Diss. (sammanfattning) Svalöv : Sveriges lantbruksuniv., 1999. / Härtill 7 uppsatser.

Methods of calculating the inbreeding coefficient In a finite population undergoing recurrent selection (self-select-intercross in succeeding generations) were investigated. It was noted that, in a population under selection, the inbreeding coefficient does not provide the experimenter with a measure of expected degree of variability; instead an index of total heterozygosity is required, and such an Index was derived. Formulas necessary to calculate both the inbreeding coefficients and the heterozygosity indexes were derived for the cases: one-locus, two-allele, random selection; k independent loci and random selection; one-locus, two-allele and effective directional selection; and k linked loci with effective directional selection. These formulas Involved defining a generalized inbreeding coefficient and a generalized index of homozygosity (or heterozygosity) in terms of vectors whose components reflected the various possible patterns of genes identical by descent at a given stage of the recurrent selection breeding program. Formulas were derived whereby the mean and the variance of the total number of loci homozygous (or heterozygous) by descent or in state may be obtained. The progress of the panmictic index and/or the index of total heterozygosity through at least twenty-five cycles of recurrent selection was observed in computer-simulated populations ranging in sizes from ten through one hundred, assuming varying recombination probabilities both in the one-locus and in the two linked-loci case and assuming both minimum and maximum inbreeding selection patterns. Tables resulting from these simulated studies could be used to estimate minimum and maximum inbreeding coefficients and/or minimum and maximum heterozygosity indexes in experimental populations for which the initial conditions approximate those assumed in the simulated populations. It was observed that the coefficient of relationship in the source population was extremely important in tracing the progress of the degree of Inbreeding and/or total homozygosity, that linkage played a major role in promoting heterozygosity in a recurrent selection system, and that careful intercrossing rather than random mating in alternate generations of the recurrent selection cycle was important in promoting maximum heterozygosity in the selected population. In the simulated populations the effect of small population sizes was observed and, in general, indications were that unless more than five complete recurrent cycles are contemplated, increasing population size results In only relatively minor increases in panmixia, especially when linked loci are involved in the selected trait and when care Is taken to avoid a maximum inbreeding selection pattern. / Ph. D.

[Truncated abstract] This thesis investigates novel methods for the genetic association analysis of haplotype data in samples of unrelated individuals, and applies these methods to the analysis of coronary heart disease and related phenotypes. Determining the inheritance pattern of genetic variants in studies of unrelated individuals can be problematic because family members of the studied individuals are often not available. For the analysis of individual genetic loci, no problem arises because the unit of interest is the observed genotype. When the unit of interest is the linear combination of alleles along one chromosome, inherited together in a haplotype, it is not always possible to determine with certainty the inheritance pattern, and therefore statistical methods to infer these patterns must be adopted. Due to genotypic heterozygosity, mutliple possible haplotype configurations can often resolve an individual's genotype measures at multiple loci. When haplotypes are not known, but are inferred statistically, an element of uncertainty is thus inherent which, if not dealt with appropriately, can result in unreliable estimates of effect sizes in an association setting. The core aim of the research described in this thesis was to develop and implement a general method for haplotype-based association analysis using multiple imputation to appropriately deal with uncertainty haplotype assignment. Regression-based approaches to association analysis provide flexible methods to investigate the influence of a covariate on a response variable, adjusting for the effects of other variables including interaction terms. ... These methods are then applied to models accommodating binary, quantitative, longitudinal and survival data. The performance of the multiple imputation method implemented was assessed using simulated data under a range of haplotypic effect sizes and genetic inheritance patterns. The multiple imputation approach performed better, on average, than ignoring haplotypic uncertainty, and provided estimates that in most cases were similar to those observed when haplotypes were known. The haplotype association methods developed in this thesis were used to investigate the genetic epidemiology of cardiovascular disease, utilising data for the cholesteryl ester transfer protein gene (CETP), the hepatic lipase (LIPC) gene and the 15- lipoxygenase (ALOX15) gene on a total of 6,487 individuals from three Western Australian studies. Results of these analyses suggested single nucleotide polymorphisms (SNPs) and haplotypes in the CETP gene were associated with increased plasma high-density lipoprotein cholesterol (HDL-C). SNPs in the LIPC gene were also associated with increased HDL-C and haplotypes in the ALOX15 gene were associated with risk of carotid plaque among individuals with premature CHD. The research presented in this thesis is both novel and important as it provides methods for the analysis of haplotypic associations with a range of response types, while incorporating information about haplotype uncertainty inherent in populationbased studies. These methods are shown to perform well for a range of simulated and real data situations, and have been written into a statistical analysis package that has been freely released to the research community.