UNIVERSITY OF ST ANDREWS
Statistics Seminars - Candlemas Semester (Term 3) 2000
____________________________________________________________
WEDNESDAY 10 MAY at 3.15 p.m.
Dr Christine HACKETT (BioSS - Scottish Crop Research Institute)
"Statistical methods for linkage analysis and QTL mapping in plants"
WEDNESDAY 10 MAY at 4.45 p.m.
Professor David BALDING (University of Reading)
"Genealogical Modelling to Locate Disease Genes from Case-Control Data"
MONDAY 15 MAY at 4 p.m.
Dr Peter JUPP (University of St Andrews)
"Improving inferential accuracy through intermediate asymptotics"
____________________________________________________________
All the seminars will be held in Lecture Theatre B of the Mathematical
Institute. Tea will be available from 3.45 p.m. on May 15, and between the
talks on May 10. Visitors will be very welcome.
Further information from:
Dr I B J Goudie email: [log in to unmask]
____________________________________________________________
THE MEETING ON 10 MAY IS A JOINT MEETING WITH THE HIGHLANDS GROUP OF THE
ROYAL STATISTICAL SOCIETY.
The meeting will be followed by a meal, the arrangements for which are
available from Ian Goudie (e-mail address as above). If you intend to come
to the meal, it would be helpful if you would inform Ian Goudie, preferably
by e-mail, by noon on Monday 8 May.
____________________________________________________________
SEMINAR ABSTRACTS
Dr Christine HACKETT (BioSS - Scottish Crop Research Institute)
"Statistical methods for linkage analysis and QTL mapping in plants"
The association between genetics and statistics is long-standing
and many important statistical concepts were developed, for example by
Galton, Pearson and above all Fisher, in response to questions motivated by
genetics. There were many important developments in statistical genetics
in the period 1908-1940's, but analyses from this period tend to involve
characters affected by a single gene. Because there are a relatively small
number of such characters, practical analysis was limited. In the last 25
years, developments in molecular biology mean that variation can be
observed in the DNA of an organism, giving a virtually unlimited supply of
molecular markers whose inheritance can be followed.
One use of molecular markers is to develop a linkage map of a
species, with positions along each chromosome labelled by molecular
markers. Such a map enables the geneticist to locate genes controlling
important traits relative to the markers, particularly quantitative traits
i.e. those controlled by a large number of genes, and affected by the
environment. For quantitative traits, e.g yield or height, a continuous
response is observed, and the effects of the individual genes (referred to
as quantitative trait loci or QTLs) cannot be observed directly. Mapping
studies have been performed in man, domestic animals and agricultural and
forest crops. The ease with which experimental crosses can be made and
large numbers of offspring raised mean that agricultural crops are the
simplest subjects for mapping studies.
This talk will review the modelling of recombination between
molecular markers, which forms the basis for estimating a linkage map.
>From there, statistical methods for QTL mapping will be discussed. These
are based on mixture models, and thresholds for significance testing need
to considered carefully. Some current work on QTL analysis for multiple
traits and multiple environments will be described.
Professor David BALDING (University of Reading)
"Genealogical Modelling to Locate Disease Genes from Case-Control Data"
Many methods for locating disease genes are based on genetic and
phenotypic data from extended families affected by the disease. However,
these are limited in their accuracy by the small number of recombination
events underlying even a very large family tree. In contrast, the
genealogical tree underlying a population sample of case chromosomes offers
many more recombinations, but the problem now is that the tree is unknown.
This is problematic because the case chromosomes are not mutually
independent; for example, some but not others will have been affected by
particular mutation or recombination events in the past. Drawing valid
inferences from these data about disease gene location requires an
assessment of the patterns of dependence in the data due to sharing of
ancestry, which in turn requires accounting for the effects of the
underlying genealogical tree. Much of the recent literature simply ignores
the dependence problem, leading to over-optimistic assessments of
uncertainty in the inferred location. Recently some authors have offered
approximations to take the dependence into account. We propose explicitly
modelling the genealogical tree underlying the sample of case chromosomes,
within the coalescent modelling framework. We implement Bayesian inference
under the model via Markov chain Monte Carlo. This is joint work with
Andrew Morris and John Whittaker, and is funded by Pfizer UK.
Dr Peter JUPP (University of St Andrews)
"Improving inferential accuracy through intermediate asymptotics"
Much standard parametric inference is based on normal
approximations which are valid when either the sample size is large or the
data are highly concentrated. The accuracy of some of these approximations
can be increased (sometimes quite dramatically) to make them useful for
small samples and quite dispersed data, provided that the statistics used
are modified appropriately.
This talk will describe simple modifications of the likelihood
ratio and score statistics which bring their null distributions close to
their asymptotic chi-squared distributions. If time permits, I shall
discuss also modifications of Wald tests which make them invariant under
change of parameterisation. I shall endeavour not to mention the
underlying differential geometry.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|