Bruce Rannala, Ph.D.

Rannala Portrait

Genome Center
Department of Evolution and Ecology, College of Biological Sciences
Center for Population Biology

5339 Storer Hall (EVE Office)

Research Interests

Computational evolutionary and population genomics

Research in the group focuses on mathematical aspects of population genetics, phylogenetic inference, and human genetics. Topics of interest include statistical methods for linkage disequilibrium gene mapping and Bayesian phylogenetic inference, as well as more general questions in theoretical population genetics. Topics of current research include the role of hypermutability and mutator phenotypes in cancer genetics, multipoint linkage disequilibrium mapping, and methods for detecting an association between genetic markers and disease in heterogeneous populations. A unifying theme of research in the group is the application of analytic theory and computer simulation to address questions of importance in evolutionary biology and human genetics.

Grad Group Affiliations

  • Biostatistics,Statistics
  • Integrative Genetics and Genomics
  • Population Biology

Specialties / Focus

  • Molecular Evolution
  • Population and Quantitative Genetics
  • Systematics and Comparative Biology


  • EVE 131 Human Genetic Variation
  • MCB 10 Intro to Human Heredity


  • Rannala Research Group

Honors and Awards

  • CIHR Peter Lougheed Award (2001)


    • 1995 Ph.D. Biological Sciences Yale University
    • 1991 M.Sc. Zoology University of Toronto
    • 1989 B.Sc. Zoology University of British Columbia


    Z. Yang and B. Rannala. 2012. Molecular phylogenetics: principles and practice. Nature Reviews Genetics 13: 303-314.

    B. Padhukasahasram and B. Rannala. 2011. Bayesian population genomic inference of crossing-over and gene-conversion. Genetics 189: 607-619.

    Z. Yang and B. Rannala. 2010. Bayesian species delimitation using multilocus sequence data. Proceedings of the National Academy of Sciences USA 107: 9264-9269.

    Y. Wang and B. Rannala. 2009. Population genomic inference of recombination rates and hotspots. Proceedings of the National Academy of Sciences USA 106: 6210-6214.