ArticleBioinformatics (Oxford, England)2015
Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Shared genetic risk factors and causal association between psoriasis and coronary artery disease.Nature communications · 2022Pooled it
- Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants.Nature communications · 2017Pooled it
- Retrospective pharmacogenetic study of psoriasis highlights the role of KLK7 in tumour necrosis factor signalling.The British journal of dermatology · 2023Article
- Psoriasis and Genetics.Acta dermato-venereologica · 2020Review
- Hypersensitive IFN Responses in Lupus Keratinocytes Reveal Key Mechanistic Determinants in Cutaneous Lupus.Journal of immunology (Baltimore, Md. : 1950) · 2019Article
- Research Techniques Made Simple: Using Genome-Wide Association Studies to Understand Complex Cutaneous Disorders.The Journal of investigative dermatology · 2018Review
- Population genetic study of 34 X-Chromosome markers in 5 main ethnic groups of China.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationPathway analysis to reveal biological mechanisms for results from genetic association studies have great potential to better understand complex traits with major human disease impact. However, current approaches have not been optimized to maximize statistical power to identify enriched functions/pathways, especially when the genetic data derives from studies using platforms (e.g. Immunochip and Metabochip) customized to have pre-selected markers from previously identified top-rank loci. We present here a novel approach, called Minimum distance-based Enrichment Analysis for Genetic Association (MEAGA), with the potential to address both of these important concerns.
resultsMEAGA performs enrichment analysis using graphical algorithms to identify sub-graphs among genes and measure their closeness in interaction database. It also incorporates a statistic summarizing the numbers and total distances of the sub-graphs, depicting the overlap between observed genetic signals and defined function/pathway gene-sets. MEAGA uses sampling technique to approximate empirical and multiple testing-corrected P-values. We show in simulation studies that MEAGA is more powerful compared to count-based strategies in identifying disease-associated functions/pathways, and the increase in power is influenced by the shortest distances among associated genes in the interactome. We applied MEAGA to the results of a meta-analysis of psoriasis using Immunochip datasets, and showed that associated genes are significantly enriched in immune-related functions and closer with each other in the protein-protein interaction network. AVAILABILITY AND IMPLEMENTATION: http://genome.sph.umich.edu/wiki/MEAGA CONTACT: : tsoi.teen@gmail.com or goncalo@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.