Marylyn Ritchie selected to participate in the 2011 Kavli Frontiers of Science Symposia in Irvine, California. Click for more information.
Marylyn Ritchie presents at the International Society of Human Genetics 2011 in Montreal, Canada. “Meta-dimensional analysis of phenotypes”
Marylyn Ritchie presents at the Asia-Pacific Translational Bioinformatics Conference 2011 in Seoul, Korea. “Phenotype-Genotype Associations in DNA Biobanks Linked to electronic Medical Records”
Carrie Buchanan presents at the Asia-Pacific Translational Bioinformatics Conference 2011 in Seoul, Korea. “A Comparison of Cataloged Variation between International HapMap Consortium and 1000 Genomes Project”
Sarah Pendergrass presents at the International Genetic Epidemiology Society 2011 Conference in Heidelberg, Germany. ” Phenotype-Wide Association Study (PheWAS) for Exploration of Novel SNP and Phenotype Relationships within PAGE”
Kullo IJ, Ding K, Shameer K, McCarty CA, Jarvik GP, Denny JC, Ritchie MD, Zi Y, Crosslin DR, Chisholm RL, Manolio TA, Chute CG.
Complement Receptor 1 Gene Variants are Associated with Erythrocyte Sedimentation Rate. AJHG, in press. PMC n/a
Xu H,Jiang M,Oetjens M,Bowton EA,Ramirez AH,Jeff JM,Basford MA,Pulley JM,Cowan JD,Wang X,Ritchie MD,Masys DR,Roden DM,Crawford DC,Denny JC.
Facilitating pharmacogenetic studies using electronic health records and natural-language processing: a case study of warfarin. J Am Med Inform Assoc. 2011 Jul 1;18(4):387-91. PMC 3128409
Ritchie lab graduate, Dr. Stephen Turner, is now the Director of a new Bioinformatics Core in the University of Virginia School of Medicine. Congratulations, Stephen!
Ritchie lab, graduate, Dr. William Bush, was named one of Genome Technology’s PIs of Tomorrow. Congratulations, Will!
Lab Overview
Our primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new statistical methods with a focus on the detection of gene-gene interactions associated with human disease.
The study of common, complex disease can be explored through a variety of study types. One such study will involve genetic analysis of gene-gene interactions through case-control association studies. These studies will involve the development of novel statistical and computational methods, in addition to the application of existing methods. We will apply these methods to real data applications in collaboration with clinicians and epidemiologists who have collected data sets where the investigation of gene-gene interactions can be conducted. While the primary focus of the research will be gene-gene interactions, any methods developed could potentially be applied for the detection of gene-environment interactions or haplotype interactions.
Pharmacogenomics is the second type of study that we will explore. The response to treatment for many diseases is highly variable among individuals. Pharmacogenomics is based on the hypothesis that genetics will explain much of this variation. There is a growing interest in studying the association between genetic polymorphisms and drug response. Statistical methods similar to those used for detecting gene-gene interactions in case-control data may be applied to pharmacogenomics data. Our research goal is to develop a collaborative research project with pharmacologists who are collecting this exciting data. We are interested in applying currently available methods where applicable, and developing new methods as necessary for the analysis of these data.
Finally, gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases. Microarray technologies and mass spectrometry are quickly becoming tools for many researchers in human genetics, as well as many other fields of biomedical research. While these technologies provide researchers the ability to measure expression patterns for thousands of variables, the analysis and interpretation of these data, especially in terms of complex interactions, is currently the more difficult challenge due to limited statistical methodologies. Our research goal is to apply current statistical methods to gene and protein expression data to identify candidate genes associated with common human disease. In addition, we are interested in developing new methods to analyze these data where current methods are lacking.