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SPRING COURSE ANNOUNCEMENT
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Statistics 593A:
Special Topics in Statistics:
Models and inference for
genetic data on related individuals.
- Instructor: Professor E. A. Thompson
- Email: thompson@stat.washington.edu
- Time/Place: Wed: 2.30-3.50, Fri: 2.50-4.10/ THO 334
Genetics provides one of the few true highly structured stochastic systems
in the real world. Genes segregate from parents to offspring in pedigree
structures, while being ordered in the linear structure of a chromosome.
The biological process of meiosis and the demographic structure of
populations give rise to additional levels of structure.
This course will discuss models and methods for inference from genetic
data, from the meiosis level to the population level, including
likelihood computation and estimation, and sampling-based (Monte Carlo)
approaches.
The title is deliberately wide -- related individuals means that we will
probably focus mainly on pedigrees, but (a) a pedigree can be as small
as a pair of individuals, or as large as ...?, and
(b) in a structured/subdivided population
the patterns of relationship among individuals are also of interest.
The text for the course is:
- Thompson, E. A. (2000)
Statistical Inferences from Genetic Data on Pedigrees
NSF-CBMS Regional Conference Series in Probability and Statistics. Volume 6.
IMS, Beachwood, OH.
Many of you should have this book from Stat550: I have one or two copies
to LEND to others.
Knowledge of Chapters 1-4 will be assumed. The course
will be based broadly
on Chapters 5-11, with additional more recent material.
Other useful recent books include (parts of):
- Donnelly P. and Tavare S. (1997)
Progress in Population Genetics and Human Evolution
IMA Volumes in Mathematics and its Applications, Vol 87.
Springer-Verlag, New York.
- Ewens, W.J. (2004)
Mathematical Population Genetics. I. Theoretical Introduction.
(2 nd. edition) Springer-Verlag.
(Actually this one is not so relevant, but I include it as it is such a neat
book. If planning the course now, I might have decided to do it on this
area instead, with this book, -- another time...)
- Gilks, W. R., S. Richardson and D.J.Spiegelhalter (eds) (1996)
Markov Chain Monte Carlo in Practice.
Chapman and Hall, New York
- Lange, K. (1997)
Mathematical and Statistical Methods for Genetic Analysis.
Springer Verlag, New York.
- Speed, T. and Waterman, M. S. (1996)
Genetic Mapping and DNA Sequencing.
IMA Volumes in Mathematics and its Applications, Vol 81.
Springer-Verlag, New York.
To request disability accommodations, contact the Office of the ADA Coordinator ten days in advance of the event. 543-6450 (voice); 543-6452 (TDD); 685-3885 (FAX); access@u.washington.edu (email).