOC) and 11-deoxycortisol + cortisol (11DOC+C) were measured. HPAS was diagnosed based on 3 measurements: PMACTH < 106 pg/ml, 11DOC < 208 nmol/l and 11DOC+C < 400 nmol/l. SNP association was done using the PLINK analysis toolkit and statistical regression. Genetic models were assessed, and SNP functional annotation & prioritization were performed.

Among other SNPs, rs1546124 (C/G) on CRISPLD2 gene showed a statistically significant association. The genotypic comparisons (CC, CG & GG) for the mean of √PMACTH and √11DOC were statistically significant as were CG vs GG and CC vs GG for the √11DOC+C phenotype. CRISPLD2 appears to modulate both glucocorticoid and cytokine function, but the mechanism is unclear. If confirmed, rs1546124 and the other SNPs could be markers for HPAS

UniProt collates genetic variation in coding regions from numerous sources, viewable across proteins in parallel with functional annotations. ProtVar (ebi.ac.uk/protvar) is a new resource which extends this functionality to contextualise and evaluate human missense variation at a per-residue level. Users can access ProtVar by submitting lists of genomic coordinates, dbSNP IDs or protein positions in over 92% of human proteins.

All functional and structural annotations relevant to the variant position are returned to the user along with precomputed conservation scores, pathogenicity predictions and the predicted protein stability change. ProtVar also retrieves critical data on variants which are co-located at the same amino acid and calculates an allele frequency for each missense change at the residue level. Additionally, variants are mapped onto all known experimental structures and AlphaFold2 models as well as predicted protein-protein interactions where the variant is at the interface, all of which can be manipulated in an interactive viewer.

The results are browsable in the user interface, downloadable and available programmatically via an API. ProtVar offers links to UniProt and other complementary variant interpretation resources and is updated regularly to offer an accurate and efficient resource for the interpretation of missense variation.