The subject area for this thesis is detection of chromosomal regions or QTL causing
complex variation at the phenotypic level. In particular, the differentiation of sources
of additive and non additive variation. Unlike QTL mapping using divergent or inbred
lines, this study aims to explore methods within populations, facilitating direct
application of techniques such as marker assisted selection. Specifically, objectives
were to evaluate a linear model or variance components (VC) approach to explore the
existence and magnitude of variation caused by additive, dominant and imprinted
QTL segregating in general pedigrees. This has been achieved by combining
extensive simulation and analysis of real commercial poultry data. Linear models
were constructed to simultaneously estimate fixed, polygenic and QTL effects.
Different genetic models were compared by hierarchical extension to incorporate
more variance components, and likelihood ratio test statistics derived from the
comparison of full with reduced or null models. A range of additive, dominant and
imprinted QTL effects were simulated within two-generation poultry, pig and human
type pedigrees. Effects of family size and structure on power, accuracy of variance
component estimation, and distribution of the test statistic, were evaluated. Empirical
thresholds were derived by simulating populations under the null hypotheses for each
type of simulated pedigree and permutation analysis in real data. In the commercial
poultry data, dominant and imprinted QTL effects were found for bodyweight and
conformation score. Under simulation, although power to detect QTL effects was high
in two-generation livestock pedigrees, considerable variation was found in power and
behaviour of test statistics. Power to detect dominance was greater in pig and poultry
than human type pedigrees with theoretical thresholds increasingly conservative as the
number of dams per sire decreased, highlighting the need for empirical derivation of
the critical test statistic. The detection of variance caused by imprinted genes and in
particular estimates of variance components were also heavily dependent upon the
number of sire and dam families used to estimate them. Results showed that VC
analysis can be used to routinely detect genetic effects including imprinting and
dominance in complex pedigrees. The work presented is the most extensive
evaluation of the detection of non additive QTL using VC methods to date. Results
challenge standard assumptions made about power and null distributions and show
that optimal use of methodology is dependent on pedigree structure
