Rice (Oryza sativa) is the staple food for more than 3.5 billion people worldwide. As the population continues to grow, rice yield will need to increase by 1% every year for the next 30 years to keep up with the growth. In the US, Arkansas accounts for more than 50% of rice production. Over the last 68 years, rice production has continued to grow in Mississippi, placing it in fourth place after Arkansas, Louisiana, and California. Due to increasing rice acreage, regionally and worldwide, the need to develop abiotic stress-tolerant rice has increased. Unfortunately, current rice breeding programs lack genetic diversity, and many traits have been lost through the domestication of cultivated rice. Currently, stressors stemming from the continued effects of climate change continue to impact rice. To counteract the impacts of climate change, research has shifted to evaluating wild and weedy relatives of rice to improve breeding techniques. Weedy rice (Oryza sativa ssp.) is a genetically similar, noxious weed in rice with increased competitive ability. Studies have demonstrated that weedy rice has increased genetic variability and inherent tolerance to abiotic stressors. The aims of this study were to 1) screen a weedy rice mini-germplasm for tolerance to cold, heat, and complete submergence-stress, 2) utilize simple sequence repeat (SSR) markers and single nucleotide polymorphisms to evaluate the genetic diversity of the weedy rice population, and 3) use genome-wide association (GWAS) to identify SNPs associated with candidate genes within the population.

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<p>Hypercholesterolemia is the presence of high levels of cholesterol in the blood, and it is one of the major factors for the development of long-term complications in T1D patients.</p> <p>In the thesis, we studied 1303 Caucasians with type 1 diabetes in the Diabetes Control and Complications Trial (DCCT). With the experience of diabetes study, many factors are associated with diabetes complications, they are age, gender, cohort, treatment, diabetes duration, body mass index (BMI), exercise, insulin dose, etc. We mainly focus on which factors are associated with total cholesterol (CHL) analysis in the thesis.</p> <p>Many measures were collected monthly, quarterly or yearly for average 6.5 years from 1983 to 1993. We used annually lipid measures of DCCT because of their values are sufficient and complete, and they belong to longitudinal data.</p> <p>Different methods are discussed in the study, and linear mixed effect models are the appropriate approach to the study. The details of model selection with CHL model analysis are shown, which includes fixed effect selection, random effects selection, and residual correlation structure selection. Then the SNPs were added on three models individually in GWAS. We found locus (rs7412) is not only genome-wide associated with CHL, but also genome-wide associated with LDL.</p> <p>We will assess whether these SNPs are diabetes-specific in the future, and we will add dietary data in the three models to identify locus are associated with the interaction of diet and SNPs.</p> / Master of Science (MSc)

京都大学 / 新制・課程博士 / 博士(理学) / 甲第25485号 / 理博第5066号 / 新制||理||1722(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 明里 宏文, 准教授 足立 幾磨, 教授 今井 啓雄 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

In multiple species, genome-wide association (GWA) studies have become an increasingly prevalent method of identifying the quantitative trait loci (QTLs) that underlie complex traits. Despite this, relatively few GWA analyses using high-density genomic markers have been carried out on highly quantitative traits in wheat. We utilized single-nucleotide polymorphism (SNP) data generated via a genotyping-by-sequencing (GBS) protocol to perform GWA on multiple yield-related traits using a panel of 329 soft red winter wheat genotypes grown in four environments. In addition, the SNP data was used to examine linkage disequilibrium and population structure within the testing panel. The results indicated that an alien translocation from the species Triticum timopheevii was responsible for the majority of observed population structure. In addition, a total of 50 significant marker-trait associations were identified. However, a subsequent study cast some doubt upon the reproducibility and reliability of plant QTLs identified via GWA analyses. We used two highly-related panels of different genotypes grown in different sets of environments to attempt to identify highly stable QTLs. No QTLs were shared across panels for any trait, suggesting that QTL-by-environment and QTL-by-genetic background interaction effects are significant, even when testing across many environments. In light of the challenges involved in QTL mapping, prediction of phenotypes using whole-genome marker data is an attractive alternative. However, many evaluations of genomic prediction in crop species have utilized univariate models adapted from animal breeding. These models cannot directly account for genotype-by-environment interaction, and hence are often not suitable for use with lower-heritability traits assessed in multiple environments. We sought to test genomic prediction models capable of more ad-hoc analyses, utilizing highly unbalanced experimental designs consisting of individuals with varying degrees of relatedness. The results suggest that these designs can successfully be used to generate reasonably accurate phenotypic predictions. In addition, multivariate models can dramatically increase predictive accuracy for some traits, though this depends upon the quantity and characteristics of genotype-by-environment interaction. / Ph. D. / Quantitative traits are those traits that can display a wide range of variability within a population of individuals. These traits are influenced by the interaction of many different genes, and are also influenced by the environment to varying degrees. Traditionally, geneticists who studied quantitative traits had to rely on statistical models, while the biological causes of variation in the expression of these traits remained largely unknown. However, the advent of DNA marker technology granted geneticists the ability to identify specific regions of the genome that highly influence quantitative traits. Many studies have since attempted to find these <i>quantitative trait loci</i> (QTLs) across a wide range of traits and species. However, we are faced with something of a paradox when we attempt to find QTLs. Theory tells us that an idealized, truly quantitative trait arises due to the effects of many genes, each with an infinitesimal effect on the trait in question. Therefore, the more quantitative a trait, the fewer QTLs we should expect to find. In addition, QTLs may not be reliable, due to the effects of different environments and different genetic backgrounds within a population. A more recent trend involves using all available marker data simultaneously to predict a particular line’s performance. This method entails ignoring the genomic underpinnings of a trait, and instead focusing solely on its expression, much like classical quantitative genetics. The obvious downside of this method is that it cannot be used to increase our understanding of what is giving rise to the variations in the trait’s expression that we observe. The studies described in this dissertation were designed to 1) test whether we could identify QTLs for highly quantitative yield-related traits in winter wheat, 2) test the reliability of identified QTLs, and 3) use the DNA marker data to instead generate predictions of line performance. The results indicate that while we can identify QTLs for highly quantitative traits in winter wheat, these QTLs may not be very reliable. Therefore, predictive models may be a good alternative to identifying QTLs, and these methods can be readily implemented within breeding programs.

Die Linksventrikuläre Noncompaction des Myokards (LVNC) ist eine seltene primäre Herzmuskelerkrankung. Es wird angenommen, dass es sich um eine embryonale Entwicklungsstörung des Myokards handelt. Mutationen in dem X-chromosomalen Gen TAZ sind verantwortlich für Fälle von frühkindlicher LVNC während die genetische Ursache autosomal-dominant vererbter adulter LVNC weitgehend unbekannt ist. In dieser Arbeit wurde die genetische Ursache der LVNC in der Familie LVNC-105 untersucht. Weiterhin wurden in einem großen Kollektiv von LVNC-Indexpatienten Kandidatengenanalysen durchgeführt. Bei der Familie LVNC-105 zeigte die genomweite Kopplungsanalyse nur signifikant hohe 2-Punkt-LOD-Werte auf Chromosom 11p15. Der maximale 2-Punkt-LOD-Wert betrug 5,06 bei D11S902 und der Lokus konnte auf 3,2 Mb (4,9 cM) eingeengt werden. Unter den 40 Genen des Erkrankungslokus war das Kandidatengen CSRP3, das bereits für 2 andere Kardiomyopathien, die dilatative und die hypertrophe Kardiomyopathie (DCM und HCM), als Krankheitsgen beschrieben wurde. Die Sequenzierung des genomischen Bereichs von CSRP3 zeigte keine Mutation bei den betroffenen Familienmitgliedern. Auch die Analyse von weiteren, im Lokus enthaltenen Gene ergab keine Mutation in kodierenden Exons. Auch Untersuchungen auf Transkriptebene offenbarten keine genetische Veränderung. Bei der Sequenzierung der LVNC-Kandidatengene LDB3, LMNA, Nkx2.5 und\linebreak BMP10 bei 63 erwachsenen Indexpatienten mit isolierter LVNC wurde nur eine Mutation in LDB3 gefunden. Erstmals wurden auch 7 Gene, die für sarkomere Proteine kodieren und als Krankheitsgene für HCM und DCM bekannt sind, mittels DHPLC untersucht. Es wurden Mutationen in einem großen Anteil der LVNC-Indexpatienten (19%) in MYH7, ACTC, TPM1 und TNNT2 identifiziert. Klinische Untersuchungen zeigten bei 7 von 12 Patienten mit Mutationen das Vorliegen einer familiären LVNC. In 4 autosomal-dominanten LVNC-Familien kosegregierten die MYH7 Mutationen mit der Erkrankung. MYH7 war mit einem Anteil von 13% das häufigste Krankheitsgen. Die Mutationen in MYH7 lagen vorwiegend in der ATP-Bindungsstelle. LVNC gehört damit zum Spektrum der Kardiomyopathien, die durch Mutationen in sarkomeren Proteinen hervorgerufen werden können. / Left ventricular noncompaction of the myocardium (LVNC) constitutes a rare primary cardiomyopathy. The mechanistic basis is assumed to be an arrest in embryonic cardiac development. Mutations in the X-linked TAZ gene are responsible for cases of infantile LVNC whereas the genetic base of late-onset LVNC in most patients is still unresolved. The objectives of this dissertation were to investigate the genetic defect in family LVNC-105 with autosomal dominant inherited LVNC and to screen a large cohort of patients with isolated LVNC for mutations in candidate genes. In kindred LVNC-105 genome wide linkage analysis revealed significant two-point LOD scores only at chromosome 11p15. A peak 2-point LOD score of 5.06 was obtained with marker D11S902 and a critical interval of 3.2 Mb (4.9 cM) was determined. Among the 40 genes within the disease region one candidate gene was CSRP3, a disease gene for hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Sequence analysis of the genomic CSRP3 region did not reveal mutations in affected family members. Also, analysis of the coding region of further candidate genes contained within the disease locus did not show mutations. Investigations of the genes on transcript level did not detect alterations. Candidate gene analysis of LDB3, LMNA, Nkx2.5 and BMP10 in 63 index patients with isolated LVNC only one mutation was detected in LDB3. For the first time 7 genes encoding sarcomere proteins, known as disease genes for HCM and DCM, were screened for mutations by DHPLC in LVNC patients. Mutations were found in a significant proportion of the cohort of LVNC index patients (19%) in MYH7, ACTC, TPM1 and TNNT2. Clinical evaluations demonstrated familial disease in 7 of 12 probands with sarcomere gene mutations. MYH7 mutations segregated with the disease in 4 autosomal dominant LVNC kindreds. MYH7 was identified as the most prevalent LVNC disease gene (13%) in this cohort. Modified residues in MYH7 were mainly located within the ATP binding site. In conclusion, LVNC belongs to the spectrum of cardiomyopathies originating in molecular defects of the sarcomere.

BACKGROUND: Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with diseases of the colon including inflammatory bowel diseases (IBD) and colorectal cancer (CRC). However, the functional role of many of these SNPs is largely unknown and tissue-specific resources are lacking. Expression quantitative trait loci (eQTL) mapping identifies target genes of disease-associated SNPs. This study provides a comprehensive eQTL map of distal colonic samples obtained from 40 healthy African Americans and demonstrates their relevance for GWAS of colonic diseases. RESULTS: 8.4 million imputed SNPs were tested for their associations with 16,252 expression probes representing 12,363 unique genes. 1,941 significant cis-eQTL, corresponding to 122 independent signals, were identified at a false discovery rate (FDR) of 0.01. Overall, among colon cis-eQTL, there was significant enrichment for GWAS variants for IBD (Crohn's disease [CD] and ulcerative colitis [UC]) and CRC as well as type 2 diabetes and body mass index. ERAP2, ADCY3, INPP5E, UBA7, SFMBT1, NXPE1 and REXO2 were identified as target genes for IBD-associated variants. The CRC-associated eQTL rs3802842 was associated with the expression of C11orf93 (COLCA2). Enrichment of colon eQTL near transcription start sites and for active histone marks was demonstrated, and eQTL with high population differentiation were identified. CONCLUSIONS: Through the comprehensive study of eQTL in the human colon, this study identified novel target genes for IBD- and CRC-associated genetic variants. Moreover, bioinformatic characterization of colon eQTL provides a tissue-specific tool to improve understanding of biological differences in diseases between different ethnic groups.

Type 2 diabetes (T2D) has a genetic component which is only partially understood. The majority of genetic variance in disease susceptibility is unaccounted for, whilst the precise transcripts and molecular mechanisms through which most risk variants exert their effect is unclear. A complete understanding of T2D susceptibility mechanisms could have benefits in risk prediction, and in drug discovery through the identification of novel therapeutic targets. Work presented in this thesis aims to define relevant transcripts and disease mechanisms at known susceptibility loci, and to identify disease association with classes of genetic variation other than common single nucleotide polymorphisms (SNPs). KCNQ1 contains intronic variants associated with T2D susceptibility and β-cell dysfunction, but only maternally-inherited alleles confer increased disease risk. It maps within an imprinted domain with an established role in congenital and islet-specific growth phenotypes. Using human adult islet and foetal pancreas samples, I refined the transcripts and developmental stage at which T2D susceptibility must be conferred by demonstrating developmentally plastic monoallelic and biallelic expression. I identified a potential risk mechanism through the effect of T2D risk alleles upon DNA methylation. The disease-associated regions identified through genome-wide association (GWA) studies often contain multiple transcripts. I performed mRNA expression profiling of genes within loci associated with raised proinsulin/insulin ratios in human islets and metabolically relevant tissues. Some genes (notably CT62) were not expressed and therefore excluded from consideration for a risk effect, whilst others (for example C2CD4A) were highlighted as good regional candidates due to specific expression in relevant tissues. GWA studies for T2D risk have focused predominantly upon common single nucleotide polymorphisms. As part of a consortium conducing GWA analysis for copy number variation (CNV) and T2D risk, I optimised and compared alternative methods of CNV genotyping, before using this information to validate two signals of disease association. I genotyped three rare single nucleotide variants emerging from an association study with T2D risk based on imputed data, providing an indication of imputation accuracy and more powerful disease association analysis. These data underscore the challenge of translating association signals to causal mechanisms, and of identifying alternative forms of genomic variation which contribute to T2D risk. My work highlights candidates for functional analysis around proinsulin-associated loci, and makes significant progress towards uncovering risk mechanisms at the KCNQ1 locus.