title: Novel integration of hospital electronic medical records and gene expression measurements to identify genetic markers of maturation.
abstract: Traditionally, the elucidation of genes involved in maturation and aging has been studied in a temporal fashion by examining gene expression at different time points in an organism's life as well as by knocking out, knocking in, and mutating genes thought to be involved. Here, we propose an in silico method to combine clinical electronic medical record (EMR) data and gene expression measurements in the context of disease to identify genes that may be involved in the process of human maturation and aging. First we show that absolute lymphocyte count may serve as a biomarker for maturation by using statistical methods to compare trends among different clinical laboratory tests in response to an increase in age. We then propose using the rate of decay for absolute lymphocyte count across 12 diseases as a proxy for differences in aging. We correlate the differing rates with gene expression across the same diseases to find maturation/aging related genes. Among the 53 genes with strongest correlations between expression profile and change in rate of decay, we found genes previously implicated in the process of aging, including MGMT (DNA repair), TERF2 (telomere stability), POLD1 (DNA replication and repair), and POLG (mtDNA replication).
authors.affiliation: Stanford Medical Informatics, Department of Medicine, Stanford University School of Medicine, Stanford, California 94305-5479, USA.
authors.forename: David P
authors.initials: DP
authors.lastname: Chen
authors.forename: Susan C
authors.initials: SC
authors.lastname: Weber
authors.forename: Philip S
authors.initials: PS
authors.lastname: Constantinou
authors.forename: Todd A
authors.initials: TA
authors.lastname: Ferris
authors.forename: Henry J
authors.initials: HJ
authors.lastname: Lowe
authors.forename: Atul J
authors.initials: AJ
authors.lastname: Butte
mesh_terms: D000293:Adolescent; D000375:Aging / Q000097:blood / Q000235:genetics*; D000704:Analysis of Variance; D002648:Child; D002675:Child, Preschool; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005819:Genetic Markers; D006753:Hospital Records; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D018655:Lymphocyte Count; D016347:Medical Records Systems, Computerized
chemical_list: D005819:Genetic Markers