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Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease


Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.


Vyšlo v časopise: Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease. PLoS Genet 9(10): e32767. doi:10.1371/journal.pgen.1003770
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003770

Souhrn

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.


Zdroje

1. AltshulerD, DalyMJ, LanderES (2008) Genetic mapping in human disease. Science 322: 881–888.

2. FrazerKA, MurraySS, SchorkNJ, TopolEJ (2009) Human genetic variation and its contribution to complex traits. Nature Reviews Genetics 10: 241–251.

3. McCarthyMI, AbecasisGR, CardonLR, GoldsteinDB, LittleJ, et al. (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Reviews Genetics 9: 356–369.

4. PearsonTA, ManolioTA (2008) How to interpret a genome-wide association study. Journal of the American Medical Association 299: 1335–1344.

5. AbrahamC, ChoJH (2009) Inflammatory bowel disease. New England Journal of Medicine 361: 2066–2078.

6. BarrettJC, HansoulS, NicolaeDL, ChoJH, DuerrRH, et al. (2008) Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nature Genetics 40: 955–962.

7. FrankeA, McGovernDPB, BarrettJC, WangK, Radford-SmithGL, et al. (2010) Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci. Nature Genetics 42: 1118–1125.

8. KhorB, GardetA, XavierRJ (2011) Genetics and pathogenesis of inflammatory bowel disease. Nature 474: 307–317.

9. StappenbeckTS, RiouxJD, MizoguchiA, SaitohT, HuettA, et al. (2011) Crohn disease: a current perspective on genetics, autophagy and immunity. Autophagy 7: 355–374.

10. Van LimbergenJ, WilsonDC, SatsangiJ (2009) The genetics of Crohn's disease. Annual Review of Genomics and Human Genetics 10: 89–116.

11. BallardD, AbrahamC, ChoJ, ZhaoH (2010) Pathway analysis comparison using Crohn's disease genome wide association studies. BMC Medical Genomics 3: 25.

12. BraunR, BuetowK (2011) Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genetics 7: e1002101.

13. ChenX, WangL, HuB, GuoM, BarnardJ, et al. (2010) Pathway-based analysis for genome-wide association studies using supervised principal components. Genetic Epidemiology 34: 716–724.

14. ChenLS, HutterCM, PotterJD, LiuY, PrenticeRL, et al. (2010) Insights into colon cancer etiology via a regularized approach to gene set analysis of GWAS data. American Journal of Human Genetics 86: 860–871.

15. De la CruzO, WenX, KeB, SongM, NicolaeDL (2010) Gene, region and pathway level analyses in whole-genome studies. Genetic Epidemiology 34: 222–231.

16. EleftherohorinouH, WrightV, HoggartC, HartikainenA, JarvelinM, et al. (2009) Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS ONE 4: e8068.

17. HoldenM, DengS, WojnowskiL, KulleB (2008) GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatics 24: 2784–2785.

18. JiaP, WangL, FanousAH, ChenX, KendlerKS, et al. (2012) A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia. Journal of Medical Genetics 49: 96–103.

19. LeePH, O'DushlaineC, ThomasB, PurcellSM (2012) INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics 28: 1797–1799.

20. RamananVK, ShenL, MooreJH, SaykinAJ (2012) Pathway analysis of genomic data: concepts, methods and prospects for future development. Trends in Genetics 28: 323–332.

21. RuanoD, AbecasisGR, GlaserB, LipsES, CornelisseLN, et al. (2010) Functional gene group analysis reveals a role of synaptic heterotrimeric G proteins in cognitive ability. American Journal of Human Genetics 86: 113–125.

22. ShahbabaB, ShachafCM, YuZ (2012) A pathway analysis method for genome-wide association studies. Statistics in Medicine 31: 988–1000.

23. TorkamaniA, TopolEJ, SchorkNJ (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92: 265–272.

24. WangK, LiM, HakonarsonH (2010) Analysing biological pathways in genome-wide association studies. Nature Reviews Genetics 11: 843–854.

25. WangL, JiaP, WolfingerRD, ChenX, ZhaoZ (2011) Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98: 1–8.

26. WuMC, KraftP, EpsteinMP, TaylorDM, ChanockSJ, et al. (2010) Powerful SNP-set analysis for case-control genome-wide association studies. American Journal of Human Genetics 86: 929–942.

27. Yaspan BL, Veatch OJ (2011) Strategies for pathway analysis from GWAS data, John Wiley and Sons, Inc., volume 71, chapter 1.20. pp. 1–15.

28. YuK, LiQ, BergenAW, PfeifferRM, RosenbergPS, et al. (2009) Pathway analysis by adaptive combination of P-values. Genetic Epidemiology 33: 700–709.

29. CantorRM, LangeK, SinsheimerJS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. American Journal of Human Genetics 86: 6–22.

30. HartwellL (2004) Robust interactions. Science 303: 774–775.

31. HirschhornJN (2009) Genomewide association studies—illuminating biologic pathways. New England Journal of Medicine 360: 1699–1701.

32. SchadtEE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461: 218–223.

33. EichlerEE, FlintJ, GibsonG, KongA, LealSM, et al. (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nature Reviews Genetics 11: 446–450.

34. ManolioTA, CollinsFS, CoxNJ, GoldsteinDB, HindorffLA, et al. (2009) Finding the missing heritability of complex diseases. Nature 461: 747–753.

35. RiouxJD, AbbasAK (2005) Paths to understanding the genetic basis of autoimmune disease. Nature 435: 584–589.

36. RopersHH (2007) New perspectives for the elucidation of genetic disorders. American Journal of Human Genetics 81: 199–207.

37. AertsS, LambrechtsD, MaityS, Van LooP, CoessensB, et al. (2006) Gene prioritization through genomic data fusion. Nature Biotechnology 24: 537–544.

38. BaranziniSE, GalweyNW, WangJ, KhankhanianP, LindbergR, et al. (2009) Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Human Molecular Genetics 18: 2078–2090.

39. ChenM, ChoJ, ZhaoH (2011) Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics 7: e1001353.

40. FrankeL, van BakelH, FokkensL, de JongED, Egmont-PetersenM, et al. (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. American Journal of Human Genetics 78: 1011–1025.

41. LageK, KarlbergEO, StorlingZM, OlasonPI, PedersenAG, et al. (2007) A human phenomeinteractome network of protein complexes implicated in genetic disorders. Nature Biotechnology 25: 309–316.

42. RaychaudhuriS, PlengeRM, RossinEJ, NgACY, PurcellSM, et al. (2009) Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genetics 5: e1000534.

43. SacconeSF, SacconeNL, SwanGE, MaddenPAF, GoateAM, et al. (2008) Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence. Bioinformatics 24: 1805–1811.

44. TrancheventL, CapdevilaFB, NitschD, De MoorB, De CausmaeckerP, et al. (2011) A guide to web tools to prioritize candidate genes. Briefings in Bioinformatics 12: 22–32.

45. WuX, JiangR, ZhangMQ, LiS (2008) Network-based global inference of human disease genes. Molecular Systems Biology 4: 189.

46. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene Ontology: tool for the unification of biology. Nature Genetics 25: 25–29.

47. BoyleAP, HongEL, HariharanM, ChengY, SchaubMA, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Research 22: 1790–1797.

48. DegnerJF, PaiAA, Pique-RegiR, VeyrierasJ, GaffneyDJ, et al. (2012) DNase I sensitivity QTLs are a major determinant of human expression variation. Nature 482: 390–394.

49. NicolaeDL, GamazonE, ZhangW, DuanS, DolanME, et al. (2010) Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genetics 6: e1000888.

50. SchaubMA, BoyleAP, KundajeA, BatzoglouS, SnyderM (2012) Linking disease associations with regulatory information in the human genome. Genome Research 22: 1748–1759.

51. WardLD, KellisM (2012) Interpreting noncoding genetic variation in complex traits and human disease. Nature Biotechnology 30: 1095–1106.

52. CapanuM, OrlowI, BerwickM, HummerAJ, ThomasDC, et al. (2008) The use of hierarchical models for estimating relative risks of individual genetic variants: an application to a study of melanoma. Statistics in Medicine 27: 1973–1992.

53. CapanuM, ConcannonP, HaileRW, BernsteinL, MaloneKE, et al. (2011) Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling. Genetic Epidemiology 35: 389–397.

54. ChenGK, WitteJS (2007) Enriching the analysis of genome-wide association studies with hierarchical modeling. American Journal of Human Genetics 81: 397–404.

55. CooperGM, ShendureJ (2011) Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Reviews Genetics 12: 628–640.

56. FridleyBL, SerieD, JenkinsG, WhiteK, BamletW, et al. (2010) Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies. Genetic Epidemiology 34: 418–426.

57. FridleyBL, LundS, JenkinsGD, WangL (2012) A Bayesian integrative genomic model for pathway analysis of complex traits. Genetic Epidemiology 36: 352–359.

58. GaffneyDJ, VeyrierasJ, DegnerJF, RogerP, PaiAA, et al. (2012) Dissecting the regulatory architecture of gene expression QTLs. Genome Biology 13: R7.

59. LeeS, DudleyAM, DrubinD, SilverPA, KroganNJ, et al. (2009) Learning a prior on regulatory potential from eQTL data. PLoS Genetics 5: e1000358.

60. LewingerJP, ContiDV, BaurleyJW, TricheTJ, ThomasDC (2007) Hierarchical Bayes prioritization of marker associations from a genome-wide association scan for further investigation. Genetic Epidemiology 31: 871–883.

61. SwartzMD, KimmelM, MuellerP, AmosCI (2006) Stochastic search gene suggestion: a Bayesian hierarchical model for gene mapping. Biometrics 62: 495–503.

62. VeyrierasJB, KudaravalliS, KimSY, DermitzakisET, GiladY, et al. (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genetics 4: e1000214.

63. BaderGD, CaryMP, SanderC (2006) Pathguide: a pathway resource list. Nucleic Acids Research 34: D504–D506.

64. Bauer-MehrenA, FurlongLI, SanzF (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Molecular Systems Biology 5: 290.

65. Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.

66. AsklandK, ReadC, MooreJ (2009) Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Human Genetics 125: 63–79.

67. EleftherohorinouH, HoggartCJ, WrightVJ, LevinM, CoinLJM (2011) Pathway-driven gene stability selection of two rheumatoid arthritis GWAS identifies and validates new susceptibility genes in receptor mediated signalling pathways. Human Molecular Genetics 20: 3494–3506.

68. FreudenbergJ, LeeAT, SiminovitchKA, AmosCI, BallardD, et al. (2010) Locus category based analysis of a large genome-wide association study of rheumatoid arthritis. Human Molecular Genetics 19: 3863–3872.

69. HolmansP, GreenEK, PahwaJS, FerreiraMAR, PurcellSM, et al. (2009) Gene Ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. American Journal of Human Genetics 85: 13–24.

70. LuoL, PengG, ZhuY, DongH, AmosCI, et al. (2010) Genome-wide gene and pathway analysis. European Journal of Human Genetics 18: 1045–1053.

71. O'DushlaineC, KennyE, HeronE, DonohoeG, GillM, et al. (2011) Molecular pathways involved in neuronal cell adhesion and membrane scaffolding contribute to schizophrenia and bipolar disorder susceptibility. Molecular Psychiatry 16: 286–292.

72. PerryJRB, McCarthyMI, HattersleyAT, ZegginiE, WeedonMN, et al. (2009) Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes 58: 1463–1467.

73. WangK, ZhangH, KugathasanS, AnneseV, BradfieldJP, et al. (2009) Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn disease. American Journal of Human Genetics 84: 399–405.

74. ZhongH, YangX, KaplanLM, MolonyC, SchadtEE (2010) Integrating pathway analysis and genetics of gene expression for genome-wide association studies. American Journal of Human Genetics 86: 581–591.

75. CarbonettoP, StephensM (2012) Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7: 73–108.

76. GuanY, StephensM (2011) Bayesian variable selection regression for genome-wide association studies, and other large-scale problems. Annals of Applied Statistics 5: 1780–1815.

77. LiY, WillerC, SannaS, AbecasisG (2009) Genotype imputation. Annual Review of Genomics and Human Genetics 10: 387–406.

78. MarchiniJ, HowieB (2010) Genotype imputation for genome-wide association studies. Nature Reviews Genetics 11: 499–511.

79. ServinB, StephensM (2007) Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genetics 3: e114.

80. ZhouX, CarbonettoP, StephensM (2013) Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genetics 9: e1003264.

81. KassRE, RafteryAE (1995) Bayes factors. Journal of the American Statistical Association 90: 773–795.

82. StephensM, BaldingDJ (2009) Bayesian statistical methods for genetic association studies. Nature Reviews Genetics 10: 681–690.

83. FernandoMMA, StevensCR, WalshEC, De JagerPL, GoyetteP, et al. (2008) Defining the role of the MHC in autoimmunity: a review and pooled analysis. PLoS Genetics 4: e1000024.

84. PolychronakosC, LiQ (2011) Understanding type 1 diabetes through genetics: advances and prospects. Nature Reviews Genetics 12: 781–792.

85. BartonA, ThomsonW, KeX, EyreS, HinksA, et al. (2008) Rheumatoid arthritis susceptibility loci at chromosomes 10p15, 12q13 and 22q13. Nature genetics 40: 1156–1159.

86. CooperJD, WalkerNM, SmythDJ, DownesK, HealyBC, et al. (2009) Follow-up of 1715 SNPs from the Wellcome Trust Case Control Consortium genome-wide association study in type I diabetes families. Genes and Immunity 10: S85–S94.

87. ParkesM, BarrettJC, PrescottNJ, TremellingM, AndersonCA, et al. (2007) Sequence variants in the autophagy gene IRGM and multiple other replicating loci contribute to Crohn's disease susceptibility. Nature Genetics 39: 830–832.

88. ThomsonW, BartonA, KeX, EyreS, HinksA, et al. (2007) Rheumatoid arthritis association at 6q23. Nature Genetics 39: 1431–1433.

89. ToddJA, WalkerNM, CooperJD, SmythDJ, DownesK, et al. (2007) Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nature Genetics 39: 857–864.

90. MHC Sequencing Consortium (1999) Complete sequence and gene map of a human major histocompatibility complex. Nature 401: 921–923.

91. HortonR, WilmingL, RandV, LoveringRC, BrufordEA, et al. (2004) Gene map of the extended human MHC. Nature Reviews Genetics 5: 889–899.

92. ImbodenJB (2009) The immunopathogenesis of rheumatoid arthritis. Annual Review of Pathology 4: 417–434.

93. BergerJO, SellkeT (1987) Testing a point null hypothesis: the irreconcilability of P values and evidence. Journal of the American Statistical Association 82: 112–122.

94. O'SheaJJ, MaA, LipskyP (2002) Cytokines and autoimmunity. Nature Reviews Immunology 2: 37–45.

95. GodessartN, KunkelSL (2001) Chemokines in autoimmune disease. Current Opinion in Immunology 13: 670–675.

96. ZhernakovaA, van DiemenCC, WijmengaC (2009) Detecting shared pathogenesis from the shared genetics of immune-related diseases. Nature Reviews Genetics 10: 43–55.

97. HampeJ, FrankeA, RosenstielP, TillA, TeuberM, et al. (2007) A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nature Genetics 39: 207–211.

98. RiouxJD, XavierRJ, TaylorKD, SilverbergMS, GoyetteP, et al. (2007) Genome-wide association study identifies new susceptibility loci for Crohn disease and implicates autophagy in disease pathogenesis. Nature Genetics 39: 596–604.

99. HomerCR, RichmondAL, RebertNA, AchkarJ, McDonaldC (2010) ATG16L1 and NOD2 interact in an autophagy-dependent antibacterial pathway implicated in Crohn's disease pathogenesis. Gastroenterology 139: 1630–1641.

100. GargG, TylerJR, YangJHM, CutlerAJ, DownesK, et al. (2012) Type 1 diabetes-associated IL2RA variation lowers IL-2 signaling and contributes to diminished CD4+CD25+ regulatory T cell function. Journal of Immunology 188: 4644–4653.

101. SakaguchiS, YamaguchiT, NomuraT, OnoM (2008) Regulatory T cells and immune tolerance. Cell 133: 775–787.

102. ToddJA (2010) Etiology of type 1 diabetes. Immunity 32: 457–467.

103. van BelleTL, CoppietersKT, von HerrathMG (2011) Type 1 diabetes: etiology, immunology, and therapeutic strategies. Physiological Reviews 91: 79–118.

104. ChistiakovDA, VoronovaNV, ChistiakovPA (2008) The crucial role of IL-2/IL-2RA-mediated immune regulation in the pathogenesis of type 1 diabetes, an evidence coming from genetic and animal model studies. Immunology Letters 118: 1–5.

105. HulmeMA, WasserfallCH, AtkinsonMA, BruskoTM (2012) Central role for interleukin-2 in type 1 diabetes. Diabetes 61: 14–22.

106. DörigRE, MarcilA, ChopraA, RichardsonCD (1993) The human CD46 molecule is a receptor for measles virus (Edmonston strain). Cell 75: 295–305.

107. NanicheD (2009) Human immunology of measles virus infection. Current Topics in Microbiology and Immunology 330: 151–171.

108. TatsuoH, OnoN, TanakaK, YanagiY (2000) SLAM (CDw150) is a cellular receptor for measles virus. Nature 406: 893–789.

109. RosenauBJ, SchurPH (2009) Association of measles virus with rheumatoid arthritis. Journal of Rheumatology 36: 893–897.

110. McInnesIB, SchettG (2011) The pathogenesis of rheumatoid arthritis. New England Journal of Medicine 365: 2205–2219.

111. MehraeinY, LennerzC, EhlhardtS, RembergerK, OjakA, et al. (2004) Virus antibodies in serum and synovial fluid of patients with rheumatoid arthritis and other connective tissue diseases. Modern Pathology 17: 781–789.

112. CotsapasC, VoightBF, RossinE, LageK, NealeBM, et al. (2011) Pervasive sharing of genetic effects in autoimmune disease. PLoS Genetics 7: e1002254.

113. TaitKF, MarshallT, BermanJ, Carr-SmithJ, RoweB, et al. (2004) Clustering of autoimmune disease in parents of siblings from the Type 1 diabetes Warren repository. Diabetic Medicine 21: 358–362.

114. BonizziG, KarinM (2004) The two NF-kappaB activation pathways and their role in innate and adaptive immunity. Trends in Immunology 25: 280–288.

115. CharoIF, RansohoffRM (2006) The many roles of chemokines and chemokine receptors in inflammation. New England Journal of Medicine 354: 610–621.

116. DongC, DavisRJ, FlavellRA (2002) MAP kinases in the immune response. Annual Review of Immunology 20: 55–72.

117. PaoLI, BadourK, SiminovitchKA, NeelBG (2007) Nonreceptor protein-tyrosine phosphatases in immune cell signaling. Annual Review of Immunology 25: 473–523.

118. BallardDH, AporntewanC, LeeJ, LeeJ, WuZ, et al. (2009) A pathway analysis applied to Genetic Analysis Workshop 16 genome-wide rheumatoid arthritis data. BMC Proceedings 3: S91.

119. BeyeneJ, HuP, HamidJS, ParkhomenkoE, PatersonAD, et al. (2009) Pathway-based analysis of a genome-wide case-control association study of rheumatoid arthritis. BMC Proceedings 3: S128.

120. PengG, LuoL, SiuH, ZhuY, HuP, et al. (2010) Gene and pathway-based second-wave analysis of genome-wide association studies. European Journal of Human Genetics 18: 111–117.

121. WangL, JiaP, WolfingerRD, ChenX, GraysonBL, et al. (2011) An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies. Bioinformatics 27: 686–692.

122. AlejandroEU, KalynyakTB, TaghizadehF, GwiazdaKS, RawstronEK, et al. (2010) Acute insulin signaling in pancreatic beta-cells is mediated by multiple Raf-1 dependent pathways. Endocrinology 151: 502–512.

123. SumaraG, FormentiniI, CollinsS, SumaraI, WindakR, et al. (2009) Regulation of PKD by the MAPK p38δ in insulin secretion and glucose homeostasis. Cell 136: 235–248.

124. BottiniN, VangT, CuccaF, MustelinT (2006) Role of PTPN22 in type 1 diabetes and other autoimmune diseases. Seminars in Immunology 18: 207–213.

125. BartokB, FiresteinGS (2010) Fibroblast-like synoviocytes: key effector cells in rheumatoid arthritis. Immunological Reviews 233: 233–255.

126. MurphySH, SuzukiK, DownesM, WelchGL, De JesusP, et al. (2011) Tumor suppressor protein (p)53 is a regulator of NF-kappaB repression by the glucocorticoid receptor. Proceedings of the National Academy of Sciences 108: 17117–17122.

127. IchimuraA, HirasawaA, Poulain-GodefroyO, BonnefondA, HaraT, et al. (2012) Dysfunction of lipid sensor GPR120 leads to obesity in both mouse and human. Nature 483: 350–354.

128. FraylingTM, TimpsonNJ, WeedonMN, ZegginiE, FreathyRM, et al. (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316: 889–894.

129. McCarthyMI (2010) Genomics, type 2 diabetes, and obesity. New England Journal of Medicine 363: 2339–2350.

130. BarrettJC, ClaytonDG, ConcannonP, AkolkarB, CooperJD, et al. (2009) Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics 41: 703–707.

131. EyreS, BowesJ, DiogoD, LeeA, BartonA, et al. (2012) High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nature Genetics 44: 1336–1340.

132. JostinsL, RipkeS, WeersmaRK, DuerrRH, McGovernDP, et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491: 119–124.

133. FisherSA, TremellingM, AndersonCA, GwilliamR, BumpsteadS, et al. (2008) Genetic determinants of ulcerative colitis include the ECM1 locus and five loci implicated in Crohn's disease. Nature Genetics 40: 710–712.

134. BottoloL, RichardsonS (2010) Evolutionary stochastic search for Bayesian model exploration. Bayesian Analysis 5: 583–618.

135. FridleyBL (2009) Bayesian variable and model selection methods for genetic association studies. Genetic Epidemiology 33: 27–37.

136. HeQ, LinD (2011) A variable selection method for genome-wide association studies. Bioinformatics 27: 1–8.

137. HoggartCJ, WhittakerJC, De IorioM, BaldingDJ (2008) Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genetics 4: e1000130.

138. HungRJ, BaragattiM, ThomasD, McKayJ, Szeszenia-DabrowskaN, et al. (2007) Inherited predisposition of lung cancer: a hierarchical modeling approach to DNA repair and cell cycle control pathways. Cancer Epidemiology, Biomarkers and Prevention 16: 2736–2744.

139. LogsdonBA, HoffmanGE, MezeyJG (2010) A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis. BMC Bioinformatics 11: 58.

140. SeguraV, VilhjálmssonBJ, PlattA, KorteA, SerenÜ, et al. (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nature Genetics 44: 825–830.

141. YiN, XuS (2008) Bayesian Lasso for quantitative trait loci mapping. Genetics 179: 1045–1055.

142. WuTT, ChenYF, HastieT, SobelE, LangeK (2009) Genome-wide association analysis by Lasso penalized logistic regression. Bioinformatics 25: 714–721.

143. GuanY, StephensM (2008) Practical issues in imputation-based association mapping. PLoS Genetics 4: e1000279.

144. International HapMap Consortium (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449: 851–861.

145. DemirE, CaryMP, PaleyS, FukudaK, LemerC, et al. (2010) The BioPAX community standard for pathway data sharing. Nature Biotechnology 28: 935–942.

146. KanehisaM, GotoS, FurumichiM, TanabeM, HirakawaM (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research 38: D355–D360.

147. CaspiR, AltmanT, DaleJM, DreherK, et al. (2010) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research 38: D473–D479.

148. SohD, DongD, GuoY, WongL (2010) Consistency, comprehensiveness, and compatibility of pathway databases. BMC Bioinformatics 11: 449.

149. StobbeM, HoutenS, JansenG, van KampenA, MoerlandP (2011) Critical assessment of human metabolic pathway databases: a stepping stone for future integration. BMC Systems Biology 5: 165.

150. CooksonW, LiangL, AbecasisG, MoffattM, LathropM (2009) Mapping complex disease traits with global gene expression. Nature Reviews Genetics 10: 184–194.

151. DixonAL, LiangL, MoffattMF, ChenW, HeathS, et al. (2007) A genome-wide association study of global gene expression. Nature Genetics 39: 1202–1207.

152. StrangerBE, NicaAC, ForrestMS, DimasA, BirdCP, et al. (2007) Population genomics of human gene expression. Nature Genetics 39: 1217–1224.

153. GeorgeEI, McCullochRE (1993) Variable selection via Gibbs sampling. Journal of the American Statistical Association 88: 881–889.

154. BodmerW, BonillaC (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nature Genetics 40: 695–701.

155. IoannidisJPA, TrikalinosTA, KhouryMJ (2006) Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases. American Journal of Epidemiology 164: 609–614.

156. O'BrienSM, DunsonDB (2004) Bayesian multivariate logistic regression. Biometrics 60: 739–746.

157. HoetingJA, MadiganD, RafteryAE, VolinskyCT (1999) Bayesian model averaging: a tutorial. Statistical Science 14: 382–401.

158. MitchellTJ, BeauchampJJ (1988) Bayesian variable selection in linear regression. Journal of the American Statistical Association 83: 1023–1032.

159. StoreyJD (2003) The positive false discovery rate: a Bayesian interpretation and the q-value. Annals of Statistics 31: 2013–2035.

160. StoreyJD, TibshiraniR (2003) Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences 100: 9440–9445.

161. ZegginiE, ScottLJ, SaxenaR, VoightBF, MarchiniJL, et al. (2008) Meta-analysis of genomewide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature Genetics 40: 638–645.

162. PirinenM, DonnellyP, SpencerCCA (2012) Including known covariates can reduce power to detect genetic effects in case-control studies. Nature Genetics 44: 848–851.

163. SchaeferCF, AnthonyK, KrupaS, BuchoffJ, DayM, et al. (2009) PID: the Pathway Interaction Database. Nucleic Acids Research 37: D674–D679.

164. GeerLY, Marchler-BauerA, GeerRC, HanL, HeJ, et al. (2010) The NCBI BioSystems database. Nucleic Acids Research 38: D492–D496.

165. CeramiEG, GrossBE, DemirE, RodchenkovI, BaburÖ, et al. (2011) Pathway Commons, a web resource for biological pathway data. Nucleic Acids Research 39: D685–D690.

166. KandasamyK, MohanSS, RajuR, KeerthikumarS, KumarGSS, et al. (2010) NetPath: a public resource of curated signal transduction pathways. Genome Biology 11: R3.

167. CroftD, O'KellyG, WuG, HawR, GillespieM, et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Research 39: D691–D697.

168. RomeroP, WaggJ, GreenM, KaiserD, KrummenackerM, et al. (2004) Computational prediction of human metabolic pathways from the complete human genome. Genome Biology 6: R2.

169. PicoAR, KelderT, van IerselMP, HanspersK, ConklinBR, et al. (2008) WikiPathways: pathway editing for the people. PLoS Biology 6: e184.

170. KelderT, van IerselMP, HanspersK, KutmonM, ConklinBR, et al. (2012) WikiPathways: building research communities on biological pathways. Nucleic Acids Research 40: D1301–D1307.

171. MiH, DongQ, MuruganujanA, GaudetP, LewisS, et al. (2010) PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the Gene Ontology Consortium. Nucleic Acids Research 38: D204–D210.

172. SilverbergMS, DuerrRH, BrantSR, BromfieldG, DattaLW, et al. (2007) Refined genomic localization and ethnic differences observed for the IBD5 association with Crohn's disease. European Journal of Human Genetics 15: 328–335.

173. Van LimbergenJ, RussellRK, NimmoER, SatsangiJ (2007) The genetics of inflammatory bowel disease. American Journal of Gastroenterology 102: 2820–2831.

174. RiouxJD, GoyetteP, VyseTJ, HammarströmL, FernandoMMA, et al. (2009) Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases. Proceedings of the National Academy of Sciences 106: 18680–18685.

175. PlengeRM, SeielstadM, PadyukovL, LeeAT, RemmersEF, et al. (2007) TRAF1-C5 as a risk locus for rheumatoid arthritis—a genomewide study. New England Journal of Medicine 357: 1199–1209.

176. StahlEA, RaychaudhuriS, RemmersEF, XieG, EyreS, et al. (2010) Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nature Genetics 42: 508–514.

177. BartonA, EyreS, KeX, HinksA, BowesJ, et al. (2009) Identification of AF4/fmr2 family, member 3 (AFF3) as a novel rheumatoid arthritis susceptibility locus and confirmation of two further pan-autoimmune susceptibility genes. Human Molecular Genetics 18: 2518–2522.

178. RaychaudhuriS, RemmersEF, LeeAT, HackettR, GuiducciC, et al. (2008) Common variants at CD40 and other loci confer risk of rheumatoid arthritis. Nature Genetics 40: 1216–1223.

179. CooperJD, WalkerNM, HealyBC, SmythDJ, DownesK, et al. (2009) Analysis of 55 autoimmune disease and type II diabetes loci: further confirmation of chromosomes 4q27, 12q13.2 and 12q24.13 as type I diabetes loci, and support for a new locus, 12q13.3–q14.1. Genes and Immunity 10: S95–S120.

180. HuangJ, EllinghausD, FrankeA, HowieB, LiY (2012) 1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 data. European Journal of Human Genetics 20: 801–805.

181. LoweCE, CooperJD, BruskoT, WalkerNM, SmythDJ, et al. (2007) Large-scale genetic fine mapping and genotype-phenotype associations implicate polymorphism in the IL2RA region in type 1 diabetes. Nature Genetics 39: 1074–1082.

182. VellaA, CooperJD, LoweCE, WalkerN, NutlandS, et al. (2005) Localization of a type 1 diabetes locus in the IL2RA/CD25 region by use of tag single-nucleotide polymorphisms. American Journal of Human Genetics 76: 773–779.

183. HakonarsonH, GrantSFA, BradfieldJP, MarchandL, KimCE, et al. (2007) A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature 448: 591–594.

184. CooperJD, SmythDJ, SmilesAM, PlagnolV, WalkerNM, et al. (2008) Meta-analysis of genomewide association study data identifies additional type 1 diabetes risk loci. Nature Genetics 40: 1399–1401.

185. McVeanGAT, MyersSR, HuntS, DeloukasP, BentleyDR, et al. (2004) The fine-scale structure of recombination rate variation in the human genome. Science 304: 581–584.

186. DreszerTR, KarolchikD, ZweigAS, HinrichsAS, et al. (2012) The UCSC Genome Browser database: extensions and updates 2011. Nucleic Acids Research 40: D918–D923.

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Genetika Reprodukčná medicína

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PLOS Genetics


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