Advisory Board

Professor Jeffrey L. Thorne

Jeffrey L. Thorne, Ph.D. is Professor of Genetics and Statistics, Bioinformatics Research Center, North Carolina State University. He is on the Editorial Boards of Evolutionary Bioinformatics, Journal of Experimental Zoology-B: Molecular and Developmental Evolution, Molecular Biology and Evolution, and Systematic Biology.
Jeff studies evolution. He does this by developing statistical techniques for analyzing DNA and protein sequence data. His main efforts concern:
(1) Improving probabilistic models of DNA sequence evolution by incorporating phenotype and reconciling these models with population genetics
The relationship between phenotype and survival of the genotype is central to both genetics and evolution. The field of population genetics has a rich body of theory for explaining how within-species genetic variation is shaped by fitness, mutation, recombination, population size, and population structure. However, this theory does not purport to map genotypes to phenotypes nor does it map phenotypes to fitness. A wide variety of computational biology schemes aim to predict phenotype from genotype.
He is working to improve models of molecular evolution by incorporating these computational biology prediction systems. He has concentrated on protein tertiary structure and RNA secondary structure, but is very excited by the potential to quantify the impacts on evolution of diverse other aspects of phenotype. Rather than designing his statistical techniques exclusively for understanding within-species genetic variation, he has been attempting to apply population genetic theory to data sets representing sequences from different species. This is a challenging endeavor but a paucity of intraspecific genetic variation means that many of the most important evolutionary questions can only be addressed via interspecific comparisons.
(2) Evolution of the rate of evolution
Evolutionary analysis of DNA and protein sequences is typically performed by either assuming that all evolutionary lineages change at the same rate or by avoiding any attempt to directly consider the fact that the rate of evolution changes over time. Factors that affect the rate of molecular evolution (e.g., mutation, population size, generation time, selection) change over time and therefore the rate of molecular evolution is extremely unlikely to be identical for different evolutionary lineages.
However, it is reasonable to expect an autocorrelation of rates over time. Closely related evolutionary lineages tend to evolve at similar rates and distantly related lineages might evolve at more different rates. His collaborators (especially Hirohisa Kishino of the University of Tokyo) and him are developing methods for estimating dates of evolutionary events from molecular sequence data. These methods lack the restrictive and implausible assumption that rates of evolution have been constant over time. He also feels that these methods have great potential for illuminating patterns of evolutionary rate variation over time.
Jeff authored Models and Their Evolution and Protein evolution constraints and model-based techniques to study them, and coauthored A TABU search algorithm for maximum parsimony phylogeny inference, Population genetics without intraspecific data, Quantifying the impact of protein tertiary structure on molecular evolution, Testing for spatial clustering of amino acid replacements within protein tertiary structure, and Dependence among sites in RNA evolution.
Jeff’s undergraduate degrees were earned in Molecular Biology and in Mathematics from the University of Wisconsin-Madison in 1986. In 1991, he earned a Ph.D. in Genetics from the University of Washington.