Author Correction: Predicting the clinical impact of human mutation with deep neural networks (Nature Genetics, (2018), 50, 8, (1161-1170), 10.1038/s41588-018-0167-z);Predicting the clinical impact of human mutation with deep neural networks

Nature Genetics

Volumn 50, Issue 8, 2018, Pages 1161-1170

  Sundaram, Laksshman ^a,b,c   Gao, Hong ^a   Padigepati, Samskruthi Reddy ^a,c   McRae, Jeremy F ^a   Li, Yanjun ^c   Kosmicki, Jack A ^a,d   Fritzilas, Nondas ^a   Hakenberg, Jörg ^a   Dutta, Anindita ^a   Shon, John ^a   Xu, Jinbo ^e   Batzloglou, Serafim ^a   Li, Xiaolin ^c   Farh, Kyle Kai How ^a  

^a ILLUMINA INC   (United States)

^b Stanford University   (United States)

^c UNIVERSITY OF FLORIDA   (United States)

^d MASSACHUSETTS GENERAL HOSPITAL   (United States)

^e TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DEEP NEURAL NETWORK; GENE IDENTIFICATION; GENE SEQUENCE; GENETIC VARIATION; INTELLECTUAL IMPAIRMENT; MISSENSE MUTATION; MUTATION; NERVE CELL NETWORK; NONHUMAN; PATHOGENICITY; PRIMATE; PRIORITY JOURNAL; RARE DISEASE; ANIMAL; EXOME; GENETIC PREDISPOSITION; GENETICS; HUMAN; HUMAN GENOME; PATHOLOGY; PHYSIOLOGY;

ANIMALS; EXOME; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; HUMANS; INTELLECTUAL DISABILITY; MUTATION; NERVE NET; PRIMATES;

EID: 85050539279     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/s41588-018-0329-z     Document Type: Erratum

References (92)

1. MacArthur, D. G. et al. Guidelines for investigating causality of sequence variants in human disease. Nature 508, 469–476 (2014)
2. Rehm, H. L. et al. ClinGen--the Clinical Genome Resource. N. Engl. J. Med. 372, 2235–2242 (2015)
3. Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745–755 (2011)
4. Rehm, H. L. Evolving health care through personal genomics. Nat. Rev. Genet. 18, 259–267 (2017)
5. Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015)
6. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016)
7. Mallick, S. et al. The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. Nature 538, 201–206 (2016)
8. Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015)
9. Liu, X., Jian, X. & Boerwinkle, E. dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Human. Mutat. 32, 894–899 (2011)
10. Chimpanzee Sequencing Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69–87 (2005)
11. Takahata, N. Allelic genealogy and human evolution. Mol. Biol. Evol. 10, 2–22 (1993)
12. Asthana, S., Schmidt, S., & Sunyaev, S. A limited role for balancing selection. Trends Genet. 21, 30–32 (2005)
13. Leffler, E. M. et al. Multiple instances of ancient balancing selection shared between humans and chimpanzees. Science 339, 1578–1582 (2013)
14. Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944–950 (2014)
15. Ohta, T. Slightly deleterious mutant substitutions in evolution. Nature 246, 96–98 (1973)
16. Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502–510 (2001)
17. Whiffin, N. et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet. Med. 19, 1151–1158 (2017)
18. Prado-Martinez, J. et al. Great ape genome diversity and population history. Nature 499, 471–475 (2013)
19. Klein, J., Satta, Y., O’HUigin, C., & Takahata, N. The molecular descent of the major histocompatibility complex. Annu. Rev. Immunol. 11, 269–295 (1993)
20. Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge Univ. Press, Canbridge, UK, 1983)
21. de Manuel, M. et al. Chimpanzee genomic diversity reveals ancient admixture with bonobos. Science 354, 477–481 (2016)
22. Locke, D. P. et al. Comparative and demographic analysis of orang-utan genomes. Nature 469, 529–533 (2011)
23. Rhesus Macaque Genome Sequencing Analysis Consortium. Evolutionary and biomedical insights from the rhesus macaque genome. Science 316, 222–234 (2007)
24. Worley, K. C. et al. The common marmoset genome provides insight into primate biology and evolution. Nat. Genet. 46, 850–857 (2014)
25. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001)
26. Schrago, C. G., & Russo, C. A. Timing the origin of New World monkeys. Mol. Biol. Evol. 20, 1620–1625 (2003)
27. Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862–868 (2016)
28. Brandon, E. P., Idzerda, R. L. & McKnight, G. S. Targeting the mouse genome: a compendium of knockouts (Part II). Curr. Biol. 5, 758–765 (1995)
29. Lieschke, J. G. & Currie, P. D. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 8, 353–367 (2007)
30. Sittig, L. J. et al. Genetic background limits generalizability of genotype-phenotype relationships. Neuron 91, 1253–1259 (2016)
31. Bazykin, G. A. et al. Extensive parallelism in protein evolution. Biol. Direct 2, 20 (2007)
32. Ng, P. C., & Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 11, 863–874 (2001)
33. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010)
34. Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553–1561 (2009)
35. Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010)
36. Reva, B., Antipin, Y., & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011)
37. Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125–2137 (2015)
38. Carter, H., Douville, C., Stenson, P. D., Cooper, D. N., & Karchin, R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genom. 14 Suppl 3, S3 (2013)
39. Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., & Chan, A. P. Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688 (2012)
40. Gulko, B., Hubisz, M. J., Gronau, I., & Siepel, A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276–283 (2015)
41. Shihab, H. A. et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31, 1536–1543 (2015)
42. Quang, D., Chen, Y., & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015)
43. Bell, C. J. et al. Comprehensive carrier testing for severe childhood recessive diseases by next generation sequencing. Sci. Transl. Med. 3, 65ra64 (2011)
44. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014)
45. Smedley, D. et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am. J. Hum. Genet. 99, 595–606 (2016)
46. Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016)
47. Jagadeesh, K. A. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 48, 1581–1586 (2016)
48. Grimm, D. G. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human. Mutat. 36, 513–523 (2015)
49. He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 770–778 (2016)
50. Heffernan, R. et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci. Rep. 5, 11476 (2015)
51. Wang, S., Peng, J., Ma, J. & Xu, J. Protein secondary structure prediction using deep convolutional neural fields. Sci. Rep. 6, 18962–18962 (2016)
52. Harpak, A., Bhaskar, A., & Pritchard, J. K. Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS Genet. 12 e1006489 (2016)
53. Payandeh, J., Scheuer, T., Zheng, N. & Catterall, W. A. The crystal structure of a voltage-gated sodium channel. Nature 475, 353–358 (2011)
54. Shen, H. et al. Structure of a eukaryotic voltage-gated sodium channel at near-atomic resolution. Science 355, eaal4326 (2017)
55. Nakamura, K. et al. Clinical spectrum of SCN2A mutations expanding to Ohtahara syndrome. Neurology 81, 992–998 (2013)
56. Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10915–10919 (1992)
57. Li, W. H., Wu, C. I. & Luo, C. C. Nonrandomness of point mutation as reflected in nucleotide substitutions in pseudogenes and its evolutionary implications. J. Molec. Evol. 21, 58–71 (1984)
58. Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862–864 (1974)
59. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. Gradient based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
60. Vissers, L. E., Gilissen, C., & Veltman, J. A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9–18 (2016)
61. Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242–245 (2012)
62. Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237–241 (2012)
63. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014)
64. Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223–228 (2015)
65. Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433–438 (2017)
66. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014)
67. Zhu, X., Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773–781, 10.1038/nn.3713 (2014)
68. Leffler, E. M. et al. Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol. 10, e1001388 (2012)
69. Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3, e1600946 (2017)
70. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002)
71. Tyner, C. et al. The UCSC Genome Browser database: 2017 update. Nucleic Acids Res. 45, D626–D634 (2017)
72. Kabsch, W., & Sander, C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637 (1983)
73. Joosten, R. P. et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 39, D411–419 (2011)
74. He, K., Zhang, X., Ren, S., & Sun, J. Identity mappings in deep residual networks. in 14th European Conference on Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9908; 630–645 (Springer, Cham, Switzerland; 2016)
75. Ionita-Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214–220 (2016)
76. Li, B. et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25, 2744–2750 (2009)
77. Lu, Q. et al. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci. Rep. 5, 10576 (2015)
78. Shihab, H. A. et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Human. Mutat. 34, 57–65 (2013)
79. Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, e1001025 (2010)
80. Liu, X., Wu, C., Li, C., & Boerwinkle, E. dbNSFPv3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Human. Mutat. 37, 235–241 (2016)
81. Jain, S., White, M., Radivojac, P. Recovering true classifier performance in positive-unlabeled learning. in Proceedings Thirty-First AAAI Conference on Artificial Intelligence. 2066–2072 (AAAI Press, San Francisco; 2017)
82. de Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921–1929 (2012)
83. Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285–299 (2012)
84. O’Roak, B. J. et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485, 246–250 (2012)
85. Rauch, A. et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet 380, 1674–1682 (2012)
86. Epi, K. C. et al. De novo mutations in epileptic encephalopathies. Nature 501, 217–221 (2013)
87. EuroEPINOMICS-RES Consortium, Epilepsy Phenome/Genome Project, Epi4K Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am. J. Hum. Genet. 95, 360–370 (2014)
88. Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014)
89. Lelieveld, S. H. et al. Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability. Nat. Neurosci. 19, 1194–1196 (2016)
90. Famiglietti, M. L. et al. Genetic variations and diseases in UniProtKB/Swiss-Prot: the ins and outs of expert manual curation. Human. Mutat. 35, 927–935 (2014)
91. Horaitis, O., Talbot, C. C.Jr., Phommarinh, M., Phillips, K. M., & Cotton, R. G. A database of locus-specific databases. Nat. Genet. 39, 425 (2007)
92. Stenson, P. D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1–9 (2014)