Literature DB >> 25459301

Pitfalls in the application of gene-set analysis to genetics studies.

Adriana Estela Sedeño-Cortés, Paul Pavlidis.   

Abstract

Gene-set analysis (GSA) (‘enrichment’) is a popular approach for the interpretation of genome-wide association studies (GWASs). GSA is most commonly applied to the analysis of transcriptomes, but from the outset it has been considered useful for any study that provides rankings or ‘hit lists’ of genes. The recent review by Mooney et al. [1] is a valuable resource for geneticists wishing to apply GSA to the output of GWASs. Here we describe some additional points of practical importance if the methods are to be applied and interpreted soundly.

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Year:  2014        PMID: 25459301      PMCID: PMC5369409          DOI: 10.1016/j.tig.2014.10.001

Source DB:  PubMed          Journal:  Trends Genet        ISSN: 0168-9525            Impact factor:   11.639


  11 in total

Review 1.  Functional and genomic context in pathway analysis of GWAS data.

Authors:  Michael A Mooney; Joel T Nigg; Shannon K McWeeney; Beth Wilmot
Journal:  Trends Genet       Date:  2014-08-22       Impact factor: 11.639

2.  Impact of ontology evolution on functional analyses.

Authors:  Anika Groß; Michael Hartung; Kay Prüfer; Janet Kelso; Erhard Rahm
Journal:  Bioinformatics       Date:  2012-09-06       Impact factor: 6.937

3.  GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies.

Authors:  Marit Holden; Shiwei Deng; Leszek Wojnowski; Bettina Kulle
Journal:  Bioinformatics       Date:  2008-10-14       Impact factor: 6.937

4.  Pathway analysis of seven common diseases assessed by genome-wide association.

Authors:  Ali Torkamani; Eric J Topol; Nicholas J Schork
Journal:  Genomics       Date:  2008-09-16       Impact factor: 5.736

5.  The impact of focused Gene Ontology curation of specific mammalian systems.

Authors:  Yasmin Alam-Faruque; Rachael P Huntley; Varsha K Khodiyar; Evelyn B Camon; Emily C Dimmer; Tony Sawford; Maria J Martin; Claire O'Donovan; Philippa J Talmud; Peter Scambler; Rolf Apweiler; Ruth C Lovering
Journal:  PLoS One       Date:  2011-12-09       Impact factor: 3.240

6.  A methodology for the analysis of differential coexpression across the human lifespan.

Authors:  Jesse Gillis; Paul Pavlidis
Journal:  BMC Bioinformatics       Date:  2009-09-22       Impact factor: 3.169

7.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.

Authors: 
Journal:  Nature       Date:  2007-06-07       Impact factor: 49.962

8.  Retraction for Dixson et al., Identification of gene ontologies linked to prefrontal-hippocampal functional coupling in the human brain.

Authors:  Luanna Dixson; Henrik Walter; Michael Schneider; Susanne Erk; Axel Schäfer; Leila Haddad; Oliver Grimm; Manuel Mattheisen; Markus M Nöthen; Sven Cichon; Stephanie H Witt; Marcella Rietschel; Sebastian Mohnke; Nina Seiferth; Andreas Heinz; Heike Tost; Andreas Meyer-Lindenberg
Journal:  Proc Natl Acad Sci U S A       Date:  2014-09-02       Impact factor: 11.205

9.  A task-based approach for Gene Ontology evaluation.

Authors:  Erik L Clarke; Salvatore Loguercio; Benjamin M Good; Andrew I Su
Journal:  J Biomed Semantics       Date:  2013-04-15

10.  Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt.

Authors:  Rachael P Huntley; Tony Sawford; Maria J Martin; Claire O'Donovan
Journal:  Gigascience       Date:  2014-03-18       Impact factor: 6.524

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  9 in total

1.  ‘Pitfalls in the application of gene set analysis to genetics studies’: a response.

Authors:  Michael A Mooney; Joel T Nigg; Shannon K McWeeney; Beth Wilmot
Journal:  Trends Genet       Date:  2014-12       Impact factor: 11.639

Review 2.  Gene set analysis: A step-by-step guide.

Authors:  Michael A Mooney; Beth Wilmot
Journal:  Am J Med Genet B Neuropsychiatr Genet       Date:  2015-06-08       Impact factor: 3.568

3.  Monitoring changes in the Gene Ontology and their impact on genomic data analysis.

Authors:  Matthew Jacobson; Adriana Estela Sedeño-Cortés; Paul Pavlidis
Journal:  Gigascience       Date:  2018-08-01       Impact factor: 6.524

4.  Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts.

Authors:  Ailin Song; Jingwen Yan; Sungeun Kim; Shannon Leigh Risacher; Aaron K Wong; Andrew J Saykin; Li Shen; Casey S Greene
Journal:  BioData Min       Date:  2016-01-19       Impact factor: 2.522

5.  An application of MeSH enrichment analysis in livestock.

Authors:  G Morota; F Peñagaricano; J L Petersen; D C Ciobanu; K Tsuyuzaki; I Nikaido
Journal:  Anim Genet       Date:  2015-06-02       Impact factor: 3.169

6.  Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.

Authors:  Lingzhao Fang; Goutam Sahana; Peipei Ma; Guosheng Su; Ying Yu; Shengli Zhang; Mogens Sandø Lund; Peter Sørensen
Journal:  BMC Genomics       Date:  2017-08-10       Impact factor: 3.969

7.  Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.

Authors:  Vinicius Tragante; Johannes M I H Gho; Janine F Felix; Ramachandran S Vasan; Nicholas L Smith; Benjamin F Voight; Colin Palmer; Pim van der Harst; Jason H Moore; Folkert W Asselbergs
Journal:  BioData Min       Date:  2017-05-26       Impact factor: 2.522

8.  Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection.

Authors:  Lingzhao Fang; Goutam Sahana; Peipei Ma; Guosheng Su; Ying Yu; Shengli Zhang; Mogens Sandø Lund; Peter Sørensen
Journal:  Genet Sel Evol       Date:  2017-05-12       Impact factor: 4.297

9.  Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression.

Authors:  Wennie Wu; Derek Howard; Etienne Sibille; Leon French
Journal:  Transl Psychiatry       Date:  2021-01-04       Impact factor: 6.222

  9 in total

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