INRICH: interval-based enrichment analysis for genome-wide association studies
Open accessAbstract
Summary: Here we present INRICH (INterval enRICHment analysis), a pathway-based genome-wide association analysis tool that tests for enriched association signals of predefined gene-sets across independent genomic intervals. INRICH has wide applicability, fast running time and, most importantly, robustness to potential genomic biases and confounding factors. Such factors, including varying gene size and single-nucleotide polymorphism density, linkage disequilibrium within and between genes and overlapping genes with similar annotations, are often not accounted for by existing gene-set enrichment methods. By using a genomic permutation procedure, we generate experiment-wide empirical significance values, corrected for the total number of sets tested, implicitly taking overlap of sets into account. By simulation we confirm a properly controlled type I error rate and reasonable power of INRICH under diverse parameter settings. As a proof of principle, we describe the application of INRICH on the NHGRI GWAS catalog. Availability: A standalone C++ program, user manual and datasets can be freely downloaded from: http://atgu.mgh.harvard.edu/inrich/. Contact: shaun@atgu.mgh.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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.
Integrity signals
Registry
Cites
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.
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?
The 21 common Alzheimer's disease susceptibility variants did not associate with m109 in trans-eQTL analysis.
“The 21 common AD variants did not associate with m109 (i.e., trans-eQTL analysis), consistent with the observed sparsity of trans-associations (trans-eQTLs) reported in studies with larger sample sizes32.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [32]
Trans-eQTL associations are sparse, as reported in larger studies.
“consistent with the observed sparsity of trans-associations (trans-eQTLs) reported in studies with larger sample sizes32.”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [32]
INRICH, ALIGATOR, and MAGENTA are best-SNP-per-gene methods that count pathway genes with independent SNPs above a significance threshold, correcting for LD and genomic structure via Monte-Carlo simulation.
“INRICH45, ALIGATOR9 and MAGENTA46 are “best SNP per gene” methods that count the number of genes in a pathway where a number of independent SNPs exceed a predefined significance, and adjust for LD and genomic structure with corrected statistics derived by Monte-Carlo simulation.”
From Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways (2015) · cites this paper as [45]
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
Self-citationEffectiveness of Antipsychotic Drugs in Patients with Chronic Schizophrenia
Lieberman, J. A., Stroup, T. S., McEvoy, J. P., Swartz, M. S., Rosenheck, R. A., Perkins, D. O., Keefe, R. S. E., Davis, S. M., Davis, C. E., Lebowitz, B. D., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa051688 · Cited by 5,892 · see citing works
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013 · see citing works
KEGG for representation and analysis of molecular networks involving diseases and drugs
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp896 · Cited by 2,322 · see citing works
Psychiatric GWAS Consortium Bipolar Disorder Working Group, Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., Edenberg, H. J., Nurnberger, J. I., Rietschel, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.943 · Cited by 1,344 · see citing works
Self-citationPathway-Based Approaches for Analysis of Genomewide Association Studies
Wang, K., Li, M., & Bućan, M. (2007) · The American Journal of Human Genetics · doi:10.1086/522374 · Cited by 858 · see citing works
Gene Ontology Analysis of GWA Study Data Sets Provides Insights into the Biology of Bipolar Disorder
Holmans, P., Green, E. K., Pahwa, J. S., Ferreira, M. A. R., Purcell, S. M., Sklar, P., Owen, M. J., O'Donovan, M. C., & Craddock, N. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.05.011 · Cited by 378 · see citing works
Self-citationRaychaudhuri, S., Korn, J. M., McCarroll, S. A., The International Schizophrenia Consortium, Altshuler, D., Sklar, P., Purcell, S., & Daly, M. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001097 · Cited by 131 · see citing works
Self-citationStrategies and issues in the detection of pathway enrichment in genome-wide association studies
Hong, M., Yudi Pawitan, Y., Magnusson, P. K. E., & Prince, J. A. (2009) · Human Genetics · doi:10.1007/s00439-009-0676-z · Cited by 118 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083 · see citing works
MAGMA: Generalized Gene-Set Analysis of GWAS Data
de Leeuw, C. A., Mooij, J. M., Heskes, T., & Posthuma, D. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004219 · Cited by 3,653 · see citing works
Fritsche, L. G., Igl, W., Bailey, J. N. C., Grassmann, F., Sengupta, S., Bragg-Gresham, J. L., Burdon, K. P., Hebbring, S. J., Wen, C., Gorski, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3448 · Cited by 1,544 · see citing works
Genome-wide association analysis identifies 13 new risk loci for schizophrenia
Multicenter Genetic Studies of Schizophrenia Consortium, Ripke, S., Psychosis Endophenotypes International Consortium, Wellcome Trust Case Control Consortium 2, O'Dushlaine, C., Chambert, K., Moran, J. L., Kähler, A. K., Akterin, S., Bergen, S. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2742 · Cited by 1,496 · see citing works
Self-citationGene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Self-citationGenetic architectures of psychiatric disorders: the emerging picture and its implications
Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3240 · Cited by 1,177 · see citing works
Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways
The Network and Pathway Analysis Subgroup of the Psychiatric Genomics Consortium (2015) · Nature Neuroscience · doi:10.1038/nn.3922 · Cited by 755 · see citing works
Seven new loci associated with age-related macular degeneration
The AMD Gene Consortium (2013) · Nature Genetics · doi:10.1038/ng.2578 · Cited by 788 · see citing works
Mostafavi, S., Gaiteri, C., Sullivan, S. E., White, C. C., Tasaki, S., Xu, J., Taga, M., Klein, H.-U., Patrick, E., Komashko, V., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0154-9 · Cited by 509 · see citing works
The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium (2017) · Molecular Autism · doi:10.1186/s13229-017-0137-9 · Cited by 587 · see citing works
Genome-wide association study of obsessive-compulsive disorder
Stewart, S. E., Yu, D., Scharf, J. M., Neale, B. M., Fagerness, J. A., Mathews, C. A., Arnold, P. D., Evans, P. D., Gamazon, E. R., Osiecki, L., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.85 · Cited by 384 · see citing works
Self-citationNativio, R., Lan, Y., Donahue, G., Sidoli, S., Berson, A., Srinivasan, A. R., Shcherbakova, O., Amlie-Wolf, A., Nie, J., Cui, X., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0696-0 · Cited by 380 · see citing works
Identifying Recent Adaptations in Large-Scale Genomic Data
Grossman, S. R., Andersen, K. G., Shlyakhter, I., Tabrizi, S., Winnicki, S., Yen, A., Park, D. J., Griesemer, D., Karlsson, E. K., Wong, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.01.035 · Cited by 368 · see citing works
Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies
(2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70171-1 · Cited by 293 · see citing works
Sey, N. Y. A., Hu, B., Mah, W., Fauni, H., McAfee, J. C., Rajarajan, P., Brennand, K. J., Akbarian, S., & Won, H. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0603-0 · Cited by 297 · see citing works
Watson, C. T., Roussos, P., Garg, P., Ho, D. J., Azam, N., Katsel, P. L., Haroutunian, V., & Sharp, A. J. (2016) · Genome Medicine · doi:10.1186/s13073-015-0258-8 · Cited by 201 · see citing works
Genome-wide, integrative analysis implicates microRNA dysregulation in autism spectrum disorder
Wu, Y. E., Parikshak, N. N., Belgard, T. G., & Geschwind, D. H. (2016) · Nature Neuroscience · doi:10.1038/nn.4373 · Cited by 195 · see citing works
Yang, L., Neale, B. M., Liu, L., Lee, S. H., Wray, N. R., Ji, N., Li, H., Qian, Q., Wang, D., Li, J., et al. (2013) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32169 · Cited by 171 · see citing works
Genome-wide analysis of attention deficit hyperactivity disorder in Norway
Zayats, T., Athanasiu, L., Sonderby, I., Djurovic, S., Westlye, L. T., Tamnes, C. K., Fladby, T., Aase, H., Zeiner, P., Reichborn-Kjennerud, T., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122501 · Cited by 83 · see citing works
A meta-analysis of gene expression quantitative trait loci in brain
Kim, Y., Xia, K., Tao, R., Giusti-Rodriguez, P., Vladimirov, V, van den Oord, E., & Sullivan, P. F. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.96 · Cited by 73 · see citing works
Genetic risk variants for social anxiety
Stein, M. B., Chen, C., Jain, S., Jensen, K. P., He, F., Heeringa, S. G., Kessler, R. C., Maihofer, A., Nock, M. K., Ripke, S., et al. (2017) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32520 · Cited by 72 · see citing works
Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach
Mooney, M. A., McWeeney, S. K., Faraone, S. V., Hinney, A., Hebebrand, J., IMAGE2 Consortium, German ADHD GWAS Group, Nigg, J. T., & Wilmot, B. (2016) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32446 · Cited by 48 · see citing works
Enrichment pathway analysis. The inflammatory genetic background in Bipolar Disorder
Drago, A., Crisafulli, C., Calabrò, M., & Serretti, A. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.03.032 · Cited by 47 · see citing works
SanGiovanni, J. P., & Lee, P. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071239 · Cited by 26 · see citing works
Self-citationLatendresse, S. J., Musci, R., & Maher, B. S. (2018) · Prevention Science · doi:10.1007/s11121-017-0785-1 · Cited by 12 · see citing works
- Authors
- Lee, P. H., O'Dushlaine, C., Thomas, B., & Purcell, S. M.
- Affiliations
- Broad Institute (US)
- 1 Analytic and Translational Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, MA 02114, 2Department of Psychiatry, Harvard Medical School, Boston, MA 02115, 3Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142 and 4Center for Statistical Genetics, Mount Sinai School of Medicine, New York, NY 10029, USA
- Corresponding author
- Purcell, Shaun M.
- ORCID
- Purcell, Shaun M.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-04-17
- Volume
- 28
- Issue
- 13
- Pages
- 1797-1799
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3381960
- OpenAlex
- W2136322773
- Cited by
- 238
- References
- 10
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 37 · 2020: 20 · 2019: 18 · 2018: 17 · 2017: 23 · 2016: 24 · 2015: 30 · 2014: 25 · 2013: 14 · 2012: 7
- FWCI
- 9.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (U01MH0855513)
- 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 › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- gene set enrichment analysis, genome-wide association study, genomic intervals, linkage disequilibrium, type I error control
- MeSH terms
- Genes, Humans, Software, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genomics, Genome-Wide Association Study
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
