CoChat
Sign Up
Cited by 81Bioinformatics and Genomic NetworksGenetic Associations and EpidemiologyBiomedical Text Mining and Ontologies

Efficient pathway enrichment and network analysis of GWAS summary data using GSA-SNP2

Open access
Yoon, Sora, Nguyen, Hai C T, Yoo, Yun J, Kim, Jinhwan, Baik, Bukyung, Kim, Sounkou, Kim, Jin, Kim, Sangsoo, Nam, Dougu·2018·Nucleic Acids Research
DOI 10.1093/nar/gky175PMC PMC6007455
Open this paper in CoChat — free accountOpen PDF

Abstract

Pathway-based analysis in genome-wide association study (GWAS) is being widely used to uncover novel multi-genic functional associations. Many of these pathway-based methods have been used to test the enrichment of the associated genes in the pathways, but exhibited low powers and were highly affected by free parameters. We present the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score. In a comparative study using simulated and real GWAS data, GSA-SNP2 exhibited high power and best prioritized gold standard positive pathways compared with six existing enrichment-based methods and two self-contained methods (alternative pathway analysis approach). Based on these results, the difference between pathway analysis approaches was investigated and the effects of the gene correlation structures on the pathway enrichment analysis were also discussed. In addition, GSA-SNP2 is able to visualize protein interaction networks within and across the significant pathways so that the user can prioritize the core subnetworks for further studies. GSA-SNP2 is freely available at https://sourceforge.net/projects/gsasnp2.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gky175

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • GSA-SNP2 identified 16 pathways significantly associated with EduYears in the EAS population and 27 in the EUR population.

    “In total, 16 and 27 pathways were identified as significantly associated with EduYears in EAS and EUR populations, respectively (Fig. 2c).”

    From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [28]

    The Results and Discussion section on real GWAS data describes analyses of type 2 diabetes and height datasets, not EduYears in EAS and EUR populations. Searches for “EduYears,” “educational attainment,” and the EAS/EUR pathway counts found no passage reporting 16 and 27 significant pathways. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 43

  1. [1]Yano K., Yamamoto E., Aya K., Takeuchi H., Lo P.C., Hu L., Yamasaki M., Yoshida S., Kitano H., Hirano K. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 2016; 48: 927– 934. 27322545
  2. [2]Wang K., Li M., Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat. Rev. Genet. 2010; 11: 843– 854. 21085203
  3. [3]Liu J.Z., McRae A.F., Nyholt D.R., Medland S.E., Wray N.R., Brown K.M., Hayward N.K., Montgomery G.W., Visscher P.M., Martin N.G. A versatile gene-based test for genome-wide association studies. Am. J. Hum. Genet. 2010; 87: 139– 145. 20598278 PMC2896770
  4. [4]Li M.X., Gui H.S., Kwan J.S., Sham P.C. GATES: a rapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet. 2011; 88: 283– 293. 21397060 PMC3059433
  5. [5]Goeman J.J., Buhlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007; 23: 980– 987. 17303618
  6. [6]Wang K., Li M., Bucan M. Pathway-based approaches for analysis of genomewide association studies. Am. J. Hum. Genet. 2007; 81: 1278– 1283. 17966091 PMC2276352
  7. [7]Holden M., Deng S., Wojnowski L., Kulle B. GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies. Bioinformatics. 2008; 24: 2784– 2785. 18854360
  8. [8]Zhang K., Cui S., Chang S., Zhang L., Wang J. i-GSEA4GWAS: a web server for identification of pathways/gene sets associated with traits by applying an improved gene set enrichment analysis to genome-wide association study. Nucleic Acids Res. 2010; 38: W90– W95. 20435672 PMC2896119
  9. [9]Weng L., Macciardi F., Subramanian A., Guffanti G., Potkin S.G., Yu Z., Xie X. SNP-based pathway enrichment analysis for genome-wide association studies. BMC Bioinformatics. 2011; 12: 99. 21496265 PMC3102637
  10. [10]Segre A.V., Groop L., Mootha V.K., Daly M.J., 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. 20714348 PMC2920848
  11. [11]Nam D., Kim J., Kim S.Y., Kim S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 2010; 38: W749– W754. 20501604 PMC2896081
  12. [13]Kofler R., Schlotterer C. Gowinda: unbiased analysis of gene set enrichment for genome-wide association studies. Bioinformatics. 2012; 28: 2084– 2085. 22635606 PMC3400962
  13. [14]Lee P.H., O’Dushlaine C., Thomas B., Purcell S.M. INRICH: interval-based enrichment analysis for genome-wide association studies. Bioinformatics. 2012; 28: 1797– 1799. 22513993 PMC3381960
  14. [16]Kwak I.Y., Pan W. Adaptive gene- and pathway-trait association testing with GWAS summary statistics. Bioinformatics. 2016; 32: 1178– 1184. 26656570 PMC5860182
  15. [17]Zhang H., Wheeler W., Hyland P.L., Yang Y., Shi J., Chatterjee N., Yu K. A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations. PLoS Genet. 2016; 12: e1006122. 27362418 PMC4928884
  16. [18]Nam D., Kim S.Y. Gene-set approach for expression pattern analysis. Brief. Bioinform. 2008; 9: 189– 197. 18202032
  17. [19]Wang Q., Yu H., Zhao Z., Jia P. EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics. 2015; 31: 2591– 2594. 25805723 PMC4514922
  18. [21]de Leeuw C.A., Neale B.M., Heskes T., Posthuma D. The statistical properties of gene-set analysis. Nat. Rev. Genet. 2016; 17: 353– 364. 27070863
  19. [22]Newton M.A., Quintana F.A., Den Boon J.A., Sengupta S., Ahlquist P. Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis. Ann. Appl. Stat. 2007; 1: 85– 106.
  20. [24]Pickrell J.K., Marioni J.C., Pai A.A., Degner J.F., Engelhardt B.E., Nkadori E., Veyrieras J.B., Stephens M., Gilad Y., Pritchard J.K. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010; 464: 768– 772. 20220758 PMC3089435
  21. [25]Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. Stat. Methodol. 1995; 57: 289– 300.
  22. [26]Nam D. Effect of the absolute statistic on gene-sampling gene-set analysis methods. Stat. Methods Med. Res. 2017; 26: 1248– 1260. 25733546
  23. [27]Croft D., O’Kelly G., Wu G., Haw R., Gillespie M., Matthews L., Caudy M., Garapati P., Gopinath G., Jassal B. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 2011; 39: D691– D697. 21067998 PMC3013646
  24. [28]Liberzon A. A description of the Molecular Signatures Database (MSigDB) web site. Methods Mol. Biol. 2014; 1150: 153– 160. 24743996
  25. [29]Morris A.P., Voight B.F., Teslovich T.M., Ferreira T., Segre A.V., Steinthorsdottir V., Strawbridge R.J., Khan H., Grallert H., Mahajan A. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 2012; 44: 981– 990. 22885922 PMC3442244
  26. [30]Lango Allen H., Estrada K., Lettre G., Berndt S.I., Weedon M.N., Rivadeneira F., Willer C.J., Jackson A.U., Vedantam S., Raychaudhuri S. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010; 467: 832– 838. 20881960 PMC2955183
  27. [31]Cho Y.S., Go M.J., Kim Y.J., Heo J.Y., Oh J.H., Ban H.J., Yoon D., Lee M.H., Kim D.J., Park M. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat. Genet. 2009; 41: 527– 534. 19396169
  28. [32]Mishra A., Macgregor S., Liu J.Z., McRae A.F., Nyholt D.R., Medland S.E., Wray N.R., Brown K.M., Hayward N.K., Montgomery G.W. VEGAS2: software for more flexible gene-based testing. Twin Res. Hum. Genet. 2015; 18: 86– 91. 25518859
  29. [34]Wood A.R., Esko T., Yang J., Vedantam S., Pers T.H., Gustafsson S., Chu A.Y., Estrada K., Luan J., Kutalik Z. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 2014; 46: 1173– 1186. 25282103 PMC4250049
  30. [36]Klingseisen A., Jackson A.P. Mechanisms and pathways of growth failure in primordial dwarfism. Genes Dev. 2011; 25: 2011– 2024. 21979914 PMC3197200
  31. [37]Bartholdi D., Krajewska-Walasek M., Ounap K., Gaspar H., Chrzanowska K.H., Ilyana H., Kayserili H., Lurie I.W., Schinzel A., Baumer A. Epigenetic mutations of the imprinted IGF2-H19 domain in Silver-Russell syndrome (SRS): results from a large cohort of patients with SRS and SRS-like phenotypes. J. Med. Genet. 2009; 46: 192– 197. 19066168
  32. [39]Schwartz N.B., Domowicz M. Chondrodysplasias due to proteoglycan defects. Glycobiology. 2002; 12: 57r– 68r. 12042245
  33. [40]Kim H., Kim I.Y., Lee S.Y., Jeong D. Bimodal actions of reactive oxygen species in the differentiation and bone-resorbing functions of osteoclasts. FEBS Lett. 2006; 580: 5661– 5665. 16996506
  34. [41]Smith L.B., Belanger J.M., Oberbauer A.M. Fibroblast growth factor receptor 3 effects on proliferation and telomerase activity in sheep growth plate chondrocytes. J. Anim. Sci. Biotechnol. 2012; 3: 39. 23216972 PMC3541258
  35. [42]Wu D., Smyth G.K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012; 40: e133. 22638577 PMC3458527
  36. [43]Szklarczyk D., Franceschini A., Wyder S., Forslund K., Heller D., Huerta-Cepas J., Simonovic M., Roth A., Santos A., Tsafou K.P. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015; 43: D447– D452. 25352553 PMC4383874
  37. [44]Alanis-Lobato G., Andrade-Navarro M.A., Schaefer M.H. HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res. 2017; 45: D408– D414. 27794551 PMC5210659
  38. [45]Safran M., Dalah I., Alexander J., Rosen N., Iny Stein T., Shmoish M., Nativ N., Bahir I., Doniger T., Krug H. GeneCards Version 3: the human gene integrator. Database. 2010; 2010: baq020. 20689021 PMC2938269
  39. [46]Sherry S.T., Ward M.H., Kholodov M., Baker J., Phan L., Smigielski E.M., Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001; 29: 308– 311. 11125122 PMC29783
  40. [47]Tessneer K.L., Jackson R.M., Griesel B.A., Olson A.L. Rab5 activity regulates GLUT4 sorting into insulin-responsive and non-insulin-responsive endosomal compartments: a potential mechanism for development of insulin resistance. Endocrinology. 2014; 155: 3315– 3328. 24932807 PMC4138579
  41. [48]Huang J., Imamura T., Olefsky J.M. Insulin can regulate GLUT4 internalization by signaling to Rab5 and the motor protein dynein. Proc. Natl. Acad. Sci. U.S.A. 2001; 98: 13084– 13089. 11687655 PMC60828
  42. [49]Moller D.E. Potential role of TNF-alpha in the pathogenesis of insulin resistance and type 2 diabetes. Trends Endocrinol. Metab. 2000; 11: 212– 217. 10878750
  43. [50]Huang S.H., Czech M.P. The GLUT4 glucose transporter. Cell Metab. 2007; 5: 237– 252. 17403369

Cited by

Sorted by citations

Authors
Yoon, S., Nguyen, H. C. T., Yoo, Y. J., Kim, J., Baik, B., Kim, S., Kim, J., Kim, S., & Nam, D.
Affiliations
  • Ulsan National Institute of Science and Technology (KR)
  • School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
  • Seoul National University (KR)
  • Soongsil University (KR)
Corresponding author
Nam, Dougu
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2018-03-19
Volume
46
Issue
10
Pages
e60-e60
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gky175
PMC
PMC6007455
OpenAlex
W2794999358
Cited by
81
References
8
Citations by year
2026: 1 · 2025: 4 · 2024: 10 · 2023: 10 · 2022: 11 · 2021: 14 · 2020: 20 · 2019: 8 · 2018: 3
FWCI
4.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion, cc-by-nc)
  • Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
Funders
  • National Research Foundation (2014M3C9A3068555)
  • Korean government, Basic Science Research Program (2017R1E1A1A03070107, 2015R1A1A3A04001269)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
pathway enrichment, genome-wide association study, protein-protein interaction network, type I error control
MeSH terms
Body Height, Diabetes Mellitus, Type 2, Humans, Programming Languages, Software, Polymorphism, Single Nucleotide, Databases, Genetic, Asian People, Genome-Wide Association Study, Protein Interaction Maps

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review