Background: Gene set testing, or pathway analysis, has become a critical tool for the analysis of high- dimensional genomic data. Although the function of many genes is strongly linked to tissue context, with recent experiments characterizing the tissue-specificity of most human protein-coding genes, little support is available for tissue-specific gene set testing. An important opportunity therefore exists to create novel bioinformatics methods that use the comprehensive data on the tissue-specificity of human genes to create tissue-specific gene set collections, annotations and testing methods. Candidate: Dr. Frost is a faculty member in the Department of Biomedical Data Science at the Geisel School of Medicine at Dartmouth. His academic training at Stanford, Harvard and Dartmouth, strong record of research productivity and extensive medical informatics consulting experience make him well qualified to achieve his proposed career and research aims and transition to research independence. Environment: The Geisel School of Medicine at Dartmouth is an optimal environment for Dr. Frost's career development and research. Dr. Frost's team of experienced mentors and advisors, along with a wide network of other collaborating faculty, will provide a supportive and invigorating intellectual en- vironment. Numerous core facilities, including a state-of-the-art supercomputing cluster, will provide the necessary technical infrastructure and regular seminars, grand rounds and workshops will provide exposure to recent advances in topics including biomedical informatics, biostatistics and genomics. Career Development Plan: The proposed career development plan will achieve the three primary aims of 1) biomedical expertise, 2) training for research leadership and 3) research independence through a combination of scientific education, career development and mentored research. All of these activities will be guided by Dr. Frost's mentoring and advising team, lead by Dr. Christopher Amos. To achieve the first aim, Dr. Frost will improve his biomedical domain knowledge, focusing on genomics and ge- nomic medicine, through classes, workshops, short courses, seminars and conferences. To achieve the second aim, Dr. Frost will pursue training in translational science, the responsible conduct of research and grant writing.
The third aim will be realized through the development of a competitive R01 grant. Research Plan: Dr. Frost will address the challenge of tissue-specific gene set testing by developing and evaluating bioinformatics methods that leverage information on tissue-specific gene function to create gene set collections and testing methods that target a single human tissue type. These methods will 1) filter out gene sets not relevant to the tissue, 2) eliminate gene set annotations inappropriate for the tissue, and 3) leverage tissue-specific gene relationships to improve gene set enrichment.
Gene set testing, or pathway analysis, has become an indispensable tool for the analysis and in- terpretation of high dimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed using tissue ag- nostic gene set collections, which impacts statistical power, interpretability and replication of results. To address this challenge, we will develop and evaluate novel bioinformatics methods that leverage information about tissue-specific gene function to create gene set collections and gene set testing methods that target a specific human tissue type.
|Frost, H Robert (2018) Computation and application of tissue-specific gene set weights. Bioinformatics 34:2957-2964|
|Gorlov, Ivan P; Pikielny, Claudio W; Frost, Hildreth R et al. (2018) Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples. BMC Bioinformatics 19:430|
|Frost, H Robert; Amos, Christopher I (2017) Gene set selection via LASSO penalized regression (SLPR). Nucleic Acids Res 45:e114|
|Li, Zhigang; Frost, H R; Tosteson, Tor D et al. (2017) A semiparametric joint model for terminal trend of quality of life and survival in palliative care research. Stat Med 36:4692-4704|
|Frost, H Robert; Amos, Christopher I (2016) Unsupervised gene set testing based on random matrix theory. BMC Bioinformatics 17:442|
|Frost, H Robert; Shen, Li; Saykin, Andrew J et al. (2016) Identifying significant gene-environment interactions using a combination of screening testing and hierarchical false discovery rate control. Genet Epidemiol 40:544-557|
|Frost, H Robert; Amos, Christopher I; Moore, Jason H (2016) A global test for gene-gene interactions based on random matrix theory. Genet Epidemiol 40:689-701|