Davorka Gulisija

computational geneticist @Penn

 
davorka

about

My research uses a combination of analytical and statistical modeling, computational techniques, simulations and genomic data to elucidate evolutionary mechanisms of adaptation to novel environments. Numerous empirical studies reported rapid evolution in response to novel environments. We now understand that rapid evolution may occur via rapid change in allele frequencies or via polygenic adaptation. The underlying evolutionary mechanisms of rapid adaptation, however, are still poorly understood. My research addresses fundamental questions about adaptation: How do populations rapidly adapt to changing conditions? How do genetic interactions modulate rapid adaptation in changing environments? How do mechanisms of rapid adaptation change as the number of selected loci increases? What is the genetic basis for polygenic adaptation? How can we characterize polygenic adaptation within populations?

I completed my MS in Statistics, MS in Quantitative Genetics, and PhD in Population Genetics at the University of Wisconsin-Madison, primarily under the guidance of Daniel Gianola, James F. Crow, and Yuseob Kim. I am currently a postdoctoral fellow in Joshua Plotkin's group at the University of Pennsylvania.

selected publications

  • Gulisija D., Carol. E. Lee, and Y. Kim. Evolution of multi-locus balanced polymorphism in temporally variable environments. In prep.
  • Gulisija D., A. I. Vazquez, G. de los Campos, M. Posavi, and J. B. Plotkin. Non-parametric inference of epistasis in human complex traits. In prep.
  • Gulisija D. and J. B. Plotkin. 2017. Phenotypic plasticity promotes recombination and gene clustering in periodic environments. Nature Communications 8: 2041. [paper]
  • Gulisija D., Y. Kim, and J. B. Plotkin. 2016. Phenotypic plasticity promotes balanced polymorphism in periodic environments by a genomic storage effect. Genetics 202: 1437–1448. [paper]
  • Gulisija D. and Y. Kim. 2015. Emergence of long-term balanced polymorphism under cyclic selection of spatially variable magnitude. Evolution 69: 979 – 992. [paper]
  • Kim Y. and D. Gulisija. 2010. Signatures of recent directional selection under different models of population expansion during colonization of new selective environments. Genetics 184: 571 – 585. [paper]
  • Gulisija D. and J. F. Crow. 2007. Inferring purging from pedigree data. Evolution 61: 1043-1051. [paper]
  • Gulisija D., D. Gianola, and K. A. Weigel. 2007. Nonparametric analysis of the impact of inbreeding on production in Jersey cows. J. Dairy Science 90: 493-500. [paper]
  • Gulisija D., D. Gianola, K. A. Weigel, and M. A. Toro. 2006. Between-founder heterogeneity in inbreeding depression for production in Jersey cows. Livestock Science 104:244-253. [paper]

    • Google Scholar

    teaching

    Education is one of the most transformative forces in the world today. I approach this privilege and responsibility with passion and dedication. @UW-Madison, I have had an opportunity to TA (and frequently guest lecture):

  • Evolutionary Biology (Spring 2009, 2010, 2011, 2012, 2013; Fall 2011, 2012)
  • Introductory Biology (Fall 2006, 2007, 2008, 2009, 2010, 2013)
  • Comparative and Evolutionary Physiology Laboratory (Spring 2007), and
  • Linear Models with Applications in Biology and Agriculture (Fall 2005).

    • I also enjoy mentoring, particularly evolution and applied mathematics projects. In addition, I have taken numerous opportunities to receive additional teaching training @UWMadison, including Training for Communication-B instructors, Zoology Department TA training, Graduate Assistants' Equity Workshops for TAs, and attended several Teaching and Learning symposiums.

    I am most interested in teaching and mentoring in mathematical biology, evolutionary biology/theory, population genetics/genomics, quantitative genetics, and computational statistics.

    code

    • HCS.c – Heterogenous Cyclic Selection simulator. This C source code simulates evolutionary dynamics under heterogeneous cyclic selection in a subdivided population. Available at Dryad.
    • GS.c – Genomic Storage simulations. This code simulates evolutionary dynamics at a plasticity modifier and a target locus under periodic selection as described in Gulisija at al. (2016). Available as a supplement at Genetics.
    • RecombinationGS – This package contains C programs used to perform stability analysis (Jacob.c), frequency based (Recfreq.c) and individually based (IndRec.c) forward-in-time evolutionary simulations of recombination rate between the evolving plasticity and target locus under the genomic storage model presented in Gulisija and Plotkin (2017). Available on GitHub.

      • contact

      davorka@sas.upenn.edu

      433 S. University Avenue
      Philadelphia, PA 19104