Annotating and prioritizing genomic variants using the Ensembl Variant Effect Predictor—A tutorial

Hunt, Sarah; Moore, Benjamin; Amode, Ridwan M; Armean, Irina M.; Lemos, Diana; Mushtaq, Aleena; Parton, Andrew; Schuilenburg, Helen; Szpak, Michał; Thormann, Anja; Perry, Emily; Trevanion, Stephen J.; Flicek, Paul; Yates, Andrew D; Cunningham, Fiona

doi:10.1002/humu.24298

Cited by 41 publications

(22 citation statements)

References 35 publications

(43 reference statements)

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“…There were 6,207 SNPs retained for further analysis. Notably, 96% (n=5,964) of the SNPs have been previously identified and are recorded in the Ensembl variant database (65, 66), which includes the dbSNP ((67) version 150), while 243 SNPs were not identified in Ensembl variant database (Additional file 2). Most of the SNPs are in 3 prime UTRs (n=1,553), and a smaller proportion (n=483) were annotated as missense variants (Additional file 2).…”

Section: Resultsmentioning

confidence: 99%

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

Marrella

Biase

2022

Preprint

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Background: A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are the normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. Results: In this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 38 significant eQTLs (P<5x10-8) following the ANOVA model and 15 significant eQTLs (P<5x10-8) following the additive model. Using a differential gene expression framework, we identified 2,471 and nine significant eQTLs (P<5x10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. Conclusions: Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed, thus this may be more suitable for finding genes whose expression are impacted by genetic variants. Our approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.

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Section: Resultsmentioning

confidence: 99%

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

Marrella

Biase

2022

Preprint

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“…The top 1% was used as the significance cut-off value. The candidate selective sweeps discovered using the above-mentioned approaches (F ST and log2 (θπ)) were annotated using the Variant Effect Predictor (VEP) tool [ 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Analysis Identifies Candidate Genes Encoding Feather Color in Ducks

Guo

Jiang

Wang

et al. 2022

Genes

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Comparative population genomics and genome-wide association studies (GWAS) offer opportunities to discover human-driven detectable signatures within the genome. From the point of view of evolutionary biology, the identification of genes associated with the domestication of traits is of interest for the elucidation of the selection of these traits. To this end, an F2 population of ducks, consisting of 275 ducks, was genotyped using a whole genome re-sequence containing 12.6 Mb single nucleotide polymorphisms (SNPs) and four plumage colors. GWAS was used to identify the candidate and potential SNPs of four plumage colors in ducks (white, spot, grey, and black plumage). In addition, FST and genetic diversity (π ratio) were used to screen signals of the selective sweep, which relate to the four plumage colors. Major genomic regions associated with white, spotted, and black feathers overlapped with their candidate selection regions, whereas no such overlap was observed with grey plumage. In addition, MITF and EDNRB2 are functional candidate genes that contribute to white and black plumage due to their indirect involvement in the melanogenesis pathway. This study provides new insights into the genetic factors that may influence the diversity of plumage color.

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“…, missense, frameshift, stop gained or lost) are retained and inconsequential variants are removed. VEP is able of identifying rapidly 4 – 5 million variants (all types, including SNPs) present in a typical sequenced genome [ 32 , 33 ]. A table with present SNVs for all samples was constructed and used for analyses.…”

Section: Methodsmentioning

confidence: 99%

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

Bosquet

Cardillo

Pineda

et al. 2022

IJMS

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The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. Methods: In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. Results: The prediction model with SNV had an AUC of 1.00 (95% CI 1.00–1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. Conclusions: Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

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Annotating and prioritizing genomic variants using the Ensembl Variant Effect Predictor—A tutorial

Cited by 41 publications

References 35 publications

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

Genome-Wide Analysis Identifies Candidate Genes Encoding Feather Color in Ducks

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

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