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With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many classic association study methods. Leveraging the non-parametric prediction capabilities of iRF, new genomic insights are used to improve Genomic Selection and Progeny Prediction. This capability enables breeders to make informed selections for crosses without first requiring a full progeny trial. A new algorithm, Tensor Iterative Random Forest (TiRF), expands upon the foundation of iRF, to provide information on relationships not only between the features and targets of the model, but also the between the targets themselves. This algorithm is validated with the capture of information from gene regulatory networks from the DREAM competition. The impact of the SARS-CoV-2 virus has necessitated a method that can capture the changing nature of the genetic architecture of the virus and incorporate potential recombination events, paving the way for a better understanding of how the virus has changed and will change. A new method is introduced that identifies likely parents of haplotypes designated to be the result of recombination. Together, these new methods aim to provide a stronger insight into genetic architecture complexities.

The genus Populus consists of many ecologically and economically important forest tree species. Their rapid growth makes them one of the most productive hardwoods growing in temperate latitudes. Populus spp. frequently hybridize where their ranges overlap, and poplar hybrids are the most frequently planted genotypes for fiber production. To better understand the genomics of hybridization in Populus, we sampled and sequenced the genome of 574 poplar trees from six east-west transects across the hybrid zone between Populus trichocarpa and Populus balsamifera in western North America. I used these data to characterize population structure within and between transects, and hybridization between the species. There was a consistent transition from greater P. balsamifera ancestry in the north and east to greater P. trichocarpa ancestry in the south and west. Hybridization between the species was common across each of the six transects, though more common in colder climates. The results also showed that both latitude and longitude affect the genetic structure of this species complex, and that subtle introgression from P. balsamifera may facilitate adaptation of P. trichocarpa to colder climates.

The ability to undergo plastic and evolutionary change in adaptive traits is key for plant survival under future climate change. Both processes require genetic variation in adaptive traits within populations. Thus, for my dissertation I measured genetic variation in nonstructural carbohydrates (NSC) storage concentrations, a critical trait that provides resilience to tree species during times of stress, within two common gardens. Both gardens had the same 1,100 genotypes, sourced from 19 different populations, and clonally replicated 3x at each site, as well as full genomic sequences for all genotypes. Common gardens are ideal as they control for differences in environment, allowing researchers to parse phenotypic differences due to genetics (ie. genetic variation). I sampled the branches, stems, and roots of black cottonwood (Populus trichocarpa) trees in two different Department of Energy (DOE) common gardens in January 2017. In Chapter 1, I demonstrate the presence of locally adapted, genetic variation in NSC storage in the stems and roots of black cottonwood trees, indicating the potential for range-wide adaptive evolution. Using a novel model of allele frequency distribution and climate, I predict that northern populations will be limited in their ability to adapt to future climates by a lack of genetic variation, while southern populations have high genetic diversity, but are at risk of local extinction due to more intense selective pressures. In Chapter 2, I compare genetic variation in NSC stores to genetic variation in other traits to look for locally adapted tradeoffs. I discover a tradeoff between NSC storage and diameter growth/fungal pathogen resistance when traits are relativized for differences in carbon supply. This tradeoff is not current locally adapted, but populations exhibit high variation in the degree to which plants store vs. grow/defend, indicating an evolutionary potential. Finally, in Chapter 3 I explore genetic and plastic variation in branch total NSC concentrations and the proportion of NSC that resides in soluble sugars (ie. affect cell osmotic balance) and insoluble starch. I find genetic and plastic variation in both traits. Moving from the coastal (Clatskanie) to continental (Corvallis) garden, there was a 50% decrease in the average amount NSC stored as starch. There was no difference in the total amount of NSC concentrations between the two gardens. However, trees in Clatskanie grew much faster and were larger than trees in Corvallis, despite maintaining the same concentration of NSC in their tissues. Our findings suggest that a NSC storage – growth tradeoff may also be plastic.

Poplar species are promising sources of cellulosic biomass for biofuels because of their fast growth rate, high cellulose content and moderate lignin content. There is an increasing movement on integrating multiple layers of ’omics data in a systems biology approach to understand gene-phenotype relationships and assist in plant breeding programs. This dissertation involves the use of network and signal processing techniques for the combined analysis of these various data types, for the goals of (1) increasing fundamental knowledge of P. trichocarpa and (2) facilitating the generation of hypotheses about target genes and phenotypes of interest. A data integration “Lines of Evidence” method is presented for the identification and prioritization of target genes involved in functions of interest. A new post-GWAS method, Pleiotropy Decomposition, is presented, which extracts pleiotropic relationships between genes and phenotypes from GWAS results, allowing for identification of genes with signatures favorable to genome editing. Continuous wavelet transform signal processing analysis is applied in the characterization of genome distributions of various features (including variant density, gene density, and methylation profiles) in order to identify chromosome structures such as the centromere. This resulted in the approximate centromere locations on all P. trichocarpa chromosomes, which had previously not been adequately reported in the scientific literature. Discrete wavelet transform signal processing followed by correlation analysis was applied to genomic features from various data types including transposable element density, methylation density, SNP density, gene density, centromere position and putative ancestral centromere position. Subsequent correlation analysis of the resulting wavelet coefficients identified scale-specific relationships between these genomic features, and provide insights into the evolution of the genome structure of P. trichocarpa. These methods have provided strategies to both increase fundamental knowledge about the P. trichocarpa system, as well as to identify new target genes related to biofuels targets. We intend that these approaches will ultimately be used in the designing of better plants for more efficient and sustainable production of bioenergy.

The project goals for this dissertation were to manipulate Populus trichocarpa source-sink relationships to optimize this woody crop species for specific agricultural traits such as increased growth rate, stress tolerance and/or improvements in overall biomass accumulation. We targeted specific tissues such as xylem, where alterations in the relationship of source and sink tissues can lead to the control of xylem cell deposition or of various wood properties. This led to the characterization of 165 protein-protein interactions and 20 protein-DNA interaction which constitute numerous woody tissue related subnetworks. One such network, centered on the DIVARACATA and RADIALIS INTERACTING FACTOR (PtrDRIF), identified PtrWOX13c as an interacting protein. Characterization of PtrWOX13c shows that it displays the ability to control promoters related to lignin biosynthesis genes and overexpression phenotypes show alterations in axillary branch activity. Genes which control the differentiation and specialization of cells such as members of the WOX family are also highly responsive to abiotic stress which can force major changes in plant metabolism and nutrient mobilization. ABA, a prominent plant phytohormone with known roles in the adaptation to stress has shown novel connections in the regulation of growth promoting complexes such as TOR through antagonistic regulatory actions of the SnRK2 protein kinase in Arabidopsis. Characterization of the core ABA signaling in P. trichocarpa has identified a regulatory clade A protein phosphatase which interacts with numerous PtrSnRK2 proteins and when overexpressed in hybrid poplar results in increased height and node production potentially by indirect control of growth promoting complexes like TOR through SnRK2 inhibition. This work has also demonstrated that in addition to the involvement of phytohormones in the regulation of plant development, sugar phosphates such as T6P can exert significant control of plant architecture. Together, these studies comprise the discovery and subsequent characterization of novel wood associated networks, hormone pathways and sugar signaling in the manipulation of P. trichocarpa source-sink relationships for the promotion of biomass accumulation.

Forest trees are an important pool of biodiversity at the gene, individual and an ecosystem level. This variation is a result of complex environmental interactions, as well as neutral and selective forces acting on populations. Patterns of standing genetic variation are the result of adaption to past and contemporary climate change, but also historical demographic events, and disentangling the role of these forces is a central problem in population genomics. The overall goal of this study is to characterize the relative effects of demography and selection in the genome of Populus trichocarpa, a riparian deciduous tree species of North America. Specifically, I used a variety of methods to summarize patterns of genetic diversity and population structure in P. trichocarpa, and to reconstruct its demographic history. I subsequently incorporated these demographic insights to guide the application of several methods to identify genome-wide targets of natural selection within and among rangewide populations adapted to heterogeneous selection regimes. Results of this study provide insights into the history of divergence and differentiation in P. trichocarpa populations and help us identify the functional genetic variants contributing to phenotypic divergence and fitness of the individuals in it.

Forest tree breeding methods rank among the most efficient ways to increase productivity and quality of forests. With the advent of high-throughput genotyping technology, genome-enabled breeding has started to gain importance and may overcome some weaknesses of traditional tree breeding. Genomic Selection (GS), which involves using genome-wide markers to predict breeding values of individuals in a population, has been proposed for animal and plant breeding programs. GS enables very accurate selection decisions through estimation of genomic estimated breeding values (GEBVs). While the goal of GS is to predict phenotype from genotype, it does not identify the underlying genes that have important roles in a trait. Genome-Wide Association Studies (GWAS) approaches are therefore complementary to GS, enabling identification of these genes, which may be useful for marker-assisted selection in some traits. In this study, we first estimated heritability for several adaptive traits (cold hardiness, dbh, bud flush, height, and bud set) in a population of Populus trichocarpa and for height, diameter, and stem straightness in Pinus taeda. GEBVs accuracies were estimated using a ridge regression–best linear unbiased prediction (rrBLUP) model, and these accuracies were compared with estimated heritabilities. GWAS was also performed for the both imputed and non–imputed data of P. taeda population using TASSEL (Trait Analysis by aSSociation Evolution and Linkage) software, as well as rrBLUP and FFBSKAT (Fast Family-Based Sequence Kernel Association Test) packages in R. Heritabilities ranged from 0.34 to 0.56 for P. trichocarpa and 0.14 to 0.37 for P.taeda. GWAS identified 3244 associations for dbh, 4077 associations for stem straightness, and 5280 SNPs for height (p≤0.05) in TASSEL using the reduced model (marker data only), whereas 2729, 3272 and 3531 associations were found with the full model where we also included population structure as a covariate. FFBSKAT showed a similar number of SNP associations (2989, 3046 and 3058). There was an inflation of SNP associations (~20k) found in rrBLUP, which suggests population structure was not effectively controlled. The GEBVs accuracies ranged from 0.09 and 0.22 for P.trichocarpa and 0.09 to 0.23 for P.taeda using rrBLUP method. Testing the effect of repetation on the accuracy of GEBV for poplar showed that there was no significant difference between the number of cycles. Also, there was no significant difference the accuracy of GEBVs in pine between two different imputation methods, the marker mean value and Beagle software.

Temperate tree species exhibit seasonal growth cycling, and the timing of such transition varies with local climate. Under anthropogenic climate change, the local pattern of growth and dormancy in tree populations is expected to become uncoupled with shifting seasonal environmental signals, particularly temperature. Thus, an understanding of the genetic underpinnings of local adaptation is key to predicting the fate of tree populations in the future. In this thesis, we coupled sampling of range-wide natural accessions of P. trichocarpa with adaptive trait phenotyping and genome-wide genotyping to uncover relationships between genotype, phenotype, and environment. We detected strong correlations between adaptive phenotypes, climate, and geography, which suggested climatic selection driving adaptation of these populations to local environments. We subsequently combined genotype-phenotype association tests with sliding window analysis and identified regions strongly associated with these adaptive traits. We also compared adaptive markers identified in two independent GWAS on samples across latitude and altitude transects and found a set of associated variants shared across both transects. We further scanned the genome with three selection tests to identify regions showing evidence of recent positive and divergent selection. By comparing candidate selection regions across altitude and latitude, we detected a set of overlapping regions showing differentiation across gradients of the same climate variables. We validated the functional imortance of these selection regions by combining GWAS and showed that selection regions contain a strong signature of phenotypic associations. We also studied the distribution of deleterious allels across genome and natural populations, and found that deleterious alleles preferentially accumulate in regions of low recombination and hithihking regions. Finally, marginal populations contained more deleterious alleles compared with central populations, which is likely due to ineffective selection in small populations and recent bottlenecks associated with postglacial recolonization. These findings provide new insights into the genomic architecture underlying climatic adaptation and how selection drives adaptive evolution of tree species.

Over the last decade, mass spectrometry based proteomics has been established as the front-runner in systems-level protein expression studies. However, with the field progressing into research of more and more complex samples, novel challenges have been raised with respect to efficient protein extraction and computational matching. In this dissertation, various aspects in the proteomics workflow, including experimental and computational approaches, have been developed, optimized and systematically evaluated. In this work, some of the critical factors with respect to proteomics sample preparation, like available biomass, detergent removal methods, and intact protein fractionation to achieve deeper proteome measurements were evaluated. The presented work will help the broader scientific community to carefully design proteomics experiments especially in biomass limited samples. A second major area of focus in this dissertation is comprehensive characterization of post-translational modifications (PTMs) in different biological systems. PTMs are critical for functioning of both the prokaryotic and eukaryotic species and this dissertation will highlight some of the experimental strategies to explore the diversity of PTMs in microbial isolates via application of alternate protease and multiple fragmentation schemes. The PTM discovery approach will be further extended into a complex eukaryotic model trees species Populus trichocarpa using recently developed sequence tagging methods to carryout broad scale PTM search and a complete blind PTM search. Although the work presented in this dissertation mainly revolves around prokaryotic and eukaryotic species involved in environmental proteomics, the general considerations outlined in this work can be extended to every proteomics pipeline. Thus this dissertation will benefit the scientific community in carefully designing experiments before embarking on any research project involving mass spectrometry.

Local adaptation to climate in temperate forest trees involves the integration of multiple physiological, morphological, and phenological traits. Latitudinal clines for the relevant component traits are frequently observed for species that have a north-south distribution, but these relationships do not account for climatic variation within a given latitudinal band, which may be reflected in adaptive traits. We used black cottonwood (Populus trichocarpa) as a model to characterize the interplay between geography, climate, and adaptation to abiotic factors. Twelve traits (height, diameter, volume index, crown diameter, number of branches, number of sylleptic branches, relative number of branches, Relative canopy depth, Bud set, Bud flush, cold index of injury, carbon isotope ratio) were measured in a range-wide sample of 124 P. trichocarpa genotypes grown in a common garden. Heritability's were moderate to high (0.24 to 0.55) and significant population differentiation (QST > 0.3) suggested adaptive divergence. When climate variables were taken as predictors and the 12 traits as response variables in a multivariate regression tree analysis, aridity (Eref) explained the most variation, with subsequent splits grouping individuals according to mean temperature of the warmest month, frost-free period (FFP), and mean annual precipitation (MAP). This grouping matches relatively well the splits using geographic variables as predictors: the northernmost groups (short FFP and low Eref) had the lowest growth performance, and the highest cold hardiness. The groups spanning the south of British Columbia (low Eref and intermediate temperatures) displayed an average growth and cold hardiness. The group from the coast of California and Oregon (high Eref and FFP) had the best growth performance and the lowest cold hardiness. The southernmost and high-elevated group (with High Eref and low FFP) performed poorly, had a low cold hardiness and a significantly lower WUE.

We are using the yeast two-hybrid (Y2H) system to identify novel protein-protein interactions (PPI) relevant to wood formation. Bait proteins for Y2H binary assays and screening against a xylem cDNA prey library were selected from approximately 400 Populus trichocarpa genes that are at least 8-fold more highly expressed in differentiating secondary xylem versus phloem-cambium, and designated here as poplar biomass (PB) genes. Here we report some of the interactions involving selected PB proteins and efforts to characterize their functions in Populus and Arabidopsis. Members of the ROP GTPase family, PB15 in poplar and ROP11 in Arabidopsis, interact with the domain of unknown function (DUF) 620 (DUF620) proteins (e.g., PB129 in poplar). Ectopic co-expression of PB15 and PB129 in Arabidopsis caused outgrowths at the base of flower pedicels and altered leaf morphology. Interestingly, the co-expression phenotype could not be observed in transgenic plants that are only expressing either one of the interacting partners separately. Transgenics altered in expression of PB15 and/or PB129 were prepared in Populus and characterization of transgenic trees will be performed in greenhouse and field. In addition to DUF620 family proteins, ROP11 also interacts with the COP9 subunit CSN5A in Arabidopsis. We confirmed the interaction of ROP11 and CSN5A in Y2H and employed available mutants for ROP11 and CSN5A in Arabidopsis to genetically characterize this interaction. Surprisingly, loss of ROP11 was found to rescue the csn5a-2 pleiotropic phenotype. Ectopic expression of a ROP11 dominant negative mutant in the csn5a-2 background also complemented the stunted growth phenotype. Transcript analysis and gel blot assays showed that CSN5A transcript levels remained unchanged in all rescue lines, whereas CSN5A protein levels increased relative to WT. Taken together, we concluded that ROP11 negatively regulate CSN5A protein level in plant by some as yet unknown mechanism.

Wood has a wide variety of uses and is arguably the most important renewable raw material. The composition of xylem cell types in wood determines the utility of different types of wood for distinct commercial applications. Using expression profiling and phylogenetic analysis, we identified many xylem-associated regulatory genes that may control the differentiation of cells involved in wood formation in Arabidopsis and poplar. Prominent among these are NAC-domain transcription factors (NACs). In addition to their roles as regulators of xylem differentiation, NACs are regulators of meristem development, organ elongation and separation. We studied a subset of Populus and Arabidopsis NACs with putative involvement in xylem cell expansion and elongation (XND1/ANAC104, PopNAC118, PopNAC122, PopNAC128, PopNAC129), and secondary cell wall synthesis (ANAC073, PopNAC105, PopNAC154, PopNAC156, PopNAC157). Using quantitative Real-Time PCR, we evaluated expression of the selected Populus NACs in a developmental gradient and in response to bending stress. We prepared transgenic Arabidopsis and Populus plants with increased or decreased expression of select NAC genes. For dominant repression of target gene expression, we evaluated transgenic plants expressing translational fusions of NAC-EAR (ERF amphiphilic repressor) chimeras through chimeric repressor silencing-technology (CRES-T). XND1 overexpression in Populus and Arabidopsis resulted in severe stunting and suppression of xylem differentiation. Overexpression of PopNAC122, an XND1 ortholog, yielded an analogous phenotype in Arabidopsis. Populus XND1 overexpressors lacked phloem fibers and showed a reduction in cell size and number, vessel number and frequency of rays. Knowledge gained through characterization of these wood-associated regulatory genes can be used to optimize molecular breeding and genetic engineering strategies for improved wood quality and increased biomass.