Topics in Human Gene Mapping
Azra Kurbasic
Centre for Mathematical Sciences
Mathematical Statistics
Lund University
2007
ISBN 9789162870454
LUNFMS10182006

Abstract:

This thesis is interdisciplinary between Mathematical Statistics, Genetics,
and Medicine. It mainly consists of topics in mathematical modelling of the
correlation of inheritance of genes and disease in a family, a method called
linkage analysis. It is organized as follows. First, a short introduction
with the relevant background is given and then four papers are included.


The first paper discusses hypothesis testing of linkage of a disease gene
to a certain position on the chromosome. The focus is on the choice of lod
scores and its relation to pvalues. The second paper is a result of
collaboration with the research groups in Lund and Denmark in the effort
to localize the gene responsible for a malignant melanoma. Here, the theory
presented in the first paper is used. The third paper concerns modelling
of complex diseases, i.e. diseases governed by genetic contribution from
at least two loci. We have studied the contribution of a particular locus
to increased risk of relatives compared with population prevalence. Relative
risk is modelled as the product of the relative risk at the main locus and
the relative risk due to genetic contribution from other loci and shared
environmental effects. Additionally, we show how this relative risk is related
to probabilities of allele sharing identical by descent at the main locus
and the power to detect linkage. The last paper contributes to the development
of the algorithms used in the linkage and family based association analysis.
One of the most demanding issues in these analyses is how to calculate the
inheritance distribution at a certain position on the chromosome. The well
established algorithms are based on the assumption that the markers used
in the studies are in linkage equilibrium (LE). However, today's marker data
have markers in linkage disequilibrium (LD). We develop a novel hidden Markov
model algorithm for association and linkage analysis when markers are in
LD.







