This thesis looks at risk factors for multiple sclerosis (MS), a chronic, degenerative autoimmune disease which is usually diagnosed between the ages of 20 and 50 years. It is estimated to affect over 100,000 people in the UK. The research setting was Orkney and Shetland, two archipelagos situated north of mainland Scotland, and both of which have very high MS prevalence as do other countries at high latitudes. I examine genetic and environmental risk factors in Orkney and Shetland using multiple methods over four studies. I also review the vitamin D and UV exposure literatures as these are risk factors pertinent to MS in Orkney and Shetland. After devoting three chapters to introducing the purpose of the thesis, MS, and Orkney and Shetland, in the fourth chapter, I aim to establish whether the birthplace of cases show any spatial, temporal, or spatiotemporal clustering. Evidence of these kinds of clustering may indicate that there are environmental risk factors present in some areas or that were present over particular periods, which raise risk of developing MS. Although I find statistically significant temporal, spatial, and spatiotemporal clustering in Orkney, and a spatial cluster in Shetland, for multiple reasons these results need to be interpreted with caution. I conclude that the clusters are very likely to be artefacts. Furthermore, there are multiple possible alternative explanations for such clusters that could not be explored by the available data. Chapter 5 examines the heritability of MS in Orkney and Shetland to estimate the proportion of phenotypic variance attributable to additive genetic effects. I also look at the birthplaces of ancestors of cases and controls to see if any locations contribute a greater amount of ancestral DNA to the gene pool of modern MS cases, which I term ‘genetic clustering’. In Orkney I obtained a heritability estimate of 0.36 (95% CI -0.26, 0.98); in Shetland this estimate was 0.20 (95% CI -1.88, 2.28). These modest estimates are consistent with the heritability literature. The genetic clustering analyses highlight two Orkney registration districts, Kirkwall and Westray, which earlier studies identified as areas of MS clustering. I also identify three Shetland registration districts, however these locations had not shown any evidence of clustering in earlier studies. Again, I advise caution in interpreting results, particularly as all the error bars across registration districts overlap. Chapter 6 presents a scoping review to map the literature and identify evidence of an association between vitamin D and UV exposure with MS. In methodically searching the literature, I identify a large and heterogeneous evidence base comprising multiple observational, intervention, and genetic studies. Overall, many studies support an association between vitamin D deficiency and MS. There is also evidence for an association between UV exposure and MS, although UV exposure is considerably less explored than vitamin D. I finally identify gaps in the literature and make suggestions for future research. In Chapter 7 I aim to compare vitamin D levels in Orkney and mainland Scotland, and establish the determinants of vitamin D status in Orkney. I firstly compare mean vitamin D and prevalence of deficiency in cross-sectional data from studies in Orkney and mainland Scotland. I secondly use multivariable regression to identify factors associated with vitamin D levels in Orkney. I find that mean (standard deviation) vitamin D is significantly higher in Orkney compared to mainland Scotland (35.3 (18.0) and 31.7 (21.2), respectively), and prevalence of severe deficiency is lower in Orkney (6.6% to 16.2% p = 1.1 x 10-15). Factors associated with higher vitamin D in Orkney include older age, farming occupations and foreign holidays. I conclude that although mean vitamin D levels are higher in Orkney compared to mainland Scotland, there is substantial variation within the Orkney population which may influence MS risk. Chapter 8 examines the correlates and determinants of UVB exposure in Shetland. I firstly construct correlation matrices to visualise how 1) personal characteristics such as sex, occupation, and skin type, 2) physical activity, and 3) body weight and fat, correlate with UVB exposure. I then use multivariable regression to identify factors associated with UVB exposure in Shetland. I run two multivariable models. The first includes the full sample size where activity data were measured by questionnaires. The second includes both questionnaire physical activity data and step-count data from pedometers, however as only a subset of participants had been supplied with pedometers, this analysis comprises a smaller sample size. I find that the amount of skin exposed was most strongly correlated with UVB exposure. Step count and activity minutes were also moderately positively correlated, and indoor occupations moderately negatively correlated, with UVB exposure. The regression analysis using the full sample with questionnaire activity data found that factors associated with greater UVB exposure were age and ambient UVB, while working indoors was significantly associated with lower UVB exposure. The model including the pedometer data found that found that age, total steps, and the amount of ambient UVB were significantly associated with greater UVB exposure. I conclude that atmospheric conditions, working outdoors and older age are important factors in UVB exposure in Shetland. It remains to be seen how UVB exposure translates to vitamin D levels in Shetland. I found evidence for environmental and genetic risk factors for MS in Orkney and Shetland. The two environmental risk factors, vitamin D deficiency and reduced UV exposure, are more likely to affect the younger population who are still within their lifetime risk of developing MS.

Submitted by (edna.saturno@ufrpe.br) on 2017-05-18T13:59:13Z No. of bitstreams: 1 Tatiana Neres de Oliveira.pdf: 1588049 bytes, checksum: 1bec51e391cc99844e14b24b53268967 (MD5) / Made available in DSpace on 2017-05-18T13:59:13Z (GMT). No. of bitstreams: 1 Tatiana Neres de Oliveira.pdf: 1588049 bytes, checksum: 1bec51e391cc99844e14b24b53268967 (MD5) Previous issue date: 2007-03-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The work was conducted at the Itambé Experimental Station of the Pernambuco State Agricultural Research Enterprise – IPA, to characterize clones and estimate heritability and character correlation, besides estimate repeatability coefficients and evaluate adaptability and stability of eye spot incidence in Pennisetum sp. clones under pasture. Sixteen Pennisetum sp. clones were studied under pasture at 42 days intervals, and after-grazing residual of 40 cm, under the randomized blocks design, with five replicates. Characters evaluated before grazing were forage availability, average plant height, disease incidence, desirability, uncovered soil, above and below 40 cm forage mass. Residual biomass above and below 40 cm, average plant height and losses were evaluated after grazing. Heritability was estimated on the ample sense, and averages were compared by theTukey test at five percent significance. Clones Taiwan A 25 P 18 (3.8), Pusa Napier 1 P 32 (1.8), SEA P 35 (1.8), SEA P 36 (3.4), SEA P 37 (3.2), RENACE CNPGL 93F41.1 (2.0), hybrids HV 241 (1.8) and Pioneiro (2.4) had the highest Helminthosporium susceptibility notes. Except for leaf production, below 40 cm residual biomass, and after grazing losses, all evaluated variables had highheritability value (64.75 to 93.40%). Choosing higher plants leads to simultaneous selection for higher above 40 cm forage mass, desirability, lower disease incidence and higher soil coverage index. Repeatability coefficients were estimated by: analysis of variance, principal components – correlation matrix, principal components – covariance matrix, and structural analysis – correlation matrix. Maximum heritability for leaf spot was 95 %. Repeatability coefficients by the four methods ranged from 0.75 to 0.78. With regards to genotype adaptability, Mineirão (0.31), Taiwan A 25 P 18 (0.50), SEA P 36 (0.28), SEA P 37 (0.38) and Gigante de Pinda P 73 (0.46) had regression coefficients below population average (β1i), indicating unfavorable environment adaptability. Genotypes Pusa Napier 1 P 25 (1.58), Pusa Napier 1 P 27 (1.55), Pusa Napier 1 P 28 (1.47), Pusa Napier 1 P 32(1.28), Pusa Napier 1 P 33 (1.22), SEA P 35 (1.60), HV 241 (1.41) and Pioneiro (1.36) had better response in favorable environments, and Pusa Napier 1 P 31 and Roxo de Botucatu P 80 had ample adaptability. Hybrid HV 241 and Pioneiro had significant deviations from regression by the F test (P<0.05), indicating instability and unpredictability to environmental alterations. / O trabalho foi realizado na Estação Experimental de Itambé da Empresa Pernambucana de Pesquisa Agropecuária – IPA, objetivando caracterizar clones e estimar a herdabilidade e correlações, além de estimar coeficientes de repetibilidade e avaliar a adaptabilidade e estabilidade de clones de Pennisetum sp. sob pastejo a incidência de mancha ocular. Foram estudados 16 clones de Pennisetum sp. sob pastejo a intervalos de 42 dias e resíduo pós pastejo de 40 cm, num delineamento em blocos ao acaso, com cinco repetições. Os caracteres avaliados no pré pastejo foram disponibilidade de forragem, altura média das plantas, incidência de doença, desejabilidade, solo descoberto, massa de forragem acima e abaixo de 40 cm. Biomassa residual acima e abaixo de 40 cm, altura média das plantas e perdas foram avaliadas no pós pastejo. Os coeficientes de repetibilidade foram estimados por:análise de variância, componentes principais – matriz de correlação, componentes principais – matriz de covariância e análise estrutural – matriz de correlação. A herdabilidade foi estimada no sentido amplo e as médias comparadas pelo teste de Tukey a 5% de probabilidade. Os clones Taiwan A 25 P 18 (3,8), Pusa Napier 1 P 32 (1,8), SEA P 35 (1,8), SEA P 36 (3,4), SEA P 37 (3,2), RENACE CNPGL 93F41.1(2,0), o híbrido HV 241 (1,8) e o Pioneiro (2,4) apresentaram maiores notas para susceptibilidade ao Helminthosporium. Com exceção de produção de folhas, biomassa residual abaixo de 40 cm e perdas pós pastejo, todas as variáveis analisadas apresentaram valores altos para herdabilidade (64,75 a 93,4%). A escolha de plantas mais altas leva à seleção simultânea para maior produção de massa de forragem acima de 40 cm e desejabilidade, menor incidência de doenças e maior índice de cobertura do solo. O valor máximo da herdabilidade para mancha ocular foi de 95%. Os coeficientes de repetibilidade estimados pelos quatro métodos variaram de 0,75 a 0,78. Com relação à adaptabilidade dos genótipos, o Mineirão (0,31), Taiwan A 25 P 18 (0,50), SEA P 36 (0,28), SEA P 37 (0,38) e Gigante de Pinda P 73 (0,46) apresentaram coeficientes de regressão abaixo da média populacional (β1i), indicandoadaptabilidade a ambientes desfavoráveis. Os genótipos Pusa Napier 1 P 25 (1,58), Pusa Napier 1 P 27 (1,55), Pusa Napier 1 P 28 (1,47), Pusa Napier 1 P 32 (1,28), Pusa Napier 1 P 33 (1,22), SEA P 35 (1,60), HV 241 (1,41) e Pioneiro (1,36) responderam melhor em ambientes favoráveis, e o Pusa Napier 1 P 31 e Roxo de Botucatu P 80 apresentaram ampla adaptabilidade. O híbrido HV 241 e o Pioneiro apresentaram desvios significativos da regressão pelo teste F (P<0,05), sugerindo instabilidade e imprevisibilidade às alterações ambientais.

In most breeding programmes breeders use phenotypic data obtained in breeding trials to rank the performance of the parents or progeny on pre-selected performance criteria. Through this ranking the best candidates are identified and selected for breeding or production purposes. Best Linear Unbiased Prediction (BLUP), is an efficient selection method to use, combining information into a single index. Unbalanced or messy data is frequently found in tree breeding trial data. Trial individuals are related and a degree of correlation is expected between individuals over sites, which can lead to collinearity in the data which may lead to instability in certain selection models. A high degree of collinearity may cause problems and adversely affect the prediction of the breeding values in a BLUP selection index. Simulation studies have highlighted that instability is a concern and needs to be investigated in experimental data. The occurrence of instability, relating to collinearity, in BLUP of tree breeding data and possible methods to deal with it were investigated in this study. Case study data from 39 forestry breeding trials (three generations) of Eucalyptus grandis and 20 trials of Pinus patula (two generations) were used. A series of BLUP predictions (rankings) using three selection traits and 10 economic weighting sets were made. Backward and forward prediction models with three different matrix inversion techniques (singular value decomposition, Gaussian elimination - partial and full pivoting) and an adapted ridge regression technique were used in calculating BLUP indices. A Delphi and Clipper version of the same BLUP programme which run with different computational numerical precision were used and compared. Predicted breeding values (forward prediction) were determined in the F1 and F2 E. grandis trials and F1 P. patula trials and realised breeding performance (backward prediction) was determined in the F2 and F3 E. grandis trials and F2 P. patula trials. The accuracy (correlation between the predicted breeding values and realised breeding performance) was estimated in order to assess the efficiency of the predictions and evaluate the different matrix inversion methods. The magnitude of the accuracy (correlations) was found to mostly be of acceptable magnitude when compared to the heritability of the compound weighted trait in the F1F2 E. grandis scenarios. Realised genetic gains were also calculated for each method used. Instability was observed in both E. grandis and P. patula breeding data in the study, and this may cause a significant loss in realised genetic gains. Instability can be identified by examining the matrix calculated from the product of the phenotypic covariance matrix with its inverse, for deviations from the expected identity pattern. Results of this study indicate that it may not always be optimal to use a higher numerical precision programme when there is collinearity in the data and instability in the matrix calculations. In some cases, where there is a large amount of collinearity, the use of a higher precision programme for BLUP calculations can significantly increase or decrease the accuracy of the rankings. The different matrix inversion techniques particularly SVD and adapted ridge regression did not perform much better than the full pivoting technique. The study found that it is beneficial to use the full pivoting Gaussian elimination matrix inversion technique in preference to the partial pivoting Gaussian elimination matrix inversion technique for both high and lower numerical precision programmes. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Genetics / unrestricted