Keith A. Crandall

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, characterizing microbiome components, and measuring 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 we focus on microbiome diversity (functional and taxonomic) and how that relates to organismal health.  We also develop methods and implement such approaches to track population dynamics of infectious diseases such as HIV, HCV, etc.  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.

For a complete publication list, please visit Dr. Crandall's GoogleScholar page.

Baghbanzadeh M, Dawson T, Sayoldin B, Frazer SA, Oakley TH, Crandall KA, Rahnavard A. deepBreaks identifies and prioritizes genotype-phenotype associations using machine learning. Sci Rep. 2025 Nov 7;15(1):39095. doi: 10.1038/s41598-025-25580-6. PMID: 41203782; PMCID: PMC12594848.

Sayer CA, Fernando E, Jimenez RR, Macfarlane NBW, Rapacciuolo G, Böhm M, Brooks TM, Contreras-MacBeath T, Cox NA, Harrison I, Hoffmann M, Jenkins R, Smith KG, Vié JC, Abbott JC, Allen DJ, Allen GR, Barrios V, Boudot JP, Carrizo SF, Charvet P, Clausnitzer V, Congiu L, Crandall KA, Cumberlidge N, Cuttelod A, Dalton J, Daniels AG, De Grave S, De Knijf G, Dijkstra KB, Dow RA, Freyhof J, García N, Gessner J, Getahun A, Gibson C, Gollock MJ, Grant MI, Groom AER, Hammer MP, Hammerson GA, Hilton-Taylor C, Hodgkinson L, Holland RA, Jabado RW, Juffe Bignoli D, Kalkman VJ, Karimov BK, Kipping J, Kottelat M, Lalèyè PA, Larson HK, Lintermans M, Lozano F, Ludwig A, Lyons TJ, Máiz-Tomé L, Molur S, Ng HH, Numa C, Palmer-Newton AF, Pike C, Pippard HE, Polaz CNM, Pollock CM, Raghavan R, Rand PS, Ravelomanana T, Reis RE, Rigby CL, Scott JA, Skelton PH, Sloat MR, Snoeks J, Stiassny MLJ, Tan HH, Taniguchi Y, Thorstad EB, Tognelli MF, Torres AG, Torres Y, Tweddle D, Watanabe K, Westrip JRS, Wright EGE, Zhang E, Darwall WRT. One-quarter of freshwater fauna threatened with extinction. Nature. 2025 Feb;638(8049):138-145. doi: 10.1038/s41586-024-08375-z. Epub 2025 Jan 8. PMID: 39779863; PMCID: PMC11798842.

De Meester L, Vázquez-Domínguez E, Kassen R, Forest F, Bellon MR, Koskella B, Scherson RA, Colli L, Hendry AP, Crandall KA, Faith DP, Starger CJ, Geeta R, Araki H, Dulloo EM, Souffreau C, Schroer S, Johnson MTJ. A link between evolution and society fostering the UN sustainable development goals. Evol Appl. 2024 Jun 14;17(6):e13728. doi: 10.1111/eva.13728. PMID: 38884021; PMCID: PMC11178947.

Frazer SA, Baghbanzadeh M, Rahnavard A, Crandall KA, Oakley TH. Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD). Gigascience. 2024 Jan 2;13:giae073. doi: 10.1093/gigascience/giae073. PMID: 39460934; PMCID: PMC11512451.

Pérez-Losada M, Crandall KA. Spatial diversity of the skin bacteriome. Front Microbiol. 2023 Sep 19;14:1257276. doi: 10.3389/fmicb.2023.1257276. PMID: 37795302; PMCID: PMC10546022.

Odom AR, Faits T, Castro-Nallar E, Crandall KA, Johnson WE. Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data. Sci Rep. 2023 Aug 26;13(1):13957. doi: 10.1038/s41598-023-40799-x. PMID: 37633998; PMCID: PMC10460424.

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