Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 143-155). Also available in print.

The overall goal of this thesis was to improve the accuracy of genomic prediction in dairy and beef cattle by developing, evaluating and enhancing novel or existent models and approaches for genomic selection. Four studies were conducted to fulfill this goal. In the first study, the impact of using genotypes imputed from low density panels for genomic prediction was evaluated and compared between a Bayesian mixture model and the Genomic Best Linear Unbiased Prediction (GBLUP) method. Results showed that for traits affected by a few large QTL, the Bayesian mixture model resulted in greater reduction in accuracy of genomic prediction, compared to GBLUP. However, for all SNP panels, scenarios and all traits studied, the Bayesian mixture model produced greater or similar accuracy, compared to the GBLUP method. In the second study, a new computing algorithm, called right-hand side updating strategy (RHSU), was proposed and compared to the conventional Gauss-Seidel residual update algorithm (GSRU) for genomic prediction. Results showed that RHSU would outperform GSRU once the sample size exceeded a fraction of the number of the SNPs. As the sample size continued to grow, the RHSU algorithm became more efficient than GSRU. In the third study, three different strategies of forming a training population for genomic prediction, within-breed, across-breed and pooling data from different breeds, were evaluated in Angus and Charolais steers using phenotypes on residual feed intake (RFI) and genotypes on the Illumina BovineSNP50 Beadchip (50k). Results suggested that using the 50k SNP panel, within-breed genomic prediction was a safe strategy; across-breed prediction resulted in the lowest accuracy; pooling data from different breeds had a potential to improve the accuracy but should be conducted with caution due to possible loss of accuracy. In the last study, a multi-task Bayesian learning model was proposed for multi-population genomic prediction. The performance of the multi-task model was evaluated in Holstein and Ayrshire dairy breeds. Results showed that the multi-task Bayesian learning model is effective and could be beneficial to smaller populations where only a limited number of training animals are available.

Introduction: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic diseases. However, warfarin use is complicated by its narrow therapeutic range and inter-individual variability in the starting dose required to achieve a stable international normalised ratio (INR). Warfarin is initiated clinically at 5mg/day then subsequent doses are adjusted accordingly to achieve a stable targeted INR. However, inter-individual variability in response to the warfarin starting dose has been observed and this is reported to be attributed to by various genetic and nongenetic factors. Non-genetics factors implicated in the warfarin dose variability include age, gender, body weight, comorbidities and concomitant drugs. Genetic factors affecting warfarin dose variability include variation in genes encoding the warfarin metabolising enzymes and targeted proteins. Genetic variants in CYP2C9 and VKORC1 have been extensively studied on how they affect warfarin dose variability, culminating in several dosing algorithms incorporating genetic (i.e., CYP2C9*2, CYP2C9*3 and VKORC1 g.-1639G>A) and non-genetic factors (i.e., age, body surface area, amiodarone, race, targeted INR, smoking and thromboembolism). However, these studies have often excluded African populations, therefore missing variants that might be important in the prediction of warfarin doses among Africans. Data on variants that specifically affect warfarin dose variability among Africans is lacking, with no dosing algorithms tailored specifically for Africans developed to date. Thus, the main aim of the study is to conduct a comprehensive evaluation of important genetic and non-genetic factors affecting warfarin response, and further make recommendations on variables important for the development of appropriate algorithms for warfarin dosing among black Africans and the Mixed Ancestry population group in Southern Africa. Method: A total of 302 black Africans and 277 Mixed Ancestry adults undergoing warfarin treatment were recruited at INR clinics in the Western Cape Province, South Africa and Harare, Zimbabwe. Their DNA samples were extracted and utilised for downstream analyses. A total of 73 candidate variants involved in either pharmacokinetics or pharmacodynamics of warfarin, were genetically characterised using a combination of allelic discrimination, Sanger sequencing, restriction fragment length polymorphism and iPLEX PGx74 Mass Array platform. Various statistical packages in STATA, R, haploview and plink were employed to determine frequency distribution, linkage disequilibrium and haplotype mapping of the studied genetic variants. Furthermore, genetic and non-genetic variables were correlated with warfarin maintenance dose and their cumulative effect on warfarin dose variability measured through a multivariate step-wise regression analysis in both the black African and Mixed Ancestry cohorts. Whole exome sequencing was done using the ion torrent Sequence ion S5 system in selected black African individuals presenting with extreme phenotypes (i.e., very low dose or very high dose) but who did not harbour variants known to significantly affect warfarin dose requirements. A workflow which applied various bioinformatics tools was employed for the analyses of the resultant raw BAM files, subsequently, population structure and frequency distribution patterns were described among our cohort and individuals in the 1000 Genomes project. Specific variants identified through WES were prioritised according to clinical significance and further genotyped in an enhanced sample size of 252 black Africans, to confirm their effect on warfarin dose requirements.

published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

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