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Document Description
Title
Bayesian
analysis
of
mixture
models
with
application
to
genetic
linkage
Author
Fang
,
Fang
,
1983-
Description
Thesis
(M.A.S)--Memorial
University
of
Newfoundland
,
2010.
Mathematics
and
Statistics
Date
2010
Pagination
viii, 52 leaves
Subject
Bayesian
statistical
decision
theory;
Linkage
(Genetics)--Mathematical
models;
Markov
processes--Numerical
solutions;
Monte
Carlo
method;
Degree
M.A.S
Degree Grantor
Memorial University of Newfoundland. Dept. of Mathematics and Statistics
Discipline
Mathematics and Statistics
Language
Eng
Notes
Includes
bibliographical
references
(leaves
47-52)
Abstract
Through
an
application
to
genetic
linkage
analysis
, this
project
describes
how the
Bayesian
approach
can
be
used
for the
mixture
model
with an
unknown
number
of
components.
Genetic
linkage
analysis
based
on a
complex
model
can
be
difficult
to
manage
when
a
large
number
of
markers
loci
and/or
large
pedigrees
are
involved
,
due
to
computation
limitations.
However
,
Markov
chain
Monte
Carlo
(MCMC)
schemes
are
one
alternative
,
utilizing
a
reversible
jump
steps
that
allow
change
on the
dimension
of
parameter
space.
Thus
, the
MCMC
samplers
with a
different
numbers
of
quantitative
trait
loci
based
on
complex
large
pedigrees
can
be
obtained
using
reversible
jump
MCMC
methodology.
The
application
of the
MCMC
scheme
is
illustrated
with a
case
study
of
genetic
linkage
to
hypercalciuria.
This
analysis
report
found
strong
evidence
for
linkage
of
hypercalciuria
to
calibrated
estimates
of
Bayes
factors
, the
so-called
L-Scores.
To
my
knowledge
this
is
the
first
time
that
urinary
calcium
excretion
has been
clearly
linked
to a
narrow
region
of the
genome.
Nevertheless
,
further
study
is
needed
to
confirm
this
finding.
Type
Text
Resource Type
Electronic
thesis
or
dissertation
Format
Image/jpeg;
Application/pdf
Source
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries
Local Identifier
a3475041
Rights
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.
Collection
Electronic
Theses
and
Dissertations
Scanning Status
Completed
PDF File
(6.01
MB)
--
http://collections.mun.ca/PDFs/theses/Fang_Fang.pdf
CONTENTdm file name
31693.cpd
