Keith A. Crandall

Keith A. Crandall

Title:
Professor of Biology
Office:
SEH 7000D
Phone: 571-553-0107
Email:
[email protected]

Areas of Expertise

Computational biology, bioinformatics, phylogenetics, infectious disease, crustacean systematics, molecular evolution, HIV, population genetics, bacterial genomics, conservation genetics.

Current Research

My research program has three main aspects. The first and central component is work on the development and testing through computer simulation of methods for the analysis of DNA sequence data. We have developed methods for estimating gene genealogies, detecting recombination, detecting selection, and measuring genetic diversity and demographic events in the history of a population. We develop software to implement many of these methods and then develop software to test our methods and many others by comparison through computer simulation. Through comparison and tests of robustness to assumption violations, we can gain great insights into why particular methods perform well or poorly and then are in a good position to redevelop improved methodology. In fact, we are currently embarking on the development of a comprehensive simulation software package that allows one, for the first time, to examine the impact of a host of population genetic phenomena at the same time (e.g., migration, mutation, recombination, selection, fluctuating population sizes, tracking geographic locations of alleles in a population, multiple locus populations, etc.). Through such studies, we gain a better understanding of the methodology we use to infer evolutionary and population demographic histories and associated parameter estimates when we apply them to empirical data. Furthermore, such studies provide valuable insights into the development of new and improved theory and methodology for inference from DNA sequence data.

The remaining two aspects of my research program deal with applications of the above methodologies in two fairly distinct arenas. The first is in molecular ecology, conservation biology, and systematics research. We have applied the methods developed and tested in our lab (and many others) to examine the populations genetics, historical demography, and molecular ecology of various species of freshwater crayfish. We have also examined the molecular systematics of a variety of organisms, from the origin of dogs to the origin of freshwater crayfishes. You will see from my CV that the systematic studies are typically collaborative studies. I firmly believe in collaboration, especially in systematic studies that require both organismal (including morphological and ecological) expertise and molecular (including phylogenetic analysis) expertise. We the freshwater crayfishes, we typically are the morphological and taxonomic experts as well. However, with all the other organismal groups, we develop (often international) teams of expertise to tackle outstanding questions in systematic biology. We then apply our results to a diversity of biogeographic and conservation biology questions. These typically lead naturally into broader issues relating to conservation biology such as diagnosing species and the relative importance of different sources of information regarding conservation priorities and conservation status such as ecological data versus genetic data.

The second focus of my empirical research is in the area of the evolution of infectious diseases. Here our main system has been HIV, but we have also now been very active in bacterial genetics, especially Neisseria gonorrhoeae. Our main goal with these research projects is to explore the population dynamics of infectious disease, particularly relative to the evolution of drug resistance. We have been heavily involved in evaluating the performance of multi-locus sequence typing (MLST) methods to track population dynamics of bacterial species. Our results suggest that these MLST are not as selectively neutral as researcher had hoped and that different MLST work differentially well in a diversity of species. We plan to continue the exploration of MLST and their application in tracking population dynamics of bacterial agents of bioterrorism, as well as tracking dynamics of infectious disease. Our extension into bacterial genomics coupled with an interested in environmental samples, has naturally led to an exploration of novel computational techniques to identify pathogens from environmental samples using next-generation sequencing approaches to collect relevant data and novel statistical and computational approaches for analyses. We are involved in all phases of this work from the molecular approaches to the statistical models to the computational implementation.

The research outlined in these three main areas in my lab has enjoyed a diversity of funding from the National Institutes of Health, the National Science Foundation, and private agencies such as the Alfred P. Sloan Foundation and the Pharmaceutical Manufacturers of America. My research program is moving evermore into the genomics and bioinformatics arena and applying these insights into conservation management, human health, and biomedical applications.

Check out the following links to read more about some of our current projects:

Decapod Tree of Life
Open Tree of Life
Gulf of Mexico Oil Spill

Education

B.A., Kalamazoo College, 1987 (Mathematics and Biology)
M.A., Washington University in St. Louis, 1993 (Statistics)
Ph.D., Washington University in St. Louis, 1993 (Biology & Biomedical Sciences)

Publications

Full text publications.

Stern, DB and KA Crandall. 2018. The evolution of gene expression underlying vision loss in cave animals. Molecular Biology and Evolution https://doi.org/10.1093/molbev/msy106.

Lewin, H.A., G. Robinson, W.J. Kress, W. Baker, J. Coddington, K. Crandall, R. Durbin, S. Edwards, F. Forest, T. Gilbert, M. Goldstein, I. Grigoriev, K. Hackett, D. Haussler, E. Jarvis, W. Johnson, A. Patrinos, S. Richards, J.C. Castilla Rubio, M.A. van Sluys, P. Soltis, X. Xu, H. Yang, and G. Zhang. In press. The Earth BioGenome Project: Sequencing Life for the Future of Life. Proceedings of the National Academy of Sciences 115(17):4325-4333. Doi/10.1073/pnas.1720115115

Pérez-Losada M, Castel AD, Lewis B, Kharfen M, Cartwright CP, Huang B, Maxwell T, Greenberg AE, Crandall KA (2017) Characterization of HIV diversity, phylodynamics and drug resistance in Washington, DC. PLoS ONE 12(9): e0185644. https://doi.org/10.1371/journal.pone.0185644

Stern, D. B., J. Breinholt, C. Pedraza Lara, M. Lopez-Mejia, C. L. Owen, H. Bracken-Grissom, J. W. Fetzner, Jr., and K. A. Crandall. 2017. Phylogenetic evidence from freshwater crayfishes that cave adaptation is not an evolutionary dead-end. Evolution 71-10:2522-2532, doi:10.1111/evo.13326

Restrepo, P., M. Movassagh, N. Alomran, C. Miller, M. Li, C. Trenkov, Y. Manchev, S. Bahl, S. Warnken, L. Spurr, T. Apanasovich, K. Crandall, N. Edwards, and A. Horvath. 2017. Overexpressed somatic alleles are enriched in functional elements in Breast Cancer. Scientific Reports 7:8287. doi:10.1038/s41598-017-08416-w

Crandall, KA and S De Grave. 2017. An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. Journal of Crustacean Biology 37(5):615-653. doi:10.1093/jcbiol/rux070

Perez-Losada M, Alamri L, Crandall KA, Freishtat RJ. 2017. Nasopharyngeal Microbiome Diversity Changes over Time in Children with Asthma. PLoS One 12: e0170543.

Hinchliff, C., S.A. Smith, J.F. Allman, J.G. Burleigh, R. Chaudhary, L.M. Coghill, K.A. Crandall, J. Deng, B.T. Drew, R. Gazis, K. Gude, D.S. Hibbett, L.A. Katz, H.D. Laughinghouse, E.J. McTavish, P.E. Midford, C.L. Owen, R. Ree, J.A. Rees, D.E. Soltis, T. Wiliams, and K.A. Cranston.  2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life.  Proceedings of the National Academy of Sciences, USA 112(41):12764-12769. doi: 10.1073/pnas.1423041112

Hong, C., S. Manimaran, Y. Shen, J.F. Perez-Rogers, A.L. Byrd, E. Castro-Nallar, K.A. Crandall, and W.E. Johnson. 2014. PathoScope 2.0: A complete computational framework for strain identification in environmental or clinical sequencing samples.  Microbiome 2:33.