Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics

PLoS Computational Biology

Volumn 12, Issue 1, 2016, Pages

  Lamparter, David ^a,b   Marbach, Daniel ^a,b   Rueedi, Rico ^a,b   Kutalik, Zoltán ^a,b,c   Bergmann, Sven ^a,b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

^c UNIVERSITY OF LAUSANNE   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; STATISTICAL TESTS; STATISTICS;

BIOLOGICAL INSIGHT; CHI-SQUARED; GENOME-WIDE ASSOCIATION STUDIES; NUMERICAL SOLUTION; P-VALUES; SCORING ALGORITHMS; SINGLE NUCLEOTIDE POLYMORPHISMS; STATISTICAL POWER; SUMMARY STATISTIC; TEST STATISTICS;

GENES;

GENE MUTATION; GENETIC POLYMORPHISM; PHENOTYPE; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICS; ALGORITHM; BIOLOGY; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HAPLOTYPE MAP; HUMAN; PROCEDURES; SOFTWARE;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENOME-WIDE ASSOCIATION STUDY; HAPMAP PROJECT; HUMANS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 84956753111     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004714     Document Type: Article

References (49)

1. Visscher PM, Brown MA, McCarthy MI, Yang J, Five years of GWAS discovery. Am J Hum Genet. 2012;90: 7–24. doi: 10.1016/j.ajhg.2011.11.029 22243964
2. Hou L, Zhao H, A review of post-GWAS prioritization approaches. Front Genet. 2013;4: 280. doi: 10.3389/fgene.2013.00280 24367376
3. Segrè A V, Groop L, Mootha VK, Daly MJ, Altshuler D, Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits. PLoS Genet. 2010;6: e1001058. doi: 10.1371/journal.pgen.1001058 20714348
4. Pers TH, Karjalainen JM, Chan Y, Westra H-J, Wood AR, Yang J, et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat Commun. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.; 2015;6: 5890. Available: doi: http://dx.doi.org/10.1038/ncomms6890 25597830
5. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46: 1173–86. doi: 10.1038/ng.3097 25282103
6. 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: 25–29. doi: 10.1038/75556 10802651
7. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M, Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 2014;42: D199–D205. doi: 10.1093/nar/gkt1076 24214961
8. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: A library of protein families and subfamilies indexed by function. Genome Res. 2003;13: 2129–2141. doi: 10.1101/gr.772403 12952881
9. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, et al. Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Res. 2011;39: D691–D697. doi: 10.1093/nar/gkq1018 21067998
10. Nishimura D, BioCarta. Biotech Softw Internet Rep. 2001;2: 117–120. doi: 10.1089/152791601750294344
11. Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. The American Society of Human Genetics; 2010;87: 139–145. doi: 10.1016/j.ajhg.2010.06.009 20598278
12. Li MX, Gui HS, Kwan JSH, Sham PC, GATES: A rapid and powerful gene-based association test using extended Simes procedure. Am J Hum Genet. 2011;88: 283–293. doi: 10.1016/j.ajhg.2011.01.019 21397060
13. Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM, Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011;21: 1109–21. doi: 10.1101/gr.118992.110 21536720
14. Wang L, Jia P, Wolfinger RD, Chen X, Grayson BL, Aune TM, et al. An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies. Bioinformatics. 2011;27: 686–692. doi: 10.1093/bioinformatics/btq728 21266443
15. Wang K, Li M, Bucan M, Pathway-based approaches for analysis of genomewide association studies. Am J Hum Genet. 2007;81: 1278–1283. doi: 10.1086/522374 17966091
16. Ehret GB, Lamparter D, Hoggart CJ, Whittaker JC, Beckmann JS, Kutalik Z, A multi-SNP locus-association method reveals a substantial fraction of the missing heritability. Am J Hum Genet. 2012;91: 863–871. doi: 10.1016/j.ajhg.2012.09.013 23122585
17. Yang J, Ferreira T, Morris AP, Medland SE, Madden PAF, Heath AC, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics. 2012. pp. 369–375. doi: 10.1038/ng.2213 22426310
18. Holmans P, Green EK, Pahwa JS, Ferreira M a R, Purcell SM, Sklar P, et al. Gene Ontology Analysis of GWA Study Data Sets Provides Insights into the Biology of Bipolar Disorder. Am J Hum Genet. 2009;85: 13–24. doi: 10.1016/j.ajhg.2009.05.011 19539887
19. Evangelou M, Smyth DJ, Fortune MD, Burren OS, Walker NM, Guo H, et al. A Method for Gene-Based Pathway Analysis Using Genomewide Association Study Summary Statistics Reveals Nine New Type 1 Diabetes Associations Genetic Epidemiology. Genet Epidemiol. 2014;38: 661–670. doi: 10.1002/gepi.21853 25371288
20. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X, Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89: 82–93. doi: 10.1016/j.ajhg.2011.05.029 21737059
21. Conneely KN, Boehnke M, So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. Am J Hum Genet. 2007;81: 1158–1168. doi: 10.1086/522036 17966093
22. Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, Clark AG, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491: 56–65. doi: 10.1038/nature11632 23128226
23. Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45: 1274–83. doi: 10.1038/ng.2797 24097068
24. The International HapMap 3 ConsortiumIntegrating common and rare genetic variation in diverse human populations. Nature. 2010;467: 52–8. doi: 10.1038/nature09298 20811451
25. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014;506: 376–81. doi: 10.1038/nature12873 24390342
26. Mishra A, Macgregor S, VEGAS2 : Software for More Flexible Gene-Based Testing. Twin Res Hum Genet. 2015;18: 86–91. doi: 10.1017/thg.2014.79 25518859
27. Firmann M, Mayor V, Vidal P, Bochud M, Pécoud A, Hayoz D, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovascular Disorders. 2008. p. 6. doi: 10.1186/1471-2261-8-6 18366642
28. Heinig M, Petretto E, Wallace C, Bottolo L, Rotival M, Lu H, et al. A trans-acting locus regulates an anti-viral expression network and type 1 diabetes risk. Nature. 2010;467: 460–464. doi: 10.1038/nature09386 20827270
29. Burren OS, Guo H, Wallace C. VSEAMS : A pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes. 2014;30: 0–26. doi: 10.1093/bioinformatics/btu571
30. Davis J, Goadrich M. The Relationship Between Precision-Recall and ROC Curves. Proc 23rd Int Conf Mach Learn—ICML’06. 2006; 233–240. doi: 10.1145/1143844.1143874
31. Franke A, McGovern DPB, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, et al. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet. Nature Publishing Group; 2010;42: 1118–25. doi: 10.1038/ng.717 21102463
32. Imielinski M, Baldassano RN, Griffiths A, Russell RK, Annese V, Dubinsky M, et al. Common variants at five new loci associated with early-onset inflammatory bowel disease. Nat Genet. 2009;41: 1335–1340. doi: 10.1038/ng.489 19915574
33. Wellcome T, Case T, Consortium C, Genome-wide association study of 14, 000 cases of seven common diseases and. Nature. 2007;447: 661–78. doi: 10.1038/nature05911 17554300
34. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42: 105–116. doi: 10.1038/ng.520 20081858
35. Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL, Ntzani EE, et al. Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. 2012;44: 491–501. doi: 10.1038/ng.2249
36. Day TF, Yang Y, Wnt and hedgehog signaling pathways in bone development. J Bone Joint Surg Am. 2008;90 Suppl 1: 19–24. doi: 10.2106/JBJS.G.01174 18292352
37. Tobacco T, Consortium G, Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet. 2010;42: 441–7. doi: 10.1038/ng.571 20418890
38. Bradley DT, Zipfel PF, Hughes AE, Complement in age-related macular degeneration: a focus on function. Eye (Lond). 2011;25: 683–693. doi: 10.1038/eye.2011.37
39. Ebrahimi KB, Handa JT, Lipids, lipoproteins, and age-related macular degeneration. J Lipids. 2011;2011: 802059. doi: 10.1155/2011/802059 21822496
40. Lee D, Williamson VS, Bigdeli TB, Riley BP, Fanous a. H, Vladimirov VI, et al. JEPEG: a summary statistics based tool for gene-level joint testing of functional variants. Bioinformatics. 2014;31: 1176–1182. doi: 10.1093/bioinformatics/btu816 25505091
41. Genz A, Numerical Computation of Multivariate Normal Probabilities. J Comput Graph Stat. 1992;1: 141–149. doi: 10.1080/10618600.1992.10477010
42. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M, KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 1999. pp. 29–34. doi: 10.1093/nar/27.1.29 9847135
43. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette M a, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102: 15545–50. doi: 10.1073/pnas.0506580102 16199517
44. Xu Z, Duan Q, Yan S, Chen W, Li M, Lange E, et al. DISSCO: direct imputation of summary statistics allowing covariates. Bioinformatics. 2015;31: 2434–2442. doi: 10.1093/bioinformatics/btv168 25810429
45. Ehret GB, Lamparter D, Hoggart CJ, Whittaker JC, Beckmann JS, Kutalik Z, A multi-SNP locus-association method reveals a substantial fraction of the missing heritability. Am J Hum Genet. 2012;91: 863–871. doi: 10.1016/j.ajhg.2012.09.013 23122585
46. Gao X, Starmer J, Martin ER, A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol. 2008;32: 361–369. doi: 10.1002/gepi.20310 18271029
47. B DR, The Distribution of a Linear Combination of x2 Random Variables. J R Stat Soc Ser C. 1980;29: 323–333.
48. Farebrother R, Algorithm AS 204: the distribution of a positive linear combination of chi2 random variables. J R Stat Soc Ser C. 1984;33: 332–339. doi: 10.2307/2347721
49. Duchesne P, Lafaye De Micheaux P, Computing the distribution of quadratic forms: Further comparisons between the Liu-Tang-Zhang approximation and exact methods. Comput Stat Data Anal. 2010;54: 858–862. doi: 10.1016/j.csda.2009.11.025