eDiVA—Classification and prioritization of pathogenic variants for clinical diagnostics

Human Mutation

Volumn 40, Issue 7, 2019, Pages 865-878

  Bosio, Mattia ^a,b,c   Drechsel, Oliver ^d   Rahman, Rubayte ^e   Muyas, Francesc ^a,b   Rabionet, Raquel ^a,b,f   Bezdan, Daniela ^a,b   Domenech Salgado, Laura ^a,b   Hor, Hyun ^g   Schott, Jean Jacques ^h,i   Munell, Francina ^j   Colobran, Roger ^j   Macaya, Alfons ^j   Estivill, Xavier ^k,l   Ossowski, Stephan ^a,b,m  

^a BARCELONA INSTITUTE OF SCIENCE AND TECHNOLOGY   (Spain)

^b UNIVERSITAT POMPEU FABRA   (Spain)

^c BARCELONA SUPERCOMPUTING CENTER   (Spain)

^d ROBERT KOCH INSTITUTE   (Germany)

^e NETHERLANDS CANCER INSTITUTE   (Netherlands)

^f UNIVERSITY OF BARCELONA   (Spain)

^g UNIVERSITY HOSPITAL ZURICH   (Switzerland)

^h UNIVERSITÉ DE NANTES   (France)

ⁱ NANTES UNIVERSITY HOSPITAL   (France)

^j HOSPITAL UNIVERSITARI VALL D HEBRON   (Spain)

^k SIDRA MEDICAL AND RESEARCH CENTER   (Qatar)

^l Dexeus Women's Health   (Spain)

^m UNIVERSITY OF TÜBINGEN   (Germany)

more..

Author keywords

disease variant prioritization; machine learning; NGS diagnostics; rare genetic disease; whole exome sequencing

Indexed keywords

ARTICLE; BENCHMARKING; CONCEPTUAL FRAMEWORK; EXOME DISEASE VARIANT ANALYSIS; GENETIC DISORDER; GENETIC VARIABILITY; HUMAN; INDEL MUTATION; LINKAGE ANALYSIS; MACHINE LEARNING; NEXT GENERATION SEQUENCING; PATHOGENICITY; PRIORITY JOURNAL; RARE DISEASE; SINGLE NUCLEOTIDE POLYMORPHISM; SPLICING DEFECT; WHOLE EXOME SEQUENCING; ALGORITHM; CHILD PARENT RELATION; GENETIC DATABASE; GENETIC PREDISPOSITION; GENETICS; MUTATION; PROCEDURES; WEB BROWSER;

ALGORITHMS; DATABASES, GENETIC; GENETIC PREDISPOSITION TO DISEASE; HUMANS; MACHINE LEARNING; MUTATION; PARENTS; RARE DISEASES; WEB BROWSER; WHOLE EXOME SEQUENCING;

EID: 85069644655     PISSN: 10597794     EISSN: 10981004     Source Type: Journal    
DOI: 10.1002/humu.23772     Document Type: Article

References (66)

1. Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., … Sunyaev, S. R. (2010). A method and server for predicting damaging missense mutations. Nature Methods, 7, 248–249.
2. 2+ Influx. PLOS One, 10, 10.
3. Bao, R., Huang, L., Andrade, J., Tan, W., Kibbe, W. A., Jiang, H., & Feng, G. (2014). Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing. Cancer Informatics, 13, 67–82.
4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
5. Choi, M., Scholl, U. I., Ji, W., Liu, T., Tikhonova, I. R., Zumbo, P., … Farhi, A. (2009). Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proceedings of the National Academy of Sciences of the United States of America, 106, 19096–19101.
6. Cingolani, P., Platts, A., Wang, L., Coon, M., Nguyen, T., Wang, L., … Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin), 6(2), 80–92.
7. Claussnitzer, M., Dankel, S. N., Kim, K.-H., Quon, G., Meuleman, W., Haugen, C., … Liu, J. (2015). FTO obesity variant circuitry and adipocyte browning in humans. New England Journal of Medicine, 373, 895–907.
8. Cutting, G. R. (2014). Annotating DNA variants is the next major goal for human genetics. American Journal of Human Genetics, 94, 5–10.
9. Dewey, F. E., Murray, M. F., Overton, J. D., Habegger, L., Leader, J. B., Fetterolf, S. N., … Pendergrass, S. A. (2016). Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science, 354, aaf6814.
10. Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35, 316–319.
11. Durst, R., Sauls, K., Peal, D. S., deVlaming, A., Toomer, K., Leyne, M., … Jett, C. (2015). Mutations in DCHS1 cause mitral valve prolapse. Nature, 525, 109–113.
12. Dong, C., Wei, P., Jian, X., Gibbs, R., Boerwinkle, E., Wang, K., & Liu, X. (2015). Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Human Molecular Genetics, 24, 2125–2137.
13. Exome Variant Server (n.d.). http://evs.gs.washington.edu/EVS/
14. Gambin, T., Akdemir, Z. C., Yuan, B., Gu, S., Chiang, T., Carvalho, C. M. B., … Bayram, Y. (2017). Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort. Nucleic Acids Research, 45, 1633–1648.
15. Garrison, E., & Marth, G. (2012). Haplotype-based variant detection from short-read sequencing. ArXiv12073907 Q-Bio, https://arxiv.org/abs/1207.3907
16. González-Pérez, A., & López-Bigas, N. (2011). Improving the assessment of the outcome of nonsynonymous SNVs with a Consensus Deleteriousness Score, Condel. American Journal of Human Genetics, 88, 440–449.
17. Grimm, D. G., Azencott, C.-A., Aicheler, F., Gieraths, U., MacArthur, D. G., Samocha, K. E., … Borgwardt, K. M. (2015). The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human Mutation, 36, 513–523.
18. Hadj-Rabia, S., Schneider, H., Navarro, E., Klein, O., Kirby, N., Huttner, K., … Grange, D. K. (2017). Automatic recognition of the XLHED phenotype from facial images. American Journal of Medical Genetics. Part A, 173, 2408–2414.
19. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, Inference, and Prediction, New York: Springer. https://www.springer.com/kr/book/9780387848570
20. Ho, M.-R., Tsai, K.-W., Chen, C., & Lin, W. (2011). dbDNV: A resource of duplicated gene nucleotide variants in human genome. Nucleic Acids Research, 39, D920–D925.
21. Hubisz, M. J., Pollard, K. S., & Siepel, A. (2011). PHAST and RPHAST: Phylogenetic analysis with space/time models. Briefings in Bioinformatics, 12, 41–51.
22. Ionita-Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. (2016). A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nature Genetics, 48, 214–220.
23. Ioannidis, N. M., Rothstein, J. H., Pejaver, V., Middha, S., McDonnell, S. K., Baheti, S., … Teerlink, C. C. (2016). REVEL: An ensemble method for predicting the pathogenicity of rare missense variants. American Journal of Human Genetics, 99, 877–885.
24. Jagadeesh, K. A., Wenger, A. M., Berger, M. J., Guturu, H., Stenson, P. D., Cooper, D. N., … Bejerano, G. (2016). M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nature Genetics, 48(12), 1581–1586.
25. Javed, A., Agrawal, S., & Ng, P. C. (2014). Phen-Gen: Combining phenotype and genotype to analyze rare disorders. Nature Methods, 11, 935–937.
26. Kimura, M. (1983). The Neutral Theory of Molecular Evolution. Cmbridge, UK: Cambridge University Press.
27. Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics, 46, 310–315.
28. Krumm, N., Sudmant, P. H., Ko, A., O'Roak, B. J., Malig, M., Coe, B. P., … Eichler, E. E. (2012). Copy number variation detection and genotyping from exome sequence data. Genome Research, 22, 1525–1532.
29. Kumar, P., Henikoff, S., & Ng, P. C. (2009). Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protocols, 4, 1073–1081.
30. Köhler, S., Doelken, S. C., Mungall, C. J., Bauer, S., Firth, H. V., Bailleul-Forestier, I., … Eppig, J. T. (2014). The Human Phenotype Ontology project: Linking molecular biology and disease through phenotype data. Nucleic Acids Research, 42, D966–D974.
31. Landrum, M. J., Lee, J. M., Riley, G. R., Jang, W., Rubinstein, W. S., Church, D. M., & Maglott, D. R. (2014). ClinVar: Public archive of relationships among sequence variation and human phenotype. Nucleic Acids Research, 42, D980–D985.
32. Lee, H., Deignan, J. L., Dorrani, N., Strom, S. P., Kantarci, S., Quintero-Rivera, F., … Fogel, B. L. (2014). Clinical exome sequencing for genetic identification of rare Mendelian disorders. Journal of the American Medical Association, 312, 1880–1887.
33. Lee, Y., Gamazon, E. R., Rebman, E., Lee, Y., Lee, S., Dolan, M. E., … Lussier, Y. A. (2012). Variants affecting exon skipping contribute to complex traits. PLOS Genetics, 8, e1002998.
34. Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., … Birnbaum, D. P. (2016). Analysis of protein-coding genetic variation in 60,706 humans. Nature, 536, 285–291.
35. Li, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinforma Oxf Engl, 27, 2987–2993.
36. Li, H. (2013). Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv13033997 Q-Bio
37. Li, M.-X., Gui, H.-S., Kwan, J. S. H., Bao, S.-Y., & Sham, P. C. (2012). A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases. Nucleic Acids Research, 40, e53–e53.
38. Louis-Dit-Picard, H., Barc, J., Trujillano, D., Miserey-Lenkei, S., Bouatia-Naji, N., Pylypenko, O., … Jeunemaitre, X. (2012). KLHL3 mutations cause familial hyperkalemic hypertension by impairing ion transport in the distal nephron. Nature Genetics, 44(4), 456–460. https://doi.org/10.1038/ng.2218
39. McCarroll, S. A., & Altshuler, D. M. (2007). Copy-number variation and association studies of human disease. Nature Genetics, 39, S37–S42.
40. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., … DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20, 1297–1303.
41. McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., … Cunningham, F. (2016). The Ensembl variant effect predictor. Genome Biology, 17, 122.
42. Muyas, F., Bosio, M., Puig, A., Susak, H., Domènech, L., Escaramis, G., … Ossowski, S. (2019). Allele balance bias identifies systematic genotyping errors and false disease associations. Human Mutation, 40, 115–126.
43. Ng, S. B., Buckingham, K. J., Lee, C., Bigham, A. W., Tabor, H. K., Dent, K. M., … Bamshad, M. J. (2010). Exome sequencing identifies the cause of a mendelian disorder. Nature Genetics, 42, 30–35.
44. Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010). Detection of nonneutral substitution rates on mammalian phylogenies. Genome Research, 20, 110–121.
45. Quang, D., Chen, Y., & Xie, X. (2015). DANN: A deep learning approach for annotating the pathogenicity of genetic variants. Bioinforma Oxf Engl, 31, 761–763.
46. Rabbani, B., Mahdieh, N., Hosomichi, K., Nakaoka, H., & Inoue, I. (2012). Next-generation sequencing: Impact of exome sequencing in characterizing Mendelian disorders. Journal of Human Genetics, 57, 621–632.
47. Ramos, M. D., Trujillano, D., Olivar, R., Sotillo, F., Ossowski, S., Manzanares, J., … Vazquez, C. (2014). Extensive sequence analysis of CFTR, SCNN1A, SCNN1B, SCNN1G and SERPINA1 suggests an oligogenic basis for cystic fibrosis-like phenotypes. Clinical Genetics, 86, 91–95.
48. Reva, B., Antipin, Y., & Sander, C. (2011). Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Research, 39, e118–e118.
49. Rhead, B., Karolchik, D., Kuhn, R. M., Hinrichs, A. S., Zweig, A. S., Fujita, P. A., … Pheasant, M. (2010). The UCSC genome browser database: Update 2010. Nucleic Acids Research, 38, D613–D619.
50. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., … Rehm, H. L. (2015). 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 Off J Am Coll Med Genet, 17, 405–424.
51. Robinson, P. N., Köhler, S., Oellrich, A., Wang, K., Mungall, C. J., Lewis, S. E., … Haendel, M. (2014). Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Research, 24, 340–348.
52. Sawyer, S. L., Hartley, T., Dyment, D. A., Beaulieu, C. L., Schwartzentruber, J., Smith, A., … Warman Chardon, J. (2016). Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: Time to address gaps in care. Clinical Genetics, 89, 275–284.
53. Seelow, D., Schwarz, J. M., & Schuelke, M. (2008). GeneDistiller—Distilling Candidate Genes from Linkage Intervals. PLoS ONE, 3(12), e3874. https://doi.org/10.1371/journal.pone.0003874
54. Sherry, S. T., Ward, M. H., Kholodov, M., Baker, J., Phan, L., Smigielski, E. M., & Sirotkin, K. (2001). dbSNP: The NCBI database of genetic variation. Nucleic Acids Research, 29, 308–311.
55. Sifrim, A., Popovic, D., Tranchevent, L.-C., Ardeshirdavani, A., Sakai, R., Konings, P., … Moreau, Y. (2013). eXtasy: Variant prioritization by genomic data fusion. Nature Methods, 10, 1083–1084.
56. Sobreira, N., Schiettecatte, F., Boehm, C., Valle, D., & Hamosh, A. (2015). New tools for mendelian disease gene identification: PhenoDB Variant Analysis Module; and GeneMatcher, a web-based tool for linking investigators with an interest in the same gene. Human Mutation, 36, 425–431.
57. Teer, J. K., Green, E. D., Mullikin, J. C., & Biesecker, L. G. (2012). VarSifter: Visualizing and analyzing exome-scale sequence variation data on a desktop computer. Bioinformatics (Oxford, England), 28, 599–600.
58. Stenson, P. D., Mort, M., Ball, E. V., Evans, K., Hayden, M., Heywood, S., … Cooper, D. N. (2017). The Human Gene Mutation Database: Towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies. Human Genetics, 136, 665–677.
59. The 1000 Genomes Project Consortium (2015). A global reference for human genetic variation. Nature, 526, 68–74.
60. Tranchevent, L.-C., Barriot, R., Yu, S., Van Vooren, S., Van Loo, P., Coessens, B., … Moreau, Y. (2008). ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Research, W377–W384. 36 (Web Server). https://doi.org/10.1093/nar/gkn325
61. Trujillano, D., Perez, B., González, J., Tornador, C., Navarrete, R., Escaramis, G., … Estivill, X. (2014). Accurate molecular diagnosis of phenylketonuria and tetrahydrobiopterin-deficient hyperphenylalaninemias using high-throughput targeted sequencing. European Journal of Human Genetics EJHG, 22, 528–534.
62. Urreizti, R., Cueto-Gonzalez, A. M., Franco-Valls, H., Mort-Farre, S., Roca-Ayats, N., Ponomarenko, J., … Hecht, J. (2017). A de novo nonsense mutation in MAGEL2 in a patient initially diagnosed as Opitz-C: Similarities between Schaaf-Yang and Opitz-C syndromes. Scientific Reports, 7, srep44138.
63. Wambach, J. A., Stettner, G. M., Haack, T. B., Writzl, K., Škofljanec, A., Maver, A., … Baldridge, D. (2017). Survival among children with “Lethal” congenital contracture syndrome 11 caused by novel mutations in the gliomedin gene (GLDN). Human Mutation, 38, 1477–1484.
64. Wang, K., Li, M., & Hakonarson, H. (2010). ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 38, e164–e164.
65. Wu, J., Li, Y., & Jiang, R. (2014). Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies. PLOS Genetics, 10, e1004237.
66. Yang, Y., Muzny, D. M., Reid, J. G., Bainbridge, M. N., Willis, A., Ward, P. A., … Person, R. (2013). Clinical whole-exome sequencing for the diagnosis of mendelian disorders. New England Journal of Medicine, 369, 1502–1511.