title: Psoriasis prediction from genome-wide SNP profiles.
abstract: BACKGROUND
  With 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.
  
  
  METHODS
  Totally 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.
  
  
  RESULTS
  The 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: Department of Epidemiology, The University of Texas M D Anderson Cancer Center, Houston, Texas 77030, USA.
authors.forename: Shenying
authors.initials: S
authors.lastname: Fang
authors.forename: Xiangzhong
authors.initials: X
authors.lastname: Fang
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D011565:Psoriasis / Q000453:epidemiology / Q000235:genetics*; D018570:Risk Assessment; D012680:Sensitivity and Specificity