Widely effective treatment for alcohol use disorder is not yet available, because the exact biological mechanisms that underlie this disorder are not completely understood. One way to gain a better understanding of these mechanisms is to examine the genetic frameworks that contribute to the risk for developing this disorder. This dissertation examines genetic association data in combination with gene expression networks in the brain to identify functional groups of genes associated with alcohol consumption and dependence. The first study took advantage of the behavioral complexity of human samples, and experimental capabilities provided by mouse models, by co-analyzing gene expression networks in the mesolimbocortical system of acute alcohol-treated mice and human genetic alcohol dependence association data. This study successfully identified ethanol-responsive gene expression networks with overrepresentation of genes suggestively associated with alcohol dependence in an independent human sample, indicating that gene expression networks in mouse models are informative for identifying mechanistic networks relevant to the risk for developing dependence. The second study aimed to identify quantitative trait loci for voluntary alcohol drinking behaviors under an intermittent ethanol access paradigm, in the genetically complex Diversity Outbred mice. After determining high heritability for alcohol consumption and dependence amongst the progenitor strains, we identified several specific genetic loci associated with these traits. One locus replicated results from a human association study of alcohol consumption, and provided insight to the potentially contributing genes. Finally, we identified alcohol consumption-correlated gene expression networks in the prefrontal cortex of these mice. We also mapped quantitative trait loci for network expression levels, some of which overlapped with the behavioral loci, indicating that the functions represented by these modules mediate the relationship between the genotypes in that region and drinking behaviors. Overall, our studies revealed neuroplastic and ubiquitin-related genes pathways involved in alcohol consumption in mice and humans, and that likely contribute to the risk for developing dependence.

<p>The focus of this thesis is on numerical algorithms for efficient solution of QTL analysis problem in genetics.</p><p>Firstly, we consider QTL mapping problems where a standard least-squares model is used for computing the model fit. We develop optimization methods for the local problems in a hybrid global-local optimization scheme for determining the optimal set of QTL locations. Here, the local problems have constant bound constraints and may be non-convex and/or flat in one or more directions. We propose an enhanced quasi-Newton method and also implement several schemes for constrained optimization. The algorithms are adopted to the QTL optimization problems. We show that it is possible to use the new schemes to solve problems with up to 6 QTLs efficiently and accurately, and that the work is reduced with up to two orders magnitude compared to using only global optimization.</p><p>Secondly, we study numerical methods for QTL mapping where variance component estimation and a REML model is used. This results in a non-linear optimization problem for computing the model fit in each set of QTL locations. Here, we compare different optimization schemes and adopt them for the specifics of the problem. The results show that our version of the active set method is efficient and robust, which is not the case for methods used earlier. We also study the matrix operations performed inside the optimization loop, and develop more efficient algorithms for the REML computations. We develop a scheme for reducing the number of objective function evaluations, and we accelerate the computations of the derivatives of the log-likelihood by introducing an efficient scheme for computing the inverse of the variance-covariance matrix and other components of the derivatives of the log-likelihood.</p>

<p>Domestication has led to genetic changes that affect quantitative traits in farm animals. Both candidate gene analysis using association tests and genome scans based on linkage analysis have been performed to understand the molecular basis underlying quantitative genetic variation in horses, pigs and chickens. To test a possible association of polymorphisms in the <i>PRKAG3</i> gene, previously found to be associated with excess glycogen content in pig skeletal muscle, with quantitative traits in the horse, the major coding part of the equine <i>PRKAG3</i> sequence was identified. Bioinformatic characterization of the equine <i>PRKAG3</i> gene was conducted. A single nucleotide polymorphism (SNP) causing a missense mutation (Pro258Leu) was found. Screening this SNP showed that the Leu258 allele was more frequent in breeds with heavy muscularity. To assess previously reported associations between polymorphisms in the <i>MC4R</i> gene and obesity-related traits further, we conducted linkage analysis between the <i>MC4R</i> locus and fatness-related traits using a Wild BoarxLarge White intercross. No significant association between segregation at the <i>MC4R</i> locus and fatness was detected in this pedigree. A genome scan of quantitative trait loci (QTLs) has been performed in an intercross between chicken lines divergently selected for growth. Divergent parental lines have been established by selecting for high and low 56-day body weight for over 40 generations. The selection has led to approximately a 9-fold difference in 56-day body weight between lines and resulted in correlated responses for a number of traits including appetite, immune response, body composition and metabolic traits. Phenotypic data on growth and other correlated traits were collected from more than 800 F2 individuals. Genome scans using 145 markers on 26 linkage groups have identified QTLs affecting growth and correlated responses to selection for 56-day body weight. No major QTL explaining a large portion of phenotypic variation in growth was revealed in this study. </p>

Domestication has led to genetic changes that affect quantitative traits in farm animals. Both candidate gene analysis using association tests and genome scans based on linkage analysis have been performed to understand the molecular basis underlying quantitative genetic variation in horses, pigs and chickens. To test a possible association of polymorphisms in the PRKAG3 gene, previously found to be associated with excess glycogen content in pig skeletal muscle, with quantitative traits in the horse, the major coding part of the equine PRKAG3 sequence was identified. Bioinformatic characterization of the equine PRKAG3 gene was conducted. A single nucleotide polymorphism (SNP) causing a missense mutation (Pro258Leu) was found. Screening this SNP showed that the Leu258 allele was more frequent in breeds with heavy muscularity. To assess previously reported associations between polymorphisms in the MC4R gene and obesity-related traits further, we conducted linkage analysis between the MC4R locus and fatness-related traits using a Wild BoarxLarge White intercross. No significant association between segregation at the MC4R locus and fatness was detected in this pedigree. A genome scan of quantitative trait loci (QTLs) has been performed in an intercross between chicken lines divergently selected for growth. Divergent parental lines have been established by selecting for high and low 56-day body weight for over 40 generations. The selection has led to approximately a 9-fold difference in 56-day body weight between lines and resulted in correlated responses for a number of traits including appetite, immune response, body composition and metabolic traits. Phenotypic data on growth and other correlated traits were collected from more than 800 F2 individuals. Genome scans using 145 markers on 26 linkage groups have identified QTLs affecting growth and correlated responses to selection for 56-day body weight. No major QTL explaining a large portion of phenotypic variation in growth was revealed in this study.

The focus of this thesis is on numerical algorithms for efficient solution of QTL analysis problem in genetics. Firstly, we consider QTL mapping problems where a standard least-squares model is used for computing the model fit. We develop optimization methods for the local problems in a hybrid global-local optimization scheme for determining the optimal set of QTL locations. Here, the local problems have constant bound constraints and may be non-convex and/or flat in one or more directions. We propose an enhanced quasi-Newton method and also implement several schemes for constrained optimization. The algorithms are adopted to the QTL optimization problems. We show that it is possible to use the new schemes to solve problems with up to 6 QTLs efficiently and accurately, and that the work is reduced with up to two orders magnitude compared to using only global optimization. Secondly, we study numerical methods for QTL mapping where variance component estimation and a REML model is used. This results in a non-linear optimization problem for computing the model fit in each set of QTL locations. Here, we compare different optimization schemes and adopt them for the specifics of the problem. The results show that our version of the active set method is efficient and robust, which is not the case for methods used earlier. We also study the matrix operations performed inside the optimization loop, and develop more efficient algorithms for the REML computations. We develop a scheme for reducing the number of objective function evaluations, and we accelerate the computations of the derivatives of the log-likelihood by introducing an efficient scheme for computing the inverse of the variance-covariance matrix and other components of the derivatives of the log-likelihood.

The ability to identify germplasm and select traits accurately is fundamental to successful plant breeding. Pedigrees and molecular markers facilitate these processes; however misleading experimental results can occur when incorrect relationships and/or cultivar names are recorded. Molecular markers can identify these inconsistencies, and with advances in genotyping technology these diagnostics can be done faster and more objectively. This study aimed to develop molecular marker assays and graphical genotyping methodologies for cultivar identification, seed purity assessment and trait selection in oat (Avena sativa L.). KBioscience’s Allele-Specific PCR (KASP™) and genotyping-by-sequencing (GBS) technologies were applied to a set of current Canadian oat cultivars to evaluate their utility for identifying cultivars and detecting intra-cultivar variation. Both KASP™ and GBS detected different extents of heterogeneity among a set of 160 seeds that originated from four seed sources of four cultivars. In both cases, the detected variation did not appear to be limited to a specific cultivar or seed source, reinforcing that all cultivars are heterogeneous. Graphical genotyping localized heterogeneity to specific chromosome regions, thereby distinguishing physical contamination from true genetic heterogeneity and heterozygosity. Pre-existing genotype data for 700 oat cultivars and breeding lines were also used to construct graphical genotypes for pedigree validation and discovery of potential sources for favourable quantitative trait loci (QTL) alleles. This methodology used historical QTLs and anchoring markers to identify 25 putative “high oil” allele carriers. The results from this study will provide diagnostic tools for cultivar identification and pedigree validation, in addition to meaningful information about existing heterogeneity and possible QTL locations in current cultivars.

Diplodia ear rot caused by the fungus Stenocarpella maydis (Berkeley) and Stenocarpella macrospora (Earle) have become one of the most important limiting factors for the production of Corn (Zea mays L.) in Brazil. The fungus can attack the stalks, leaves and the grain causing significant reductions on yield and the overall quality of the grain, since it can produce micotoxinas that are dangerous to livestock. Resistance to ear rot by Stenocarpella sp in corn is quantitative and highly influenced by the environment and even that artificial inoculation techniques are available to screen for the disease the overall cost is very expensive. The objective of this study was the identification of quantitative trait loci (QTL s) associated with ear rot resistance by Stenocarpella sp in one breeding population composed of 141 doublehaploid progenies resulted from the cross among the resistant inbred MONDR1 and the susceptible inbred MONDS1 in testcrosses with the susceptible tester MONDS5. Testcrosses were evaluated at harvest time after artificial inoculation for ear rot at three different locations in the central region of Brazil during the 2005/06 summer season. Thru Composite interval mapping (CIM), a total of three QTL s (LOD>2.5) for ear rot resistance were identified at chromosomes 2, 3 and 5, all together accounting for up 26% of total phenotypic variation for this character. The identification of two QTL s for ear rot resistance coming from the susceptible parent MONDS1 appear to indicate the presence of the phenomena of transgressive segregation. Additionally we were able to identify six double-haploid progenies with high level of resistance to ear rot by Stenocarpella (MDH15, MDH443, MDH95, MDH2, MDH120 e MDH81), being those recommended for their incorporation into the breeding program as new breeding sources for the Central Brazil regions. / Grãos ardidos causados pelos fungos Stenocarpella maydis (Berkeley) e Stenocarpella macrospora (Earle) tem se constituído num dos maiores fatores limitantes para a produção de milho (Zea mays L.) no Brasil. Estes fungos podem causar infecções no colmo, folhas e grãos, podendo ocasionar reduções significativas na produtividade e na qualidade do grão, pela produção de micotoxinas daninhas para aves e bovinos. A resistência para podridão de grão por Stenocarpella sp apresenta herança quantitativa e pode ser altamente influenciada pelo meio ambiente, e embora existam técnicas de inoculação que facilitam a discriminação de materiais suscetíveis, isto requer de grande quantidade de recursos. O objetivo do presente trabalho foi à identificação de locos de caracteres quantitativos (QTL) associados à resistência para podridão de grãos ( grãos ardidos ) ocasionados por Stenocarpella sp numa população de 141 progênies duplo-haplóides derivadas do cruzamento entre a linhagem resistente MONDR1 e a linhagem susceptível MONDS1 em testcross com o testador susceptível MONDS5. A porcentagem de espigas infectadas por Stenocarpella sp foi registrada para cada uma das testcrosses apos da inoculação artificial em três localidades na região Central de Brasil durante a Safra agrícola 2005/06. Mediante a análise de mapeamento por intervalo composto foram identificados três QTL s com LOD>2.5 para resistência à grãos ardidos nos cromossomos 2, 3 e 5, sendo estes em conjunto, responsáveis por ate 26% de variação fenotípica para este caráter. A identificação de dois QTL s para resistência a grãos ardidos por Stenocarpella sp com origem no progenitor susceptível parece indicar a presença do fenômeno de segregação transgressiva. Adicionalmente foram identificadas seis progênies duplohaplóides com alto nível de resistência a grãos ardidos (MDH15, MDH443, MDH95, MDH2, MDH120 e MDH81), sendo estas recomendadas para sua incorporação no programa de melhoramento para a região central do Brasil. / Mestre em Genética e Bioquímica

The ability to identify germplasm and select traits accurately is fundamental to successful plant breeding. Pedigrees and molecular markers facilitate these processes; however misleading experimental results can occur when incorrect relationships and/or cultivar names are recorded. Molecular markers can identify these inconsistencies, and with advances in genotyping technology these diagnostics can be done faster and more objectively. This study aimed to develop molecular marker assays and graphical genotyping methodologies for cultivar identification, seed purity assessment and trait selection in oat (Avena sativa L.). KBioscience’s Allele-Specific PCR (KASP™) and genotyping-by-sequencing (GBS) technologies were applied to a set of current Canadian oat cultivars to evaluate their utility for identifying cultivars and detecting intra-cultivar variation. Both KASP™ and GBS detected different extents of heterogeneity among a set of 160 seeds that originated from four seed sources of four cultivars. In both cases, the detected variation did not appear to be limited to a specific cultivar or seed source, reinforcing that all cultivars are heterogeneous. Graphical genotyping localized heterogeneity to specific chromosome regions, thereby distinguishing physical contamination from true genetic heterogeneity and heterozygosity. Pre-existing genotype data for 700 oat cultivars and breeding lines were also used to construct graphical genotypes for pedigree validation and discovery of potential sources for favourable quantitative trait loci (QTL) alleles. This methodology used historical QTLs and anchoring markers to identify 25 putative “high oil” allele carriers. The results from this study will provide diagnostic tools for cultivar identification and pedigree validation, in addition to meaningful information about existing heterogeneity and possible QTL locations in current cultivars.

Neurogenesis in the adult hippocampus contributes to learning and memory in the healthy brain but is dysregulated in metabolic and neurodegenerative diseases. The molecular relationships between neural stem cell activity, adult neurogenesis, and global metabolism are largely unknown. Here we applied unbiased systems genetics methods to quantify genetic covariation among adult neurogenesis and metabolic phenotypes in peripheral tissues of a genetically diverse family of rat strains, derived from a cross between the spontaneously hypertensive (SHR/OlaIpcv) strain and Brown Norway (BN-Lx/Cub). The HXB/BXH family is a very well established model to dissect genetic variants that modulate metabolic and cardiovascular diseases and we have accumulated deep phenome and transcriptome data in a FAIR-compliant resource for systematic and integrative analyses. Here we measured rates of precursor cell proliferation, survival of new neurons, and gene expression in the hippocampus of the entire HXB/BXH family, including both parents. These data were combined with published metabolic phenotypes to detect a neurometabolic quantitative trait locus (QTL) for serum glucose and neuronal survival on Chromosome 16: 62.1–66.3 Mb. We subsequently fine-mapped the key phenotype to a locus that includes the Telo2-interacting protein 2 gene (Tti2)—a chaperone that modulates the activity and stability of PIKK kinases. To verify the hypothesis that differences in neurogenesis and glucose levels are caused by a polymorphism in Tti2, we generated a targeted frameshift mutation on the SHR/OlaIpcv background. Heterozygous SHR-Tti2+/- mutants had lower rates of hippocampal neurogenesis and hallmarks of dysglycemia compared to wild-type littermates. Our findings highlight Tti2 as a causal genetic link between glucose metabolism and structural brain plasticity. In humans, more than 800 genomic variants are linked to TTI2 expression, seven of which have associations to protein and blood stem cell factor concentrations, blood pressure and frontotemporal dementia.

Flax (Linum usitatissimum L.) is a self-pollinated crop widely cultivated for fiber and oil production. Flaxseed is renowned for its health attributes but the presence of compounds, such as the heavy metal cadmium (Cd), is undesirable. Genomic studies in flax have produced large amounts of data in the last 15 years, providing useful resources to improve the genetic of this crop using genomics-based technologies and strategies. The goal of this thesis is therefore to capitalize on these advances to address the Cd problem and to propose solutions to improve breeding efficiencies. To find genomic-based solutions to Cd content, to the currently low breeding efficiency and to abiotic stress resistance in flax, this study utilized four major strategies: (1) genomic cross prediction, (2) gene family identification, (3) genome-wide association study (GWAS) and (4) genomic selection (GS). Characterization of the ATP-binding cassette (ABC) transporter and heavy metal associated (HMA) gene families was performed using the flax genome sequence. A total of 198 ABC transporter and 12 HMA genes were identified in the flax genome, of which nine were orthologous to Cd-associated genes in Arabidopsis, rice and maize. A transcriptomic analysis of eight tissues provided some support towards the functional annotation of these genes and confirmed the expression of these ABC transporter and HMA genes in flax seeds and other tissues. A diversity panel of 168 flax accessions was grown in the field at multiple locations and years and the seed content of 24 heavy metals (HMs) was measured. The panel was also sequenced and a single nucleotide polymorphism (SNP) dataset of nearly 43,000 SNPs was defined. A GWAS was conducted using these genotypic and phenotypic data and a total of 355 non-redundant quantitative trait nucleotides (QTNs) were identified for ten of the 24 metal contents. Overall, a total of 24 major and 331 minor effect QTNs were detected, including 11 that were pleiotropic. After allelic tests, 108 non-redundant QTNs were retained for eight of the ten metals and ranging from one for copper (Cu) to 70 for strontium (Sr). A total of 20 candidate genes for HM accumulation were identified at 12 of the 24 major QTN loci, of which five belonged to the ABC transporter family. Many of the metal contents, including Cd, appeared to be controlled by many genes of small effects; hence, GS is better suited than marker-assisted selection for application in breeding. To test this, predictive ability using ten GS statistical models was evaluated using trait-specific QTN and the random genome-wide 43K SNP datasets. Significantly higher predictive abilities were observed from the GS models built with the dataset made of QTNs associated with metal contents (70-80%) compared to that of the 43K dataset (10-25%). This study showed the feasibility of using GS to improve the predictive ability of polygenic traits such as metal content in seeds. GS can be applied in early generation selection to accelerate the improvement of abiotic stress resistance and either select low-Cd lines or discard high-Cd lines. These findings validate the use of a QTL-based strategy as a highly effective method for improving the efficiency of predictive ability of GS for highly complex traits such as resistance or tolerance to HM accumulation. Identification of both large and minor effect QTNs and/or pleiotropic effects hold potential for flax breeding improvement. Candidate gene functional validation can be performed using methods such as genome editing or targeting induced local lesions in genomes (TILLING).