Medium screen shot 2014 04 18 at 11.49.06 am

Mark Gmail

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BIOGRAPHY


I came from Philadelphia, and took my undergraduate degree at the University of Wisconsin, Madison, in 1964. My Ph.D. thesis work was with Richard Lewontin, then at the University of Chicago. After a year's postdoctoral work at the Institute of Animal Genetics in Edinburgh, Scotland, I got a job here in the Department of Genetics. I was also an adjunct faculty member in Zoology 1990-2002 and since then a joint member of Genome Sciences and of Biology Department.

I originally trained as a theoretical population geneticist, and did primarily that for the first decade of work here. But then a side interest in inference of phylogenies came to be my primary research area. I released the PHYLIP package of programs to infer phylogenies for the first time in 1980, and published a 2004 book Inferring Phylogenies. Since about 1990 I have concentrated on how trees of genes in populations allow inference of parameters and more recently on evolutionary models of quantitative characters changing on phylogenies.

Research Overview

We have lately been working on methods for estimating population parameters (such as effective population size, mutation rate, and so on) from population samples of molecular sequences. The genes at one locus in a population are related by a "gene tree" that depicts which ones are descended from recent common ancestors.

We have been using a computationally intensive method known as Markov Chain Monte Carlo Integration to make approximate calculations of the statistical likelihoods for different values of the population parameters. We are now distributing a free package of computer programs, LAMARC, to do these calculations. We think that these methods will become the standard way of analyzing population samples of sequences. My colleagues in this work have been Mary Kuhner, Jon Yamato, and Peter Beerli.

I have also been working lately on models and inference methods for quantitative characters varying between species and within-species, allowing us to infer correlated evolution of different characters. One important case is discrete 0/1 phenotypes, which can be caused by an underlying polygenic quantitative character. Years ago Sewall Wright made such a model, called the threshold model. I have adapted this to inference of covariation between characters in their evolution, by assuming that the underlying characters evolve in a correlated fashion, but that the 0/1 characters just show which of the underlying characters exceed the threshold that separates the two states. Making inferences about the covariation requires Markov chain Monte Carlo methods. I have also been working on inferring whether characters measured in different populations of a single species are under natural selection which is affected by some measured environments. There the problem is to correct for the similarities of populations that are connected by gene flow.

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