Mendelian Randomization (MR) methods use genetic variants as instrumental variables (IV) to infer causal relationships between an exposure and an outcome, which overcomes the inability to infer such a relationship in observational studies due to unobserved confounders. There are several MR methods, including the inverse variance weighted (IVW) method, which has been extended to deal with correlated IVs; the median method, which provides consistent causal estimates in the presence of pleiotropy when less than half of the genetic variants are invalid IVs but assumes independent IVs. In this dissertation, we propose two new methods to improve precision for MR analysis. In the first chapter, we extend the median method to correlated IVs: the quasi-boots median method, that accounts for IV correlation in the standard error estimation using a quasi-bootstrap method. Simulation studies show that this method outperforms existing median methods under the correlated IVs setting with and without the presence of pleiotropic effects. In the second chapter, to overcome the lack of an effective solution to account for sample overlap in current IVW methods, we propose a new overall causal effect estimator by exploring the distribution of the estimator for individual IVs under the independent IVs setting, which we name the IVW-GH method. In the final chapter, we extend the IVW-GH method to correlated IVs. In simulation studies, the IVW-GH method outperforms the existing IVW methods under the one-sample setting for independent IVs and shows reasonable results for other settings. We apply these proposed methods to genome-wide association results from the Framingham Heart Study Offspring Study and the Million Veteran Program to identify potential causal relationships between a number of proteins and lipids. All the proposed methods are able to identify some proteins known to be related to lipids. In addition, the quasi-boots median method is robust to pleiotropic effects in the real data application. Consequently, the newly proposed quasi-boots median method and IVW-GH method may provide additional insights for identifying causal relationships. / 2025-01-23T00:00:00Z

In D. melanogaster, resistance to DDT is conferred by the upregulation of a cytochrome P450 enzyme, CYP6G1. Resistant flies have tandemly duplicated Cyp6g1 alleles that possess the LTR (Long Terminal Repeat) of an Accord retrotransposon inserted in the cis-regulatory region, 291bp upstream of the transcription start site. This DDT resistance allele (DDT-R) has been shown to have pleiotropic fitness benefits for female flies in at least one genetic background and with evidence of sexually antagonistic selection at this locus. In this thesis, I first review the role of transposable elements in conferring insecticide resistance and the evidence to date regarding the pleiotropic effects of DDT-R in D. melanogaster. By conducting life history and behavioural tests on flies of two genetic backgrounds I examine the sex-specific effects of expressing DDT-R in the absence of DDT. Finally I develop a single locus population genetics model based on these sex-specific effects and test the model using replicate laboratory populations. The first main finding is that DDT-R incurred a male mating cost that depended on the genetic background in which DDT-R was found and that this cost coincided with strong epistasis between genetic background and DDT-R that influenced male size (Chapter 3). Following on from this result, it was confirmed that the effect of DDT-R on male size does contribute to lowered mating success but does not fully explain this fitness cost (Chapter4). Additionally, resistant males were found to have a lowered rate of courtship behaviour driven by aborted chasing of females and lower male-male aggression than susceptible males (Chapter 4). Fitness assays in wild caught strain females revealed that DDT-R confers a fecundity increase but unlike previous work, no offspring viability increases were detected (Chapter 5). Thus as with male costs, specific pleiotropic female fitness benefits to resistance depend on genetic background. Modelling of DDT-R using a simple single-locus approach (Chapter 6) provides, for the first time, a unifying explanation for past and present DDT-R frequencies in nature and in old laboratory populations. The model is consistent with an old origin for the original DDT-R mutation held at low equilibrium frequency through balancing selection of a sexually antagonistic nature. It is also consistent with continued near fixation of DDT-R long after discontinued use and matches empirical observations in laboratory populations of the Canton-S background.

<p>Examining the genetic basis across repeated origins of the same phenotypic adaptation allows us to address several questions pertaining to the genetic basis of adaptation. First, whether the genes and types of mutations that are involved in adaptation are predictable. Second, whether the underlying genetic changes can constrain future evolutionary trajectories. Here, I have focused on the genetics of blue to red flower color shifts, an adaptive shift that has repeatedly occurred across angiosperms. First, I review the literature and determine the relative contribution of functional vs. regulatory mutations to the evolution of red flowers can be predicted both on the mutational target size of each type of mutation and the degree of their associated deleterious pleiotropy. Chapter 2 characterizes the genetic basis of red flowers in Penstemon barbatus using a combination of gene expression and protein function assays. I demonstrated that multiple inactivating mutations to one anthocyanin pathway enzyme, F3'5'h, have occurred, but no mutations to any other component of the anthocyanin pathway have contributed to the evolution of red flowers. This suggests that F3'5'h may be a particularly favorable target for selection and also that evolutionary reversal to blue flowers would be highly unlikely. Chapter 3 investigates the genetic basis of an additional 12 origins of red flowers within Penstemon. Again, using a combination of gene expression and enzyme function assays, I found the genetic basis of these additional origins red flowers in Penstemon is highly predictable, involving redundant inactivating mutations to F3'5'h, and tissue-specific regulatory mutations to a second gene F3'h. Thus, the genetics of red flowers in Penstemon often involves inactivation of a non-pleiotropic gene, F3'5'h, but tissue-specific regulatory mutations to the pleiotropic gene F3'h. Furthermore, the presence of redundant inactivating mutations in many red-flowered Penstemon species indicates that the evolutionary reversal to blue flowers would be unlikely.</p> / Dissertation

Objectives: This article presents estimates of narrow-sense heritability and bivariate genetic correlation for 14 tooth crown morphological variants scored on permanent premolars, first molars, and second molars. The objective is to inform data collection and analytical practices in dental biodistance and to provide insights on the development of molar crowns as integrated structures. Materials and Methods: African American dental casts from the Menegaz-Bock collection were recorded for the Arizona State University Dental Anthropology System. Estimates of narrow-sense heritability and genetic correlation were generated using SOLAR v.8.1.1, which included assessment of age, sex, and birth year as covariates. Both continuous scale and dichotomized estimates are provided. Results: Heritability estimates were nonsignificant for the majority of variables; however, for variables yielding significant estimates, values were moderate to high in magnitude and comparable to previous studies. Comparing left and right-side heritability estimates suggests directional asymmetry in the expression of environmental variance, something not seen in anterior tooth traits. Genetic correlations were moderate among antimeres and metameres and low for different traits scored on the same tooth crown. Although several negative correlations were noted, few reached statistical significance. Results affirm some of the current data cleaning and analytical practices in dental biodistance, but others are called into question. These include the pooling of males and females and combining left and right-side data into a single dataset. Conclusions: In comparison to anterior tooth crown traits, postcanine heritabilities were more often non-significant; however, those traits with significant heritability also tended to produce higher estimates. Genetic correlations were unremarkable, in part, because they were underpowered. However, M1 results may provide insight into the complex relationship between genes, environment, and development in determining ultimate crown form.

Objectives: This article presents estimates of narrow-sense heritability and bivariate genetic correlation for a series of morphological crown variants of the anterior dentition. These results provide insight into the value of dental phenotypes as evolutionary proxies, as well as the development of tooth crowns as integrated or modular structures. Materials and Methods: African American dental casts from the Menegaz-Bock collection were scored for a standard set of dental morphological variables using the Arizona State Dental Anthropology System. Estimates of narrow-sense heritability and genetic correlations were generated using SOLAR v. 8.1.1, controlling for the covariates of age, sex, and birth year. Analyses were run using ordinal/continuous scale variables that were then dichotomized at various breakpoints, consistent with standard practices in dental anthropology. Results: Heritability estimates were low to moderate for most traits, and lower in magnitude than those reported for odontometric data from the same study sample. Only winging, canine shoveling, and canine double shoveling returned narrow-sense heritabilities that did not differ significantly from zero. Genetic correlations were high among antimeres and metameres and low for different traits scored on the same tooth crown. These results affirm standard data cleaning practices in dental biodistance. Double shoveling was atypical in returning strong negative correlations with other traits, shoveling in particular. Conclusions: Additive genetic variation contributes to dental morphological variation, although the estimates are uniformly lower than those observed for odontometrics. Patterns of genetic correlation affirm most standard practices in dental biodistance. Patterns of negative pleiotropy involving lingual and labial crown features suggest a genetic architecture and developmental complex that differentially constrain morphological variation of distinct surfaces of the same tooth crown. These patterns warrant greater consideration and cross-population validation.

Objectives: This article provides estimates of narrow-sense heritability and genetic pleiotropy for mesiodistal tooth dimensions for a sample of 20th century African American individuals. Results inform biological distance analysis and offer insights into patterns of integration in the human dentition. Materials and Methods: Maximum mesiodistal crown dimensions were measured using Hillson-FitzGerald calipers on 469 stone dental casts from the Menegaz-Bock Collection. Narrow-sense heritability estimates and genetic and phenotypic correlations were estimated using SOLAR 8.1.1 with covariate screening for age, sex, age*sex interaction, and birth year. Results: Heritability estimates were moderate (∼0.10 – 0.90; h2 mean = 0.51) for most measured variables with sex as the only significant covariate. Patterns of genetic correlation indicate strong integration across tooth classes, except molars. Comparison of these results to previously published work suggests lower overall heritability relative to other human populations and much stronger genetic integration across tooth classes than obtained from nonhuman primate genetic pleiotropy estimates. Conclusions: These results suggest that the high heritabilities previously published may reflect overestimates inherent in previous study designs; as such the standard estimate of 0.55 used in biodistance analyses may not be appropriate. For the Gullah, isolation and endogamy coupled with elevated levels of physiological and economic stress may suppress narrow-sense heritability estimates. Pleiotropy analyses suggest a more highly integrated dentition in humans than in other mammals.

The genome-wide association study (GWAS) approach has identified novel loci for a variety of complex diseases. However, for most of these disorder much of the heritability is not explained by this approach, which focuses on identifying common variants that are associated with disease risk. The unexplained heritability may be due to genetic or phenotypic heterogeneity or the influence of rare variants. The motivation behind this thesis was to uncover the unexplained heritability by applying joint analyses of sets of variants (gene-based association test) and multiple disease-related phenotypes (called multivariate gene-based association test). First, we evaluated multivariate gene-based methods for detecting association of common genetic variants with correlated phenotypes. An extensive simulation study showed that the method combining the MultiPhen and GATES software performed best for most tested scenarios especially when correlations among phenotypes are relatively low. We developed a new multivariate gene-based test using rare variants called VEMPHAS. A simulation study using VEMPHAS showed that this method correctly controls for type I error in all tested scenarios. We applied VEMPHAS to analysis of various phenotypes related to Alzheimer disease (AD) and found suggestive association (P < 4.15x10-6) with the gene TRIM22, which has been identified in a previous sequencing study of AD onset in PSEN1/2 mutation carriers. We also developed software with a graphical user interface which is designed for integrating information from different types of data sources including genetic data (from GWAS or sequencing), expression data (from RNA-Seq), and protein structures (from protein data banks). This software has several features including 1) testing associations between genetic variants and gene expressions; 2) locating amino acids, encoded by the variants, in a protein structure; and 3) retrieving genetic locations (chromosome and base pair positions) of amino acids of interest in the protein structure. The last feature can be applied for prioritizing coding variants for gene-based association testing. The methods and strategies developed for this dissertation project can effectively uncover a portion of the remaining heritability of complex diseases that is unexplained by traditional GWAS approaches.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21158号 / 農博第2284号 / 新制||農||1060(附属図書館) / 学位論文||H30||N5132(農学部図書室) / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 田中 千尋, 教授 本田 与一, 教授 宮川 恒 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DGAM

Genetic predisposition to obesity presents a paradox: how do genetic variants with such a detrimental impact on human health persist through evolutionary time? Numerous hypotheses, for instance the thrifty genotype hypothesis, attempt to explain this phenomenon, yet fail to provide a satisfying answer to the modern obesity epidemic. In this critical review, I appraise existing theories explaining the evolutionary origins of obesity and explore novel biological and sociocultural agents of evolutionary change that may help explain the distribution of obesity and leanness predisposing variants in modern human populations. Gene pleiotropy and adaptations to diverse environmental niches may explain the rise and subsequent selection of obesity risk alleles. The regulation of gene expression by epigenetic mechanisms may serve as a stochastic factor affecting the manifestation of obesity phenotypes. Finally, exposure to malnutrition and disease epidemics in the wake of colonialism, culturally mediated notions of attractiveness and desirability, and diverse mating systems – including forced copulation, consanguinity and polygamy – may play a role in shaping the human genome. In short, I posit that in order to explain ethnic variation in obesity susceptibility, we must examine the origin of physiological adaptations and understand the sociocultural experiences of individuals and populations. As an imperative first step towards the identification of important drivers of obesity gene evolution, this review will inform empirical research focused on testing evolutionary theories by way of population genetics and mathematical modelling. Ultimately, these data will promote a better understanding of the aetiology of obesity and are expected to guide the development of targeted management, treatment, and prevention strategies. / Thesis / Master of Science (MSc)

Plant hormones and their gene regulatory networks orchestrate a diverse array of metabolic and physiological changes crucial for growth, development, and environmental responses. Targeting the engineering of hormone signaling networks holds promise for enhancing plant health, crop productivity, and vigor. However, these networks are intricate, featuring negative feedback loops, extensive interconnections between pathways, pleiotropy, and overlapping gene expression. These complexities pose challenges in identifying candidate genes and parsing apart their isolated functions that could be strategically engineered to achieve desired plant phenotypes. Integration of comparative evolution, synthetic biology, and expression analysis facilitates the deconstruction of these networks. Through systems biology approaches data dimensionality can be reduced, enabling the attribution of specific phenotypes to associated genes. Here, I reviewed how the employment of these above-mentioned approaches can aid in the identification of candidate genes involved the regulation of growth and development within specific tissues, and how through synthetic biology we can explore the sequence-function space of candidate genes and their pathway modules. Candidate genes identified through this process can be evaluated through comparative evolutionary approaches, and efficiently tested in synthetic systems for engineering of their molecular functionalities in a high-throughput manner. Here, as a case study, I employ a systems biology approach to identify tissue-specific candidate genes within the auxin regulatory network in soybean shoot development. This method aims to minimize pleiotropy and off-target effects by utilizing expression analysis tissue-specificity score and principal component analysis. I primarily, focused on three pivotal components of the nuclear auxin signaling pathway: Aux/IAA transcriptional repressors, ARF transcription factors, and TIR1/AFB auxin receptors. These components collectively modulate auxin signaling, influencing various growth and environmental responses. I identified genes within the three pivotal components of auxin signaling involved in early shoot architecture development, which has advantages from weed suppression to yield in soybean cultivation. I used a yeast chassis to investigate the function of pleiotropic auxin receptors, which primarily regulate Aux/IAA levels and orchestrate transcriptional changes in response to auxin. I explored whether these receptors modulate auxin response in a concerted fashion, as they are generally not tissue specific. Here, I reported that auxin receptors interact in an epistatic manner to modulate auxin response. This case of study serves as a foundation in engineering plant genotype-phenotype via auxin signaling. / Doctor of Philosophy / Plant hormones are essential for controlling various processes that drive plant growth, development, and responses to the environment. Scientists are exploring ways to engineer the networks that regulate these hormones to improve plant health, boost crop yields, and enhance plant strength. However, these networks are complex, with many interacting parts, making it difficult to identify which genes to modify to achieve specific outcomes in plants. To tackle this challenge, researchers use a combination of approaches, including studying how these networks have evolved, analyzing large amounts of biological data, and using synthetic biology to test and refine their findings. By breaking down the complexity of these networks, they can link specific genes to particular plant traits. Once these genes–trait links are identified, they can be further tested and engineered to optimize plant characteristics. In this study, I focused on the auxin hormone, which plays a key role in numerous aspects of plant growth including soybean shoot development and Arabidopsis root development. I looked at three main components of the auxin regulatory network: Aux/IAA proteins (which act as repressors), ARF transcription factors (which control gene expression), and TIR1/AFB receptors (which detect auxin levels). These components work together to regulate how plants grow and respond to their environment. I identified key genes within these main auxin components that are important for early development of soybean shoots and minimizes off-target effects. This can help improve soybean farming by enhancing weed control and enhancing crop yields Using a synthetic biology yeast system, I studied the function of TIR1/AFB auxin receptors and how this family of receptors interact to perceive auxin and control the levels of Aux/IAA proteins, consequently controlling the plant's growth in response to auxin. I found that auxin receptors work in concert in a way that reduces their overall effect on the plants response to auxin. This research lays the groundwork for future efforts to engineer plant traits by modifying the auxin signaling pathway, which could lead to improved crop performance and resilience.