Genetic variants identified to date by genome-wide association studies only explain a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained heritability. In the first part of this dissertation, a novel approach to detect such interactions is proposed. This approach utilizes penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty. The method is tested on simulated data under various scenarios. Simulations show that with reasonable outside biological knowledge, the new method performs noticeably better than current stage-wise strategies in finding true interactions, especially when the marginal strength of main effects is weak. The proposed method is designed for single-cohort analyses. However, it is generally acknowledged that only multi-cohort analyses have sufficient power to uncover genes and gene-by-gene interactions with moderate effects on traits, such as likely underlie complex diseases. Multi-cohort, meta-analysis approaches for penalized regressions are developed and investigated in the second part of this dissertation. Specifically, I propose two different ways of utilizing data-splitting principles in multi-cohort settings and develop three procedures to conduct meta-analysis. Using the method developed in the first part of this dissertation as an example of penalized regressions, three proposed meta-analysis procedures are compared to mega-analysis using a simulation study. The results suggest that the best approach is to split the participating cohorts into two groups, to perform variable selection for each cohort in the first group, to fit regular regression model on the union of selected variables for each cohort in the second group, and lastly to conduct a meta-analysis across cohorts in the second group. In the last part of this dissertation, the novel method developed in the first part is applied to the Framingham Heart Study measures on total plasma Immunoglobulin E (IgE) concentrations, C-reactive protein levels, and Fasting Glucose. The effect of incorporating various sources of biological information on the ability to detect gene-gene interaction is explored. For IgE, for example, a number of potentially interesting interactions are identified. Some of these interactions involve pairs in human leukocyte antigen genes, which encode proteins that are the key regulators of the immune response. The remaining interactions are among genes previously found to be associated with IgE as main effects. Identification of these interactions may provide new insights into the genetic basis and mechanisms of atopic diseases.

Fusarium head blight (FHB) is a devastating wheat (Triticum aestivum L.) disease worldwide. Presently, there is insufficient FHB resistance in the Canadian wheat germplasm. Genome-wide association study (GWAS) and genomic selection (GS) can be utilized to identify sources of resistance that could benefit wheat breeding. To define the genetic architecture of FHB resistance, association panels from a spring and a winter collection were evaluated using the Wheat Illumina Infinium 90K array. A total of 206 accessions from the spring panel and 73 from the winter panel were evaluated in field trials for 3-4 years at two locations, namely Morden (Manitoba) and Ottawa (Ontario). These accessions were phenotyped for FHB incidence (INC), severity (SEV), visual rating index (VRI), and deoxynivalenol (DON) content. Significant (p < 0.05) differences among genotypes for all traits were found. Genetic characterization using the wheat 90K array identified a set of 20,501 single nucleotide polymorphisms (SNPs). The probe sequences (~100 bp) of these SNPs were mapped to the Chinese Spring reference genome v2.0 to identify 13,760 SNPs in the spring panel, and 10,421 SNPs in the winter panel covering all 21 wheat chromosomes. GWAS was performed to identify novel FHB resistance loci for INC, SEV, VRI and DON content for the spring and the combined panels separately using these 13,760 SNPs and for the winter panel using 10,421 SNPs. A total of 107, 157, 174 unique quantitative trait loci (QTNs) were identified for the four traits using two single-locus and seven multi-locus GWAS models for the spring, winter, and combined panels, respectively. These QTNs represent a valuable genetic resource for the improvement of FHB resistance in commercially grown wheat cultivars. In addition, these GWAS-defined QTNs were further used for GS to determine the breeding value (BV) of individuals as outlined below. In order to understand the role of the model and that of the marker type and density in trait prediction modelling, a GS study was conducted. GS is considered as an important tool for increasing genetic gain for economically important traits such as FHB resistance. GS uses genome-wide molecular markers to develop statistical models that predict genomic estimated breeding values (GEBVs) of an individual. Our results support genomic prediction (GP) as an alternative to phenotypic selection to predict the BVs of individuals for this trait. GS accounts for minor effect QTNs, which is beneficial when breeding for quantitative traits. Moderate to high GP accuracies can be achieved for FHB resistance-related traits when implemented in a breeding program. The correlation between the estimate of the missing phenotypic value and the observed phenotype is known as predictive ability (r). Overall, the predictive ability increased significantly using a QTN-based GP approach for FHB traits in wheat and its wild relatives. DON content had the highest predictive ability among all FHB traits, and that was in the winter panel, highlighting the importance of objectively measured traits in breeding for disease resistant genotypes. Interestingly, the winter panel contained several wild relative species that may harbor genes of interest to prevent the accumulation of mycotoxins in the grain. This study showed the usability of genomic prediction by improving the predictive ability of the FHB traits, which can be applied in early generation selection to accelerate the improvement of FHB resistance in wheat. The results show that GS can be successfully implemented in wheat breeding programs over multiple breeding cycles and can be effective for economically important traits. It is anticipated that GS will play a substantial role in the future of wheat breeding.

Avena sativa L. (2n = 6x = 42, AACCDD genome composition) or common oat is the cereal grain possessing the highest levels of water-soluble seed (1-3,1-4)-β-D-glucan (β-glucan), a hemicellulose important to human health due to its ability to lower serum LDL cholesterol levels. Understanding the mechanisms of β-glucan accumulation in oat endosperm is, consequently, of great interest. We report a genome-wide association study (GWAS) to identify quantitative trait loci (QTLs) controlling β-glucan production in oat, identifying 58 significantly associated markers. Synteny with the barley (Hordeum vulgare L.) genome identified four major regions of interest, the CslF and CslH gene families along with UGPase and AGPase as candidate genes. Subgenome-specific expression of the A, C, and D homoeologs of major β-glucan synthase AsCslF6 revealed that AsCslF6_C is the least expressed in all tissue types and time points, with low-β-glucan varieties recording the highest proportion of AsCslF6_C expression. In order to further investigate the candidate genes identified in our GWAS study and gain a greater understanding of the other cell wall polysaccharides that comprise the total fiber content in oat we sought to characterize five additional genes. Accordingly, we cloned and sequenced the three homoeologs of AsUGP and AsAGPS1. AsAGPS1 is the small subunit 1 gene of the enzyme ADP-glucose pyrophosphorylase (AGPase), which is responsible for catalyzing the first committed step in the starch biosynthesis pathway through the production of ADP-glucose. AsUGP is the gene the codes for UDP-glucose pyrophosphorylase (UGPase) an enzyme responsible for the reversible production of UDP-glucose (UDPG). UDPG is used directly or indirectly as a precursor for the biosynthesis of cell wall polysaccharides. In high β-glucan mutant line ‘OT3044’ we observed increased expression of AsUGP with a corresponding reduction of AsAGPS1 expression. Similarly, we observed an inverse expression pattern in low-fiber mutant line ‘OT3018’, wherein AsUGP expression was decreased in favor of AsAGPS1 expression. Further, we also found evidence that these changes in both AsUGP and AsAGPS1 expression are due primarily to up- or down-regulation in the A-genome homoeoalleles. Additionally, we characterized genes in the CslC family (CslC4, CslC9) and CslA family (CslA7) responsible for xyloglucan and glucomannan synthesis, respectively. High-fiber line ‘HiFi’ showed the least amount of overall expression of these three genes, raising the possibility that the increased β-glucan is due to a reduction in other hemicelluloses. After analyzing homoeolog-specific expression in multiple genes we observed that the A genome consistently had the most highly expressed homoeoallele, hinting at a universal preference for expression of this subgenome. We present hypotheses regarding multiple points in carbohydrate metabolism having the potential to alter β-glucan content in oat.

Thesis advisor: Tim van Opijnen / Thesis advisor: Welkin Johnson / Pathogenic bacteria can experience various stress factors during an infection including antibiotics and the host immune system. Whether a pathogen will establish an infection largely depends on its survival-success while enduring these stress factors. We reasoned that the ability to predict whether a pathogen will survive under and/or adapt to a stressful condition will provide great diagnostic and prognostic value. However, it is unknown what information is needed to enable such predictions. We hypothesized that under a stressful condition, a bacterium triggers responses that indicate how the stress is experienced in the genome, thereby correctly identifying a stress response holds the key to enabling such predictions. Bacterial stress responses have long been studied by determining how small groups of individual genes or pathways respond to certain environmental triggers. However, the conservation of these genes and the manner in which they respond to a stress can vary widely across species. Thus, this thesis sought to achieve a genome-wide and systems-level understanding of a bacterial stress response with the goal to identify signatures that enable predictions of survival and adaptation outcomes in a pathogen- and stress-independent manner. Here, we first set up a multi-omics framework that maps out a stress response on a genome-wide level using the human respiratory pathogen Streptococcus pneumoniae as a model organism. Under an environmental stress, gene fitness changes are determined by transposon insertion sequencing (Tn-Seq) which represents the phenotypic response. Differential expression is profiled by RNA-Seq which represents as the transcriptional response. Much to our surprise, the phenotypic response and transcriptional response are separated on different genes, meaning that differentially expressed genes are poor indicators of genes that contribute to the fitness of the bacterium. By devising and performing topological network analysis, we show that phenotypic and transcriptional responses are coordinated under evolutionary familiar stress, such as nutrient depletion and host infection, in both Gram-positive and -negative pathogens. However, such coordination is lost under the relatively unfamiliar stress of antibiotic treatment. We reasoned that this could mean that a generalizable stress response signature might exist that indicates the level to which a bacterium is adapted to a stress. By extending stress response profiling to 9 antibiotics and 3 nutrient depletion conditions, we found that such a signature indeed exists and can be captured by the level of transcriptomic disruption, defined by us as transcriptomic entropy. Centered on entropy, we constructed predictive models that perform with high accuracy for both survival outcomes and antibiotic sensitivity across 7 species. To further develop these models with the goal to eventually enable predictions on disease progression, we developed a dual RNA-Seq technique that maps out the transcriptomic responses of both S. pneumoniae and its murine host during lung infection. Preliminary data show that a high entropy is observed in the pathogen’s transcriptome during clearance (a failed infection) compared to a successful/severe infection, while the host transcriptome exhibits a pro-inflammatory and active immune response under the severe infection. Lastly, we characterized evolutionary trajectories that lead to long-term survival success of S. pneumoniae, for instance this means that the bacterium successfully adapts to the presence of an antibiotic and becomes resistant or can grow successfully in the absence of a formerly critical nutrient. These trajectories show that adaptive mutations tend to occur in genes closely related to the adapted stress. Additionally, independent of the stress, adaptation triggers rewiring of transcriptional responses resulting in a change in entropy from high to low. Most importantly, we demonstrate that by combining multi-omics profiles with additional genomic data including gene conservation and expression plasticity, and feeding this into machine learning models, that adaptive evolution can become (at least partially) predictable. Additionally, the genetic diversity in bacterial genomes across different strains and species can indeed influence a bacterium’s adaptation trajectory. In conclusion, this thesis presents a substantial collection of multi-omics stress response profiles of S. pneumoniae and other pathogenic bacteria under various environmental and clinically-relevant stresses. By demonstrating the feasibility of predictions on bacterial survival and adaptive outcomes, this thesis paves the way towards future improvements on infectious disease prognostics and forecasting the emergence of antibiotic resistance. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

No description available.

Major depression disorder (MDD) is a complex and chronic disease that ranks fourth as cause of disability worldwide. About 14 million adults in the USA are believed to have MDD, and an estimated 75 % attempt suicide making MDD a major public health problem. Neuroticism has been recognized as an endophenotype of MDD; however, few genome-wide association (GWA) analyses of neuroticism as a quantitative trait have been reported to date. The aim of this study is to identify genome-wide genetic variants affecting neuroticism using a European sample. A linear regression model was used to analyze the association with neuroticism as a continuous trait in the Netherlands Study of Depression and Anxiety and Netherlands Twin Registry population-based sample of 2,748 individuals with Perlegen 600K single nucleotide polymorphisms (SNPs). In addition, the neuroticism-associated genes/loci of the top 20 SNPs (p < 10-4) were examined with anti-social personality disorder (ASPD) in an Australian twin family study. Through GWA analysis, 32 neuroticism-associated SNPs (p < 10-4) were identified. The most significant association was observed with SNP rs4806846 within the TMPRSS9 gene (p = 7.79 × 10-6) at 19p13.3. The next best signal was in GRIN2B gene (rs220549, p = 1.05 × 10-5) at 12p12. In addition, several SNPs within GRIN2B showed borderline associations with ASPD in the Australian sample. In conclusion, these results provide a possible genetic basis for the association with neuroticism. Our findings provide a basis for replication in other populations to elucidate the potential role of these genetic variants in neuroticism and MDD along with a possible relationship between ASPD and neuroticism.

Objective: We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs). Methods: A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis. Results: A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10-7≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10-7) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence. Conclusion: NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.

Maximum number of drinks (MaxDrinks) defined as "Maximum number of alcoholic drinks consumed in a 24-h period" is an intermediate phenotype that is closely related to alcohol dependence (AD). Family, twin and adoption studies have shown that the heritability of MaxDrinks is approximately 0.5. We conducted the first genome-wide association (GWA) study and meta-analysis of MaxDrinks as a continuous phenotype. 1059 individuals were from the Collaborative Study on the Genetics of Alcoholism (COGA) sample and 1628 individuals were from the Study of Addiction - Genetics and Environment (SAGE) sample. Family sample with 3137 individuals was from the Australian twin-family study of alcohol use disorder (OZALC). Two population-based Caucasian samples (COGA and SAGE) with 1 million single-nucleotide polymorphisms (SNPs) were used for gene discovery and one family-based Caucasian sample was used for replication. Through meta-analysis we identified 162 SNPs associated with MaxDirnks (p<10-4). The most significant association with MaxDrinks was observed with SNP rs11128951 (p=4.27×10-8) near SGOL1 gene at 3p24.3. Furthermore, several SNPs (rs17144687 near DTWD2, rs12108602 near NDST4, and rs2128158 in KCNB2) showed significant associations with MaxDrinks (p<5×10-7) in the meta-analysis. Especially, 8 SNPs in DDC gene showed significant associations with MaxDrinks (p<5×10-7) in the SAGE sample. Several flanking SNPs in above genes/regions were confirmed in the OZALC family sample. In conclusions, we identified several genes/regions associated with MaxDrinks. These findings can improve the understanding about the pathogenesis of alcohol consumption phenotypes and alcohol-related disorders.

Major depression disorder (MDD) is a complex and chronic disease that ranks fourth as cause of disability worldwide. About 14 million adults in the USA are believed to have MDD, and an estimated 75 % attempt suicide making MDD a major public health problem. Neuroticism has been recognized as an endophenotype of MDD; however, few genome-wide association (GWA) analyses of neuroticism as a quantitative trait have been reported to date. The aim of this study is to identify genome-wide genetic variants affecting neuroticism using a European sample. A linear regression model was used to analyze the association with neuroticism as a continuous trait in the Netherlands Study of Depression and Anxiety and Netherlands Twin Registry population-based sample of 2,748 individuals with Perlegen 600K single nucleotide polymorphisms (SNPs). In addition, the neuroticism-associated genes/loci of the top 20 SNPs (p < 10-4) were examined with anti-social personality disorder (ASPD) in an Australian twin family study. Through GWA analysis, 32 neuroticism-associated SNPs (p < 10-4) were identified. The most significant association was observed with SNP rs4806846 within the TMPRSS9 gene (p = 7.79 × 10-6) at 19p13.3. The next best signal was in GRIN2B gene (rs220549, p = 1.05 × 10-5) at 12p12. In addition, several SNPs within GRIN2B showed borderline associations with ASPD in the Australian sample. In conclusion, these results provide a possible genetic basis for the association with neuroticism. Our findings provide a basis for replication in other populations to elucidate the potential role of these genetic variants in neuroticism and MDD along with a possible relationship between ASPD and neuroticism.

Objective: We aimed to identify novel, functional, replicable and genome-wide significant risk regions specific for alcohol dependence using genome-wide association studies (GWASs). Methods: A discovery sample (1409 European-American cases with alcohol dependence and 1518 European-American controls) and a replication sample (6438 European-Australian family subjects with 1645 alcohol dependent probands) underwent association analysis. Nineteen other cohorts with 11 different neuropsychiatric disorders served as contrast groups. Additional eight samples underwent expression quantitative locus (eQTL) analysis. Results: A genome-wide significant risk gene region (NKAIN1-SERINC2) was identified in a meta-analysis of the discovery and replication samples. This region was enriched with 74 risk SNPs (unimputed); half of them had significant cis-acting regulatory effects. The distributions of -log(p) values for the SNP-disease associations or SNP-expression associations in this region were consistent throughout eight independent samples. Furthermore, imputing across the NKAIN1-SERINC2 region, we found that among all 795 SNPs in the discovery sample, 471 SNPs were nominally associated with alcohol dependence (1.7×10-7≤p≤0.047); 53 survived region- and cohort-wide correction for multiple testing; 92 SNPs were replicated in the replication sample (0.002≤p≤0.050). This region was neither significantly associated with alcohol dependence in African-Americans, nor with other non-alcoholism diseases. Finally, transcript expression of genes in NKAIN1-SERINC2 was significantly (p<3.4×10-7) associated with expression of numerous genes in the neurotransmitter systems or metabolic pathways previously associated with alcohol dependence. Conclusion: NKAIN1-SERINC2 may harbor a causal variant(s) for alcohol dependence. It may contribute to the disease risk by way of neurotransmitter systems or metabolic pathways.