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Course Structure
Wellcome Trust Doctoral Programme in Genomic Medicine and Statistics
Structure of the four year graduate degrees in genomic
medicine and statistics
The first year of the
PhD programme will contain a series of modules covering
fundamental topics in genetics, statistical modelling and
inference, computation and epidemiology. By way of example, a
week within a course would start with a lecture, followed by a
short problem sheet and a discussion. Students would then be
given a directed reading, leading to an extended and structured
discussion. They would then be given a computer-based practical
on the taught material that would last the rest of the week,
with a guest lecture on a related topic to break up the period.
The week would finish with a discussion of the results of and
issues raised by the practical.
A laptop will be
provided to all students on the course.
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Term one (10 weeks):
Fundamental concepts in
genetics
The central dogma, the structure of genes, molecular
evolution, the selfish gene concept, protein structure and
function, networks, genetic disease, cancer, ethics in genetics. Statistical modelling
and probability theory. Basic probability, distributions, limit
theorems and their application, Markov chains, stochastic
simulation, likelihood, Bayes theorem, theoretical concepts in
statistics. |
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Term 2 (10 weeks):
Systems analysis and
complex disease
The aetiology and genetic dissection of
complex disease in humans and animal models, illustrated by
recent case studies. T he common-disease-common variant
hypothesis and the theory of sporadic mutations. Principles of
genetic association, whole-genome association, candidate gene
studies (including resequencing), copy number variants. Gene
expression traits and their relation to medical phenotypes.
Systems biology in relation to gene networks. Genetic epidemiology and
statistical inference. Methods used in epidemiological research
and how to draw causal inferences. Measures of disease
frequency, measures of effect, cross sectional studies, case
control studies, cohort studies, randomized controlled trials.
Exploratory data analysis, graphical representation, basic data
summary, linear modelling, generalised linear modelling, model
selection, model criticism, nonparametric statistics.
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Term 3 (10 weeks):
Bioinformatics and
computing
The use of computational and statistical approaches
in biology: sequence database searching, in silico gene and SNP
discovery, in silico protein function prediction. Software
packages and databases for genetic analysis. Programming in Perl
and SQL. Graphical models and
Monte Carlo Bayesian inference. Building statistical models,
hidden Markov models and their application, Markov Chain Monte
Carlo (including Gibbs sampling, the Metropolis-Hastings
algorithm, advanced MCMC).
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Friday
sessions:
Case studies
We will
take specific examples to examine and explore the various
decision processes and analysis techniques involved during a
project’s lifetime. This will include a detailed examination of
issues in experimental design, power studies, sample collection,
platform choice, signal normalisation, analysis, quality
control, validation. Examples will be drawn from HapMap, WTCCC,
MolPAGE and ongoing projects at the Gene Centre. Programming in C++ and
Perl. Object-oriented programming, classes, inheritance, the
standard template library, specialised libraries, debugging,
graphical user interfaces, writing and documenting code for
release.
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Summer period:
Following the taught
courses, students will undertake two short research projects
(seven weeks each) before making a decision about their final
research project (which is likely to be a continuation of one
project). All students will have to undertake at least one
project in collaboration with an experimental group. Their PhD
research will start in October of their second year.
Descriptions of
potential short projects will be circulated during term three.
Students will be encouraged to take advantage of the modular
structure of other relevant courses in the University to acquire
additional training wherever necessary.
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