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Thesis (Ph.D)--University of Tennessee Health Science Center, 2008. / Title from title page screen (viewed on January 6, 2009). Research advisor: Ann K. Cashion, Ph.D. Document formatted into pages (x, 102 p. : ill.). Vita. Abstract. Includes bibliographical references (p. 81-88).

The serotonergic and cholinergic systems are jointly involved in regulating sleep but this balance is theorized to be disturbed in depressed individuals (Janowsky 1972, Jouvet 1972). One potential cause of disturbed neurotransmission is genetic predisposition. The G(-1019) allele of the 5-HT1A receptor predicts an increased risk for depression compared to the wild-type C(-1019) allele. The goal of this study was to use pharmacological probes in normal controls to model the serotonergic/cholinergic imbalance of depression and its associated abnormalities in sleep structure while controlling for 5-HT1A receptor genotype. Seventeen healthy female participants homozygous for either C (n=11) or G (n=6) alleles, age 18-27 years were tested on four non-consecutive nights. Participants were given galantamine (an anti-acetylcholinesterase), buspirone (a serotonergic agonist), both drugs together, or placebos before sleeping. Buspirone suppressed tonic REM: there was a significant increase in REM latency (p<0.001). Galantamine increased tonic REM sleep, leading to more time spent in stage REM (p<0.001) and shorter REM latency (p<0.01). Galantamine and buspirone given together tended to negate the effects of each other on REM sleep measures but disrupted sleep more than either drug alone, showing lower SE and N3% and increased awakenings, Wake% and N1% (p<0.019). There was no main effect of genotype nor was there a significant multivariate interaction between genotype and drug condition. These findings are partially consistent with the literature about sleep in depression, notably short REM latency, higher percentage of total sleep time spent in REM, and increased sleep fragmentation. The C/G mutation in the 5-HT1A receptor does not appear to cause noticeable differences in the sleep patterns of healthy young females.

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 Irene Nascimento (irene.kessia@ufpe.br) on 2016-10-18T19:17:10Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao -Gilderlanio Santana de Araujo.pdf: 9533988 bytes, checksum: 951b1cf090729a87ebf3a8741ff00ad4 (MD5) / Made available in DSpace on 2016-10-18T19:17:10Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao -Gilderlanio Santana de Araujo.pdf: 9533988 bytes, checksum: 951b1cf090729a87ebf3a8741ff00ad4 (MD5) Previous issue date: 2013-03-07 / FACEPE / O desenvolvimento de técnicas de genotipagem de baixo custo (SNP arrays) e as anotações de milhares de polimorfismos de nucleotídeo único (SNPs) em bancos de dados públicos têm originado um crescente número de estudos de associação em escala genômica (do inglês, Genome-Wide Associations Studies - GWAS). Nesses estudos, um enorme número de SNPs (centenas de milhares) são avaliados com métodos estatísticos univariados de forma a encontrar SNPs associados a um determinado fenótipo. Testes univariados são incapazes de capturar relações de alta ordem entre os SNPs, algo comum em doenças genéticas complexas e são afetados pela alta correlação entre SNPs na mesma região genômica. Métodos de aprendizado de máquina, como o Random Forest (RF), têm sido aplicados em dados de GWAS para realizar a previsão de riscos de doenças e capturar os SNPs associados às mesmas. Apesar de RF ser um método com reconhecido desempenho em dados de alta dimensionalidade e na captura de relações não-lineares, o uso de todos os SNPs presentes em um estudo GWAS é computacionalmente inviável. Neste estudo propomos o uso de redes biológicas para a seleção inicial de SNPs candidatos a serem usados pela RF. A partir de um conjunto inicial de genes já relacionados à doença na literatura, usamos ferramentas de redes de interação gene-gene, para encontrar novos genes que possam estar associados a doença. Logo, é possível extrair um número reduzido de SNPs tornando a aplicação do método RF viável. Os experimentos realizados nesse estudo concentram-se em investigar quais polimorfismos podem influenciar na suscetibilidade à doença de Alzheimer (DA) e ao comprometimento cognitivo leve (MCI). O resultado final das análises é a delineação de uma metodologia para o uso de RF, para a análise de dados de GWAS, assim como a caracterização de potenciais fatores de riscos da DA. / The development of low cost genotyping techniques (SNP arrays) and annotations of thousands of single nucleotide polymorphisms (SNPs) in public databases has led to an increasing number of Genome-Wide Associations Studies (GWAS). In these studies, a large number of SNPs (hundreds of thousands) are evaluated with univariate statistical methods in order to find SNPs associated with a particular phenotype. Univariate tests are unable to capture high-order relationships among SNPs, which are common in complex genetic diseases, and are affected by the high correlation between SNPs at the same genomic region. Machine learning methods, such as the Random Forest (RF), have been applied to GWAS data to perform the prediction of the risk of diseases and capture a set of SNPs associated with them. Although, RF is a method with recognized performance in high dimensional data and capacity to capture non-linear relationships, the use of all SNPs present in GWAS data is computationally intractable. In this study we propose the use of biological networks for the initial selection of candidate SNPs to be used by RF. From an initial set of genes already related to a disease based on the literature, we use tools for construct gene-gene interaction networks, to find novel genes that might be associated with disease. Therefore, it is possible to extract a small number of SNPs making the method RF feasible. The experiments conducted in this study focus on investigating which polymorphisms may influence the susceptibility of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This work presents a delineation of a methodology on using RF for analysis of GWAS data, and characterization of potential risk factors for AD.

(Truncated abstract) The aims of this thesis are: (i) To estimate the prevalence of psychiatric illness among persons with intellectual disability and, conversely, the prevalence of intellectual disability among persons with a psychiatric illness; (ii) To describe the disability and service utilisation profile of persons with conjoint disorder; (iii) To examine, in particular, intellectual disability co-occurring with schizophrenia; and (iv) To explore the role of hereditary and environmental (specifically obstetric) risk factors in the aetiology of (i) intellectual disability and (ii) intellectual disability co-occurring with psychiatric illness. This thesis has a special interest in the relationship between intellectual disability and schizophrenia. Where data and sample sizes permit, it explores that relationship at some depth and has included sections on the putative nature of the link between intellectual disability and schizophrenia in the introductory and discussion chapters. To realise its objectives, the thesis comprises a core study focusing on aims (i) – (iii) and a supplementary study whose focus is aim (iv). It also draws on work from an ancillary study completed prior to the period of candidacy...This thesis found that, overall, 31.7% of persons with an intellectual disability had a psychiatric illness; 1.8% of persons with a psychiatric illness had an intellectual disability. The rate of schizophrenia, but not bipolar disorder or unipolar major depression, was greatly increased among cases of conjoint disorder: depending on birth cohort, 3.7-5.2% of individuals with intellectual disability had co-occurring schizophrenia. Down syndrome was much less prevalent among conjoint disorder cases despite being the most predominant cause of intellectual disability while pervasive developmental disorder was over-represented. Persons with conjoint disorder had a more severe clinical profile including higher mortality rates than those with a single disability. The supplementary study confirmed the findings in the core body of work with respect to the extent of conjoint disorder, its severity, and its relationship with pervasive development disorder and Down syndrome. Moreover, the supplementary study and the ancillary influenza study indicated a role for neurodevelopmental insults including obstetric complications in the adverse neuropsychiatric outcomes, with timing of the insult a potentially critical element in defining the specific outcome. The supplementary study also added new information on familiality in intellectual disability. It found that, in addition to parental intellectual disability status and exposure to labour and delivery complications at birth, parental psychiatric status was an independent predictor of intellectual disability in offspring as well as a predictor of conjoint disorder. In conclusion, the facility to collect and integrate records held by separate State administrative health jurisdictions, and to analyse them within the one database has had a marked impact on the capacity for this thesis to estimate the prevalence of conjoint disorder among intellectually disabled and psychiatric populations, and to understand more about its clinical manifestations and aetiological underpinnings.