Barley is a resilient crop that performs better than other cereal plants under abiotic stress conditions, including salinity stress. The understanding of salinity tolerance in crops is a major milestone to increase yield in areas affected by soil salinity. In barley, some components of salinity tolerance have been elucidated, (e.g. HVP10, which is involved in tissue tolerance), yet little research has explored the discovery of other components contributing to salinity tolerance. In this PhD project, a forward genetics approach was used, whereby two barley populations were phenotyped under controlled and field conditions for salinity tolerance. The first population is a diversity panel of two-row European spring barley, and the second population is a nested association mapping barley population with wild donors from the Fertile Crescent. The use of non-destructive high-throughput experiments conducted under controlled conditions provided insight into the understudied shoot ion-independent component of salinity tolerance. In addition, the previously known association HvHKT1;5 was detected under controlled conditions. In parallel, the field experiments increased our understanding of new components of salinity tolerance, such as the maintenance of yield and yield-related traits under saline conditions. This strategy was successful with the identification of a locus on chromosome 2H (140-145 cM), where the allele from one of the wild donors of the nested association mapping population increased yield under saline conditions in the field. When re-evaluating lines homozygous at the 2H locus, ear length, ear number per plant, yield and harvest index were all significantly higher under saline conditions for the lines carrying the wild allele. Furthermore, another interesting locus on chromosome 7H that was responsive to salt treatment and co-localized with HVP1 was identified using the diversity panel population. Loci with known flowering genes were also shown to be involved in salinity tolerance. To conclude, this PhD project shed more light on the genetic mechanisms of salinity tolerance in barley, a knowledge that can benefit breeding programs and can be extended to other crops such as wheat.

Master of Science / Department of Statistics / Haiyan Wang / Motivation: Genome-wide association studies (GWAS) provide an important avenue for investigating many common genetic variants in different individuals to see if any variant is associated with a trait. GWAS is a great tool to identify genetic factors that influence health and disease. However, the high dimensionality of the gene expression dataset makes GWAS challenging. Although a lot of promising machine learning methods, such as Support Vector Machine (SVM), have been investigated in GWAS, the question of how to improve the accuracy of the result has drawn increased attention of many researchers A lot of the studies did not apply feature selection to select a parsimonious set of relevant genes. For those that performed gene selections, they often failed to consider the possible interactions among genes. Here we modify a gene selection algorithm BMSF originally developed by Zhang et al. (2012) for improving the accuracy of cancer classification with binary responses. A continuous response version of BMSF algorithm is provided in this report so that it can be applied to perform gene selection for continuous gene expression dataset. The algorithm dramatically reduces the dimension of the gene markers under concern, thus increases the efficiency and accuracy of GWAS. Results: We applied the continuous response version of BMSF on the wheat phenotypes dataset to predict two quantitative traits based on the genotype marker data. This wheat dataset was previously studied in Long et al. (2009) for the same purpose but used only direct application of SVM regression methods. By applying our gene selection method, we filtered out a large portion of genes which are less relevant and achieved a better prediction result for the test data by building SVM regression model using only selected genes on the training data. We also applied our algorithm on simulated datasets which was generated following the setting of an example in Fan et al. (2011). The continuous response version of BMSF showed good ability to identify active variables hidden among high dimensional irrelevant variables. In comparison to the smoothing based methods in Fan et al. (2011), our method has the advantage of no ambiguity due to difference choices of the smoothing parameter.

D'importants progrès ont été réalisés dans les domaines du génotypage et du séquençage, ce qui permet de mieux comprendre la relation génotype/phénotype. Il est possible d'analyser l'architecture génétique des caractères (génétique d'association, GA), ou de prédire la valeur génétique des candidats à la sélection (sélection génomique, SG). L'objectif de cette thèse était de développer des outils pour mener ces stratégies de manière optimale. Nous avons d'abord dérivé analytiquement la puissance du modèle mixte de GA, et montré que la puissance était plus faible pour les marqueurs présentant une faible diversité, une forte différentiation entre sous groupes et une forte corrélation avec les marqueurs utilisés pour estimer l'apparentement (K). Nous avons donc considéré deux estimateurs alternatifs de K. Des simulations ont montré qu'ils sont aussi efficaces que la méthode classique pour contrôler les faux positifs et augmentent la puissance. Ces résultats ont été confirmés sur les panels corné et denté du programme Cornfed, avec une augmentation de 40% du nombre de SNP détectés. Ces panels, génotypés avec une puce 50k SNP et phénotypés pour leur précocité et leur biomasse ont permis de décrire la diversité de ces groupes et de détecter des QTL. En SG, des études ont montré l'importance de la composition du jeu de calibration sur la fiabilité des prédictions. Nous avons proposé un algorithme d'échantillonnage dérivé de la théorie du G-BLUP permettant de maximiser la fiabilité des prédictions. Par rapport à un échantillon aléatoire, il permettrait de diminuer de moitié l'effort de phénotypage pour atteindre une même fiabilité de prédiction sur les panels Cornfed. / Major progresses have been achieved in genotyping technologies, which makes it easier to decipher the relationship between genotype and phenotype. This contributed to the understanding of the genetic architecture of traits (Genome Wide Association Studies, GWAS), and to better predictions of genetic value to improve breeding efficiency (Genomic Selection, GS). The objective of this thesis was to define efficient ways of leading these approaches. We first derived analytically the power from classical GWAS mixed model and showed that it was lower for markers with a small minimum allele frequency, a strong differentiation among population subgroups and that are strongly correlated with markers used for estimating the kinship matrix K. We considered therefore two alternative estimators of K. Simulations showed that these were as efficient as classical estimators to control false positive and provided more power. We confirmed these results on true datasets collected on two maize panels, and could increase by up to 40% the number of detected associations. These panels, genotyped with a 50k SNP-array and phenotyped for flowering and biomass traits, were used to characterize the diversity of Dent and Flint groups and detect QTLs. In GS, studies highlighted the importance of relationship between the calibration set (CS) and the predicted set on the accuracy of predictions. Considering low present genotyping cost, we proposed a sampling algorithm of the CS based on the G-BLUP model, which resulted in higher accuracies than other sampling strategies for all the traits considered. It could reach the same accuracy than a randomly sampled CS with half of the phenotyping effort.

A soja está entre as principais culturas mundiais, uma vez que é uma excelente fonte de proteínas e óleo. Além disso, a espécie é aproveitada também pela indústria de biocombustíveis. Considerando a importância das novas mudanças climáticas no agronegócio; para a soja, esta situação é agravada em virtude das condições de temperatura e latitude recomendadas para a semeadura. Dessa forma, para aumentar a produtividade da cultura mesmo frente ao aquecimento global, fazse fundamental o desenvolvimento de cultivares com alta produtividade e tolerantes às altas temperaturas. Neste contexto, o objetivo geral deste trabalho foi selecionar genótipos de soja tolerantes ao calor. Uma população composta por 80 PI\'s de soja e 15 testemunhas foi avaliada sob condições de altas temperaturas, com experimentos instalados nas cidades de Teresina-PI, Piracicaba-SP e Jaboticabal- SP, no ano agrícola 2013/2014. Para a avaliação dos genótipos, foram realizadas análises univariadas e multivariadas. A seleção dos genótipos mais tolerantes a altas temperaturas foi realizada via análise de componentes principais. Nas análises de variâncias univariadas, todos os caracteres mostraram efeitos de tratamentos significativos pelo teste F. Pela análise de componentes principais no experimento conduzido em Teresina-PI, os caracteres que mais contribuíram para a variabilidade dos genótipos avaliados foram: data que metade da parcela atingiu o estádio R5, altura da planta na maturidade, período de granação e valor agronômico. Em Piracicaba-SP, os caracteres que mais contribuíram para a variabilidade foram o período de granação, massa de 100 sementes e o número de dias para a maturidade. Para a seleção dos genótipos mais tolerantes ao calor em Jaboticabal- SP, considerou-se principalmente a altura e a produtividade. Para a análise de mapeamento associativo, a fenotipagem foi realizada em Teresina-PI e avaliados quatro caracteres: altura da planta na maturidade, valor agronômico, massa de cem sementes e produtividade. A genotipagem foi realizada utilizando o chip da empresa Affymetrix. O desequilíbrio de ligação entre pares de marcadores foi calculado pelo coeficiente de determinação r2 e a análise de associação entre marcadores e o fenótipo de interesse foi realizada utilizando a abordagem de modelo linear generalizado. Foram identificadas 16 associações significativas. / Soybean (Glycine max) is one of most important crops in the world. This crop is an source of protein and oil. Beyond that, the species is also utilized for the biofuels industry. The recent climate changes are important on agribusiness, the ones on soybean crop are worse than on other crops because of the conditions of temperature and latitude recommended for planting. Thus, to increase the productivity of the crop even in face of global warming, it is essential that soybean breeding programs promote the development of cultivars highly productive and tolerant to high temperatures. In this context, the aim of this study was to select genotypes for heat tolerance. A population composed of 80 soybean PI\'s and 15 experimental checks was evaluated under high temperature conditions. The experiments were conducted in the cities of Teresina-PI, Piracicaba-SP and Jaboticabal-SP, in the 2013/2014 season. For the evaluation of the genotypes, univariate and multivariate analysis were performed, and the selection of the most genotypes for heat tolerance was performed by principal component analysis (PCA). In the univariate analyzes of variance, all characters showed significant effects of treatments by test F. In the PCA in the experiment conducted in Teresina-PI, the variables that most contributed to the variability of genotypes were: date in which half of the parcel reached R5 stage, height of the plant at maturity, grain filling period and agronomic value. In Piracicaba-SP PCA, the variables that most contributed to the variability were: grain filling period, 100-grain weight and the number of days to maturity. For the selection of the most heat-tolerant genotypes in Jaboticabal-SP, the height and the yield were the variables that most contributed to the variability. In the the association mapping analysis, the genotypes were evaluated under conditions of high temperatures in Teresina-PI and evaluated for four traits: height of the plant at maturity, agronomic value, 100 grain weight and yield. The genotyping was carried out using the Affymetrix chip. The linkage disequilibrium between pairs of markers was calculated by the determination coefficient r2 and the association analysis between markers and the phenotype of interest was performed using the generalized linear model approach. A total of 16 significant marker-trait associations were detected for the four traits.

A soja é uma das culturas mais importantes do mundo, além de ser a principal comodity brasileira. Entretanto apesar dos ganhos crescentes de produtividade a base genética da cultura no país é estreita. Sendo assim, é importante a identificação e caracterização de fontes de variabilidade para os programas de melhoramento de soja. Dado o exposto, os objetivos deste estudo foram i) avaliação da produtividade de grãos e caracteres agronômicos correlacionados; ii) caracterização da diversidade fenotípica; iii) caracterização da diversidade genética e estrutura de populações; e iv) mapeamento associativo para produtividade de grãos. Os acessos foram fenotipados nos anos agrícolas de 2012/2013 e 2013/2014, em cinco ambientes. As características avaliadas foram: altura da planta na maturidade, período de granação, valor agronômico, acamamento, massa de cem sementes, número de dias para a maturidade, inserção da primeira vagem, altura da planta no florescimento, teor de óleo e produtividade de grãos. A análise dos dados fenotípicos foi feita pelo software SELEGEN utilizando modelos mistos, e a árvore de regressão para identificação dos caracteres correlacionados foi feita pelo software JMP SAS. A diversidade fenotípica foi feita a partir de todas as características avaliadas anteriormente utilizando três tipos de análises: os métodos de agrupamento de Ward e Average Linkage no software Power Marker, e pela análise de componentes principais no software JMP SAS. A genotipagem dos acessos foi realizada pelo Axiom&reg; Soybean Genotyping Array contendo 10017 SNPs polimórficos para os acessos genotipados. A partir dos dados de marcadores foi feita a caracterização da diversidade genética pelo software Power Marker. Além disso, foram realizadas análises de estrutura de população pelo software STRUCTURE e pelo pacote do R, adegenet. A análise de associação foi efetuada pelo software TASSEL, utilizando o modelo misto MLM (Q+K). Duas abordagens foram utilizadas na análise de associação, a primeira utilizando as médias fenotípicas ajustadas para BLUP dos cinco ambientes e a segunda utilizando apenas as médias de cada local individualmente. Na análise fenotípica os acessos Dowling, PI 417563, PI200526, PI 377573 e PI 159922, apresentaram boa produtividade de grãos nos cincos ambientes avaliados. A caracterização molecular e fenotípica da diversidade indicou a presença de variabilidade genética no painel de acessos avaliados. Além disso, foi possível a identificação de dois grupos (k=2) em ambas as análises de estrutura da população utilizadas. No mapeamento associativo, foram detectadas sete associações marcador-característica com p<0,001 e com correção para múltiplos testes q<0,1. Dentre estas, quatro foram significativas no modelo de análise conjunta dos cinco ambientes e para o ambiente dois. As demais associações foram significativas somente para este último local. / Soybean is one of the most important crops in the world, and is Brazil\'s main commodity. However despite growing yield gains the genetic basis of culture in the country is narrow. Therefore, the identification and characterization of sources of variability for soybean breeding programs is important. On this basis, the objectives of this study were i) assessment of grain yield and agronomic traits correlated; ii) characterization of the phenotypic diversity; iii) characterization of genetic diversity and population structure; and iv) associative mapping for grain yield. The inbred lines were phenotyped in the agricultural years of 2012/2013 and 2013/2014, in five environments. The traits evaluated were: plant height at maturity, fruit filling period, agronomic value, lodging, mass of hundred seeds, number of days to maturity, first pod, plant height at flowering, oil content and grain yield. The analysis of phenotypic data was made by SELEGEN software using mixed models, and regression tree for identification of correlated traits was made by JMP SAS software. The phenotypic diversity was made from all the features previously evaluated using three types of analysis: the Ward clustering methods and Average Linkage from the Power Marker software, and the principal component analysis in SAS JMP software. Genotyping was performed by Axiom&reg; Soybean Genotyping Array containing 10017 polymorphic SNPs genotyped for the soybean lines. From the markers data was taken the genetic diversity analysis by Power Marker software. In addition, population structure analysis was performed by Structure software and the R package, adegenet. The association analysis was performed by TASSEL software using the mixed model MLM (Q + K). Two approaches were used in the association analysis, the first using the phenotypic average adjusted to BLUP values for the five enviroments and the second one using only the means of each site individually. In the phenotypic analysis the lines: Dowling, PI 417563, PI200526, PI 377573 and PI 159922 showed good grain yield in the five evaluated environments. The molecular and phenotypic characterization of diversity indicated the presence of geneticvariability in the inbreed lines. Moreover, it was possible to identify two groups (k = 2) in both population structure analysis used. In the associative mapping, were detected seven markertrait associations with p <0.001 and with correction for multiple tests q <0.1. Among these, four were significant in the pooled analysis model with five environments and at the individually environment two. The other variables were significant only for the latter location.

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No. of bitstreams: 2 Dissertacão - Clistiane dos Anjos Mendes - 2010.pdf: 3556172 bytes, checksum: 0afdcb2cfe1cae06f8e21fb23ad19fba (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2010-08-16 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Rice is consumed by more than half of the world population. The expectation of substantial population growth and consequently the increase of the consumption has demontrated that the present rice production will not supply the future’s demand. As a result, studies that aim at the increase of the yield must be prioritized by scientific co&unity. Aiming to identify genomic regions related to grain yield and its component traits (panicle number per meter, grain panicle number and hundred grain mass), it was carried out the associative mapping analysis. The analysis of association used two types of SSR markers: a) structural genome derived markers, and b) transcriptome derived markers, i.e. developed from transcripts from marker-anchored genomic regions previously related to yield and its components from 27 different QTL maps already published. The associative mapping methodology used included the whole genome scanning and candidate genes approaches. The molecular characterization used 186 accessions from Embrapa Rice Core Collection (ERiCC), whereas 76 accessions from lowland system of cultivation, and 113 upland system of cultivation. The experimental design was the randomized block with 4 repetitions, and included the cultivars BR Irga 409, Metica 1 and BRS Caiapó as controls. It were evaluated the following traits: Panicle number per meter (NPM), panicle (NGP), Hundred grain mass (PCG) and productivity. The 186 markers were genotyped by 29 transcript-derived SSR markers and 86 SSR markers derived from structural genome. For the irrigated accessions was verified positive correlation for panicle number per meter and productivity (0,2424; p<0,01) and by productivity and hundred grain mass (p<0,05; 0,1457) and negative correlation for panicle number per meter and grain number per panicle (-0,457; p<0,01). For the upland accessions it was observed positive correlation for grain number per panicle and productivity (0,4243; p<0,01). Negative correlations were observed for panicle number per meter and hundred grain weight (p<0,01; -0,2114). Based on 44 SSR markers developed in this work, 181 alelles were detected, an average of 5.5 alelles per locus, average PIC of 0.44 and 25 private alleles. The analysis with 115 SSR markers identified 43 significant associations for grain number per panicle considering the cultivation system upland and three associations for lowland system; 7 significant associations for panicle number per meter for lowland system, and 9 were significant for upland cultivation systems; 14 associations were significant for hundred grain mass in lowland system of cultivarion, and 43 were significant for upland system of cultivarion; 3 associations were significant for grain yield in upland system of cultivarion. The association analysis identified 61 SSR markers consistently associated to yield and its components. It were detected 33 associations statiscally significant to one or more traits, which validated the previous results obtained by QTL mapping. The markers RM125, RM152 e Q69JE3 showed the higher number of associations per trait. The association productivity and PCG, and NCP, respectively, were previously found in the literature. The markers associated to the evaluated traits permit to initiate a scientific program to identify the candidate genes and to proceed the marker assisted selection The accessions CNA0001107, CNA0005478, CNA0010533, CNA0003287, IR 36, CNA0003602, CNA0006413, CNA0006174, CNA0003289, CNA0002253, CNA0004098, CNA0001416, CNA0003490 e CNA0000994 showed the highest number of favorable alleles considering 16 the markers associated to yield and its components. The identified accessions from this work as having the higher number of favorable alleles from the evaluated traits will be recommended to be used as genitors in Brazilian rice breeding programs. The mixed approaches to the associative mapping used in the present work was interesting to permit that, even using a low marker density, in couple with the linkage disequilibrium found in rice, some previously mapped markers from QTL analyses were again associated to traits evaluated in field experiments. This strategy can be used to other traits of interest for rice, such as drought and cold tolerance, and disease resistance. / O arroz é consumido por mais da metade da população mundial. As expectativas de aumento substancial da população, e consequentemente aumento do consumo, têm demonstrado que a produção atual da cultura não suprirá a demanda. A produção de grãos do arroz é uma característica complexa determinada pelos seus três componentes: número de panículas, número de grãos por panícula e peso de grãos, os quais são típicos caracteres quantitativos. A evolução no mapeamento do genoma, o sequenciamento e as pesquisas em genômica funcional têm fornecido ferramentas poderosas para estudar as bases genética e molecular desses caracteres quantitativos. Buscando identificar regiões genômicas responsáveis pela produtividade e componentes de produção (número de panículas por metro, número de grãos por panícula e peso de 100 grãos), foi realizada a análise de mapeamento associativo. O mapeamento associativo foi realizado com dois tipos de marcadores SSR: a) marcadores fluorescentes, e b) marcadores desenvolvidos a partir de transcritos de regiões genômicas ancoradas por marcadores previamente relacionados à produção e seus componentes por 27 análises de QTLs publicadas na literatura. Dessa forma, nesse trabalho foi utilizada uma metodologia híbrida de mapeamento associativo incluindo as técnicas de whole genome scanning e de genes candidatos. A caracterização molecular foi realizada em 186 acessos da coleção nuclear de arroz da embrapa (CNAE), sendo 76 acessos do sistema de cultivo irrigado e 113 do sistema de cultivo de sequeiro, selecionados por não apresentarem vínculo genético. Destes três acessos foram utilizados como testemunha em ambos experimentos, sendo BR Irga 409, Metica 1 e BRS Caiapó. Os acessos foram avaliados em dois experimentos de campo, um para os genótipos do sistema de cultivo irrigado, e outro para os do sistema de cultivo de sequeiro, no delineamento de blocos casualizados com 4 repetições. Foram avaliados os seguintes caracteres: Número de panículas por metro (NPM), Número de grãos por panícula (NGP), Peso de 100 grãos (PCG) e Produtividade em kg.ha-1 (Prod). Para a análise de associação foram utilizados 29 marcadores SSR derivados de transcritos e 86 marcadores SSR derivados de sequências do DNA estrutural. Em relação as análises fenotípicas, os coeficientes de variação obtidos foram consistentes de acordo com o sistema de cultivo. Para os acessos de arroz irrigado foi verificada correlação positiva para NPM e Prod (0,2424; p<0,01) e Prod e PCG (0,1457; p<0,05) e correlação negativa para NPM e NGP (-0,457; p<0,01). Para os acessos do sistema de cultivo de sequeiro foi observada correlação positiva para NGP e Prod (0,4243; p<0,01). Correlação negativa foi observada para NPM e PCG (-0,2114; p<0,01). Em relação aos dados moleculares, para este trabalho foram desenvolvidos 44 marcadores SSR baseados em 27 mapas de QTLs. Destes, foram obtidos 181 alelos e uma média de 5,5 alelos por loco. Resultante da análise dos 44 marcadores SSR foram obtidos 29 marcadores que apresentaram padrão de bandas polimórfico, destes foram obtidas um PIC médio de 0,44 e 25 alelos privados. Para a análise de associação foram usados 29 marcadores polimórficos juntamente aos 86 marcadores fluorescentes (previamente avaliados por Borba et al. 2009), foram identificadas 43 associações significativas para NGP nos acessos de sequeiro e três associações significativas nos acessos de irrigado; 7 associações significativas para NPM nos acessos de irrigado e 9 associações significativas nos acessos de sequeiro; 14 associações foram significativas para PCG nos acessos de irrigado e 43 associações foram significativas nos acessos de sequeiro; 14 3 associações significativas para Prod foram encontradas nos acessos de sequeiro. A análise de associação identificou 61 marcadores SSR associados de forma consistente à produção e aos seus componentes. Foram detectadas 33 associações estatisticamente significativas a um ou mais caracteres, as quais validaram os resultados encontrados por meio das análises de mapeamento de QTLs realizadas em trabalhos anteriores. Os marcadores RM125, RM152 e Q69JE3 apresentaram maior número de associações por caracteres, sendo que algumas associações já foram previamente descritas na literatura. Os marcadores associados aos caracteres avaliados podem ser usados em programa de melhoramento visando a identificação de genes candidatos e seleção assistida. Os acessos CNA0001107, CNA0005478, CNA0010533, CNA0003287, IR 36, CNA0003602, CNA0006413, CNA0006174, CNA0003289, CNA0002253, CNA0004098, CNA0001416, CNA0003490 e CNA0000994 foram os que apresentaram o maior número de alelos favoráveis nos locos marcadores associados aos caracteres produção e seus componentes. Os acessos identificados por este trabalho como tendo o maior número de alelos favoráveis para os caracteres avaliadas serão recomendados para uso como genitores em programas de melhoramento de arroz do Brasil. A metodologia híbrida de mapeamento associativo utilizado por este trabalho foi interessante por permitir que, mesmo utilizando baixa resolução, juntamente com o desequilíbrio de ligação encontrado em arroz, determinados locos previamente mapeados em análises de QTLs foram novamente associados aos caracteres avaliados nos ensaios de campo. Esta estratégia pode ser repetida para outros caracteres de interesse em arroz, como tolerância à seca e frio e resistência a doenças.

In this dissertation, the posterior probability of linkage (PPL) framework is extended to the analysis of case-control (CC) data and three new linkage disequilibrium (LD) statistics are introduced. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the unified analysis of these disparate data structures. They also provide the other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets of subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, being implemented within the PPL framework, the new statistics readily allow linkage information obtained from distinct data, to be incorporated into LD analyses in the form of a prior probability distribution. Performance of the proposed LD statistics is examined using simulated data. In addition, the effects of key modeling violations on performance are assessed. These statistics are also applied to a previously published type 1 diabetes (T1D) family dataset with a few candidate genes with previously reported weak associations, and another T1D CC dataset also previously published as a genome-wide association (GWA) study with some strong associations reported. The new LD statistics under the PPLD framework confirm most of the findings in the published work and also find some new SNPs suspected of being associated with T1D. Sequential updating between the family dataset and the CC dataset dramatically increased the association signal strength for a CTLA4 SNP genotyped in both studies. Linkage information gleaned from the family dataset is also combined into the LD analysis of the CC dataset to demonstrate the utility of this unique feature of the PPL framework, and specifically for the new LD statistics.

Evidence indicates seed oil melting point is likely an adaptive quantitative trait in many flowering plant species. An adaptive hypothesis suggests selection has changed the melting point of seed oils to covary with germination temperatures because of a trade-off between total energy stores and the rate of energy acquisition during germination under competition. The predicted differences in relative fitness under different temperatures have not yet been tested and little is known about the genetic basis of differences in oil composition. I used Arabidopsis thaliana to: (1) assess the fitness consequences of high and low melting point seeds germinating at different temperatures, (2) assess what genes underlie natural variation in seed oil composition, and (3) consider how these genes may be used to create oils with particular characteristics. To assess the effects of seed oil melting point on timing of seedling emergence and fitness, I competed high and low melting point lines of A. thaliana under cold and warm temperatures. Emergence timing between these lines was not significantly different at either temperature, which comported with warm temperature predictions but not cold temperature predictions. Under all conditions, plants competing against high melting point lines had lower fitness relative to those against low melting point lines, which matched expectations for undifferentiated emergence times. To assess the genetic basis of naturally occurring variation in seed oil melting point, the seed oil compositions of 391 accessions of A. thaliana were used in a genome-wide association study. Twelve genes were tightly linked with SNPs significantly associated with seed oil melting point variation. Seven encoded lipid synthesis enzymes or regulatory products. The remaining 5 encoded products with no clear relation to seed oil melting point. Results suggest selection can alter quantitative trait variation in response to local conditions through a small set of genes. 268 seed-expressed, candidate genes were linked to 103 SNPs associated with A. thaliana seed oil fatty acids. Eight genes were involved in lipid metabolism, and thirty-four encoded regulatory products. I discuss how knowledge of these genes can be used to breed and engineer desirable oil compositions for industry and nutrition. / text

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Plant-herbivore interactions are among the most common of ecological interactions. It is therefore not surprising that plants have evolved multiple mechanisms to defend themselves, using both constitutive chemical and physical barriers and by induced responses which are only expressed after herbivory has occurred. Herbivores, on the other hand, respond to these plant defenses by evolving counter-adaptations which makes defenses less effective or even useless. Adaptation can occur at different geographical scales, with varying coevolutionary interactions across a spatially heterogenous landscape. By looking at the underlying genes responsible for these defensive traits and herbivore related phenotypic traits, it is possible to investigate the coevolutionary history of these plant- herbivore interactions. Here I use molecular population genetic tools to investigate the evolutionary history of several inducible defense genes in European Aspen (Populus tremula) in Sweden. Two genes, belonging to the Polyphenol oxidase gene-family (PPO1 and PPO2), show skews in their site frequency spectrum together with patterns of diversity and divergence from an outgroup which correspond to signatures of adaptive evolution (Paper II). 71 single nucleotide polymorphisms (SNPs) from seven inducible defense genes (PPO1-PPO3, TI2-TI5) show elevated levels of population differentiation compared to control genes (genes not involved in plant defense), and 10 of these defense SNPs show strong signatures of natural selection (Paper III). These 71 defense SNPs also divides a sample of Swedish P. tremula trees into three distinct geographical groups, corresponding to a Southern, Central and Northern cluster, a patterns that is not present in control SNPs (Paper III). The same geographical pattern, with a distinct Northern cluster, is also observed in several phenotypic traits related to herbivory in our common garden in Sävar (Paper IV). These phenotypic traits show patterns of apparent local maladaptation of the herbivore community to the host population which could indicate the presence of “information coevolution” between plants and herbivores (Paper IV). 15 unique defense SNPs also show significant associations to eight phenotypic traits but the causal effects of these SNP associations may be confounded by the geographic structure found in both the underlying genes and in the phenotypic traits. The co-occurrence of population structure in both defense genes and herbivore community traits may be the result from historical events during the post-glacial recolonization of Sweden. / Interaktioner mellan växter och herbivorer är bland de vanligaste ekologiska interaktionerna och det är därför inte förvånande att växter har utvecklat flera olika mekanismer för att försvara sig. Dessa försvarsmekanismer består både av konstitutiva kemiska och fysiska barriärer så väl som inducerade försvar som bara är uttryckta efter att en växt har blivit skadad genom betning. Herbivorerna å sin sida svarar på dessa försvar genom att utveckla motanpassningar som gör växternas försvar mindre effektiva eller till och med verkningslösa. Dessa anpassningar kan ske över olika geografiska skalor beroende på om de samevolutionära interaktionerna varierar i ett rumsligt heterogent landskap. Genom att studera de underliggande gener som kontrollerar dessa försvarsegenskaper tillsammans med herbivorrelaterade fenotypiska egenskaper är det möjligt att undersöka den samevolutionära historien av interaktionerna mellan växter och herbivorer. Här använder jag mig av molekylärpopulationsgenetiska verktyg för att undersöka den evolutionära historien i flera inducerade försvarsgener hos asp (Populus tremula) i Sverige. Två gener, som tillhör genfamiljen Polyphenol-oxidaser (PPO1 och PPO2), uppvisar ett frekvensmönster som man förväntar sig vid positiv selektion. Detta mönster kan också ses i dessa geners diversitet samt i divergens från en utgrupp (Uppsats II). 71 ”single nucleotide polymorphisms” (SNPar) från 7 inducerade försvarsgener (PPO1-PPO3, TI2-TI5) visar förhöjda nivåer av populationsdifferentiering jämfört med kontrollgener (gener som inte är involverade i trädens försvar), och 10 av dessa försvars-SNPar visar även tecken på naturlig selektion (Uppsats III). Dessa 71 försvars-SNPar delar in ett urval av svenska aspar i tre distinkta geografiska grupper som beskriver ett sydligt, centralt och nordligt kluster som inte förekommer hos kontroll-SNPar (Uppsats III). Samma geografiska mönster, med ett distinkt nordligt kluster, återfinns däremot i ett antal fenotypiska egenskaper som är relaterade till herbivori i ett odlingsförsök utanför Sävar (Uppsats IV). Dessa fenotypiska egenskaper visar tecken på lokal felanpassning hos herbivorsamhället till den lokala värdpopulationen, vilket kan indikera förekomsten av ett ”samevolutionärt informationsutbyte” mellan växter och herbivorer (Uppsats IV). 15 unika försvars-SNPar påvisar också signifikanta associationer med 8 olika fenotypiska egenskaper, men om dessa har en verklig effekt eller inte är svårt att säga på grund av den geografiska strukturen som förekommer både hos de underliggande generna och hos de fenotypiska egenskaperna. Att denna populationsstruktur förekommer hos både försvarsgener och egenskaper som är förknippade med herbivorsamhället kan däremot vara ett resultat av historiska händelser som skett under aspens post-glaciala återkolonisation av Sverige.