Inferring the molecular and phenotypic impact of amino acid variants with MutPred2
Open accessPublished version of a preprint
Abstract
Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.
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The estimated prevalence of pathogenic variants in the gnomAD reference set was 4.41%.
“This procedure yielded a prior probability of pathogenicity (prevalence in the data set) of 4.41%, higher than that estimated previously for an exome of a healthy individual19 using a similar procedure but lower than the prevalence of 10% assumed by Tavtigian et al.9 for clinical sequencing data.”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [19]
The Results section on estimating pathogenic missense variants reports 5.8% for unlabeled training variants and up to 1.3% for variants in a presumably healthy genome, not a prevalence in gnomAD. The Results section on external-tool evaluation mentions gnomAD as a source of unlabeled variants but gives no 4.41% estimate for that set (pages unavailable in our copy). (LLM, conf=0.76)
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationBreiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,380 · see citing works
A method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194 · see citing works
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W.-Z., & Godzik, A. (2006) · Bioinform. · doi:10.1093/bioinformatics/btl158 · Cited by 10,994 · see citing works
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587 · see citing works
Self-citationThe mutational constraint spectrum quantified from variation in 141,456 humans
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434 · see citing works
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658 · see citing works
dbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502 · see citing works
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183 · see citing works
UniProt: a hub for protein information
The UniProt Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku989 · Cited by 5,106 · see citing works
ClinVar: improving access to variant interpretations and supporting evidence
Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1153 · Cited by 4,196 · see citing works
A direct adaptive method for faster backpropagation learning: the RPROP algorithm
Riedmiller, M., & Braun, H. (2002) · IEEE International Conference on Neural Networks · doi:10.1109/icnn.1993.298623 · Cited by 3,917 · see citing works
MutationTaster2: mutation prediction for the deep-sequencing age
Schwarz, J. M., Cooper, D. N., Schuelke, M., & Seelow, D. (2014) · Nature Methods · doi:10.1038/nmeth.2890 · Cited by 3,652 · see citing works
Self-citationClinVar: public archive of interpretations of clinically relevant variants
Landrum, M. J., Lee, J. M., Benson, M., Brown, G., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Hoover, J., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1222 · Cited by 2,758 · see citing works
Synaptic, transcriptional and chromatin genes disrupted in autism
Study, T. D., De Rubeis, S., Autism, H. M. C. F., UK10K Consortium, The Autism Sequencing Consortium, He, X., Goldberg, A. P., Poultney, C. S., Samocha, K., Ercument Cicek, A., et al. (2014) · Nature · doi:10.1038/nature13772 · Cited by 2,746 · see citing works
Predicting Deleterious Amino Acid SubstitutionsPreprint
Ng, P. C., & Henikoff, S. (2001) · Genome Research · doi:10.1101/gr.176601 · Cited by 2,667 · see citing works
The contribution of de novo coding mutations to autism spectrum disorder
Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K. E., et al. (2014) · Nature · doi:10.1038/nature13908 · Cited by 2,585 · see citing works
Detection of nonneutral substitution rates on mammalian phylogenies
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010) · Genome Research · doi:10.1101/gr.097857.109 · Cited by 2,353 · see citing works
Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations
O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., Levy, R., Ko, A., Lee, C., Smith, J. D., et al. (2012) · Nature · doi:10.1038/nature10989 · Cited by 2,165 · see citing works
Rate of de novo mutations and the importance of father’s age to disease risk
Kong, A., Frigge, M. L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S. A., Sigurdsson, A., Jonasdottir, A., Jonasdottir, A., et al. (2012) · Nature · doi:10.1038/nature11396 · Cited by 2,134 · see citing works
De novo mutations revealed by whole-exome sequencing are strongly associated with autism
Sanders, S. J., Murtha, M. T., Gupta, A. R., Murdoch, J. D., Raubeson, M. J., Willsey, A. J., Ercan-Sencicek, A. G., DiLullo, N. M., Parikshak, N. N., Stein, J. L., et al. (2012) · Nature · doi:10.1038/nature10945 · Cited by 2,084 · see citing works
Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++
Davydov, E. V., Goode, D. L., Sirota, M., Cooper, G. M., Sidow, A., & Batzoglou, S. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001025 · Cited by 1,757 · see citing works
Patterns and rates of exonic de novo mutations in autism spectrum disorders
Neale, B. M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K. E., Sabo, A., Lin, C.-F., Stevens, C., Wang, L.-S., Makarov, V., et al. (2012) · Nature · doi:10.1038/nature11011 · Cited by 1,734 · see citing works
De novo mutations in schizophrenia implicate synaptic networks
Fromer, M., Pocklington, A. J., Kavanagh, D. H., Williams, H. J., Dwyer, S., Gormley, P., Georgieva, L., Rees, E., Palta, P., Ruderfer, D. M., et al. (2014) · Nature · doi:10.1038/nature12929 · Cited by 1,617 · see citing works
Diagnostic exome sequencing in persons with severe intellectual disability
de Ligt, J., Willemsen, M. H., van Bon, B. W. M., Kleefstra, T., Yntema, H. G., Kroes, T., Vulto-van Silfhout, A. T., Koolen, D. A., de Vries, P., Gilissen, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1206524 · Cited by 1,546 · see citing works
De novo mutations in epileptic encephalopathies
Epi4K Consortium, & Epilepsy Phenome/Genome Project (2013) · Nature · doi:10.1038/nature12439 · Cited by 1,514 · see citing works
De Novo Gene Disruptions in Children on the Autistic Spectrum
Iossifov, I., Ronemus, M., Levy, D., Wang, Z., Hakker, I., Rosenbaum, J., Yamrom, B., Lee, Y.-H., Narzisi, G., Leotta, A., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.009 · Cited by 1,446 · see citing works
The BioGRID interaction database: 2019 update
Oughtred, R., Stark, C., Breitkreutz, B., Rust, J., Boucher, L., Chang, C., Kolas, N. K., O’Donnell, L., Leung, G. C., McAdam, R. F., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1079 · Cited by 1,440 · see citing works
Stenson, P. D., Mort, M. E., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., & Cooper, D. N. (2017) · Human Genetics · doi:10.1007/s00439-017-1779-6 · Cited by 1,348 · see citing works
Self-citationA Proteome-Scale Map of the Human Interactome Network
Rolland, T., Taşan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., Yi, S. S., Lemmens, I. H., Fontanillo, C., Mosca, R., et al. (2014) · Cell · doi:10.1016/j.cell.2014.10.050 · Cited by 1,342 · see citing works
Self-citationShihab, H. A., Gough, J., Cooper, D. N., Stenson, P. D., Barker, G., Edwards, K. J., Day, I. N. M., & Gaunt, T. R. (2012) · Human Mutation · doi:10.1002/humu.22225 · Cited by 1,319 · see citing works
Self-citationMultiplex Targeted Sequencing Identifies Recurrently Mutated Genes in Autism Spectrum Disorders
O’Roak, B. J., Vives, L., Fu, W., Egertson, J. D., Stanaway, I. B., Phelps, I. G., Carvill, G., Kumar, A., Lee, C., Ankenman, K., et al. (2012) · Science · doi:10.1126/science.1227764 · Cited by 1,240 · see citing works
Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations
O'Roak, B. J., Deriziotis, P., Lee, C., Vives, L., Schwartz, J. J., Girirajan, S., Karakoc, E., MacKenzie, A. P., Ng, S. B., Baker, C., et al. (2011) · Nature Genetics · doi:10.1038/ng.835 · Cited by 1,203 · see citing works
Gene splicing and mutagenesis by PCR-driven overlap extension
Heckman, K. L., & Pease, L. R. (2007) · Nature Protocols · doi:10.1038/nprot.2007.132 · Cited by 1,150 · see citing works
Genome sequencing identifies major causes of severe intellectual disability
Gilissen, C., Hehir-Kwa, J. Y., Thung, D. T., van de Vorst, M., van Bon, B. W. M., Willemsen, M. H., Kwint, M., Janssen, I. M., Hoischen, A., Schenck, A., et al. (2014) · Nature · doi:10.1038/nature13394 · Cited by 1,125 · see citing works
Large-scale discovery of novel genetic causes of developmental disorders
Study, T. D. D. D. (2015) · Nature · doi:10.1038/nature14135 · Cited by 1,124 · see citing works
Dong, C., Wei, P., Jian, X., Gibbs, R. A., Boerwinkle, E., Wang, K., & Liu, X. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu733 · Cited by 1,106 · see citing works
Prediction of deleterious human alleles
Sunyaev, S. R. (2001) · Human Molecular Genetics · doi:10.1093/hmg/10.6.591 · Cited by 1,042 · see citing works
Rauch, A., Wieczorek, D., Graf, E., Wieland, T., Endele, S., Schwarzmayr, T., Albrecht, B., Bartholdi, D., Beygo, J., Di Donato, N., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61480-9 · Cited by 1,041 · see citing works
Liu, X., Wu, C., Li, C., & Boerwinkle, E. (2015) · Human Mutation · doi:10.1002/humu.22932 · Cited by 957 · see citing works
The BioGRID interaction database: 2015 update
Chatr‐aryamontri, A., Breitkreutz, B., Oughtred, R., Boucher, L., Heinicke, S., Chen, D., Stark, C., Breitkreutz, A., Kolas, N. K., O’Donnell, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1204 · Cited by 884 · see citing works
Automated inference of molecular mechanisms of disease from amino acid substitutions
Li, B., Krishnan, V. G., Mort, M. E., Xin, F., Kamati, K. K., Cooper, D. N., Mooney, S. D., & Predrag Radivojac, A. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp528 · Cited by 818 · see citing works
Self-citationStenson, P. D., Mort, M. E., Ball, E. V., Chapman, M., Evans, K., Azevedo, L., Hayden, M., Heywood, S., Millar, D. S., Phillips, A. D., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02199-3 · Cited by 793 · see citing works
Self-citationSNPs, protein structure, and disease
Wang, Z., & Moult, J. (2001) · Human Mutation · doi:10.1002/humu.22 · Cited by 726 · see citing works
Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies
Schnoes, A. M., Brown, S. D., Dodevski, I., & Babbitt, P. C. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000605 · Cited by 695 · see citing works
Accounting for Human Polymorphisms Predicted to Affect Protein FunctionPreprint
Ng, P. C., & Henikoff, S. (2002) · Genome Research · doi:10.1101/gr.212802 · Cited by 695 · see citing works
Functional annotations improve the predictive score of human disease-related mutations in proteins
Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L., & Casadio, R. (2009) · Human Mutation · doi:10.1002/humu.21047 · Cited by 685 · see citing works
The UCSC Genome Browser database: 2014 update
Karolchik, D., Barber, G. P., Casper, J., Clawson, H., Cline, M. S., Diekhans, M., Dreszer, T. R., Fujita, P. A., Guruvadoo, L., Haeussler, M., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1168 · Cited by 670 · see citing works
Allosteric binding sites on cell-surface receptors: novel targets for drug discovery
Christopoulos, A. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd746 · Cited by 665 · see citing works
Positive and Negative Selection on the Human Genome
Fay, J. C., Wyckoff, G. J., & Wu, C. (2001) · Genetics · doi:10.1093/genetics/158.3.1227 · Cited by 646 · see citing works
Francioli, L. C., Menelaou, A., Pulit, S. L., Elbers, C. C., Kloosterman, W. P., van Setten, J., Nijman, I. J., Renkens, I., de Bakker, P. I. W., van Dijk, F., et al. (2014) · Nature Genetics · doi:10.1038/ng.3021 · Cited by 642 · see citing works
Widespread macromolecular interaction perturbations in human genetic disorders
Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., et al. (2015) · Cell · doi:10.1016/j.cell.2015.04.013 · Cited by 594 · see citing works
Status of Large-scale Analysis of Post-translational Modifications by Mass Spectrometry
Olsen, J. V., & Mann, M. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o113.034181 · Cited by 586 · see citing works
Identifying Mendelian disease genes with the Variant Effect Scoring Tool
Carter, H. K., Douville, C. B., Stenson, P. D., Cooper, D. N., & Karchin, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-s3-s3 · Cited by 566 · see citing works
Self-citationNeedles in stacks of needles: finding disease-causal variants in a wealth of genomic data
Cooper, G. M., & Shendure, J. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3046 · Cited by 563 · see citing works
Xie, H. M., Vučetić, S., Iakoucheva, L. M., Oldfield, C. J., Dunker, A. K., Uversky, V. N., & Zoran Obradovic, A. (2007) · Journal of Proteome Research · doi:10.1021/pr060392u · Cited by 556 · see citing works
Self-citationPerformance of mutation pathogenicity prediction methods on missense variants
Thusberg, J., Olatubosun, A., & Vihinen, M. (2011) · Human Mutation · doi:10.1002/humu.21445 · Cited by 537 · see citing works
Gulsuner, S., Walsh, T., Watts, A. C., Lee, M. K., Thornton, A. M., Casadei, S., Rippey, C., Shahin, H., Braff, D., Cadenhead, K. S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.049 · Cited by 536 · see citing works
Whole-genome sequencing of quartet families with autism spectrum disorder
Yuen, R. K. C., Thiruvahindrapuram, B., Merico, D., Walker, S., Tammimies, K., Hoang, N., Chrysler, C., Nalpathamkalam, T., Pellecchia, G., Liu, Y., et al. (2015) · Nature Medicine · doi:10.1038/nm.3792 · Cited by 514 · see citing works
Gene hunting in autism spectrum disorder: on the path to precision medicine
Geschwind, D. H., & State, M. W. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00044-7 · Cited by 481 · see citing works
Jiang, Y.-H., Yuen, R. K. C., Jin, X., Wang, M., Chen, N., Wu, X., Ju, J., Mei, J., Shi, Y., He, M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.012 · Cited by 474 · see citing works
Exome sequencing supports a de novo mutational paradigm for schizophrenia
Xu, B., Roos, J. L., Dexheimer, P. J., Boone, B. E., Plummer, B., Levy, S., Gogos, J. A., & Karayiorgou, M. (2011) · Nature Genetics · doi:10.1038/ng.902 · Cited by 461 · see citing works
Dudev, T., & Lim, C. (2013) · Chemical Reviews · doi:10.1021/cr4004665 · Cited by 454 · see citing works
De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia
Xu, B., Ionita-Laza, I., Roos, J. L., Boone, B., Woodrick, S., Sun, Y., Levy, S., Gogos, J. A., & Karayiorgou, M. (2012) · Nature Genetics · doi:10.1038/ng.2446 · Cited by 442 · see citing works
De Novo Mutations in Synaptic Transmission Genes Including DNM1 Cause Epileptic Encephalopathies
Appenzeller, S., Balling, R., Barisic, N., Baulac, S., Caglayan, H., Craiu, D., De Jonghe, P., Depienne, C., Dimova, P., Djémié, T., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.08.013 · Cited by 439 · see citing works
Increased exonic de novo mutation rate in individuals with schizophrenia
Girard, S. L., Gauthier, J., Noreau, A., Xiong, L., Zhou, S., Jouan, L., Dionne-Laporte, A., Spiegelman, D., Henrion, E., Diallo, O., et al. (2011) · Nature Genetics · doi:10.1038/ng.886 · Cited by 438 · see citing works
Network of immune-neuroendocrine interactions.
Besedovsky, H. O., & Sorkin, E. (1977) · PubMed · Cited by 410 · see citing works
Chasman, D. I., & Adams, R. M. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4510 · Cited by 393 · see citing works
Proportionally more deleterious genetic variation in European than in African populations
Lohmueller, K. E., Indap, A. R., Schmidt, S., Boyko, A. R., Hernandez, R. D., Hubisz, M. J., Sninsky, J. J., White, T. J., Sunyaev, S. R., Nielsen, R., et al. (2008) · Nature · doi:10.1038/nature06611 · Cited by 363 · see citing works
Edgetic perturbation models of human inherited disorders
Zhong, Q., Simonis, N., Li, Q., Charloteaux, B., Heuze, F., Klitgord, N., Tam, S., Yu, H., Venkatesan, K., Mou, D., et al. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.80 · Cited by 359 · see citing works
McCarthy, S. E., Gillis, J., Kramer, M., Lihm, J., Yoon, S. C., Berstein, Y., Mistry, M., Pavlidis, P., Solomon, R., Ghiban, E., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.29 · Cited by 332 · see citing works
Xue, Y., Chen, Y., Ayub, Q., Huang, N., Ball, E. V., Mort, M. E., Phillips, A. D., Shaw, K., Stenson, P. D., Cooper, D. N., et al. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.10.015 · Cited by 275 · see citing works
Self-citationDrugs for Allosteric Sites on Receptors
Wenthur, C. J., Gentry, P. R., Mathews, T. P., & Lindsley, C. W. (2011) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010611-134525 · Cited by 274 · see citing works
Turner, T. N., Hormozdiari, F., Duyzend, M., McClymont, S. A., Hook, P. W., Iossifov, I. H., Raja, A. N., Baker, C. A., Hoekzema, K., Stessman, H. A. F., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.023 · Cited by 263 · see citing works
Getting to the cores of autism
Iakoucheva, L. M., Muotri, A. R., & Sebat, J. (2019) · Cell · doi:10.1016/j.cell.2019.07.037 · Cited by 260 · see citing works
Self-citationKondrashov, A. S. (1995) · Journal of Theoretical Biology · doi:10.1006/jtbi.1995.0167 · Cited by 237 · see citing works
Protein interactome reveals converging molecular pathways among autism disorders
Sakai, Y., Shaw, C. A., Dawson, B. C., Dugas, D. V., Al-Mohtaseb, Z., Hill, D. E., & Zoghbi, H. Y. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002166 · Cited by 226 · see citing works
Comparison of predicted and actual consequences of missense mutations
Miosge, L. A., Field, M. A., Sontani, Y., Cho, V., Johnson, S., Palkova, A., Balakishnan, B., Liang, R., Zhang, Y., Lyon, S., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1511585112 · Cited by 219 · see citing works
Learning from positive and unlabeled examples
Denis, F., Gilleron, R., & Letouzey, F. (2005) · Theoretical Computer Science · doi:10.1016/j.tcs.2005.09.007 · Cited by 219 · see citing works
Genome-wide characteristics of de novo mutations in autism
Yuen, R. K., Merico, D., Cao, H., Pellecchia, G., Alipanahi, B., Thiruvahindrapuram, B., Tong, X., Sun, Y., Cao, D., Zhang, T., et al. (2016) · npj Genomic Medicine · doi:10.1038/npjgenmed.2016.27 · Cited by 216 · see citing works
CHD8 Is an ATP-Dependent Chromatin Remodeling Factor That Regulates β-Catenin Target Genes
Thompson, B. A., Tremblay, V., Lin, G., & Bochar, D. A. (2008) · Molecular and Cellular Biology · doi:10.1128/mcb.00322-08 · Cited by 208 · see citing works
Self-citationBuxbaum, J. D., Daly, M. J., Devlin, B., Lehner, T., Roeder, K., & State, M. W. (2012) · Neuron · doi:10.1016/j.neuron.2012.12.008 · Cited by 185 · see citing works
Humbert, S., Subedi, S. D., Cohn, J., Zeng, B., Bi, Y., Chen, X. X., Zhu, T., McNicholas, P. D., & Rothstein, S. J. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-3 · Cited by 177 · see citing works
Lin, G. N., Corominas, R., Lemmens, I., Yang, X., Tavernier, J., Hill, D. E., Vidal, M., Sebat, J., & Iakoucheva, L. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.010 · Cited by 155 · see citing works
Self-citationEasy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar
Mottaz, A., David, F. P. A., Veuthey, A., & Yip, Y. L. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq028 · Cited by 155 · see citing works
Edgotype: a fundamental link between genotype and phenotype
Sahni, N., Yi, S. S., Zhong, Q., Jailkhani, N., Charloteaux, B., Cusick, M. E., & Vidal, M. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.11.002 · Cited by 147 · see citing works
Corominas, R., Yang, X., Lin, G. N., Kang, S., Shen, Y., Ghamsari, L., Broly, M., Rodriguez, M., Tam, S., Wanamaker, S. A., et al. (2014) · Nature Communications · doi:10.1038/ncomms4650 · Cited by 140 · see citing works
Self-citationLearning from Corrupted Binary Labels via Class-Probability Estimation
Menon, A. K., van Rooyen, B., Ong, C. S., & Williamson, B. (2015) · ANU Open Research (Australian National University) · Cited by 135 · see citing works
Schnoes, A. M., Ream, D. C., Thorman, A. W., Babbitt, P. C., & Friedberg, I. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003063 · Cited by 129 · see citing works
Spectrum of disease-causing mutations in protein secondary structures
Khan, S., & Vihinen, M. (2007) · BMC Structural Biology · doi:10.1186/1472-6807-7-56 · Cited by 125 · see citing works
Towards precision medicine: advances in computational approaches for the analysis of human variants
Peterson, T. A., Doughty, E., & Kann, M. G. (2013) · Journal of Molecular Biology · doi:10.1016/j.jmb.2013.08.008 · Cited by 123 · see citing works
Variation Interpretation Predictors: Principles, Types, Performance, and Choice
Niroula, A., & Vihinen, M. (2016) · Human Mutation · doi:10.1002/humu.22987 · Cited by 121 · see citing works
The ORFeome Collaboration: a genome-scale human ORF-clone resource
Wiemann, S., Jöcker, A., Wellenreuther, R., Heil, O., Hotz‐Wagenblatt, A., Pennacchio, C., Hu, Y., Hunter, P. D., Park, J., Seiler, C., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3776 · Cited by 117 · see citing works
Role of Duplicate Genes in Robustness against Deleterious Human Mutations
Hsiao, T., & Vitkup, D. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000014 · Cited by 96 · see citing works
Classification with asymmetric label noise: Consistency and maximal denoising
Blanchard, G., Flaska, M., Handy, G., Pozzi, S. A., & Scott, C. (2016) · Electronic Journal of Statistics · doi:10.1214/16-ejs1193 · Cited by 93 · see citing works
Whole-exome sequencing and neurite outgrowth analysis in autism spectrum disorder
Hashimoto, R., Nakazawa, T., Tsurusaki, Y., Yasuda, Y., Nagayasu, K., Matsumura, K., Kawashima, H., Yamamori, H., Fujimoto, M., Ohi, K., et al. (2015) · Journal of Human Genetics · doi:10.1038/jhg.2015.141 · Cited by 91 · see citing works
Exome Sequencing in 53 Sporadic Cases of Schizophrenia Identifies 18 Putative Candidate Genes
Guipponi, M., Santoni, F. A., Setola, V., Gehrig, C., Rothärmel, M., Cuenca, M., Guillin, O., Dikeos, D. G., Georgantopoulos, G., Papadimitriou, G. N., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112745 · Cited by 89 · see citing works
Analysis of conditional heterozygous STXBP1 mutations in human neurons
Patzke, C., Han, Y., Covy, J. P., Yi, F., Maxeiner, S., Wernig, M., & Südhof, T. C. (2015) · Journal of Clinical Investigation · doi:10.1172/jci78612 · Cited by 83 · see citing works
Estimating the class prior and posterior from noisy positives and unlabeled dataPreprint
Jain, S., White, M., & Radivojac, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.08561 · Cited by 83 · see citing works
Self-citationEvolutionary diagnosis method for variants in personal exomes
Kumar, S., Sanderford, M. D., Gray, V. E., Ye, J., & Liu, L. (2012) · Nature Methods · doi:10.1038/nmeth.2147 · Cited by 64 · see citing works
Variation Ontology for annotation of variation effects and mechanismsPreprint
Vihinen, M. (2013) · Genome Research · doi:10.1101/gr.157495.113 · Cited by 62 · see citing works
Human Monogenic Disease Genes Have Frequently Functionally Redundant Paralogs
Chen, W., Zhao, X., van Noort, V., & Peer Bork, B. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003073 · Cited by 59 · see citing works
Protein function in precision medicine: deep understanding with machine learning
Rost, B., Predrag Radivojac, A., & Bromberg, Y. (2016) · FEBS Letters · doi:10.1002/1873-3468.12307 · Cited by 51 · see citing works
Recovering True Classifier Performance in Positive-Unlabeled Learning
Jain, S., White, M., & Predrag Radivojac, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10937 · Cited by 49 · see citing works
From De Novo Mutations to Personalized Therapeutic Interventions in Autism
Brandler, W. M., & Sebat, J. L. (2015) · Annual Review of Medicine · doi:10.1146/annurev-med-091113-024550 · Cited by 41 · see citing works
Self-citationLatinne, P., Saerens, M., & Decaestecker, C. (2001) · Cited by 36 · see citing works
Das, J., Lee, H. R., Sagar, A., Fragoza, R., Liang, J. H., Wei, X., Wang, X., Mort, M. E., Stenson, P. D., Cooper, D. N., et al. (2014) · Human Mutation · doi:10.1002/humu.22534 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Pejaver, V., Byrne, A. B., Feng, B.-J., Pagel, K. A., Mooney, S. D., Karchin, R., O’Donnell-Luria, A., Harrison, S. M., Tavtigian, S. V., Greenblatt, M. S., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.10.013 · Cited by 589 · see citing works
“Interest-ingly, the local posterior probability curves showed that, at appropriate thresholds, several tools could provide strong evidence for pathogenicity (BayesDel, VEST4), benignity (CADD), or both (MutPred2, REVEL), as shown in Figure 3, Table 2, and Table S1.”
MethodResultInfluentialSelf-citationBig Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325 · see citing works
Self-citationStein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53 · see citing works
Self-citationRitelli, M., Cinquina, V., Venturini, M., Pezzaioli, L., Formenti, A. M., Chiarelli, N., & Colombi, M. (2019) · Genes · doi:10.3390/genes10020135 · Cited by 33 · see citing works
Oppermann, H., Marcos-Grañeda, E., Weiss, L. A., Gurnett, C. A., Jelsig, A. M., Vineke, S. H., Isidor, B., Mercier, S., Magnussen, K., Zacher, P., et al. (2023) · European Journal of Human Genetics · doi:10.1038/s41431-023-01445-2 · Cited by 18 · see citing works
Li, C., Zhang, L., Zhuo, Z., Su, F., Li, H., Xu, S., Liu, Y., Zhang, Z., Xie, Y., Yu, X., et al. (2023) · Theranostics · doi:10.7150/thno.79362 · Cited by 10 · see citing works
Hossen, M. A., Jahan, U. M. S., Hossain, M. A., Asif, K. H., Rahman, A., Ahmed, S., Uddin, M. M., Amin, M. F., Barik, M. A., Islam, M. J., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0320550 · Cited by 8 · see citing works
Heterozygous loss-of-function variants in DOCK4 cause neurodevelopmental delay and microcephaly
Herbst, C., Bothe, V., Wegler, M., Axer-Schaefer, S., Audebert-Bellanger, S., Gecz, J., Cogne, B., Feldman, H. B., Horn, A. H. C., Hurst, A. C. E., et al. (2024) · Human Genetics · doi:10.1007/s00439-024-02655-4 · Cited by 7 · see citing works
“0.0 (Karczewski et al. 2020) below 1%, impact on protein function via different in silico programs (CADD (Rentzsch et al. 2021), REVEL (Ioannidis et al. 2016), MutPred2 (Pejaver et al. 2020) and Metadome (Wiel et al. 2019)) and involvement of candidate genes in neuronal processes.”
BackgroundInfluentialUtilizing protein structure graph embeddings to predict the pathogenicity of missense variants
Danner, M., Begemann, M., Elbracht, M., Kurth, I., & Krause, J. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf097 · Cited by 2 · see citing works
- Authors
- Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., Mort, M., Cooper, D. N., Sebat, J., Iakoucheva, L. M., Mooney, S. D., & Radivojac, P.
- Affiliations
- University of Washington (US)
- University of California San Diego (US)
- Indiana University Bloomington (US)
- Johns Hopkins University (US)
- Shanghai Jiao Tong University (CN)
- Cardiff University (GB)
- Corresponding author
- Iakoucheva, Lilia M.
- Mooney, Sean D.
- ORCID
- Venue
- Nature Communications
- Published
- 2020-11-20
- Volume
- 11
- Issue
- 1
- Pages
- 5918-5918
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7680112
- OpenAlex
- W3104285666
- Cited by
- 923
- References
- 110
- Citations by year
- 2026: 114 · 2025: 187 · 2024: 176 · 2023: 134 · 2022: 94 · 2021: 92 · 2020: 58 · 2019: 41 · 2018: 20 · 2017: 5
- FWCI
- 56.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Washington Research Foundation (Fund for Innovation in Data-Intensive Discovery)
- Gordon and Betty Moore Foundation (Moore/Sloan Data Science Environments Project)
- Alfred P. Sloan Foundation (Moore/Sloan Data Science Environments Project)
- U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (K99 LM012992, R01 LM009722)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01 MH076431, R01 MH104766, R01 MH109885, R01 MH105524)
- Simons Foundation (345469)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- amino acid substitutions, pathogenicity prediction, protein structure-function relationship, monogenic disorders, de novo mutations, neurodevelopmental disorders, genetic disorders
- MeSH terms
- Humans, Mutation, Phenotype, Proteins, Software, Models, Statistical, Genome, Human, Computational Biology, Amino Acid Substitution, Genetic Predisposition to Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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