The publication of a draft equine genome sequence and the release by Illumina of a 50,000 marker single-nucleotide polymorphism (SNP) genotyping chip has provided equine researchers with the opportunity to use new approaches to study the relationships between genotype and phenotype. In particular, it is hoped that the use of high-density markers applied to population samples will enable progress to be made with regard to more complex diseases. The first objective of this thesis is to explore the potential for the equine SNP chip to enable such studies to be performed in the horse. The second objective is to investigate the genetic background of osteochondrosis (OC) in the horse. These objectives have been tackled using 348 Thoroughbreds from the US, divided into cases and controls, and a further 836 UK Thoroughbreds, the majority with no phenotype data. All horses had been genotyped with the Illumina Equine SNP50 BeadChip. Linkage disequilibrium (LD) is the non-random association of alleles at neighbouring loci. The reliance of many genomic methodologies on LD between neutral markers and causal variants makes it an important characteristic of genome structure. In this thesis, the genomic data has been used to study the extent of LD in the Thoroughbred and the results considered in terms of genome coverage. Results suggest that the SNP chip offers good coverage of the genome. Published theoretical relationships between LD and historical effective population size (Ne) were exploited to enable accuracy predictions for genome-wide evaluation (GWE) to be made. A subsequent in-depth exploration of this theory cast some doubt on the reliability of this approach in the estimation of Ne, but the general conclusion that the Thoroughbred population has a small Ne which should enable GWE to be carried out efficiently in this population, remains valid. In the course of these studies, possible errors embedded within the current sequence assembly were identified using empirical approaches. Osteochondrosis is a developmental orthopaedic disease which affects the joints of young horses. Osteochondrosis is considered multifactorial in origin with a variety of environmental factors and heredity having been implicated. In this thesis, a genome-wide association study was carried out to identify quantitative trait loci (QTL) associated with OC. A single SNP was found to be significantly associated with OC. The low heritability of OC combined with the apparent lack of major QTL suggests GWE as an alternative approach to tackle this disease. A GWE analysis was carried out on the same dataset but the resulting genomic breeding values had no predictive ability for OC status. This, combined with the small number of significant QTL, indicates a lack of power which could be addressed in the future by increasing sample size. An alternative to genotyping more horses for the 50K SNP chip would be to use a low-density SNP panel and impute remaining genotypes. The final chapter of this thesis examines the feasibility of this approach in the Thoroughbred. Results suggest that genotyping only a subset of samples at high density and the remainder at lower density could be an effective strategy to enable greater progress to be made in the arena of equine genomics. Finally, this thesis provides an outlook on the future for genomics in the horse.

Made available in DSpace on 2015-03-26T13:55:13Z (GMT). No. of bitstreams: 1 texto completo.pdf: 833034 bytes, checksum: c1160b57dd7b16db04bc27109bc4f8f1 (MD5) Previous issue date: 2012-07-23 / Fundação de Amparo a Pesquisa do Estado de Minas Gerais / From the beginning of the century, advances in genotyping enabled the development of new classes of markers, among which stand out single nucleotide polymorphisms (SNPs). Due to the availability of these markers it has been proposed genomic selection, consisting of simultaneous analysis of large number of markers distributed throughout the genome; its success depends on the method used for prediction of genomic breeding values. The objective of this study was to compare the methods RR-BLUP and Bayesian LASSO to calculate estimated genomic breeding values (GEBVs) and also to determine which method provides more accurate results for genomic selection in pigs. A total of 622 boarswere genotyped for 2,500 SNPs, and phenotyped for the following traits: concentration of androstenone, concentration of skatole, backfat thickness and loin depth. The R software packages rrBLUP and BLR were used respectively for the implementation of the RR-BLUP method and Bayesian LASSO method. Genetic correlations between the traits were calculated by the correlation between the vectors of GEBVs. The Bayesian LASSO method reached higher accuracy values in three traits: concentration of androstenone (0.65), concentration of skatole (0.58) and loin depth (0.33), and RR-BLUP was more accurate (0.61) for backfat thickness. Genetic correlations calculated, show that exists a small genetic correlation (0.03) between backfat thickness and loin depth. Between the concentrations of androstenone and skatole also exists a genetic correlation (0.24)that is consistent with results from other studies. Thus, concerning to the estimates of effects of markers, for all traits the found peaks were in regions where are reported QTLs inPIGQTLdatabase and other studies. / A partir do início do século XXI, avanços na genotipagem permitiram o desenvolvimento de novas classes de marcadores, entre os quais se destacam os polimorfismos de nucleotídeos simples (SNPs). Devido à disponibilidade desses marcadores, foi proposta a Seleção Genômica, que consiste em uma análise simultânea de um grande número de marcadores distribuídos ao longo do genoma, cujo sucesso depende do método utilizado de predição de valores genéticos genômicos. O objetivo deste estudo foi comparar os métodos RR-BLUP e LASSO Bayesiano para cálculo dos valores genéticos genômicos estimados (GEBVs) e determinar qual método apresenta resultados mais acurados para a seleção genômica em suínos. Foram genotipados 622 suínos machos não castrados para 2.500 SNPs, e fenotipados para as seguintes características: concentração de androstenona, concentração de skatol, espessura de gordura subcutânea e profundidade de lombo. Os pacotes rrBLUP e BLR do software R foram utilizados respectivamente para a implementação do método RR-BLUP e LASSO Bayesiano. As correlações genéticas entre as característicasforam calculadas por meio da correlação entre os vetores de GEBVs. O método LASSO Bayesiano apresentou valores mais elevados de acurácia em três características: concentração de androstenona (0,65), concentração deskatol (0,58), e profundidade de lombo (0,33), e o RR-BLUP foi mais acurado para espessura de gordura subcutânea (0,61). As correlações genéticas calculadas, mostram que existe uma pequena correlação genética entre espessura de gordura subcutânea e profundidade de lombo (0,03). Entre as concentrações de androstenona e skatol também existe correlação genética (0,24) que é consistente com os resultados de outros estudos. Assim, com relação às estimativas de efeitos de marcadores, para todas as características os picos encontrados estão em regiões onde se encontram QTLs relatados no PIGQTLdatabase e em outros estudos.