Alzheimer's disease is a complex neurodegenerative disease whose basic etiology and genetic structure remains elusive, despite decades of intensive investigation. To date, the significant genetic markers identified have no obvious functional effects, and are unlikely to play a role in Alzheimer's disease etiology, themselves. These markers are likely linked to other genetic variations, rare or common. Regardless of what causal mutations are found, research has demonstrated that no single gene determines Alzheimer's disease development and progression. It is clear that Alzheimer's disease development and progression are based on a set of interactions between genes and environmental variables. This dissertation focuses on gene-gene interactions (epistasis) and their effects on Alzheimer's disease case-control status. We genotyped the top Alzheimer's disease genetic markers as found on AlzGene.org (accessed 2014), and tested for interactions that were associated with Alzheimer's disease case- control status. We identified two potential gene-gene interactions between rs11136000 (CLU) and rs670139 (MS4A4E) (synergy factor = 3.81; p = 0.016), and rs3865444 (CD33) and rs670139 (MS4A4E) (synergy factor = 5.31; p = 0.003). Based on one data set alone, however, it is difficult to know whether the interactions are real. We replicated the CLU-MS4A4E interaction in an independent data set from the Alzheimer's Disease Genetics Consortium (synergy factor = 2.37, p = 0.007) using a meta-analysis. We also identified potential dosage (synergy factor = 2.98, p = 0.05) and APOE ε4 effects (synergy factor = 4.75, p = 0.005) in Cache County that did not replicate independently. The APOE ε4 effect is an association with Alzheimer's disease case-control status in APOE ε4 negative individuals. There is minor evidence both the dosage (synergy factor = 1.73, p = 0.02) and APOE ε4 (synergy factor = 2.08, p = 0.004) effects are real, however, because they replicate when including the Cache County data in the meta-analysis. These results demonstrate the importance of understanding the role of epistasis in Alzheimer's disease. During this research, we also developed a novel tool known as the Variant Tool Chest. The Variant Tool Chest has played an integral part in this research and other projects, and was developed to fill numerous gaps in next-generation sequence data analysis. Critical features include advanced, genotype-aware set operations on single- or multi-sample variant call format (VCF) files. These features are critical for genetics studies using next-generation sequencing data, and were used to perform important analyses in the third study of this dissertation.By understanding the role of epistasis in Alzheimer's disease, researchers will begin to untangle the complex nature of Alzheimer's disease etiology. With this information, therapies and diagnostics will be possible, alleviating millions of patients, their families and caregivers of the painful experience Alzheimer's disease inflicts upon them.

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Determining the genetic basis of reproductive isolation is a fundamental goal in evolutionary biology. Intrinsic reproductive isolation often arises due to epistasis between divergent interacting genes. The rapid evolution of hybrid male sterility is known to have several causes, including the exposure of recessive X-linked incompatibilities in males and the rapid evolution of male reproductive traits. Despite these insights, little is known about the genetics of reproductive isolation during the early stages of speciation. This deficiency inspired parallel studies on the molecular evolution of male reproduction in house mice and the genetic basis of hybrid male sterility between two mouse species, Mus domesticus and M. musculus. Evolutionary analysis of 946 genes showed that the intensity of positive selection varies across sperm development and acts primarily on phenotypes that develop late in spermatogenesis (Appendix A). Several reciprocal crosses between wild-derived strains of M. musculus and M. domesticus were used to examine F1 hybrid male sterility (Appendix B). These crosses revealed hybrid male sterility linked to the M. musculus X chromosome and a novel sterility polymorphism within M. musculus. A large introgression experiment was used to further dissect the genetic basis of X-linked incompatibilities between M. musculus and M. domesticus (Appendix C). Introgression of the M. musculus X chromosome into a M. domesticus genetic background produced male sterility and involved a minimum of four factors. No sterility factors were uncovered on the M. domesticus X chromosome. These data demonstrate the complex genetic basis of hybrid sterility in mice and provide numerous X-linked candidate sterility genes. The molecular evolution of five rapidly evolving candidate genes was examined using population and phylogenetic sampling in Mus (Appendix D). Four of these loci showed evidence of positive natural selection. One locus, 4933436I01Rik, showed divergent protein evolution between M. domesticus and M. musculus and was one of a handful of testis-expressed genes within a narrow interval involved in hybrid male sterility. In summary, these data demonstrate that hybrid male sterility has a complex genetic basis between two closely related species of house mice and provide a foundation for the identification of specific mutations that isolate these species.

Many phenotypes are controlled by factors which include the genes, the environment, interactions between genes and interaction between the genotypes and the environment. Great strides have been made to understand how these various factors affect traits of agricultural, medical and environmental importance. The chicken is regarded as a model organism whose study would not only assist efforts towards increased agricultural productivity but also provide insight into the genetic determination of traits with potential application in understanding human health and disease. Detection of genomic regions or loci responsible for controlling quantitative traits (QTL) in poultry has focussed mainly on growth and production traits with limited information on reproductive traits. Most of the reported results have used additive-dominance models which are easy to implement because they ignore epistatic gene action despite indications that it may be important for traits with low heritability and high heterosis. The thesis presents results on the detection of loci and genetic mechanisms involved in sexual maturity traits through modelling both additive-dominance gene actions and epistasis. The study was conducted on an F2 broiler x White Leghorn layer cross for QTL detection for age, weight, abdominal fat, ovary weight, oviduct weight, comb weight, number of ovarian yellow follicles, a score for the persistence of the right oviduct and bone density. In addition, body weight QTL at 3, 6, 12, 24, 48 and 72 weeks of age, QTL for growth rate between the successive ages and QTL for the parameters of the growth curve were also detected. Most of the QTL for traits at sexual maturity acted additively. A few of the QTL explained a modest proportion of the phenotypic variation with most of the QTL explaining a small component of the cumulative proportion of the variation explained by the QTL. Body weight QTL were critical in determining the attainment of puberty. The broiler allele had positive effects on weight at first egg and negative effects on age at first egg. Most QTL affecting weight at first egg overlapped with QTL for age at first egg and for early growth rate (6-9 weeks) suggesting that growth rate QTL are intimately related to the onset of puberty. Specific QTL for early and adult growth were detected but most QTL had varying influence on growth throughout life. Chromosome 4 harboured most of QTL for the assessed traits which explained the highest proportion of the phenotypic variation in the traits confirming its critical role in influencing traits of economic importance. There was no evidence for epistasis for almost all the studied traits. Evidence for role of epistasis was significant for ovary weight and suggestive for both growth rate and abdominal fat.

Master of Science / Department of Agronomy / Guorong Zhang / Guihua Bai / Wheat streak mosaic virus (WSMV) is a disease that causes significant yield losses in wheat (Triticum aestivum L.). Host resistance is the primary approach for control. KS06HW79 is a wheat line with WSMV resistance up to 21°C. To study the inheritance of resistance in KS06HW79, it was crossed with two WSMV-susceptible wheat genotypes, KS020638-M-5 and Brawl CL Plus. Parental lines, F₁, F₂, and check varieties were mechanically inoculated and evaluated for WSMV resistance at 21°C in growth chambers. The segregation pattern in two F₂ populations fit a one-recessive-gene model (1 resistant : 3 susceptible) and a dominant-suppression-epistasis model (3 resistant : 13 susceptible). To determine which model was a better fit, WSMV resistance was evaluated for F₂:₃ families generated from resistant F₂ plants in both crosses. Approximately two thirds of the F₂:₃ families in each cross showed segregation for WSMV resistance, suggesting that the dominant-suppression epistasis model better explained the WSMV resistance in KS06HW79. This model was also supported by two KS06HW79-derived doubled haploid populations, which had a segregation ratio of 1 resistant : 3 susceptible. Therefore, the WSMV resistance in KS06HW79 is likely controlled by two dominant genes, one of which is a suppressor.

O mapeamento genético é constituído por procedimentos experimentais e estatísticos que buscam detectar genes associados à etiologia e regulação de doenças, além de estimar os efeitos genéticos e as localizações genômicas correspondentes. Considerando delineamentos experimentais que envolvem cruzamentos controlados de animais ou plantas, diferentes formulações de modelos de regressão podem ser adotados na identificação de QTLs (do inglês, quantitative trait loci), incluindo seus efeitos principais e possíveis efeitos de interação (epistasia). A dificuldade nestes casos de mapeamento é a comparação de modelos que não necessariamente são encaixados e envolvem um espaço de busca de alta dimensão. Para este trabalho, descrevemos um método geral para melhorar a eficiência computacional em mapeamento simultâneo de múltiplos QTLs e de seus efeitos de interação. A literatura tem usado métodos de busca exaustiva ou busca condicional. Propomos o uso do algoritmo genético para pesquisar o espaço multilocos, sendo este mais útil para genomas maiores e mapas densos de marcadores moleculares. Por meio de estudos de simulações mostramos que a busca baseada no algoritmo genético tem eficiência, em geral, mais alta que aquela de um método de busca condicional e que esta eficiência é comparável àquela de uma busca exaustiva. Na formalização do algoritmo genético pesquisamos o comportamento de parâmetros tais como: probabilidade de recombinação, probabilidade de mutação, tamanho amostral, quantidade de gerações, quantidade de soluções e tamanho do genoma, para diferentes funções objetivo: BIC (do inglês, Bayesian Information Criterion), AIC (do inglês, Akaike Information Criterion) e SSE, a soma de quadrados dos resíduos de um modelo ajustado. A aplicação das metodologias propostas é também considerada na análise de um conjunto de dados genotípicos e fenotípicos de ratos provenientes de um delineamento F2. / Genetic mapping is defined in terms of experimental and statistical procedures applied for detection and localization of genes associated to the etiology and regulation of diseases. Considering experimental designs in controlled crossings of animals or plants, different formulations of regression models can be adopted in the identification of QTL\'s (Quantitative Trait Loci) to the inclusion of the main and interaction effects between genes (epistasis). The difficulty in these approaches of gene mapping is the comparison of models that are not necessarily nested and involves a multiloci search space of high dimension. In this work, we describe a general method to improve the computational efficiency in simultaneous mapping of multiples QTL\'s and their interactions effects. The literature has used methods of exhausting search or conditional search. We consider the genetic algorithm to search the multiloci space, looking for epistatics loci distributed on the genome. Compared to the others procedures, the advantage to use such algorithm increases more for set of genes bigger and dense maps of molecular markers. Simulation studies have shown that the search based on the genetic algorithm has efficiency, in general, higher than the conditional search and that its efficiency is comparable to that one of an exhausting search. For formalization of the genetic algorithm we consider different values of the parameters as recombination probability, mutation probability, sample size, number of generations, number of solutions and size of the set of genes. We evaluate different objective functions under the genetic algorithm: BIC, AIC and SSE. In addition, we used the sample phenotypic and genotypic data bank. Briefly, the study examined blood pressure variation before and after a salt loading experiment in an intercross (F2) progeny.

O mapeamento genético é constituído por procedimentos experimentais e estatísticos que buscam detectar genes associados à etiologia e regulação de doenças, além de estimar os efeitos genéticos e as localizações genômicas correspondentes. Considerando delineamentos experimentais que envolvem cruzamentos controlados de animais ou plantas, diferentes formulações de modelos de regressão podem ser adotados na identificação de QTLs (do inglês, quantitative trait loci), incluindo seus efeitos principais e possíveis efeitos de interação (epistasia). A dificuldade nestes casos de mapeamento é a comparação de modelos que não necessariamente são encaixados e envolvem um espaço de busca de alta dimensão. Para este trabalho, descrevemos um método geral para melhorar a eficiência computacional em mapeamento simultâneo de múltiplos QTLs e de seus efeitos de interação. A literatura tem usado métodos de busca exaustiva ou busca condicional. Propomos o uso do algoritmo genético para pesquisar o espaço multilocos, sendo este mais útil para genomas maiores e mapas densos de marcadores moleculares. Por meio de estudos de simulações mostramos que a busca baseada no algoritmo genético tem eficiência, em geral, mais alta que aquela de um método de busca condicional e que esta eficiência é comparável àquela de uma busca exaustiva. Na formalização do algoritmo genético pesquisamos o comportamento de parâmetros tais como: probabilidade de recombinação, probabilidade de mutação, tamanho amostral, quantidade de gerações, quantidade de soluções e tamanho do genoma, para diferentes funções objetivo: BIC (do inglês, Bayesian Information Criterion), AIC (do inglês, Akaike Information Criterion) e SSE, a soma de quadrados dos resíduos de um modelo ajustado. A aplicação das metodologias propostas é também considerada na análise de um conjunto de dados genotípicos e fenotípicos de ratos provenientes de um delineamento F2. / Genetic mapping is defined in terms of experimental and statistical procedures applied for detection and localization of genes associated to the etiology and regulation of diseases. Considering experimental designs in controlled crossings of animals or plants, different formulations of regression models can be adopted in the identification of QTL\'s (Quantitative Trait Loci) to the inclusion of the main and interaction effects between genes (epistasis). The difficulty in these approaches of gene mapping is the comparison of models that are not necessarily nested and involves a multiloci search space of high dimension. In this work, we describe a general method to improve the computational efficiency in simultaneous mapping of multiples QTL\'s and their interactions effects. The literature has used methods of exhausting search or conditional search. We consider the genetic algorithm to search the multiloci space, looking for epistatics loci distributed on the genome. Compared to the others procedures, the advantage to use such algorithm increases more for set of genes bigger and dense maps of molecular markers. Simulation studies have shown that the search based on the genetic algorithm has efficiency, in general, higher than the conditional search and that its efficiency is comparable to that one of an exhausting search. For formalization of the genetic algorithm we consider different values of the parameters as recombination probability, mutation probability, sample size, number of generations, number of solutions and size of the set of genes. We evaluate different objective functions under the genetic algorithm: BIC, AIC and SSE. In addition, we used the sample phenotypic and genotypic data bank. Briefly, the study examined blood pressure variation before and after a salt loading experiment in an intercross (F2) progeny.

Major Depressive Disorder is a devastating psychiatric illness with a complex genetic and environmental component that affects 10% of the UK population. Previous studies have shown that that individuals with depression show poorer performance on measures of cognitive domains such as memory, attention, language and executive functioning. A major risk factor for depression is a higher level of neuroticism, which has been shown to be associated with depression throughout life. Understanding cognitive performance in depression and neuroticism could lead to a better understanding of the aetiology of depression. The first aim of this thesis focused on assessing phenotypic and genetic differences in cognitive performance between healthy controls and depressed individuals and also between single episode and recurrent depression. A second aim was determining the capability of two decision-tree based methods to detect simulated gene-gene interactions. The third aim was to develop a novel statistical methodology for simultaneously analysing single SNP, additive and interacting genetic components associated with neuroticism using machine leaning. To assess the phenotypic and genetic differences in depression, 7,012 unrelated Generation Scotland participants (of which 1,042 were clinically diagnosed with depression) were analysed. Significant differences in cognitive performance were observed in two domains: processing speed and vocabulary. Individuals with recurrent depression showed lower processing speed scores compared to both controls and individuals with single episode depression. Higher vocabulary scores were observed in depressed individuals compared to controls and in individuals with recurrent depression compared to controls. These significant differences could not be tied to significant single locus associations. Derived polygenic scores using the large CHARGE processing speed GWAS explained up to 1% of variation in processing speed performance among individuals with single episode and recurrent depression. Two greedy non-parametric decision-tree based methods - C5.0 and logic regression - were applied to simulated gene-gene interaction data from Generation Scotland. Several gene-gene interactions were simulated under multiple scenarios (e.g. size, strength of association levels and the presence of a polygenic component) to assess the power and type I error. C5.0 was found to have an increased power with a conservative type I error using simulated data. C5.0 was applied to years of education as a proxy of educational attainment in 6,765 Generation Scotland participants. Multiple interacting loci were detected that were associated with years of education, some most notably located in genes known to be associated with reading and spelling (RCAN3) and neurodevelopmental traits (NPAS3). C5.0 was incorporated in a novel methodology called Machine-learning for Additive and Interaction Combined Analysis (MAICA). MAICA allows for a simultaneous analysis of single locus, polygenic components, and gene-gene interaction risk factors by means of a machine learning implementation. MAICA was applied on neuroticism scores in both Generation Scotland and UK Biobank. The MAICA model in Generation Scotland included 151 single loci and 11 gene-gene interaction sets, and explained ~6.5% of variation in neuroticism scores. Applying the same model to UK Biobank did not lead to a statistically significant prediction of neuroticism scores. The results presented in this thesis showed that individuals with depression performed significantly lower on the processing speed tests but higher on vocabulary test and that 1% of variation in processing speed can be explained by using a large processing speed GWAS. Evidence was provided that C5.0 had increased power and acceptable type I error rates versus logic regression when epistatic models exist - even with a strong underlying polygenic component, and that MAICA is an efficient tool to assess single locus, polygenic and epistatic components simultaneously. MAICA is open-source, and will provide a useful tool for other researchers of complex human traits who are interested in exploring the relative contributions of these different genomic architectures.

Mainstream evolutionary biology lacks a mature theory of phenotype. Following from the Modern Synthesis, researchers tend to assume an unrealistically simple mapping of genotype to phenotype, or else trust that the complexities of developmental architecture can be adequately captured by measuring trait variances and covariances. In contrast, the growing field of evolutionary developmental biology (evo-devo) explicitly examines the relationship between developmental architecture and evolutionary change, but lacks a rigorous quantitative and predictive framework. In my dissertation, I strive to integrate quantitative genetics and evo-devo, using both theoretical and empirical studies of plasticity. My first paper explores the effect of realistic development on the evolution of phenotypic plasticity when there is migration between two discrete environments. The model I use reveals that nonadditive developmental interactions can constrain the evolution of phenotypic plasticity in the presence of stabilizing selection. In my second paper, I examine the manner in which the genetically controlled responsiveness of traits to each other is shaped by selection and can in turn shape the phenotypic response to selection. Here, results indicate that developmental entanglement through plasticity can facilitate rapid multivariate adaptation in response to a novel selective pressure. In my final paper, I examine patterns of gene expression underlying ancestral plasticity and adaptive loss of melanin in Daphnia melanica. My results indicate that the developmental mechanism underlying ancestral plasticity has been co-opted to facilitate rapid adaptation to an introduced predator.

Gene regulatory networks are dynamic and continuously remodelled in response to internal and external stimuli. To understand how these networks alter cellular phenotype in response towards specific challenges, my first project sought to develop a methodology to explore how the strength of genetic interactions changes according to environmental context. Defined as sensitivity-based epistasis, the results obtained using this methodology were compared to those generated under the conventional fitness-based approach. By integrating this information with gene expression profiles and physical interaction datasets, we demonstrate that sensitivity-based epistasis specifically highlights genetic interactions with a dynamic component. Having investigated how an external stimulus regulates network dynamics, we next sought to understand of how genome positioning impacts transcription kinetics. This feat was accomplished by cloning two gene-reporter constructs, representing contrasting promoter architectures, across 128 loci along chromosome III in S.Cerevisiae. By comparing expression and noise measurements for promoters with “covered” and “open” chromatin structures against a stochastic model for eukaryotic gene expression, we demonstrate that while promoter structure regulates burst frequency (the rate of promoter activation), positional effects in turn appear to primarily modulate burst size (the number of mRNA produced per gene activation event). By integrating these datasets with information describing global chromatin structure, we suggest that the acetylation state of chromatin regulates burst size across the genome. Interestingly, this hypothesis is further supported by nicotinamide-mediated inhibition of Sir2 which would appear to modulate burst size globally across the genome.