ArticleTrends in genetics : TIG2014
Pitfalls in the application of gene-set analysis to genetics studies.
Article in Trends in genetics : TIG, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Differential and spatial expression meta-analysis of genes identified in genome-wide association studies of depression.Translational psychiatry · 2021Pooled it
- Monitoring changes in the Gene Ontology and their impact on genomic data analysis.GigaScience · 2018Article
- Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.BMC genomics · 2017Article
- 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.Genetics, selection, evolution : GSE · 2017Article
- Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype.BioData mining · 2017Article
- Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts.BioData mining · 2016Article
- Gene set analysis: A step-by-step guide.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2015Review
- An application of MeSH enrichment analysis in livestock.Animal genetics · 2015Article
- ‘Pitfalls in the application of gene set analysis to genetics studies’: a response.Trends in genetics : TIG · 2014Article
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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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