%0 Journal Article
%T Sample matching by inferred agonal stress in gene expression analyses of the brain
%A Jun Z Li
%A Fan Meng
%A Larisa Tsavaler
%A Simon J Evans
%A Prabhakara V Choudary
%A Hiroaki Tomita
%A Marquis P Vawter
%A David Walsh
%A Vida Shokoohi
%A Tisha Chung
%A William E Bunney
%A Edward G Jones
%A Huda Akil
%A Stanley J Watson
%A Richard M Myers
%J BMC Genomics
%D 2007
%I BioMed Central
%R 10.1186/1471-2164-8-336
%X We developed an Agonal Stress Rating (ASR) system that evaluates each sample's degree of stress based on gene expression data, and used ASRs in post hoc sample matching or covariate analysis. While gene expression patterns are generally correlated across different brain regions, we found strong region-region differences in empirical ASRs in many subjects that likely reflect inter-individual variabilities in local structure or function, resulting in region-specific vulnerability to agonal stress.Variation of agonal stress across different brain regions differs between individuals, revealing a new level of complexity for gene expression studies of brain tissues. The Agonal Stress Ratings quantitatively assess each sample's extent of regulatory response to agonal stress, and allow a strong control of this important confounder.Comparing cases and controls is one of the most widely used methods in genetic and epidemiological research to identify disease risk factors at the population level. From the study design standpoint, to maximize the power of detecting a true effect it is important to understand the major sources of phenotypic variation, and to minimize sample heterogeneity accordingly. Furthermore, to reduce the number of spurious positive findings due to confounding factors, it is important to match cases and controls on "well-established determinants" [1] that are not themselves the variables of direct interest. In practice, however, it is often difficult to declare a priori which variables, out of many that are examined, are the established risk factors. Occasionally, the major factors affecting the phenotypic outcome may be truly strong and well known, such as cigarette smoking as a risk factor for lung cancer, or older age for Alzheimer's disease. In most other situations, however, particularly those concerning multifactorial diseases such as cancer and psychiatric disorders, there are usually numerous contributing factors for the observed phenotype, but thei
%U http://www.biomedcentral.com/1471-2164/8/336