Comparison of predicted and actual consequences of missense mutations

Miosge, Lisa A.; Field, Matthew A.; Sontani, Yovina; Cho, Vicky; Johnson, Simon C.; Palkova, Anna; Balakishnan, Bhavani; Liang, Rong; Zhang, Yafei; Lyon, Stephen; Beutler, Bruce; Whittle, Belinda; Bertram, Edward M.; Enders, Anselm; Goodnow, Christopher C.; Andrews, T. Daniel

doi:10.1073/pnas.1511585112

Cited by 196 publications

(182 citation statements)

References 91 publications

(59 reference statements)

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“…However, different computational prediction algorithms often give conflicting information. 9,10 Furthermore, a recent evaluation of predictor performance on 21 human disease-associated genes revealed that at sensitive thresholds detecting 90% of pathogenic variation, false predictions are made 30% of the time. 11 At more stringent thresholds yielding errors 10% of the time, only 20% of pathogenic variants are captured.…”

Section: Introductionmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

288

277

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Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of ''lookup tables'' of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

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Section: Introductionmentioning

confidence: 99%

Variant Interpretation: Functional Assays to the Rescue

Starita

Ahituv

Dunham

et al. 2017

The American Journal of Human Genetics

288

277

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“…In patients suffering from a monogenic disease, at most two variants are disease causing [true positives (TP)], and the other 20,000 or so proteincoding exome variants are false positives (FP; type I error). Several variant-level metrics predicting the biochemical impact of DNA mutations (7)(8)(9) can be used to prioritize candidate variants for a phenotype of interest (10,11). Gene-level metrics aim to prioritize the genes themselves, providing information that can be used for the further prioritization of variants.…”

mentioning

confidence: 99%

The human gene damage index as a gene-level approach to prioritizing exome variants

Itan

Shang

Boisson

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

213

228

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The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.

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“…and has a much broader range of data from model organisms. Combined Annotation Dependent Depletion (CADD) 35 and PolyPhen 36 focus on predicting the pathogenicity of an amino acid change. These two tools incorporate a combination of homology, structural, and machine learning analysis to predict whether or not a single amino acid change is likely to disrupt protein function.…”

Section: Discussionmentioning

confidence: 99%

“…37,38 However, there are cases where additional population frequency data and model organism phenotypic data are needed to improve variant interpretation. 35,39 The Monarch Initiative 40 addresses the challenge of annotating the human genome by gathering data on known phenotypes in other organisms (phenotype-centric) to assist in variant analysis whereas MARRVEL provides a gene-centric toolkit including non-vertebrate model organisms and protein alignments. Although most bioinformatics tools and strategies are useful guides, combining multiple resources often provides a better view of the variant and higher predictive value when analyzing variants and genes.…”

Section: Discussionmentioning

confidence: 99%

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

Wang¹,

Al‐Ouran²,

Hu³

et al. 2017

The American Journal of Human Genetics

189

140

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One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

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Comparison of predicted and actual consequences of missense mutations

Cited by 196 publications

References 91 publications

Variant Interpretation: Functional Assays to the Rescue

Variant Interpretation: Functional Assays to the Rescue

The human gene damage index as a gene-level approach to prioritizing exome variants

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

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