Heat stress adversely affects wheat production in many regions of the world and is particularly detrimental during reproductive development. The objective of this study was to identify quantitative trait loci (QTL) associated with improved heat tolerance in hexaploid bread wheat (Triticum aestivum). To accomplish this objective, an analysis of both the phenotypic and genetic responses of two recombinant inbred line (RIL) populations was conducted. RIL populations Halberd x Cutter and Halberd x Karl 92 (H/K) both derive heat tolerance from Halberd and segregate in their response to heat stress. A heat susceptibility index (HSI) was calculated from the reduction of three yield components; kernel number, kernel weight, and single kernel weight, following a three-day 38 degrees C heat stress treatment during early grain-filling. The HSI, as well as temperature depression of the main spike and flag leaf were used as measurements of heat tolerance. Genetic linkage maps were constructed for both populations and were used in combination with phenotypic data and statistical software to detect QTL for heat tolerance. In a comparison across the two across populations, seven common QTL regions were identified for HSI, located on chromosomes 1B, 3B, 4A, 5A, 5B, and 6D. Subsequent analysis of temperature depression in the H/K population identified seven QTL that co-localized for both cooler organ temperature and improved HSI. Four of the beneficial alleles at these loci were contributed Halberd. The genetic effect of combining QTL, including QHkw.tam-1B, QHkwm.tam-5A.1, and QHskm.tam-6D showed the potential benefit of selection for multiple heat tolerant alleles simultaneously. Analysis of the H/K population in the field under abiotic stress detected QTL on chromosome 3B and 5A, which were in agreement with results from the greenhouse study. The locus QYld.tam-3B was pleiotropic for both temperature depression and HSI in both experiments and was associated with higher biomass and yield under field conditions. The results presented here represent a comprehensive analysis of both the phenotypic response of wheat to high temperature stress and the genetic loci associated with improved heat tolerance and will be valuable for future understanding and improvement of heat stress tolerance in wheat.

Variable selection methods play important roles in modeling high-dimensional data and are key to data-driven scientific discoveries. In this thesis, we consider the problem of variable selection with interaction detection. Instead of building a predictive model of the response given combinations of predictors, we start by modeling the conditional distribution of predictors given partitions based on responses. We use this inverse modeling perspective as motivation to propose a stepwise procedure for effectively detecting interaction with few assumptions on parametric form. The proposed procedure is able to detect pairwise interactions among p predictors with a computational time of \(O(p)\) instead of \(O(p^2)\) under moderate conditions. We establish consistency of the proposed procedure in variable selection under a diverging number of predictors and sample size. We demonstrate its excellent empirical performance in comparison with some existing methods through simulation studies as well as real data examples. Next, we combine the forward and inverse modeling perspectives under the Bayesian framework to detect pleiotropic and epistatic effects in effects in expression quantitative loci (eQTLs) studies. We augment the Bayesian partition model proposed by Zhang et al. (2010) to capture complex dependence structure among gene expression and genetic markers. In particular, we propose a sequential partition prior to model the asymmetric roles played by the response and the predictors, and we develop an efficient dynamic programming algorithm for sampling latent individual partitions. The augmented partition model significantly improves the power in detecting eQTLs compared to previous methods in both simulations and real data examples pertaining to yeast. Finally, we study the application of Bayesian partition models in the unsupervised learning of transcription factor (TF) families based on protein binding microarray (PBM). The problem of TF subclass identification can be viewed as the clustering of TFs with variable selection on their binding DNA sequences. Our model provides simultaneous identification of TF families and their shared sequence preferences, as well as DNA sequences bound preferentially by individual members of TF families. Our analysis may aid in deciphering cis regulatory codes and determinants of protein-DNA binding specificity. / Statistics

I examined phenotypic and genetic variation in growth traits in 30 families of commercial Fraser strain Arctic charr (Salvelinus alpinus) reared in freshwater (FRW) and brackish water (BRW) in Eastern Canada. I detected family by treatment interactions for all traits [body weight (BW), condition factor (K) and specific growth rate (SGR)] across all measurement dates and growth intervals, however, mean family BW in FRW was correlated phenotypically with BRW BW. In addition, FRW fish showed significantly greater survival than those transferred to BRW and fish which survived until the conclusion of the experiment were significantly heavier in BW at the baseline assessment than their full-sibs that died. These observations suggest that BW in FRW and BW in BRW should be analyzed as separate but correlated traits in Arctic charr breeding programs. I then tested the potential for genetic improvement in this species by calculating genetic parameters for BW and K, and tested if previously identified quantitative trait loci (QTL) for these traits were detectable across the broodstock. QTL with experiment-wide and chromosome-wide significance for body size and condition factor were detected on multiple linkage groups. Heritability for BW and K was moderate in FRW (0.29-0.38) but lower in BRW (0.14-0.17). Genetic correlations for BW across environments were positive and moderate (0.33-0.67), however equivalent K correlations were weaker (0.24-0.37). This information was then used to predict the rate of genetic change following one generation of selection for BW using phenotypic selection and genomic methodologies including marker-only selection and marker assisted selection. The greatest response in the rate of genetic change was achieved by selecting only from families in which significant BW QTL had been identified. As such, marker assisted selection showed the greatest gain in genetic response with 5.4% in FRW and 4.3% in BRW. These results have applications to commercial aquaculture as the Canadian aquaculture industry is attempting to diversify with alternative species. Such genetic improvement strategies will aid in developing a strain of Arctic charr characterised by increased BW. / Funding provided through the NSERC Strategic grants program. The project was sponsored by CanAqua Seafoods Ltd. in collaboration with the Coastal Zones Research Institute.

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<p>While local adaptation has been frequently demonstrated via reciprocal transplant experiments, our understanding of the genetic basis of it remains minimal. There is a notable lack of studies that identify naturally segregating variants, determine the traits controlled by these variants and characterize their fitness effects in the field. Such studies are critical for understanding how spatially varying selective pressures can drive population divergence and maintain genetic variation. The experiments presented here aim to characterize the genetic basis of local adaptation to serpentine soils in Mimulus guttatus. First, I show that serpentine and non-serpentine populations of M. guttatus are locally adapted to soil habitat wherein non-serpentine plants are unable to survive on serpentine soils. Serpentine tolerance appears to come at a cost as serpentine plants are smaller in the juvenile stage than non-serpentine plants when grown at non-serpentine field sites. These size differences may limit the competitive ability of serpentine tolerant plants in non-serpentine habitats which tend to be more heavily vegetated than serpentine habitats. Next I identify environmental variables that are important selective agents in the serpentine habitat. Using hydroponic assays to isolate an individual chemical variable of serpentine soils - low calcium levels to high magnesium levels (low Ca:Mg ratio) - I show that serpentine and non-serpentine populations of M. guttatus have significant differences in tolerance to low Ca:Mg. I then characterize the genetic basis of these ecotypic differences in survival and tolerance using quantitative trait locus (QTL) mapping. I identify a single, major QTL that controls both the ability to survive on serpentine soils and tolerance to low Ca:Mg ratio which suggests that M. guttatus populations have adapted to serpentine soils through an ability to tolerate the low levels of Ca while simultaneously not suffering from Mg toxicity. Furthermore, I show that this same QTL controls ability to survive on serpentine soils in a second, geographically distant population. However, preliminary work suggests that the two populations are not equally tolerant to each other's soils indicating that either other loci also contribute to serpentine tolerance and these are not shared between the two serpentine populations or that there are different serpentine tolerance alleles at the major QTL are not functionally equivalent. This work addresses long-standing questions in evolutionary biology regarding the number and effect size of loci that underlie adaptive traits by identifying a large effect locus that contributes to adaptive differences between M. guttatus populations.</p> / Dissertation

Fusarium Head Blight (FHB) is a destructive disease caused by Fusarium graminearum that affects wheat (Triticum aestivum L.) worldwide. Breeding for resistance to FHB is arguably the best way to combat this disease. However, FHB resistance is highly complex and phenotypic screening is difficult. Molecular markers are a promising tool but breeding programs face the challenge of allocating resources in such a way that the optimum balance between phenotypic and genotypic selection is reached. An F2:3 population derived from a resistant x susceptible cross was subjected to phenotypic and genotypic selection. For phenotyping, a novel air separation method was used to measure percentage of damaged kernels (FDK). Heritability estimates were remarkably high, which was attributed to the type of cross and the quality of phenotyping. Genotypic selection was done by selecting resistance alleles at quantitative trait loci (QTL) on the 3BS (Fhb1) and the 2DL chromosomes. Fhb1 conferred a moderate but stable FHB resistance while the 2DL QTL conferred a surprisingly high level of resistance but with significant interaction with the environment. Phenotypic selection conferred higher or lower genetic gains than genotypic selection, depending on the selection intensity. Based on these results, different selection strategies are discussed.

A whole genome scan was conducted to identify and fine map QTL regions for ultrasound and carcass merit traits in beef cattle. A total of 465 steers and bulls, genotyped for 4592 SNPs, were analysed for 16 ultrasound and carcass merit traits using interval mapping, single marker regression and Bayesian shrinkage approaches. Thirty QTL and 22 SNPs associated with traits were identified by interval mapping and single marker regression respectively. In Bayesian shrinkage estimation, 218 QTL were identified, wherein 11 of the 30 QTL identified by interval mapping were confirmed. The proportions of QTL variance on the trait variations estimated by Bayesian shrinkage analysis were relatively small. They ranged from 0.1 to 4.8% compared to 6.1 to 11.7% in interval mapping because the QTL in Bayesian approach were adjusted to remove effects of other QTL in the genome. These results are useful for detection of underlying causative QTN variants. / Animal Science

The objective of this thesis was to construct radiation hybrid (RH) maps and estimate linkage disequilibrium (LD) using high density SNP markers on chromosomes 19 (BTA19) and 29 (BTA29) and use these as a tool to detect QTL in dairy and beef cattle. We have constructed RH maps of BTA19 and BTA29 consisting of 555 and 253 SNP markers respectively using a 12,000 rad whole genome RH panel. When aligned with the third draft of bovine genome sequence assembly, there was a significant internal rearrangement of the markers involving displacement, inversion and flips within the scaffolds with some scaffolds being misplaced in the genome assembly. Many of these mapped markers (370 and 186 SNP markers on BTA19 and 29 respectively) were further utilized to quantify the extent of LD using the square of the correlation coefficient (r2) and to study the pattern of selection signatures in beef (Angus) and dairy (Holstein) breeds of Bos taurus. Along the chromosomes, patterns of LD were variable in both breeds and a minimum of 30,000 informative and evenly spaced markers would be required for whole genome association studies in cattle. In addition, chromosomal regions showing evidence of selection for economically important traits in Angus and Holstein were identified. Furthermore, the dense SNP markers were used to perform chromosome-wide scan to detect QTL for different economically important traits in beef and dairy cattle. Two approaches, single marker LD regression and Bayesian Monte Carlo Markov Chain, were used to map QTL. QTL for 10 and 5 traits in dairy cattle and for 2 and 1 trait in beef cattle on BTA19 and 29 respectively were detected using both approaches of QTL mapping. The QTL detected in this study are a step towards the identification of positional candidate genes controlling these traits. In addition, we have detected several SNPs influencing economically important traits in both beef and dairy cattle. Some SNPs have been validated in an independent cattle population and has the potential of being utilized in the marker assisted selection of cattle. / Animal Science

Recent initiatives for biofuel production have increased research and development of sweet sorghum. Currently, the initial major limitation to integrating sweet sorghum into existing production systems is the lack of sweet sorghum hybrids adapted to industrial production systems. Hybrid development is now underway, and the application of genetic markers can be used to define the genetic basis of sugar yield and its components, as well as reduce the time required to deliver new sweet sorghum hybrids to market. The purpose of this research was to further characterize the genetic components that influence sweet sorghum productivity, agronomics, and composition. Specifically, a grain x sweet sorghum recombinant inbred line (RIL) population developed for quantitative trait locus (QTL) analysis related to sugar production was evaluated for 24 phenotypic traits including brix, percent moisture, and biomass yield across four environments. The 185 F4 RILs were derived from the parents 'BTx3197' and 'Rio', which are pithy stalk grain and juicy stalk sweet sorghums respectively. Following screening, two genetic maps were constructed with 372 and 381 single nucleotide polymorphisms (SNPs) evaluated using an Illumina GoldenGate assay. Analysis of the data in QTL Cartographer revealed a major and previously reported QTL for soluble solids on chromosome 3, but in contrast to previous studies, this QTL co-localized with other QTLs that have a negative influence on biomass and seed production. Therefore, selection for this QTL may not be advantageous. Because only a few QTLs for percent moisture were found, the results indicated that the pithy stalk phenotype does not have a major effect on percent moisture as measured in this study. Thus, breeding for high or low moisture content will be more challenging than previously expected. The absence of dominance effects indicated that brix must be high in both parents to produce high brix in the hybrid.