Psoriasis prediction from genome-wide SNP profiles

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.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.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.Risk classification models that utilize independent variables and outcomes through machine learning have been widely used to predict disease status in medical research. To better characterize a disease, researchers have drawn information from clinical, microarray, and single nucleotide polymorphism (SNP) data to build a disease risk model, which is then applied for clinical diagnosis and prediction of an individual's susceptibility to the disease. For example, researchers examined traditional risk factors such as age, total cholesterol, HDL cholesterol, smoking, systolic blood pressure, diabetes, and treatment for hypertension and built a classification rule with high discriminant power for diagnosing cardiovascular disease [1]. Selection of genomic biomarkers for disease classification with microarray data has been reported extensively in cancers [2-4] and other diseases[5] although in most cases high-dimensional gene expression data were obtained from a small number of observations. With the availability of high-throughput genotyping technology, data on hundre