Professor Crosslin’s academic and professional research experience have been focused on statistical genetics and bioinformatics with applications to complex diseases. Under the direction of Professor Elizabeth Hauser, his doctoral 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. Under the direction of Professors Gail Jarvik, Bruce Weir, and Debbie Nickerson, 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, and ultimately an Associate Professor position in the Department of Biomedical Informatics and Medical Education (BIME) at UW. In addition to the BIME faculty appointment, Dr. Crosslin received an affiliate faculty appointment at Kaiser Permanente Washington Health Research Institute, Seattle, WA, adjunct faculty appointments in Genome Sciences and the Institute for Public Health Genetics at UW.
In 2021, Professor Crosslin joined the Division of Biomedical Informatics and Genomics in the John W. Deming Department of Medicine, Tulane University School of Medicine, as an Associate Professor, and will serve as the Co-Director of Biomedical Informatics in the Tulane University Biomedical Sciences Graduate Program. Upon his move to Tulane University School of Medicine, Professor Crosslin received affiliate faculty appointments in the Department of Biomedical Informatics and Medical Education, Department of Genome Sciences, and the Institute for Public Health Genetics all at UW. He also received an affiliate faculty appointment at Kaiser Permanente Washington Health Research Institute, Seattle, WA. Finally, Professor Crosslin has served on an advisory board for Optum Labs (formerly UnitedHealth Group Research and Development) to guide implementation of cohort-level precision medicine efforts since 2018. When not spending time with his daughters, Dr. Crosslin enjoys training, boating, and college football.
Research Interests/Area of Study:
Applied bioinformatics, applied statistics, clinical decision support, etiology of complex diseases, human genetics and genomics, informatics, large scale statistical genetic analyses, systems integration, and implementation science.
Summary:
Professor Crosslin's current research program focuses on the area of precision medicine, with a combination of statistical genetics, biomedical informatics, implementation science, and computational / bioinformatics tools development. One implementation theme of his research program is the integration of genomics data into the electronic health record (EHR) for clinical decision support. As such, his program is in line with advancing the national electronic health information infrastructure in support of personalized medicine.
Professor Crosslin has been provided the unique opportunity to participate as a key contributor in multiple National Human Genome Research Institute (NHGRI) “Big Data” efforts. One such effort is the Electronic Medical Records & Genomics (eMERGE) Network that is currently in its fourth phase, and comprises multiple mixed-ancestry US biobanks with genotyped and/or sequence data, linked to EHRs. The eMERGE Network is on the forefront of precision medicine and discovery using mined phenotypes from the EHR, and has transitioned from discovery to genomic precision medicine implementation. Professor Crosslin is currently MPI (Jarvik, Crosslin) for the UW site’s U01 project (2020-2025) to evaluate the use of genomic information (polygenic risk scores) in the health care of diverse ancestry participants. Previously, Professor Crosslin served as the UW PI for the eMERGE Coordinating Center sub-contract (2015-2020) primarily focused on genetic data activities, Co-Chair for the eMERGE Genomics Working Group (2012-2020), and Co-I for the Kaiser Permanente/UW site project. With his colleagues, he has led and published on multiple methods and network phenotypes including pharmacogenomics, ancestry, white blood cell count, monocyte count, and varicella zoster/shingles (see publications below). This experience has exposed his group to genetics discovery, phenotyping using the electronic health record, and translational implementation.
In addition to eMERGE, Professor Crosslin is an active participant in the NHGRI’s Clinical Sequencing Evidence-Generating Research (CSER) Consortium where his team is developing computational tools to collect and harmonize outcomes and measures from participants receiving clinical sequencing results, and harmonizing genomic sequence data. He is currently Co-Chair of the CSER Sequence Analysis and Diagnostic Yield (SADY) Working Group (2020-2021). His group is also heavily involved with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) cloud data activities, both with the eMERGE Network and the CSER Consortium. Having access to these data provides excellent data resources for research and education, as well as supporting national collaborations. These established collaborations benefit his research program in implementation science and precision medicine.
With the NHGRI research focus transitioning from discovery to implementation, Professor Crosslin’s research portfolio focus has done the same. This includes evidenced-based research to assess effectiveness and implementation (hybrid trials) of precision medicine interventions. Having access to up-to-date interpretation of genetic variation, prior clinical associations, and molecular annotation will allow for more informed decisions in genetic diagnostics, and ultimately better patient outcomes. With advances in biotechnology, bioinformatic software, and molecular annotation tools, genetic health service is becoming more informed. His group is utilizing cloud-computing to assist in the implementation of informed genomics in patient care. This includes application development to assist in crowdsourcing clinical interpretation of genetics, and to assist in the communication between genetic clinician and patient. Finally, Professor Crosslin’s appointment to the OptumLabs’ (formerly UnitedHealth Group Research and Development) advisory board to guide implementation of cohort-level precision medicine efforts provide multiple precision public health research opportunities, and will bring this experience to Tulane University School of Medicine.
Representative and Recent Publications:
Crosslin D.R., Shah S.H., Nelson S.C., et al., Hauser E.R., 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., Jarvik G.P., 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., Jarvik G.P., 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., Jarvik G.P., 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., Jarvik G.P., 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 I.B., Hall T.O., Rosenthal E.A., et al., Crosslin D.R. “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. (PMCID: PMC6375696.)
Hall T.O., Stanaway I.B., Carrell D.S., et al., Crosslin, D.R., 2018: “Unfolding of hidden white blood cell count phenotypes for gene discovery using latent class mixed modeling,” Genes & Immunity, (PMCID: PMC6541537.)
Linder J., et al., Crosslin D.R., 2021: “Lessons from the eMERGE Network: Balancing genomics in discovery and in practice,” Human Genetics and Genomics Advances, 2(1).
Most of Professor Crosslin’s publications can be found at: http://www.ncbi.nlm.nih.gov/pubmed/?term=crosslin+dr
