J.L.
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Jeffrey L. Thorne Research
Overview
My colleagues and I study evolution. We do
this by developing statistical techniques
for analyzing DNA and protein sequence data. Our
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. We are working to improve models of molecular evolution
by incorporating these computational biology prediction systems.
We have concentrated on protein tertiary structure and RNA
secondary structure, but are very excited by the potential to
quantify the impacts on evolution of diverse other aspects of
phenotype. Rather than designing our statistical techniques
exclusively for understanding within-species genetic variation, we have
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.
My collaborators (especially Hirohisa Kishino of
the University of Tokyo) and I 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. We also feel that these
methods have great potential for illuminating patterns of evolutionary
rate variation over time.
J.L.
Thorne homepage
:
Research Overview
Publications
Research
Group, Alumni, & Collaborators Software
(multidivtime) Teaching