PON-P: Integrated predictor for pathogenicity of missense variants

Olatubosun, Ayodeji; Väliaho, Jouni; Härkönen, Jani; Thusberg, Janita; Vihinen, Mauno

doi:10.1002/humu.22102

Cited by 87 publications

(72 citation statements)

References 63 publications

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“…An increasingly common approach has been to (1) evaluate multiple existing prediction methods, (2) select some number of top-performing methods relative to a given benchmark data set and a particular metric of performance, and (3) train a machine-learning classifier using as features the scores from each of the top-performing methods. PON-P was the first of these, integrating a conservation method (SIFT), a structural stabilitybased method (I-Mutant), and three combined machinelearning-based methods (SNAP, PolyPhen-2, PhD-SNP), using Random Forests to make a final prediction (Olatubosun et al 2012). Numerous other studies have since followed this general approach, such as Meta-SNP (Capriotti et al 2013a), CoVEC (Frousios et al 2013), PredictSNP (Bendl et al 2014), and Meta-SVM (Dong et al 2015).…”

Section: Meta-prediction Methodsmentioning

confidence: 99%

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tang¹,

Thomas

2016

Genetics

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As personal genome sequencing becomes a reality, understanding the effects of genetic variants on phenotype-particularly the impact of germline variants on disease risk and the impact of somatic variants on cancer development and treatment-continues to increase in importance. Because of their clear potential for affecting phenotype, nonsynonymous genetic variants (variants that cause a change in the amino acid sequence of a protein encoded by a gene) have long been the target of efforts to predict the effects of genetic variation. Whole-genome sequencing is identifying large numbers of nonsynonymous variants in each genome, intensifying the need for computational methods that accurately predict which of these are likely to impact disease phenotypes. This review focuses on nonsynonymous variant prediction with two aims in mind: (1) to review the prioritization methods that have been developed to date and the principles on which they are based and (2) to discuss the challenges to further improving these methods.

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Section: Meta-prediction Methodsmentioning

confidence: 99%

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tang¹,

Thomas

2016

Genetics

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“…To distinguish variants from local polymorphisms between 200 and 248 anonymized in‐house normal control blood samples were sequenced over the positions of interest. Variant were interpreted for their deleteriousness by using SIFT (Sorting Intolerant From Tolerant), PolyPhen‐2, (Polymorphism Phenotyping version 2), Mutation Taster, Condel (CONsensus DELeteriousness score of missense SNVs), and PON‐P (Pathogenic‐or‐Not–Pipeline) (Sunyaev et al, 2001; Kumar et al, 2009; Schwarz et al, 2010; Gonzalez‐Perez and Lopez‐Bigas, 2011; Olatubosun et al, 2012). Splice‐site prediction of variants was performed with SpliceSiteFinder‐like, MaxEntScan, NNSPLICE, GeneSplicer and Human Splicefinder (Reese et al, 1997; Zhang, 1998; Pertea et al, 2001; Fairbrother et al, 2002; Cartegni et al, 2003; Yeo and Burge, 2004; Desmet et al, 2009; Houdayer et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

GREM1 and POLE variants in hereditary colorectal cancer syndromes

Rohlin

Eiengård

Lundstam

et al. 2015

Genes Chromosomes & Cancer

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Hereditary factors are thought to play a role in at least one third of patients with colorectal cancer (CRC) but only a limited proportion of these have mutations in known high‐penetrant genes. In a relatively large part of patients with a few or multiple colorectal polyps the underlying genetic cause of the disease is still unknown. Using exome sequencing in combination with linkage analyses together with detection of copy‐number variations (CNV), we have identified a duplication in the regulatory region of the GREM1 gene in a family with an attenuated/atypical polyposis syndrome. In addition, 107 patients with colorectal cancer and/or polyposis were analyzed for mutations in the candidate genes identified. We also performed screening of the exonuclease domain of the POLE gene in a subset of these patients. The duplication of 16 kb in the regulatory region of GREM1 was found to be disease‐causing in the family. Functional analyses revealed a higher expression of the GREM1 gene in colorectal tissue in duplication carriers. Screening of the exonuclease domain of POLE in additional CRC patients identified a probable causative novel variant c.1274A>G, p.Lys425Arg. In conclusion a high penetrant duplication in the regulatory region of GREM1, predisposing to CRC, was identified in a family with attenuated/atypical polyposis. A POLE variant was identified in a patient with early onset CRC and a microsatellite stable (MSS) tumor. Mutations leading to increased expression of genes can constitute disease‐causing mutations in hereditary CRC syndromes. © 2015 The Authors. Genes, Chromosomes & Cancer Published by Wiley Periodicals, Inc.

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“…MutationTaster 13 classifies single nucleotide variants (SNVs) and small insertion/deletion polymorphisms (indels) as polymorphic or pathogenic. PolyPhen-2 14 and PON-P 15 only predict the effects of non-synonymous SNVs that result in amino acid replacement. PolyPhen-2 classifies the variants as benign, possibly pathogenic or probably pathogenic, whereas PON-P defines them as neutral, unclassified or pathogenic.…”

Section: Methodsmentioning

confidence: 99%

Fine‐mapping the 2q37 and 17q11.2‐q22 loci for novel genes and sequence variants associated with a genetic predisposition to prostate cancer

Laitinen

Rantapero

Fischer

et al. 2014

Intl Journal of Cancer

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The 2q37 and 17q12-q22 loci are linked to an increased prostate cancer (PrCa) risk. No candidate gene has been localized at 2q37 and the HOXB13 variant G84E only partially explains the linkage to 17q21-q22 observed in Finland. We screened these regions by targeted DNA sequencing to search for cancer-associated variants. Altogether, four novel susceptibility alleles were identified. Two ZNF652 (17q21.3) variants, rs116890317 and rs79670217, increased the risk of both sporadic and hereditary PrCa (rs116890317: OR = 3.3 – 7.8, P = 0.003 – 3.3 × 10−5; rs79670217: OR = 1.6 – 1.9, P = 0.002 – 0.009). The HDAC4 (2q37.2) variant rs73000144 (OR = 14.6, P = 0.018) and the EFCAB13 (17q21.3) variant rs118004742 (OR = 1.8, P = 0.048) were overrepresented in patients with familial PrCa. To map the variants within 2q37 and 17q11.2-q22 that may regulate PrCa-associated genes, we combined DNA sequencing results with transcriptome data obtained by RNA sequencing. This expression quantitative trait locus (eQTL) analysis identified 272 SNPs possibly regulating six genes that were differentially expressed between cases and controls. In a modified approach, pre-filtered PrCa-associated SNPs were exploited and interestingly, a novel eQTL targeting ZNF652 was identified. The novel variants identified in this study could be utilized for PrCa risk assessment, and they further validate the suggested role of ZNF652 as a PrCa candidate gene. The regulatory regions discovered by eQTL mapping increase our understanding of the relationship between regulation of gene expression and susceptibility to PrCa and provide a valuable starting point for future functional research.

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PON-P: Integrated predictor for pathogenicity of missense variants

Cited by 87 publications

References 63 publications

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation

GREM1 and POLE variants in hereditary colorectal cancer syndromes

Fine‐mapping the 2q37 and 17q11.2‐q22 loci for novel genes and sequence variants associated with a genetic predisposition to prostate cancer

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