Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. © 2021 Society of Biological Psychiatry / National Institutes of Health / Revisión por pares

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19404号 / 医博第4055号 / 新制||医||1012(附属図書館) / 32429 / 京都大学大学院医学研究科医学専攻 / (主査)教授 野田 亮, 教授 瀬原 淳子, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Quantitative genetics is the study of continuously distributed traits and their ge- netic components. Recent developments in DNA sequencing technologies and computational systems allow researchers to conduct large scale in silico studies. However, going from raw DNA reads to genomic prediction of quantitative traits with the help of neural networks is a long and error-prone process. In the course of this thesis, many steps involved in this process will be assessed in depth. Chap- ter 2 will feature a study that compares the landscape of chloroplast genome as- sembly tools. Chapter 3 will present a software to perform genome-wide associa- tion studies using modern tools, which allow GWAS-Flow to outperform current state of the art software packages. Chapter 4 will give an in depth introduc- tion to machine learning and the nature of quantitative traits and will combine those to genomic prediction with artificial neural networks and compares the re- sults to those of algorithms based on linear mixed models. Finally, in Chapter 5 the results from the previous chapters are summarized and used to elucidate the complex nature of studies concerning quantitative genetics. / Quantitative Genetik beschäftigt sich mit kontinuierlich verteilten Merkmalen und deren genetischer Komponenten. In den letzten Jahren gab es vielfältige Entwicklungen in der Computertechnik und der Genomik, insbesondere der DNA Sequenzierung, was Forschern erlaubt großflächig angelegte in silico Studien durchzuführen. Jedoch ist es ein komplexer Prozess von rohen Sequenzdaten bis zur genomischen Vorhersage mit Hilfe von neuronalen Netzwerken zu kommen. Im Rahmen der vorliegenden Studien werden viele Schritte, die an diesem Prozess beteiligt sind beleuchtet. Kapitel 2 wird einen Vergleich zwischen einer Vielzahl an Werkzeugen zur Assemblierung von Chloroplasten Genomen ziehen. Kapitel 3 stellt eine neu entwickelte Software zur genom-weiten Assoziationskartierung vor, die bisherigen Programmen überlegen ist. Kapitel 4 stellt maschinelles Lernen und die genetischen Komponenten von quantitativen Merkmalen vor und bringt diese im Kontext der genomischen Vorhersagen zusammen. Zum Schluss in Kapitel 5 werden die vorherigen Ergebnisse im Gesamtkontext der quantitativen Genetik erläutert.

Coronary artery disease (CAD) is the leading cause of death worldwide. Genome-wide association studies (GWAS) have identified more than 200 loci associated with CAD. Here, we investigated the functional effects of a locus tagged by rs61969072 (T/G), with the common allele (T) associated with protection from CAD. Expression quantitative trait loci (eQTL) analysis demonstrated a strong association between rs61969072 and CARS2 gene expression, which increased with the T allele, in various human tissues. CARS2 encodes the mitochondrial cysteinyl-tRNA synthetase, an enzyme that attaches cysteine to its cognate tRNA. We hypothesized that CARS2 is a candidate causal gene and that CARS2 confers a protective effect against CAD. We characterized CARS2 expression in macrophages and demonstrated decreased expression in pro-inflammatory M1 macrophages. Gene expression profiling following CARS2 siRNA knockdown revealed increased levels of several pro-inflammatory cytokines. Functional enrichment analysis identified the anti-inflammatory IL-10 signaling pathway, and western blotting showed that CARS2 attenuated IL-10 pathway activation through STAT3 phosphorylation. We also demonstrated that macrophage CARS2 knockdown in a macrophage/smooth muscle cell (SMC) co-culture model elicited gene expression changes indicative of a less contractile, pro-inflammatory, SMC phenotype. We then performed an in-depth analysis of differentially expressed genes following CARS2 knockdown. Several inflammatory pathways and functions were affected, particularly Protein Kinase R (PKR), implicated in Interferon Induction and Antiviral Response. Downstream of PKR is the NF-κB signaling pathway; CARS2 knockdown led to increased NF-κB protein expression but not activation, as measured by a luciferase reporter assay. Finally, we investigated potential mitochondrial mechanisms that could lead to inflammation. Reduced CARS2 levels were found to decrease mitochondrial membrane potential. However, there was a decrease in reactive oxygen species (ROS) levels and no changes in mitochondrial DNA release, metabolism, or mitochondrial bioenergetics. While ROS are often considered harmful due to their role in oxidative damage and inflammation, studies have shown that under certain contexts, ROS can have protective effects. Further studies are required to understand the mechanisms underlying the anti-inflammatory effects of CARS2. Overall, my findings highlight a novel anti-inflammatory role of CARS2 in human macrophages, consistent with the CAD protective effect of a common GWAS-identified variant.

This work analyzes the genetic basis of three neurodegenerative diseases using several thousands of individuals of European descent to determine a range of phenotypic heritability outside of what has been identified by prior methods. By measuring additive genetic variance genome-wide, measures of its contribution to the phenotypic variance of these diseases were substantially increased, in some instances by a factor of 10 or more. Additionally, regional-mapping methods identified segments of the genome exhibiting significantly high heritability estimates associated with one of the neurodegenerative diseases, Amyotrophic lateral sclerosis. This resulted in the detection of novel candidate regions and provided conclusive evidence for the polygenic architecture of this disease. Lastly, novel risk variants associated with Parkinson's disease were identified on the X chromosome, a previously ignored genomic region. Overall, the employment of new analytic methods produced robust and novel results, adding substantial information to the neurodegenerative disease literature and connecting the anthropological perspective with growing informatics-based methods. / Anthropology

Transposable elements (Transposons; TEs) constitute the majority of DNA in genomes and are a major source of genetic polymorphisms. TEs act as potential regulators of gene expression and lead to phenotypic plasticity in plants and animals. In crops, several TEs were identified to influence alleles associated with important agronomic traits, such as apical dominance in maize and seed number in rice. Crops may harbor more TE-mediated genetic regulations than expected in view of multifunctional TEs in genomes. However, tools that accurately annotate TEs and clarify their associations with agronomic traits are still lacking, which largely limits applications of TEs in crop breeding. Here we 1) evaluate performances of popular tools and strategies to identify TEs in genomes, 2) develop a tool 'DeepTE' to annotate TEs based on deep learning models, and 3) develop a tool 'TE-marker' to identify potential TE-regulated alleles associated with agronomic traits. As a result, we propose a series of recommendations and a guideline to develop a comprehensive library to precisely identify TEs in genomes. Secondly, 'DeepTE' classifies TEs into 15-24 super families according to sequences from plants, metazoans, and fungi. For unknown sequences, this tool can distinguish non-TEs and TEs in plant species. Finally, the 'TE-marker' tool builds a TE-based marker system that is able to cluster rice populations similar to a classical SNP marker approach. This system can also detect association peaks that are equivalent to the ones produced by SNP markers. 'TE-marker' is a novel complementary approach to the classical SNP markers that it assists in revealing population structures and in identifying alleles associated with agronomic traits. / Doctor of Philosophy / Transposable elements (Transposons; TEs) are DNA fragments that can jump and integrate into new positions in the genome. TEs potentially act as regulators of gene expression and alter traits of plants and animals. In crops, several TEs were identified to influence functions of genes that control important agronomic traits, such as branching in maize and seed number in rice. However, tools that identify these associations in the crops are still lacking, which largely limits applications of TEs in crop breeding. Here we evaluated performance of popular tools and strategies that identify TEs, and provide a series of recommendations to efficiently apply these tools to the TE identification. In view of structural and sequence differences, TEs are classified into multiple families. We developed a 'DeepTE' tool to precisely cluster TEs into different families using a deep learning method. Finally, a 'TE-marker' tool was developed to build TE-based genetic markers to identify nearby alleles associated with agronomic traits. Overall, this work could promote the use of TEs as markers in improving quality and yielding crops.

Background: Genome-wide association studies (GWAS) prove to be a powerful approach to identify the genetic basis of various human[1] diseases. Here we take advantage of existing GWAS data and attempt to build a framework to understand the complex relationships among diseases. Specifically, we examined 49 diseases from all available GWAS with a cascade approach by exploiting network analysis to study the single nucleotide polymorphisms (SNP) effect on the similarity between different diseases. Proteins within perturbation subnetwork are considered to be connection points between the disease similarity networks. Results: shared disease subnetwork proteins are consistent, accurate and sensitive to measure genetic similarity between diseases. Clustering result shows the evidence of phenome similarity. Conclusion: our results prove the usefulness of genetic profiles for evaluating disease similarity and constructing disease relationship networks. / Master of Science

Genome-wide associated study (GWAS) aims to identify associated single nucleotide polymorphisms (SNP) for phenotypes. SNP has the characteristic that the number of SNPs is from hundred of thousands to millions. If p is the number of SNPs and n is the sample size, it is a p>>n variable selection problem. To solve this p>>n problem, the common method for GWAS is single marker analysis (SMA). However, since SNPs are highly correlated, SMA identifies true causal SNPs with high false discovery rate. In addition, SMA does not consider interaction between SNPs. In this dissertation, we propose novel Bayesian variable selection methods BG2 and IBG3 for non-Gaussian GWAS data. To solve ultra-high dimension problem and highly correlated SNPs problem, BG2 and IBG3 have two steps: screening step and fine-mapping step. In the screening step, BG2 and IBG3, like SMA method, only have one SNP in one model and screen to obtain a subset of most associated SNPs. In the fine-mapping step, BG2 and IBG3 consider all possible combinations of screened candidate SNPs to find the best model. Fine-mapping step helps to reduce false positives. In addition, IBG3 iterates these two steps to detect more SNPs with small effect size. In simulation studies, we compare our methods with SMA methods and fine-mapping methods. We also compare our methods with different priors for variables, including nonlocal prior, unit information prior, Zellner-g prior, and Zellner-Siow prior. Our methods are applied to substance use disorder (alcohol comsumption and cocaine dependence), human health (breast cancer), and plant science (the number of root-like structure). / Doctor of Philosophy / Genome-wide associated study (GWAS) aims to identify genomics variants for targeted phenotype, such as disease and trait. The genomics variants which we are interested in are single nucleotide polymorphisms (SNP). SNP is a substitution mutation in the DNA sequence. GWAS solves the problem that which SNP is associated with the phenotype. However, the number of possible SNPs is from hundred of thousands to millions. The common method for GWAS is called single marker analysis (SMA). SMA only considers one SNP's association with the phenotype each time. In this way, SMA does not have the problem which comes from the large number of SNPs and small sample size. However, SMA does not consider the interaction between SNPs. In addition, SNPs that are close to each other in the DNA sequance may highly correlated SNPs causing SMA to have high false discovery rate. To solve these problems, this dissertation proposes two variable selection methods (BG2 and IBG3) for non-Gaussian GWAS data. Compared with SMA methods, BG2 and IBG3 methods detect true causal SNPs with low false discovery rate. In addition, IBG3 can detect SNPs with small effect sizes. Our methods are applied to substance use disorder (alcohol comsumption and cocaine dependence), human health (breast cancer), and plant science (the number of root-like structure).

In the past decade it has become increasingly clear that a biological response is rarely caused by a single gene or protein. Rather, it is a result of a myriad of biological factors, constituting a systematic network of biological variables that span multiple granularities of biology from gene transcription to cell metabolism. Therefore it has become a significant challenge in the field of bioinformatics to integrate different levels of biology and to think of biological problems from a network perspective. In my thesis, I will discuss three projects that address this challenge. First, I will introduce two novel methods that integrate quantitative and qualitative biological data in a network approach. My aim in chapters two and three is to combine high-throughput data with biological databases to identify the causal mechanisms of action (MoA), in the form of canonical biological pathways, underlying the data for a given phenotype. In the second chapter, I will introduce an algorithm called Latent Pathway Identification Analysis (LPIA). This algorithm looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that explicitly links pathways through their common function in the cell. In chapter three, I will introduce a new method that focuses on the identification of perturbed pathways from high-throughput gene expression data, which we approach as a task in statistical modeling and inference. We develop a two-level statistical model, where (i) the first level captures the relationship between high-throughput gene expression and biological pathways, and (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation. In the fourth chapter, I will focus on the integration of high throughput data on two distinct levels of biology to elucidate associations and causal relationships amongst genotype, gene expression and glycemic traits relevant to Type 2 Diabetes. I use the Framingham heart study as well as its extension, the SABRe initiative, to identify genes whose expression may be causally linked to fasting glucose.

The amount and composition of intramuscular fat (IMF) influence the sensory characteristics, nutritional value of beef and human health. The amount of fatty acid and its composition in beef varies by breed, nutrition, sex, age or carcass finishing level. The fat deposition and composition are determined by many genes that participate directly or indirectly in adipogenesis and lipid metabolism. The selection of animals with fat amount and composition suitable for the consumer is complex due to high cost of measurement, the moderate heritability and polygenic traits (many genes are involved with these traits). In the last decade with a great advance in bovine genomics resulted in the complete genome sequencing and the development of high-density chips of SNPs. This scientific advance jointly with technological improvement allowed the identification of genes responsible for important quantitative traits in cattle. This study aimed to identify and characterize genes associated with the deposition and composition of intramuscular fat in Nellore. A genome-wide association study (genome- wide association studies, GWAS) was performed to identify genomic regions associated with traits of interest and positional candidate genes. A total RNA sequencing (RNA-Seq) analysis was applied to transcriptome study of Longissimus dorsi muscle. Three hundred and eighty six Nellore steers were used for the evaluation of lipid content and fatty acid profile of LD, and genotyping with high-density chip SNP (SNP800 Illumina BeadChip). A subset of 14 animals, seven animals for each extremes of genomic estimated values (GEBV) were used to RNA-Seq analysis. Twenty-five genomic regions (1 MB window) were associated with the deposition and composition of intramuscular fat, which explained >= 1 % of the genetic variance. These regions were identified on chromosomes 2, 3, 6, 7, 8, 9, 10, 11, 12, 17, 26 and 27, many of these have not previously been found in other breeds and in these regions important genes were identified. Genomic regions and genes identified and presented here should be contribute to a better understanding of the genetic control of deposition and fat composition in beef cattle, and can be applied in breeding programs for animals that produce a quality and healthy beef to human consumers. / A quantidade e composição da gordura intramuscular (GIM) pode influenciar as características sensoriais, o valor nutricional da carne bovina e na saúde humana. O perfil dos seus ácidos graxos pode se apresentar de maneira diversificada conforme a genética, o manejo e a nutrição dos animais de origem. A deposição e composição da gordura são determinadas por muitos genes que participam direta ou indiretamente da adipogênese e do metabolismo lipídico. A seleção de animais com teor e composição de gordura adequado para o consumidor é complexa pela difícil mensuração destas características, pela moderada herdabilidade e pelo desconhecimento dos genes envolvidos. Na última década, presenciamos um grande avanço na área da genômica bovina que resultou no sequenciamento completo do genoma e no desenvolvimento de chips de alta densidade de SNP. Este progresso científico, aliado aos avanços tecnológicos de equipamentos, resultou na identificação de genes responsáveis pela determinação de características quantitativas de interesse científico e comercial na bovinocultura. Este estudo teve como objetivo identificar e caracterizar genes associados à deposição e composição de gordura intramuscular em bovinos Nelore. Para este fim foi conduzido um estudo de associação genômica (Genome-wide association studies, GWAS) para identificar regiões genômicas associadas às características de interesse e identificar genes candidatos posicionais. Para o estudo de expressão diferencial foi conduzido um estudo do transcriptoma a partir do sequenciamento de RNA total (RNA-Seq) do músculo Longissimus dorsi. Foram utilizados 386 Nelores para a avaliação do teor de lipídeos total e perfil de ácidos graxos do músculo LD e, genotipagem com chip de alta densidade de SNP (Illumina SNP800 BeadChip). Um subconjunto de 14 animais, sendo sete animais de cada extremo para os valores genômicos estimados (GEBV) foi utilizado para o estudo de RNA-Seq. Foram encontradas 25 regiões genômicas (intervalos de 1 MB) associadas com deposição e composição de gordura intramuscular, as quais explicaram >= 1% da variância genética. Estas regiões foram identificadas nos cromossomos 2, 3, 6, 7, 8, 9, 10, 11, 12, 17, 26 e 27, muitas destas não foram previamente detectadas em outras raças. Nestas regiões foram identificados importantes genes e podem ajudar no entendimento da base genética envolvida na deposição e composição de gordura. As regiões genômicas e genes aqui identificados e apresentados contribuem para um melhor entendimento do controle genético da deposição e composição de gordura em gado de corte e ainda podem ser aplicados em programas de seleção genética de animais que produzam carne com qualidade e com perfil de gordura saudável ao homem.