ArticleBMC dermatology2011
Psoriasis prediction from genome-wide SNP profiles.
Article in BMC dermatology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diagnostic Test Accuracy of Genetic Tests in Diagnosing Psoriasis: A Systematic Review.Cureus · 2022Review
- Robust Reference Powered Association Test of Genome-Wide Association Studies.Frontiers in genetics · 2019Article
- Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.Scientific reports · 2018Article
- Nuclear Norm Clustering: a promising alternative method for clustering tasks.Scientific reports · 2018Article
- Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances.Oncotarget · 2018Article
- Random Bits Forest: a Strong Classifier/Regressor for Big Data.Scientific reports · 2016Article
- Phenotype prediction from genome-wide association studies: application to smoking behaviors.BMC systems biology · 2012Article
- Effects of Thai medicinal herb extracts with anti-psoriatic activity on the expression on NF-κB signaling biomarkers in HaCaT keratinocytes.Molecules (Basel, Switzerland) · 2011Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundWith the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data.
methodsTotally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance.
resultsThe best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study.
conclusionsThe fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.
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