A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Genome Medicine

Volumn 8, Issue 1, 2016, Pages

  James, Regis A ^a   Campbell, Ian M ^a   Chen, Edward S ^a   Boone, Philip M ^a   Rao, Mitchell A ^a   Bainbridge, Matthew N ^a   Lupski, James R ^a,b   Yang, Yaping ^a   Eng, Christine M ^a   Posey, Jennifer E ^a   Shaw, Chad A ^a,c  

^a BAYLOR COLLEGE OF MEDICINE   (United States)

^b TEXAS CHILDREN S HOSPITAL   (United States)

^c Rice University   (United States)

Author keywords

Disease gene discovery; Exome; Semantic similarity; Variant prioritization

Indexed keywords

ALGORITHM; ARTICLE; BIOMEDICINE; COHORT ANALYSIS; COMPARATIVE STUDY; DATA ANALYSIS SOFTWARE; DIFFERENTIAL DIAGNOSIS; EXOME; GENETIC DATABASE; GENETIC VARIABILITY; GENOME ANALYSIS; GENOTYPE; HUMAN; HUMAN COMPUTER INTERACTION; INFORMATION PROCESSING; MENDELIAN INHERITANCE IN MAN DATABASE; MOLECULAR DIAGNOSIS; PHENOTYPE; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SEMANTICS; BIOLOGY; DISEASES; GENETIC VARIATION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; PILOT STUDY; PROCEDURES; SOFTWARE;

COMPUTATIONAL BIOLOGY; DIAGNOSIS, DIFFERENTIAL; DISEASE; GENETIC VARIATION; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHENOTYPE; PILOT PROJECTS; RETROSPECTIVE STUDIES; SOFTWARE;

EID: 84956744263     PISSN: None     EISSN: 1756994X     Source Type: Journal    
DOI: 10.1186/s13073-016-0261-8     Document Type: Article

References (55)

1. Dewey FE, Grove ME, Pan C, Goldstein BA, Bernstein JA, Chaib H, et al. Clinical interpretation and implications of whole-genome sequencing. JAMA. 2014;311:1035.
2. Feero WG, Manolio TA, Khoury MJ. Translational research is a key to nongeneticist physicians' genomics education. Genet Med. 2014;16(12):871-3.
3. Bauer DC, Gaff C, Dinger ME, Caramins M, Buske FA, Fenech M, et al. Genomics and personalised whole-of-life healthcare. Trends Mol Med. 2014;20:479-86.
4. Ong FS, Lin JC, Das K, Grosu DS, Fan J-B. Translational utility of next-generation sequencing. Genomics. 2013;102:137-9.
5. Frese K, Katus H, Meder B. Next-generation sequencing: from understanding biology to personalized medicine. Biology. 2013;2:378-98.
6. Green ED, Guyer MS, Guyer MS. Charting a course for genomic medicine from base pairs to bedside. Nature. 2011;470:204-13.
7. Lochmüller H. Rare diseases need global solutions: new international initiatives in rare disease omics research. Newsletter British Soc Gen Med. 2013;1:2-3.
8. Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet. 2013;14:681-91.
9. Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat. 2012;33:803-8.
10. Loscalzo J, Kohane I, Barabási A-L. Human disease classification in the postgenomic era: A complex systems approach to human pathobiology. Mol Syst Biol. 2007;3:124.
11. Li M-X, Kwan JSH, Bao S-Y, Yang W, Ho S-L, Song Y-Q, et al. Predicting Mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. PLoS Genet. 2013;9:e1003143.
12. Bromberg Y. Chapter 15: Disease gene prioritization. PLoS Comput Biol. 2013;9:e1002902.
13. Chen Y, Zhang W, Gan M, Jiang R. Constructing human phenome-interactome networks for the prioritization of candidate genes. Stat Its Inter. 2012;5:137-48.
14. Green RC, Berg JS, Grody WW, Kalia SS, Korf BR, Martin CL, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15:565-74.
15. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, 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. 2015;17:405-24.
16. Richards CS, Bale S, Bellissimo DB, Das S, Grody WW, Hegde MR, et al. ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions 2007. Genet Med. 2008;10:294-300.
17. Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2013;42:D980-5.
18. Zemojtel T, Köhler S, Mackenroth L, Jäger M, Hecht J, Krawitz P, et al. Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med. 2014;6:252ra123.
19. Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008;83:610-5.
20. Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S, Ott CE, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet. 2009;85:457-64.
21. Masino AJ, Dechene ET, Dulik MC, Wilkens A, Spinner NB, Krantz ID, et al. Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology. BMC Bioinformatics. 2014;15:248.
22. Javed A, Agrawal S, Ng PC. Phen-Gen: combining phenotype and genotype to analyze rare disorders. Nat Meth. 2014;11:935-7.
23. Robinson PN, Kohler S, Oellrich A, Sanger Mouse Genetics Project, Wang K, Mungall CJ, et al. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Res. 2014;24:340-8.
24. Haendel MA, Vasilevsky N, Brush M, Hochheiser HS, Jacobsen J, Oellrich A, et al. Disease insights through cross-species phenotype comparisons. Mamm Genome. 2015;26:548-55.
25. Sifrim A, Popovic D, Tranchevent L-C, Ardeshirdavani A, Sakai R, Konings P, et al. eXtasy: variant prioritization by genomic data fusion. Nat Meth. 2013;10:1083-4.
26. Singleton MV, Guthery SL, Voelkerding KV, Chen K, Kennedy B, Margraf RL, et al. Phevor combines multiple biomedical ontologies for accurate identification of disease-causing alleles in single individuals and small nuclear families. Am J Hum Genet. 2014;94:599-610.
27. Hamosh A, Sobreira N, Hoover-Fong J, Sutton VR, Boehm C, Schiettecatte F, et al. PhenoDB: a new web-based tool for the collection, storage, and analysis of phenotypic features. Hum Mutat. 2013;34:566-71.
28. Girdea M, Dumitriu S, Fiume M, Bowdin S, Boycott KM, Chénier S, et al. PhenoTips: patient phenotyping software for clinical and research use. Hum Mutat. 2013;34:1057-65.
29. Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barbási A-L. The human disease network. Proc Natl Acad Sci USA. 2007;104:8685-90.
30. Amberger J, Bocchini CA, Scott AF, Hamosh A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 2009;37:D793-6.
31. Yang Y, Muzny DM, Reid JG, Bainbridge MN, Willis A, Ward PA, et al. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N Engl J Med. 2013;369:1502-11.
32. Garla VN, Brandt C. Semantic similarity in the biomedical domain: an evaluation across knowledge sources. BMC Bioinformatics. 2012;13:261.
33. Amberger J, Bocchini C, Hamosh A. A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®). Hum Mutat. 2011;32:564-7.
34. McKusick VA. Mendelian Inheritance in Man and its online version, OMIM. Am J Hum Genet. 2007;80:588-604.
35. Hamosh A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2004;33:D514-7.
36. Mazandu GK, Mulder NJ. Information content-based gene ontology semantic similarity approaches: toward a unified framework theory. BioMed Res Int. 2013;2013:1-11.
37. Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. 1995;1:448-453.
38. Mistry M, Pavlidis P. Gene Ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics. 2008;9:327.
39. Mazandu GK, Mulder NJ. A Topology-based metric for measuring term similarity in the gene ontology. Advances Bioinform. 2012;2012:1-17.
40. Cohen Y, Cohen JY. Statistics and Data with R: an applied approach through examples. Chichester: John Wiley & Sons, Ltd; 2008.
41. Goh KI, Choi IG. Exploring the human diseasome: the human disease network. Brief Funct Genomics. 2012;11:533-42.
42. Exome Aggregation Consortium (ExAC). ExAC Browser home page. http://exac.broadinstitute.org. Accessed 2015.
43. Groza T, Kohler S, Doelken S, Collier N, Oellrich A, Smedley D, et al. Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora. Database. 2015;2015:bav005-bav005.
44. Kohler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2013;42:D966-74.
45. Torgerson WS. Multidimensional scaling: I Theory and method. Psychometrika. 1952;17:401-19.
46. Schwarz JM, Rödelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Meth. 2010;7:575-6.
47. Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, et al. PINA v2.0: mining interactome modules. Nucleic Acids Res. 2011;40:D862-5.
48. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat Meth. 2015;12:115-21.
49. RStudio, Inc. The RStudio Shiny web application framework. http://shiny.rstudio.com. Accessed 2015.
50. Cantelon M, Holowaychuk TJ, Harter M, Rajlich N. Node.js in action. Shelter Island, NY: Manning Publications; 2013.
51. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Meth. 2010;7:248-9.
52. Ng PC. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812-4.
53. Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci USA. 2004;101:6062-7.
54. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-9.
55. Yang JH, Li JH, Jiang S, Zhou H, Qu LH. ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res. 2012;41:D177-87.