SEMINAR IN LEICESTER
There will be a seminar at 4.30 pm THIS THURSDAY,
22nd June 2000 in Room G20, Department of Epidemiology
and Public Health, 22-28 Princess Rd West, Leicester.
Dr Nuala Sheehan will speak on:
'COMPUTATIONAL PROBLEMS IN GENETIC ANALYSES ON VERY
LARGE COMPLEX PEDIGREES'
Nuala is to join our Department early next month - and
is currently at the Department of Mathematical Sciences,
Loughborough University,
All those interested are welcome to attend.
This seminar is part of the Nottingham and Leicester
Universities Statistical Seminars series.
If you are travelling from outside Leicester,
please contact Eve Kilbourne (email: [log in to unmask]
or Tel: 0116-252-3276),
Abstract
=======
Analyses of genetic data observed on groups of related
individuals frequently require the computation of
probabilities on pedigrees. Exact methods are
computationally intensive and often impossible on large and
complex networks. Simulation of genes descending a pedigree
is easily done, but simulation in the presence of observed
genetic data on current individuals is far harder. The
number of possible genotypic configurations on a large
pedigree can be immense and the proportion that are
compatible with observed data minute. Markov chain Monte
Carlo provides a method of sampling from the space of
genotypic configurations simulated under a genetic model
conditional upon observed data and estimates of required
probability and likelihood functions can be obtained. The
underlying Markov chaim is irreducible for most traits
determined by a diallelic locus but this may not be the
case when a multiallelic locus is involved. Proposed
solutions which do not identify all the noncommunicating
classes of genotypic configurations are often inefficient
with highly polymorphic loci. This is a particularly
serious problem in linkage analysis, because highly
polymorphic markers are much more informative and thus are
preferred. For example, the mapping of a quantitative
trait locus from incomplete marker data on a large pedigree
can pose enormous computational problems.
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