Role for protein-protein interaction databases in human genetics

Expert Review of Proteomics

Volumn 6, Issue 6, 2009, Pages 647-659

  Pattin, Kristine A ^b,c   Moore, Jason H ^a,b,d,e,f  

^a DARTMOUTH HITCHCOCK MEDICAL CENTER   (United States)

^b DARTMOUTH MEDICAL SCHOOL   (United States)

^c NORRIS COTTON CANCER CENTER   (United States)

^d Univ of New Hampshire   (United States)

^e University of Vermont   (United States)

^f TRANSLATIONAL GENOMICS RESEARCH INSTITUTE   (United States)

Author keywords

Epistasis; Expert knowledge; Multifactor dimensionality reduction; Protein protein interaction; Single nucleotide polymorphism

Indexed keywords

MULTIDRUG RESISTANCE PROTEIN; PROTEIN;

CAENORHABDITIS ELEGANS; EPISTASIS; FUNCTIONAL GENOMICS; GASTRINOMA; GENE EXPRESSION; GENE REGULATORY NETWORK; GENE REPLICATION; GENETIC ANALYSIS; GENETIC DATABASE; GENETIC EPIDEMIOLOGY; GENETIC VARIABILITY; GENOME; GENOTYPE PHENOTYPE CORRELATION; HUMAN; HUMAN GENETICS; HUNTINGTON CHOREA; MULTIDRUG RESISTANCE; NONHUMAN; PARATHYROID ADENOMA; PATHOGENESIS; PROGNOSIS; PROLACTINOMA; PROTEIN FUNCTION; PROTEIN INTERACTION; PROTEIN PROCESSING; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REVIEW; SINGLE NUCLEOTIDE POLYMORPHISM;

DATABASES, GENETIC; HUMANS; PROTEIN BINDING; PROTEIN INTERACTION MAPPING;

EID: 74049112301     PISSN: 14789450     EISSN: 17448387     Source Type: Journal    
DOI: 10.1586/epr.09.86     Document Type: Review

References (98)

1. Wasinger VC, Cordwell SJ, Cerpa-Poljak A et al. Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 16, 1090-1094 (1995).
2. Moore JH, Ritchie MD. The challenges of whole-genome approaches to common diseases. JAMA 291, 1642-1643 (2004). •• Elaborates on the challenged of conducting whole-genome studies and potential ways to address these issues.
3. Persidis A. Proteomics. Nat. Biotechnol. 16, 393-394 (1998).
4. Wang Z, Moult J. SNPs, protein structure, and disease. Hum. Mutat. 4, 263-270 (2001).
5. Cavallo A, Martin AC. Mapping SNPs to protein sequence and structure data. Bioinformatics 21(8), 1443-1450 (2005). • Provides an understanding of how mutations such as SNPs can have an effect on protein structure.
6. Kim BC, Kim WY, Park D et al. SNP@Promoter: a database of human SNPs (single nucleotide polymorphisms) within the putative promoter regions. BMC Bioinformatics 9(Suppl. 1), S2 (2009).
7. Bauer-Mehern A, Furlong LI, Raustschka M et al. From SNPs to pathways: integration of functional effect of sequence variations on models of cell signaling pathways. BMC Bioinformatics 10(Suppl. 8), S6 (2009).
8. Ryan M, Deikhans M, Lein S et al. LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics 25(11), 1431-1432 (2008).
9. Cordell HJ. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum. Mol. Genet. 11, 2463-2468 (2002).
10. Moore JH, Williams SM. Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. BioEssays 27, 637-646 (2005). • Provides a description of both biological and statistical epistasis (i.e., gene-gene interaction), its role in common complex diseases, and the challenges of detecting epistasis in genetic association studies.
11. Bateson W. Mendel's Principles of Heredity. Cambridge University Press, Cambridge, UK (1909).
12. Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399-433 (1918).
13. Falchetti A, Marini F, Luzi E, Tonelli F, Brandi ML. Multiple endocrine neoplasms. Best Pract. Res. Clin. Rheumatol. 22(1), 149-163 (2008).
14. Gao LG, Luo F, Hui RT, Zhou XL. Recent molecular biological progress in Marfan syndrome and Marfan-associated disorders. Ageing Res. Rev. DOI:10.1016/j.arr.2009.09.001 (2009) (Epub ahead of print).
15. Avery L, Wasserman S. Ordering gene function: the interpretation of epistasis in regulatory hierarchies. Trends Genet. 8(9), 312-316 (1992).
16. Goodwin EB, Ellis RE. Turning clustering loops: sex determination in Caenorhabditis elegans. Curr. Biol. 12, R111-R120 (2002).
17. Tong AH, Lesage G, Bader GD et al. Global mapping of the yeast genetic interaction network. Science 303(5659), 808-813 (2004).
18. Lehrer B, Crombie C, Tischler J et al. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat. Genet. 38(8), 896-903 (2006).
19. Hirschorn JN, Lohmueller K, Byrne E et al. A comprehensive review of genetic association studies. Genet. Med. 4, 45-61 (2002).
20. Fink U. The future of genetic association studies in Alzheimer disease. J. Neural Transm. 110, 253-266 (2003).
21. Moore JH. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56, 73-82 (2003).
22. Wiltshire S, Bell JT, Groves C et al. Type 2 diabetes susceptibility loci on chromosomes 1q21-25 and 10q23-26 in Northern Europeans. Ann. Hum. Genet. 70, 726-737 (2006).
23. Abou Jamra R, Fuerst R, Kaneva R et al. The first genomewide interaction and locus-heterogeneity linkage scan in bipolar affective disorder: strong evidence of epistatic effects between loci on chromosomes 2q and 6q. Am. J. Hum. Genet. 81, 974-986 (2007).
24. Askland K, Read C, Moore JH. Pathway-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Hum. Genet. 125, 63-79 (2009).
25. Tsai CT, Hwang JJ, Ritchie MD et al. Renin-angiotensin system gene polymorphisms and coronary artery disease in a large angiographic cohort: detection of high order gene-gene interaction. Atherosclerosis 195(1) 172-180 (2006).
26. Spencer CC, Su Z, Donnelly P et al. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet. 5(5), E1000477 (2009).
27. Clark AG, Boerwinkle E, Hixson J et al. Determinants of the success of wholegenome association testing. Genome Res. 15, 1463-1467 (2005).
28. Templeton AR. Epistasis and complex traits. In: Epistasis and Evolutionary Process. Wolf J, Brodie B 3rd, and Wade M (Eds). Oxford University Press, NY, USA (2000).
29. Sing CF, Stengard JH, Kardia SL. Genes, environment, and cardiovascular disease. Arterioscler. Thromb. Vasc. Biol. 23, 1190-1196 (2003).
30. Thornton-Wells TA, Moore JH, Haines JL. Genetics, statistics, and human disease: analytical retooling for complexity. Trends Genet. 20, 640-647 (2004).
31. Rea TJ, Brown CM, Sing CF. Complex adaptive system models and the genetic analysis of plasma HDL-cholesterol concentration. Perspect. Biol. Med. 49(4), 490-503 (2006).
32. Moore JH, Williams SM. Epistasis and its implications for personal genetics. Am. J. Hum. Genet. 85(3), 309-320 (2009).
33. Ritchie MD, Hahn LW, Roodi N et al. Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138-147 (2001).
34. Ritchie MD, Hahn LW, Moore JH. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet. Epidemiol. 24, 150-157 (2003).
35. Nelson MR, Kardia SL, Ferrell RE et al. A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res. 11, 458-470 (2001).
36. Reif DM, White BC, Moore JH. Integrated analysis of genetic, genomic and proteomic data. Expert Rev. Proteomics 1, 1095-1104 (2004).
37. Kooperberg C, Ruczinski I. Identifying interacting SNPs using Monte Carlo logic regression. Genet. Epidemiol. 28, 157-170 (2005).
38. Young SS, Ge N. Recursive partitioning analysis of complex disease pharmacogenetic studies. I. Motivation and overview. Pharmacogenomics 6(1), 65-75 (2005).
39. Millstein J, Conti DV, Gilliland FD et al. A testing framework for identifying susceptibility genes in the presence of epistasis. Am. J. Hum. Genet. 78, 15-27 (2006).
40. Zheng T, Wang H, Lo SH. backward genotype-trait association (BGTA) - based dissection of complex traits in case-control design. Hum. Hered. 62, 196-212 (2006).
41. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat. Genet. 39(9), 1167-1173 (2007).
42. Chen X, Liu CT, Zhang M, Zhang H. A forest-based approach to identifying gene and gene gene interactions. PNAS 104(49), 19199-19203 (2007).
43. Park MY, Hastie T. Penalized logistic regression for detecting gene interactions. Biostatistics 9(1), 30-50 (2008).
44. Motsinger-Reif AA, Dudek SM, Hahn LW et al. Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet. Epidemiol. 32, 325-340 (2008).
45. Xiang W, Yang C, Yang Q et al. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association studies. BMC Bioinformatics 10, 13 (2009).
46. Hahn LW, Ritchie MD, Moore JH. Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19, 376-382 (2003).
47. Hahn LW, Moore JH. Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol. 4(2), 183-194 (2004).
48. Moore JH. Computational analysis of gene-gene interactions in common human diseases using multifactor dimensionality reduction. Expert Rev. Mol. Diagn. 4(6), 795-803 (2004).
49. Moore JH, Gilbert JC, Tsai CT et al. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J. Theor. Biol. 241, 252-261 (2006).
50. Moore JH. Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. Zhu X, Davidson I (Eds). IGA Press, PA, USA, 17-30 (2007).
51. Altshuler D, Brooks LD, Chakravarti A et al. A haplotype map of the human genome. Nature 437, 1299-1320 (2005).
52. Risch NJ, Merikangas KR. The future of genetic studies of complex human disease. Science 273, 1516-1517 (1996).
53. Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95-108 (2005).
54. Wang WY, Barratt BJ, Clayton DG et al. Genome-wide association studies: Theoretical and practical concerns. Nat. Rev. Genet. 6, 109-118 (2005).
55. Kingsmore SF, Lindquist IE, Mudge J et al. Genome-wide association studies: progress in identifying genetic biomarkers in common, complex diseases. Biomarker Insights 2, 283-292 (2007). • Discusses the basics of genome-wide association studies, how they are useful for biomarker discover in complex diseases compared with other methods, as well as their limitations.
56. Greene CS, Moore JH. Ant colony optimization for genome-wide genetic analysis. Lect. Notes Comp. Sci. 5217, 27-47 (2008).
57. Moore JH. From genotypes to genometypes: putting the genome back in genome-wide association studies. Eur. J. Hum. Genet. 17(10), 1205-1206 (2009).
58. Emily M, Mailund T, Hain J et al. Using biological networks to search for interacting loci in genome-wide association studies. Eur. J. Hum. Genet. 17(10), 1231-1240 (2009).
59. Shriner D, Tesfaye BM, Padilla MA et al. Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies. Nucleic Acids Res. 36(4), e26 (2008).
60. Greene CS, Gilmore JM, Kiralis J, Andrews PC, Moore JH. Optimal use of expert knowledge in ant colony optimization for the analysis of epistasis in human disease. In: Lecture Notes in Computer Science: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (Volume 5483/2009). Springer Berlin/Heidelberg, Germany (2009).
61. Yates JR. Mass spectrometry: from genomics to proteomics. Trends Genet. 16, 5-8 (2000).
62. Pellegrini M, Haynor D, Johnson JM. Protein interaction networks. Expert Rev. Proteomics 1(2), 239-249 (2004). • Reviews several experimental approaches used to measure protein-protein, protein-DNA, and gene-gene interactions and how these approaches have been scaled up to produce genome-wide data used to depict interaction networks.
63. Tong AH, Evangelista M, Parsons AB et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364-2368 (2001).
64. Shrabanek L, Saini HK, Bader GD et al. Computational prediction of protein-protein interactions. Mol. Biotechnol. 38, 1-17 (2007).
65. Pellegrini M, Marcotte EM, Thompson MJ et al. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA 96, 4285-4288 (1999).
66. Tan SH, Zhang Z, Ng SK. ADVICE: Automated Detection and Validation of Interaction by Co-Evolution. Nucleic Acids Res. 32(Web Server issue), W69-W72 (2004).
67. Makino T, Gojobori T. Evolution of protein-protein interaction network. Genome Dyn. 3, 13-29 (2007).
68. Kim S, Shin SY, Lee IH, Kim SJ, Sriram R, Zhang BT. PIE: an online prediction system for protein-protein interactions from text. Nucleic Acids Res. 36(Web Server Issue), W411-W415 (2008).
69. Ta HX, Holm L. Evaluation of different domain based methods in protein interaction prediction. Biochem. Biophys. Rex. Commun. DOI: 10.1016/j.bbrc.2009.09.130 (2009) (Epub ahead of print).
70. Li SH, Li XJ. Huntington-protein interactions and the pathogenesis of Huntington's disease. Trends Genet. 20, 146-154 (2004).
71. Kaltenbach LS, Romero E, Becklin RR et al. Huntingtin interacting proteins are genetic modifiers of neurodegeneration. PloS Genet. 3(5), e82 (2007).
72. Limviphuvadh V, Tanaka S, Goto S et al. The commonality of protein interaction networks determined in neurodegenerative disorders. Bioinformatics 23, 2129-2138 (2007).
73. Mathivanan S, Periaswamy B, Gandi T et al. An evaluation of human protein-protein interaction data in the public domain. BMC Bioinformatics. 7(Suppl. 5), S19 (2006). •• Gives a thorough review and evaluation of a number or protein-protein interaction databases comparing their overlap and a number of other statistics.
74. Chautard E, Thierry-Meig N, Ricard-Blum S. Interaction networks: from protein function to drug discovery. Pathol. Biol. (Paris) 57(4), 324-333 (2009).
75. Lehne B, Schlitt T. Protein-protein interaction databases: keeping up with growing interactomes. Hum. Genomics 3(3), 291-297 (2009).
76. Prasad TSK, Goel R, Kandasamy K et al. Human Protein Reference Database - 2009 update. Nucleic Acids Res. 37(Database issue), D767-D772 (2009)
77. Matthews L, Gopinath G, Gillespie M et al. Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res. 37(Database issue), D619-D622 (2009).
78. Jensen LJ, Kuhn M, Stark M et al. STRING 8 - a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37(Database issue), D412-416 (2009).
79. von Mering C, Jensen LJ, Kuhn M et al. STRING 7: recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 35(Database issue), D358-D362 (2007).
80. Chaurasia G, Malhotra S, Russ J et al. UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic Acids Res. 37(Database issue), D657-D660 (2009).
81. Franke L, van-Bakel H, Fokkens L et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet. 78, 1011-1025 (2006).
82. Lim J, Hao T, Shaw C et al. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 125(4), 801-814 (2006).
83. Raul TJ, Venkatesan K, Hao T et al. Towards a proteome-scale map of the human protein-protein interaction network Nature 437, 1173-1178 (2005).
84. Stelzl U, Worm U, Lalowski M et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 857-968 (2005).
85. Coutinho AM, Sousa I, Martins M et al. Evidence for epistasis between SLC6A4 and ITGB3 in autism etiology and in the determination of platelet serotonin levels. Hum. Genet. 121, 243-256 (2007).
86. Asselbergs FW, Williams SM, Hebert PR et al. Epistatic effects of polymorphisms in genes from the renin-angiotensin, bradykinin, and fibrinolytic systems on plasma t-PA and PAI-1 levels. Genomics 89(3), 362-369 (2007).
87. Broberg K, Huynh E, Engstrom KS et al. Association between polymorphisms in RMI1, TOP3A, and BLM and risk of cancer: a case-control study. BMC Cancer 9, 140 (2009).
88. Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac. Symp. Biocomput. 368-379 (2009).
89. Saccone SF, Saccone NL, Swan GE et al. Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence. Bioinformatics 24, 1805-1811 (2008).
90. Yu K, Li Q, Bergen AW et al. Pathway analysis by adaptive combination of P-values. Genet. Epidemiol. DOI: 10.1002/gepi.20422 (2009) (Epub ahead of print).
91. Wilke RA, Mareedu RK, Moore JH. The pathway less traveled: moving from candidate genes to candidate pathways in the analysis of genome-wide data from large scale pharmacogenetic association studies. Curr. Pharmacogenomics Personalized Med. 6, 150-159 (2008).
92. Lievens S, Lemmens I, Tavernier J. Mammalian two-hybrids come of age. Trends Biochem. Sci. DOI:10.1016/j.tibs.2009.06.009 (2009) (Epub ahead of print).
93. Chanock SJ, Manolio T, Boehnke M et al. Replicating genotype-phenotype associations. Nature 447(7145), 655-660 (2007).
94. Carlson CS. Agnosticism and equity in genome-wide association studies. Nat. Genet. 38(6), 605-606 (2006). • Describes the standpoint of genomic agnosticisms where each SNP is considered to be equally functional as applied to two studies, and how some believe this approach to have certain advantages.
95. Breitkreutz BJ, Stark C, Reguly T et al. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res. 36(Database issue), D637-D640 (2008).
96. Alfarano C, Andrade CE, Anthony K et al. The Biomolecular Interaction Network Database and related tools: 2005 update. Nucleic Acids Res. 33(Database issue), D418-D424 (2005).
97. Chatr-aryamontri A, Ceol A, Palazzi LM et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 35(Database issue), D572-D574 (2007).
98. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32(Database issue), D449-D451 (2004).