title: Phenotype prediction by integrative network analysis of SNP and gene expression microarrays.
abstract: A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.
authors.affiliation: Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA. hsun-hsien.chang@childrens.harvard.edu
authors.forename: Hsun-Hsien
authors.initials: HH
authors.lastname: Chang
authors.forename: Michael
authors.initials: M
authors.lastname: McGeachie
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D015973:Gene Expression Regulation, Leukemic; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D040641:Quantitative Trait Loci; D015203:Reproducibility of Results