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Moscow

Department of Biological Sciences
biosci@uidaho.edu
phone: (208) 885-6280
fax:(208) 885-7905
Life Sciences South 252
875 Perimeter Drive MS 3051
Moscow, ID 83844-3051

Paul Hohenlohe

Paul Hohenlohe, Ph.D.


Office: LSS 256
Phone: (208) 885-4031
Email: hohenlohe@uidaho.edu
Mailing Address: University of Idaho
Dept. of Biological Sciences
875 Perimeter MS 3051
Moscow, Idaho 83844-3051

College of Science
Department of Biological Sciences
Department of Statistics
Assistant Professor

Home Town: Washington, DC
Campus Locations: Moscow
With UI Since 2011


  • Research/Focus Areas
    • Evolutionary Genomics
    • Bioinformatics
    • Mathematical Biology
    • Quantitative Genetics
  • Biography
    I earned my B.A. in Biology from Williams College and moved to Alaska for a year, where I worked for The Wilderness Society and taught at the University of Alaska.  Following my graduate work, conducted at Friday Harbor Laboratories of the University of Washington, I worked for several years as a conservation biologist and Regional Interagency Malacologist (slug and snail expert) for the federal Northwest Forest Plan.  I conducted postdoctoral work in quantitative genetics and evolutionary genomics with Steve Arnold at Oregon State University and Bill Cresko at the University of Oregon.
  • Selected Publications
    • Catchen JM, Amores A, Hohenlohe PA, Cresko WA, Postlethwait JH.  2011.  Stacks: building and genotyping loci de novo from short-read sequences.  G3 Genes Genomes Genetics 1: 171-182.
    • Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML.  2011.  Genome-wide genetic marker discovery and genotyping using next-generation sequencing.  Nature Reviews Genetics 12: 499-510.
    • Hohenlohe PA, Amish S, Catchen JM, Allendorf FW, Luikart G.  2011.  RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow trout and westslope cutthroat trout.  Molecular Ecology Resources 11(Supp. 1): 117-122.
    • Hohenlohe PA, Phillips PC, Cresko WA.  2010.  Using population genomics to detect selection in natural populations: key concepts and methodological considerations.  International Journal of Plant Sciences 171(9): 1059-1071.
    • Allendorf FW, Hohenlohe PA, Luikart G.  2010.  Genomics and the future of conservation genetics.  Nature Reviews Genetics 11(10): 697-709.
    • Hohenlohe PA, Arnold SJ.  2010.  The dimensionality of mate choice, sexual isolation, and speciation.  Proceedings of the National Academy of Sciences 10.1073/pnas.1003537107.
    • Emerson KJ, Merz CR, Catchen JM, Hohenlohe PA, Cresko WA, Bradshaw WE, Holzapfel CM.  2010.  Resolving post-glacial phylogeography using high-throughput sequencing.  Proceedings of the National Academy of Sciences 107(37): 16196-16200.
    • Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, Cresko WA.  2010.  Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags.  PLoS Genetics 6(2): e1000862 (23 pp).
    • Hohenlohe PA, Arnold SJ.  2008.  MIPoD: A hypothesis-testing framework for microevolutionary inference from patterns of divergence.  American Naturalist 171(3): 366-385.
  • Research Projects
    • Genomic architecture of evolution
      The genetic basis of complex, multivariate phenotypes depends on the distribution and interactions of many loci across the genome.  Evolutionary forces shape this genomic architecture of phenotypic variation, and genomic architecture in turn constrains and bends the responses to natural selection and the trajectories of diversification.  Modern sequencing technology now allows us to take an empirical genome-scale view of evolution in a myriad of species.
    • Our research addresses a range of related questions:
      What is the genomic architecture of multivariate phenotypes in natural populations?  How do interactions between population structure, gene flow, and divergent natural selection shape genomic architecture to facilitate rapid evolution?  How many directions in phenotypic space are available to evolution?  How wrong are the traditional quantitative genetic assumptions about the structure of continuous phenotypic variation, and what is a better model?
    • Cancer as an evolutionary process
      The cell lineages in a developing tumor form a highly heterogeneous, evolving population, and prognosis and treatment options depend on this evolutionary process.  We are seeking to combine evolutionary genomic theory and high-throughput sequencing to understand evolution in cancer at the genomic scale.
    • Conservation genomics
      Evolutionary genomic approaches have powerful applications to conservation of species and ecosystems.  We are collaborating with a number of researchers to develop large sets of genetic markers, assess phylogeographic structure, detect hybridization and introgression, and estimate patterns of genetic variation in natural populations.