dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs

Liu, Xiaoming; Li, Chang; Mou, Chengcheng; Dong, Yibo; Tu, Yi-Cheng

doi:10.1186/s13073-020-00803-9

Cited by 392 publications

(370 citation statements)

References 56 publications

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“…Logical clustering of the functional annotation weight emerged, including NAFLD weighting liver cell types higher than any other disease, and autoimmune disease (ulcerative colitis, crohn’s disease, celiac disease and rheumatoid arthritis) all weighting enhancers-1 higher than any other disease (Figure 6D, Table S40-S46). Similar deleterious weights were created from SIFT, PROVEAN and other scores 34,35 , although a meaningful trend did not emerge (Table S47-S48). The clear patterns that emerged from these analyses show that it is likely possible to limit a PRS’s component variants to a strict functional class and still maintain high predictive accuracy.…”

Section: Resultsmentioning

confidence: 95%

A systematic framework for assessing the clinical impact of polygenic risk scores

Kulm

Mezey

Elemento

2020

Preprint

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The estimate of an individual's genetic susceptibility to a disease can provide critical information when setting screening schedules, prescribing medication and making lifestyle change recommendations. The polygenic risk score is the predominant susceptibility metric, with many methods available to describe its construction. However, these methods have never been comprehensively compared or the predictive value of their outputs systematically assessed, leaving the clinical utility of polygenic risk scores uncertain. This study aims to resolve this uncertainty by deeply comparing the maximum possible, currently available, 15 polygenic risk scoring methods to 25 well-powered, UK Biobank derived, disease phenotypes. Our results show that simpler methods, which employ heuristics, bested complex, methods, which predominately model linkage disequilibrium. Accuracy was assessed with AUC improvement, the difference in area under the receiver operating curve generated by two logistic regression models, both of which share the covariates of age, sex, and principal components, while the second model also contains the polygenic risk score. To better determine the maximal utility of polygenic risk scores, straightforward score ensembles, which bested all methods across all traits in the training data-set, were evaluated in the withheld data-set. The score ensembles revealed that the accuracy gained by considering a polygenic risk score varied greatly, with AUC improvement greater than 0.05 for 9 traits. Many additional analyses revealed widespread pleiotropy across scores, large variations between assessment statistics, peculiar patterns amongst phenotype definitions, and wide ranges in the optimal number of variants used for scoring. If these many variable aspects of score creation can be well controlled and documented, simple methods can easily generate polygenic risk score that well predict an individual's future liability of certain diseases.

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Section: Resultsmentioning

confidence: 95%

A systematic framework for assessing the clinical impact of polygenic risk scores

Kulm

Mezey

Elemento

2020

Preprint

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“…dbNSFP database, which comprehensive functional prediction and annotation for human nonsynonymous and splice-site SNVs, was valuable resource for training set construction in pathogenicity prediction of novel variants. 40…”

Section: Discussionmentioning

confidence: 99%

The global carrier frequency and genetic prevalence of Upshaw-Schulman syndrome

Zhao

Fan

Sun³

2021

Preprint

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Purpose: Upshaw-Schulman syndrome (USS) is an autosomal recessive disease of thrombotic microangiopathy, caused by pathogenic variants in ADAMTS13. We aimed to (1) perform data mining pathogenicity of ADAMTS13 variants, (2) estimate carrier frequency and genetic prevalence of USS from gnomAD data, and (3) curated ADAMTS13 gene pathogenic variants database. Methods: PubMed and Scopus were comprehensive retrieved. All previously reported pathogenic ADAMTS13 variants were compiled and annotated with gnomAD allele frequencies. Pooled global and population-specific carrier frequency and genetic prevalence for USS were calculated using Hardy-Weinberg equation. Results: we mined reported disease-causing variants, of these were present in gnomAD exomes v2.1.1, filtering by allele frequency, pathogenicity of variants were classified by American College of Medical Genetics and Genomics criteria. The genetic prevalence and carrier frequency of USS was 0.43 per 1 million (95% CI: [0.36, 0.55]) and 1.31 per thousand, respectively. Combining known with novel pathogenic/likely pathogenic variants, the genetic prevalence and carrier frequency are 1 per 1 million (95% CI: [0.89, 1.37]) and 2.1 per thousand, respectively. Conclusion: the genetic prevalence and carrier frequency of Upshaw-Schulman syndrome are within range of previously rough estimated.

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“…Variant annotation was achieved using the Highlander software (Helaers et al., under revision, https://sites.uclouvain.be/highlander/) or the VarSome database (https://varsome.com/), 7 both annotating variants using dbNSFP. 8…”

Section: Case Descriptionmentioning

confidence: 99%

Pulmonary arterial hypertension‐associated genetic variants in combined post‐capillary and pre‐capillary pulmonary hypertension: a case report

et al. 2021

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dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs

Cited by 392 publications

References 56 publications

A systematic framework for assessing the clinical impact of polygenic risk scores

A systematic framework for assessing the clinical impact of polygenic risk scores

The global carrier frequency and genetic prevalence of Upshaw-Schulman syndrome

Pulmonary arterial hypertension‐associated genetic variants in combined post‐capillary and pre‐capillary pulmonary hypertension: a case report

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