Human genetics and genomics, applied bioinformatics, applied statistics, clinical decision support, systems integration and implementation, large scale statistical genetic analyses, etiology of complex diseases.
Dr. Crosslin’s academic and professional research experience have been focused on statistical genetics and bioinformatics with applications to complex diseases. His doctorate research in Computational Biology and Bioinformatics at Duke University focused on the central theme of modeling metabolic pathways through dimension reduction techniques of genomics data to understand the etiology of complex traits such as cardiovascular disease. Dr. Crosslin’s postdoctoral training at the University of Washington (UW) focused on the areas of clinical applications of genetics, statistical genetics, and sequencing technologies. This led to an Acting Instructor faculty position in Genome Sciences at UW. Along with the BIME faculty appointment, Dr. Crosslin has an affiliate faculty appointment at Group Health Research Institute, Seattle, WA, and an adjunct faculty appointment in Genome Sciences. Dr. Crosslin’s research program focuses on translational bioinformatics with a combination of bioinformatics, statistical association analyses, and computational tools development for applied research. Specifically, his research focuses on integrating genetic data into the electronic health record (EHR) for clinical decision support (CDS). All efforts will advance the national electronic health information infrastructure in support of personalized medicine. Dr. Crosslin has been and will continue to be affiliated with one such NHGRI effort. The Electronic Medical Records & Genomics (eMERGE) Network is on the forefront of precision medicine and discovery using mined phenotypes, and has transitioned from discovery to interpretation and integration into the EHR for CDS. When not spending time with his daughters, Dr. Crosslin enjoys training, camping, kayaking, and college football.
Crosslin D.R., Shah S.H., Nelson S.C., et al., 2009: “Genetic effects in the leukotriene biosynthesis pathway and association with atherosclerosis,” Human Genetics, 125:217-229. (PMCID: PMC2759090.)
Crosslin D.R., McDavid A., Weston N., et al., 2012: “Genetic variants associated with the white blood cell count in 13,923 subjects in the eMERGE Network,” Human Genetics, 131(4):639-652. (PMCID: PMC3640990.)
Crosslin D.R., Carrell D.S., Burt A., et al., 2014: “Genetic variation in the HLA region is associated with susceptibility to herpes zoster,” Genes & Immunity, 16(1):1-7. (PMCID: PMC4308645.)
Crosslin D.R., Tromp G., Burt A., et al., 2014: “Controlling for population structure and genotyping platform bias in the eMERGE multi-institutional biobank linked to Electronic Health Records” Frontiers in Genetics, 5:352, eCollection. (PMCID: PMC4220165.)
Crosslin D.R., Robertson P.D., Carrell D.S., et al., 2015: “Prospective participant selection and ranking to maximize actionable pharmacogenetic variants and discovery in the eMERGE Network,” Genome Medicine, 7(1):67, eCollection. (PMCID: PMC4517371.)
Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP; eMERGE Network, Crosslin DR. “The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype” Genet Epidemiol. 2018 Oct 8. doi: 10.1002/gepi.22167. [Epub ahead of print]
Hall T.O., Stanaway I.B., Carrell D.S., Carroll R.J., Denny J.C., Hakonarson H., Larson E.B., Mentch F.D., Peissig P.L., Pendergrass S.A., Rosenthal E.A., Jarvik G.P., Crosslin, D.R., 2018: “Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling,” Genes & Immunity, doi: 10.1038/s41435-018-0051-y. [Epub ahead of print]