Human genotype-phenotype databases: Aims, challenges and opportunities

Nature Reviews Genetics

Volumn 16, Issue 12, 2015, Pages 702-715

  Brookes, Anthony J ^a,b   Robinson, Peter N ^c,d,e  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b GLENFIELD HOSPITAL   (United Kingdom)

^c CHARITÉ UNIVERSITÄTSMEDIZIN BERLIN   (Germany)

^d MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^e FREIE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EXOME; GENETIC ANALYSIS; GENETIC DATABASE; GENETIC IDENTIFICATION; GENETIC VARIABILITY; GENETICS; GENOME; GENOTYPE; GENOTYPE PHENOTYPE DATABASE; HEALTH CARE; HUMAN; INFORMATION PROCESSING; MEDICAL PRACTICE; NONHUMAN; PATHOGENICITY; PHENOTYPE; PRIORITY JOURNAL; REVIEW; GENOMICS;

DATABASES, GENETIC; GENETIC VARIATION; GENOMICS; GENOTYPE; HUMANS; PHENOTYPE;

EID: 84947613905     PISSN: 14710056     EISSN: 14710064     Source Type: Journal    
DOI: 10.1038/nrg3932     Document Type: Review

References (115)

1. Johnston, J. J. & Biesecker, L. G. Databases of genomic variation and phenotypes: existing resources and future needs. Hum. Mol. Genet. 22, R27-R31 (2013).
2. Rehm, H. L. Disease-targeted sequencing: A cornerstone in the clinic. Nat. Rev. Genet. 14, 295-300 (2013).
3. Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N. Engl. J. Med. 369, 1502-1511 (2013).
4. Zemojtel, T. et al. Effective diagnosis of genetic disease by computational phenotype analysis of the diseaseassociated genome. Sci. Transl. Med. 6, 252ra123 (2014).
5. Saunders, C. J. et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci. Transl. Med. 4, 154ra135 (2012).
6. Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nat. Rev. Genet. 12, 628-640 (2011).
7. Li, M. X. et al. Predicting Mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies. PLoS Genet. 9, e1003143 (2013).
8. Pelak, K. et al. The characterization of twenty sequenced human genomes. PLoS Genet. 6, e1001111 (2010).
9. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823-828 (2012).
10. Horaitis, O. et al. A database of locus-specific databases. Nat. Genet. 39, 425 (2007).
11. Patrinos, G. P. et al. Human Variome Project country nodes: documenting genetic information within a country. Hum. Mutat. 33, 1513-1519 (2012).
12. Fokkema, I. F. et al. LOVD v.2.0: The next generation in gene variant databases. Hum. Mutat. 32, 557-563 (2011).
13. Beroud, C., Collod-Beroud, G., Boileau, C., Soussi, T. & Junien, C. UMD (Universal Mutation Database): A generic software to build and analyze locus-specific databases. Hum. Mutat. 15, 86-94 (2000).
14. Polvi, A. et al. The Finnish disease heritage database (FinDis) update-A database for the genes mutated in the Finnish disease heritage brought to the next-generation sequencing era. Hum. Mutat. 34, 1458-1466 (2013).
15. Dalgleish, R. et al. Locus Reference Genomic sequences: an improved basis for describing human DNA variants. Genome Med. 2, 24 (2010).
16. MacArthur, J. A. et al. Locus Reference Genomic: reference sequences for the reporting of clinically relevant sequence variants. Nucleic Acids Res. 42, D873-D878 (2014).
17. Gout, A. M. et al. Analysis of published PKD1 gene sequence variants. Nat. Genet. 39, 427-428 (2007).
18. Bell, C. J. et al. Carrier testing for severe childhood recessive diseases by next-generation sequencing. Sci. Transl. Med. 3, 65ra4 (2011).
19. Chen, S. N. et al. Human molecular genetic and functional studies identify TRIM63, encoding muscle RING finger protein 1, as a novel gene for human hypertrophic cardiomyopathy. Circ. Res. 111, 907-919 (2012).
20. Ploski, R. et al. Does p.Q247X in TRIM63 cause human hypertrophic cardiomyopathy? Circ. Res. 114, e2-e5 (2014).
21. Witt, C. C. et al. Cooperative control of striated muscle mass and metabolism by MuRF1 and MuRF2. EMBO J. 27, 350-360 (2008).
22. Plon, S. E. et al. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum. Mutat. 29, 1282-1291 (2008).
23. Sosnay, P. R. et al. Defining the disease liability of variants in the cystic fibrosis transmembrane conductance regulator gene. Nat. Genet. 45, 1160-1167 (2013).
24. den Dunnen, J., Cutting, G. R. & Paalman, M. H. Mandatory variant submission-our experiences. Hum. Mutat. 33, 1 (2012).
25. Terry, S. F. Disease advocacy organizations catalyze translational research. Front. Genet. 4, 101 (2013).
26. Wicks, P. et al. Sharing health data for better outcomes on PatientsLikeMe. J. Med. Internet. Res. 12, e19 (2010).
27. Kirkpatrick, B. E. et al. GenomeConnect: matchmaking between patients, clinical laboratories and researchers to improve genomic knowledge. Hum. Mutat. 36, 974-978 (2015).
28. McAllister, M. & Dearing, A. Patient reported outcomes and patient empowerment in clinical genetics services. Clin. Genet. 88, 114-121 (2015).
29. The Lancet Editorial. Patient empowerment-who empowers whom? Lancet 379, 1677 (2012).
30. Abecasis, G. R. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061-1073 (2010).
31. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64-69 (2012).
32. Ball, M. P. et al. A public resource facilitating clinical use of genomes. Proc. Natl Acad. Sci. USA 109, 11920-11927 (2012).
33. Zhang, J. et al. International Cancer Genome Consortium Data Portal-A one-stop shop for cancer genomics data. Database (Oxford) 2011, bar026 (2011).
34. Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113-1120 (2013).
35. Petrovski, S. & Goldstein, D. B. Phenomics and the interpretation of personal genomes. Sci. Transl. Med. 6, 254fs35 (2014).
36. Dorschner, M. O. et al. Actionable, pathogenic incidental findings in 1,000 participants' exomes. Am. J. Hum. Genet. 93, 631-640 (2013).
37. Dyment, D. A. et al. Whole-exome sequencing broadens the phenotypic spectrum of rare pediatric epilepsy: A retrospective study. Clin. Genet. 88, 34-40 (2015).
38. Firth, H. V. et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am. J. Hum. Genet. 84, 524-533 (2009).
39. Gonzalez, M. A. et al. GEnomes Management Application (GEM.app): A new software tool for largescale collaborative genome analysis. Hum. Mutat. 34, 842-846 (2013).
40. Denny, J. C. et al. Systematic comparison of phenomewide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102-1110 (2013).
41. Schaefer, C. & RPGEH GO Project Collaboration. The Kaiser Permanente Research Program on Genes, Environment and Health: A resource for genetic epidemiology in adult health and aging. Clin. Med. Res. 9, 177-178 (2011).
42. Lappalainen, I. et al. The European Genome-phenome Archive of human data consented for biomedical research. Nat. Genet. 47, 692-695 (2015).
43. Tryka, K. A. et al. NCBI's Database of Genotypes and Phenotypes: dbGaP. Nucleic Acids Res. 42, D975-D979 (2014).
44. Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417-428 (2011).
45. Murphy, S. N. et al. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J. Am. Med. Inform. Assoc. 17, 124-130 (2010).
46. Adamusiak, T. et al. Observ-OM and Observ-TAB: universal syntax solutions for the integration, search, and exchange of phenotype and genotype information. Hum. Mutat. 33, 867-873 (2012).
47. Beck, T., Hastings, R. K., Gollapudi, S., Free, R. C. & Brookes, A. J. GWAS Central: A comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur. J. Hum. Genet. 22, 949-952 (2014).
48. Gaye, A. et al. DataSHIELD: taking the analysis to the data, not the data to the analysis. Int. J. Epidemiol. 43, 1929-1944 (2014).
49. Karr, A. et al. Secure, privacy-preserving analysis of distributed databases. Technometrics 49, 335-345 (2007).
50. Cariaso, M. & Lennon, G. SNPedia: A wiki supporting personal genome annotation, interpretation and analysis. Nucleic Acids Res. 40, D1308-D1312 (2012).
51. Rappaport, N. et al. MalaCards: an integrated compendium for diseases and their annotation. Database (Oxford) 2013, bat018 (2013).
52. Lopes, P., Dalgleish, R. & Oliveira, J. L. WAVe: web analysis of the variome. Hum. Mutat. 32, 729-734 (2011).
53. Glusman, G., Caballero, J., Mauldin, D. E., Hood, L. & Roach, J. C. Kaviar: an accessible system for testing SNV novelty. Bioinformatics 27, 3216-3217 (2011).
54. Philippakis, A. A. et al. The matchmaker exchange: A platform for rare disease gene discovery. Hum. Mutat. 36, 915-921 (2015).
55. Manolio, T. A. et al. Global implementation of genomic medicine: we are not alone. Sci. Transl. Med. 7, 290ps13 (2015).
56. Hayden, E. C. Geneticists push for global data-sharing. Nature 498, 16-17 (2013).
57. Gottlieb, M. M. et al. GeneYenta: A phenotype-based rare disease case matching tool based on online dating algorithms for the acceleration of exome interpretation. Hum. Mutat. 36, 432-438 (2015).
58. Lancaster, O. et al. Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts. Hum. Mutat. 36, 957-964 (2015).
59. Wellcome Trust. Enhancing discoverability of public health and epidemiology research data. Wellcome Trust [online], http://www.wellcome.ac.uk/About-us/Policy/Spotlight-issues/Data-sharing/Public-health-andepidemiology/WTP054675.htm (2014).
60. Digital Curation Centre (DCC). Jisc Research Data Registry and Discovery Service DCC http://www.dcc. ac.uk/projects/research-data-registry-pilot (2014).
61. Cotton, R. G. et al. The Human Variome Project. Science 322, 861-862 (2008).
62. Rehm, H. L. et al. ClinGen-The Clinical Genome Resource. N. Engl. J. Med. 372, 2235-2242 (2015).
63. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980-D985 (2014).
64. Beaulieu, C. L. et al. FORGE Canada Consortium: outcomes of a 2-year national rare-disease genediscovery project. Am. J. Hum. Genet. 94, 809-817 (2014).
65. Buske, O. J. PhenomeCentral: A portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum. Mutat. 36, 931-940 (2015).
66. Thompson, R. et al. RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research. J. Gen. Intern. Med. 29, S780-S787 (2014).
67. Conley, J. M., Cook-Deegan, R. & Lázaro-Muñoz, G. Myriad after Myriad: The proprietary data dilemma. N. C. J. Law Technol. 15, 597-637 (2014).
68. Riggs, E. R., Jackson, L., Miller, D. T. & Van Vooren, S. Phenotypic information in genomic variant databases enhances clinical care and research: The International Standards for Cytogenomic Arrays Consortium experience. Hum. Mutat. 33, 787-796 (2012).
69. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993-998 (2010).
70. Banda, Y. et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics 200, 1285-1295 (2015).
71. Knoppers, B. M. Framework for responsible sharing of genomic and health-related data. HUGO J. 8, 3 (2014).
72. Mascalzoni, D. et al. International Charter of principles for sharing bio-specimens and data. Eur. J. Hum. Genet. 23, 721-728 (2015).
73. Rath, A. et al. Representation of rare diseases in health information systems: The Orphanet approach to serve a wide range of end users. Hum. Mutat. 33, 803-808 (2012).
74. Köhler, S. et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42, D966-D974 (2014).
75. Varga, E. A. & Moll, S. Cardiology patient pages. Prothrombin 20210 mutation (factor II mutation). Circulation 110, e15-e18 (2004).
76. Beaudet, A. L. & Tsui, L. C. A suggested nomenclature for designating mutations. Hum. Mutat. 2, 245-248 (1993).
77. den Dunnen, J. T. & Antonarakis, S. E. Nomenclature for the description of human sequence variations. Hum. Genet. 109, 121-124 (2001).
78. Taschner, P. E. & den Dunnen, J. T. Describing structural changes by extending HGVS sequence variation nomenclature. Hum. Mutat. 32, 507-511 (2011).
79. Laros, J. F., Blavier, A., den Dunnen, J. T. & Taschner, P. E. A formalized description of the standard human variant nomenclature in Extended Backus-Naur Form. BMC Bioinformatics 12, S5 (2011).
80. Wildeman, M., van Ophuizen, E., den Dunnen, J. T. & Taschner, P. E. Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation nomenclature checker. Hum. Mutat. 29, 6-13 (2008).
81. Hart, R. K. et al. A Python package for parsing, validating, mapping and formatting sequence variants using HGVS nomenclature. Bioinformatics 31, 268-270 (2015).
82. Cock, P. J., Fields, C. J., Goto, N., Heuer, M. L. & Rice, P. M. The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 38, 1767-1771 (2010).
83. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).
84. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156-2158 (2011).
85. Byrne, M. et al. VarioML framework for comprehensive variation data representation and exchange. BMC Bioinformatics 13, 254 (2012).
86. Vihinen, M. Variation Ontology for annotation of variation effects and mechanisms. Genome Res. 24, 356-364 (2014).
87. Kibbe, W. A. et al. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 43, D1071-D1078 (2015).
88. Groza, T. et al. The Human Phenotype Ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97, 111-124 (2015).
89. Wright, C. F. et al. Genetic diagnosis of developmental disorders in the DDD study: A scalable analysis of genome-wide research data. Lancet 385, 1305-1314 (2015).
90. Sifrim, A. et al. eXtasy: variant prioritization by genomic data fusion. Nat. Methods 10, 1083-1084 (2013).
91. Robinson, P. N. et al. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Res. 24, 340-348 (2014).
92. Singleton, M. V. 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. 94, 599-610 (2014).
93. Javed, A., Agrawal, S. & Ng, P. C. Phen-Gen: combining phenotype and genotype to analyze rare disorders. Nat. Methods 11, 935-937 (2014).
94. Westbury, S. K. et al. Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disorders. Genome Med. 7, 36 (2015).
95. Adam, D. Mental health: on the spectrum. Nature 496, 416-418 (2013).
96. Stenson, P. D. et al. Human Gene Mutation Database: towards a comprehensive central mutation database. J. Med. Genet. 45, 124-126 (2008).
97. Abel, O., Powell, J. F., Andersen, P. M. & Al-Chalabi, A. ALSoD: A user-friendly online bioinformatics tool for amyotrophic lateral sclerosis genetics. Hum. Mutat. 33, 1345-1351 (2012).
98. Chandrasekharappa, S. C. et al. Massively parallel sequencing, aCGH, and RNA-seq technologies provide a comprehensive molecular diagnosis of Fanconi anemia. Blood 121, e138-e148 (2013).
99. Dalgleish, R. The human type I collagen mutation database. Nucleic Acids Res. 25, 181-187 (1997).
100. Piirila, H., Valiaho, J. & Vihinen, M. Immunodeficiency mutation databases (IDbases). Hum. Mutat. 27, 1200-1208 (2006).
101. Ruiz-Pesini, E. et al. An enhanced MITOMAP with a global mtDNA mutational phylogeny. Nucleic Acids Res. 35, D823-D828 (2007).
102. Papadopoulos, P. et al. Developments in FINDbase worldwide database for clinically relevant genomic variation allele frequencies. Nucleic Acids Res. 42, D1020-D1026 (2014).
103. Bragin, E. et al. DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variation. Nucleic Acids Res. 42, D993-D1000 (2014).
104. Hamosh, A. et al. PhenoDB: A new web-based tool for the collection, storage, and analysis of phenotypic features. Hum. Mutat. 34, 566-571 (2013).
105. Sobreira, N., Schiettecatte, F., Valle, D. & Hamosh, A. GeneMatcher: A matching tool for connecting investigators with an interest in the same gene. Hum. Mutat. 36, 928-930 (2015).
106. Amberger, J., Bocchini, C. & Hamosh, A. A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®). Hum. Mutat. 32, 564-567 (2011).
107. Mungall, C.J. et al. Use of model organism and disease databases to support matchmaking for human disease gene discovery. Hum. Mutat. 36, 979-984 (2015).
108. Wilks, C. et al. The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data. Database (Oxford) 2014, bau093 (2014).
109. Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945-D950 (2011).
110. Cheng, W. C. et al. DriverDB: an exome sequencing database for cancer driver gene identification. Nucleic Acids Res. 42, D1048-D1054 (2014).
111. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001-D1006 (2014).
112. Li, M. J. et al. GWASdb: A database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 40, D1047-D1054 (2012).
113. Koike, A., Nishida, N., Inoue, I., Tsuji, S. & Tokunaga, K. Genome-wide association database developed in the Japanese Integrated Database Project. J. Hum. Genet. 54, 543-546 (2009).
114. Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92, 414-417 (2012).
115. Carey, J. C., Allanson, J. E., Hennekam, R. C. & Biesecker, L. G. Standard terminology for phenotypic variations: The elements of morphology project, its current progress, and future directions. Hum. Mutat. 33, 781-786 (2012).