SNP Data Science for Classification of Bipolar Disorder I and Bipolar Disorder II.

ABSTRACT

Bipolar disorder I (BD-I) and bipolar disorder II (BD-II) have specific characteristics and clear diagnostic criteria, but quite different treatment guidelines. In clinical practice, BD-II is commonly mistaken as a mild form of BD-I. This study uses data science technique to identify the important Single Nucleotide Polymorphisms (SNPs) significantly affecting the classifications of BD-I and BD-II, and develops a set of complementary diagnostic classifiers to enhance the diagnostic process. Screening assessments and SNP genotypes of 316 Han Chinese were performed with the Affymetrix Axiom Genome-Wide TWB Array Plate. The results show that the classifier constructed by 23 SNPs reached the area under curve of ROC (AUC) level of 0.939, while the classifier constructed by 42 SNPs reached the AUC level of 0.9574, which is a mere addition of 1.84 percent. The accuracy rate of classification increased by 3.46 percent. This study also uses Gene Ontology (GO) and Pathway to conduct a functional analysis and identify significant items, including calcium ion binding, GABA-A receptor activity, Rap1 signaling pathway, ECM proteoglycans, IL12-mediated signaling events, Nicotine addiction), and PI3K-Akt signaling pathway. The study can address time-consuming SNPs identification and also quantify the effect of SNP-SNP interactions.