Bayesian biomarker identification based on marker-expression proteomics data

• Published in: Genomics (San Diego, Calif.) Vol. 92; no. 6; pp. 384 - 392
• Main Authors: Bhattacharjee, M., Botting, C.H., Sillanpää, M.J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2008
• Subjects: Association analysis; Bayes Theorem; Bayesian hierarchical models; Biomarkers - analysis; CFS; Fatigue Syndrome, Chronic - genetics; Fatigue Syndrome, Chronic - metabolism; Gene Frequency; Genetic Carrier Screening; Genetic Markers; High-throughput analysis; Humans; Markov Chains; Mathematical Computing; Models, Biological; Monte Carlo Method; Polymorphism, Single Nucleotide; Proteomics - statistics & numerical data; Regression Analysis; Variable selection; Association analysis; CFS; Bayesian hierarchical models; High-throughput analysis; Variable selection
• ISSN: 0888-7543; 1089-8646
• DOI: 10.1016/j.ygeno.2008.06.006

We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic–epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS). CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.