Biomedical and Translational Informatics Program
100 North Academy Avenue
Danville, PA 17822
MS, Applied Statistics, Vanderbilt University, 2002
PhD, Statistical Genetics, Vanderbilt University, 2004
Genomics, Genetics, Pharmacogenetics, Biostatistics, Epidemiology
Bioinformatics; Translational Informatics; Systems Genomics; Epistasis; Genetic Epidemiology
The past several decades of biomedical research have been focused on generating new tools for the collection of biological data including: genetics, genomics, proteomics, clinical laboratory data, electronic health records, metabolomics, epidemiological data, pharmacogenomics, etc. This explosion of ‘omics has led to a wealth of information just waiting to be interrogated. To achieve our goals of identifying the etiology and treatment for diseases of public health interest, it is time to change the way we think about data analysis. We have been hindered in our ability to exploit these laboratory advances because strategies for analyzing these data have not kept pace. An integrative approach is needed that accommodates multiple analytical methods and/or multiple data sources/types to maximize our information extraction. It is my vision that the future of biomedical research will embrace this integrative and collaborative approach for the dissection of common, complex disease.
Meta-dimensional analysis is new field of biomedical research that includes the integration of data from multiple ‘omics of biology. The rationale depends on the fact that any single underlying analytical scheme in one data type will reveal some important results and that multiple analytical approaches and multiple datasets will reveal different subsets of important and potentially novel results. Once results are obtained from any single dataset or analytical approach, these results can be viewed in light of the results from other analyses to best understand the full meaning of the data.
This paradigm shift is EXTREMELY important as with all of the resources that have gone into the dissection of common, complex diseases, little is currently known about the biology of such disorders. We must change the way we interrogate these data in radical ways so that we can begin to unravel the complexities of diseases such as pharmacogenomics, cardiovascular disease, neurodevelopmental disorders, and cancer.
Kim D, Li R, Dudek SM, Wallace JR, Ritchie MD.
Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.
Pac Symp Biocomput
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. (2015 Feb). Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet , 16(2):85-97.
Hall MA, Verma A, Brown-Gentry KD, Goodloe R, Boston J, Wilson S, McClellan B, Sutcliffe C, Dilks HH, Gillani NB, Jin H, Mayo P, Allen M, Schnetz-Boutaud N, Crawford DC, Ritchie MD, Pendergrass SA. (2014, Dec). Detection of Pleiotropy through a Phenome-Wide Association Study (PheWAS) of Epidemiologic Data as Part of the Environmental Architecture for Genes Linked to Environment (EAGLE) Study. PLoS Genet , 10(12):e1004678.
Kim D, Li R, Dudek SM, Frase AT, Pendergrass SA, Ritchie MD. (2014 Sep ). Knowledge-driven genomic interactions: an application in ovarian cancer. BioData Min , 7:20.
Pendergrass SA, Frase A, Wallace J, Wolfe D, Katiyar N, Moore C, Ritchie MD. (2013 Dec ). Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development. BioData Min , 6(1):25.