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: 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

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

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.

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

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.

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.

A seca é um dos estresses abióticos mais importantes na cultura do milho, o qual ocasiona reduções significativas na produção de grãos. A arquitetura genética da tolerância à seca é complexa, fazendo-se necessária a melhor compreensão desse caráter. Estudos envolvendo mapeamento associativo são úteis por explorarem a variação genética de caracteres quantitativos e, adicionalmente, levam em conta informações acerca de genótipos, ambientes e interações genótipo por ambiente (G × E). Ao considerar efeitos de G × E em modelos de mapeamento associativo há possibilidade de identificar regiões no genoma associadas à condições e ambientes específicos. Este trabalho teve como objetivo detectar associações relacionadas à tolerância à seca em milho por meio de um modelo de mapeamento associativo para múltiplos ambientes e múltiplos locos, o qual permitiu distinguir associações com efeitos ambiente-específico daquelas com efeitos principais e de interação associação por ambiente (QEI). O painel associativo foi composto por 190 linhagens, classificadas de acordo com os grupos heteróticos quanto ao tipo de grão. Marcadores SNPs (∼500k) foram utilizados para a genotipagem do painel associativo. Duas linhagens (L228-3 e L3) foram usadas como testadores comuns e os híbridos obtidos foram avaliados em duas localidades (Janaúba-MG e Teresina-PI), dois anos agrícolas (2010 e 2011), sob duas condições de tratamento (irrigado e não irrigado). Ao total, consideraram-se seis caracteres: peso de grãos, intervalo de florescimento, florescimento feminino e masculino, altura de planta e de espiga. Consideraram-se dois grupos de mapeamento, agrupados de acordo com os testadores utilizados. SNPs foram úteis para testar associações ao longo do genoma do milho e investigar o relacionamento genético entre indivíduos. O modelo de mapeamento associativo, com inclusão de informações sobre interação G × E, detectou o total de 179 associações, e o maior número de associações foram relacionadas aos caracteres de florescimento. A maioria das associações (168) apresentaram QEI significativo, sendo que o tamanho e a magnitude desses efeitos distinguiram-se de acordo com o ambiente em avaliação. Apenas o caráter florescimento feminino não apresentou associações com efeitos estáveis ao longo dos ambientes em estudo. A detecção de algumas associações em posições próximas do genoma evidenciam possíveis efeitos de pleiotropia. Algumas associações foram co-localizadas em regiões do genoma do milho relacionadas à tolerância à seca, sendo que algumas dessas associações estavam envolvidas a fatores pertencentes à vias metabólicas de interesse. O presente estudo forneceu informações úteis para a compreensão da base genética da tolerância à seca em milho sob os ambientes específicos em avaliação. / Drought is a severe stress factor in maize production and causes significant reduction in grain yield. Genetic architecture of drought tolerance is complex and a better understanding of this trait is required. Association mapping studies are useful to explore quantitative traits and simultaneously account for genetic backgrounds including genotype, environment and genotype-by-environment (G × E) interactions. By accounting for G × E into association mapping models it is possible to identify regions associated with specific environment and conditions. The main goal of this study was detect significant associations related to drought tolerance in maize via multi-environment-multi- locus association mapping model, distinguishing information about specific-environment effects from main and association-by-environment (QEI) effects. Our association panel was composed by 190 inbred lines classified according to heterotic groups. The panel was genotyped with ∼500K SNPs. Two inbred lines (L228-3 and L3) were used as common testers and testcrosses were assessed in two locations (Janaúba-MG e Teresina-PI), two years (2010 and 2011), under two treatment conditions (well-watered and water-stressed). A total of six traits were evaluated including grain yield, anthesis-silking interval, female and male flowering time, plant and ear height. Two mapping groups were considered, grouped by common testers. SNPs were used to test significant association along the maize genome and also to account for population structure and relatedness coefficient. Our mapping model detected a total of 179 associations and the highest number of associations were related to the flowering time measures. The most associations (168) showed significant QEI and the size and magnitude of those effects were distinguished by environment conditions. Only female flowering trait did not show stable effects across all environments. Mapped associations in nearby positions indicate plausible pleiotropy effects. Some associations were co-located in maize genome regions related to metabolic pathway factors. Our study support the detection of significant associations along the maize genome and contributes for understanding of the genetic basis of drought tolerance in maize.

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.