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An Evolutionary Framework for Association Testing in Resequencing Studies
Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4.</p
Unravelling the complex reproductive tactics of male humpback whales : an integrative analysis of paternity, age, testosterone, and genetic diversity
How the underlying forces of sexual selection impact reproductive tactics including elaborate acoustic displays in cetaceans remains poorly understood. Here, I combined 26 years (1995-2020) of photo-identification, behavioural, (epi)genetic, and endocrine data from an endangered population of humpback whales (New Caledonia), to explore male reproductive success, age, physiology, and population dynamics over almost a third of the lifespan of a humpback whale. First, I conducted a paternity analysis on 177 known mother-offspring pairs and confirmed previous findings of low variation in reproductive success in male humpback whales. Second, epigenetic age estimates of 485 males revealed a left-skewed population age structure in the first half of the study period that became more balanced in the second half. Further, older males (> 23 years) more often engaged in certain reproductive tactics (singing and escorting) and were more successful in siring offspring once the population age structure stabilised, suggesting reproductive tactics and reproductive success in male humpback whales may be age-dependent. Third, using enzyme immunoassays on 457 blubber samples, I observed a seasonal decline in male testosterone in the population over the breeding season. Testosterone levels appeared highest during puberty, then decreased and levelled off at the onset of maturity, yet were highly variable at any point during the breeding season and across males of all ages. Lastly, I investigated the influence of genetic diversity at the major histocompatibility complex (MHC) class I and class IIa (DQB and DRB-a) on patterns of male reproductive success in humpback whales. Mating pairs shared fewer alleles than expected under random mating at MHC class I and IIa, thus, providing evidence of an MHC-mediated female mate choice in humpback whales. This thesis provides novel, critical insights into the evolutionary consequences of commercial whaling on the demography, patterns of reproduction and sexual selection of exploited populations of baleen whales."This work was supported by a University of St Andrews School of Biology Ph.D. Scholarship and the Louis M. Herman Research Scholarship 2022 to Franca Eichenberger. Sample collection and analyses from 2018-2020 were supported by grants to Ellen C. Garland (Royal Society University Research Fellowship (UF160081 & URF\R\221020), Royal Society Research Fellows Enhancement Award (RGF\EA\180213), Royal Society Research Grants for Research Fellows 2018 (RGF\R1\181014), National Geographic Grant (#NGS-50654R-18), Carnegie Trust Research Incentive Grant (RIG007772), British Ecological Society Small Research Grant (SR18/1288) and School of Biology Research Committee funding)."--Fundin
Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data
Programa de Doctorado en BiotecnologĂa, IngenierĂa y TecnologĂa QuĂmicaLĂnea de InvestigaciĂłn: IngenierĂa, Ciencia de Datos y BioinformĂĄticaClave Programa: DBICĂłdigo LĂnea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques.
Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformĂĄtic
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Molecular signals of arms race evolution between RNA viruses and their hosts
Viruses are intracellular parasites that hijack their hostsâ cellular machinery to replicate themselves. This creates an evolutionary âarms raceâ between hosts and viruses, where the former develop mechanisms to restrict viral infection and the latter evolve ways to circumvent these molecular barriers. In this thesis, I explore examples of this virus-host molecular interplay, focusing on events in the evolutionary histories of both viruses and hosts. The thesis begins by examining how recombination, the exchange of genetic material between related viruses, expands the genomic diversity of the Sarbecovirus subgenus, which includes SARS-CoV responsible for the 2002 SARS epidemic and SARS-CoV-2 responsible for the COVID-19 pandemic. On the host side, I examine the evolutionary interaction between RNA viruses and two interferon-stimulated genes expressed in hosts. First, I show how the 2â˛-5â˛-oligoadenylate synthetase 1 (OAS1) gene of horseshoe bats (Rhinolophoidea), the reservoir host of sarbecoviruses, lost its anti-coronaviral activity at the base of this bat superfamily. By reconstructing the Rhinolophoidea common ancestor OAS1 protein, I first validate the loss of antiviral function and highlight the implications of this event in the virus-host association between sarbecoviruses and horseshoe bat hosts. Second, I focus on the evolution of the human butyrophilin subfamily 3 member A3 (BTN3A3) gene which restricts infection by avian influenza A viruses (IAV). The evolutionary analysis reveals that BTN3A3âs anti-IAV function was gained within the primates and that specific amino acid substitutions need to be acquired in IAVsâ NP protein to evade the human BTN3A3 activity. Gain of BTN3A3-evasion-conferring substitutions correlate with all major human IAV pandemics and epidemics, making these NP residues key markers for IAV transmissibility potential to humans. In the final part of the thesis, I present a novel approach for evaluating dinucleotide compositional biases in virus genomes. An application of my metric on the Flaviviridae virus family uncovers how ancestral host shifts of these viruses correlate with adaptive shifts in their genomesâ dinucleotide representation. Collectively, the contents of this thesis extend our understanding of how viruses interact with their hosts along their intertangled evolution and provide insights into virus host switching and pandemic preparedness
Gut-brain interactions affecting metabolic health and central appetite regulation in diabetes, obesity and aging
The central aim of this thesis was to study the effects of gut microbiota on host energy metabolism and central regulation of appetite. We specifically studied the interaction between gut microbiota-derived short-chain fatty acids (SCFAs), postprandial glucose metabolism and central regulation of appetite. In addition, we studied probable determinants that affect this interaction, specifically: host genetics, bariatric surgery, dietary intake and hypoglycemic medication.First, we studied the involvement of microbiota-derived short-chain fatty acids in glucose tolerance. In an observational study we found an association of intestinal availability of SCFAs acetate and butyrate with postprandial insulin and glucose responses. Hereafter, we performed a clinical trial, administering acetate intravenously at a constant rate and studied the effects on glucose tolerance and central regulation of appetite. The acetate intervention did not have a significant effect on these outcome measures, suggesting the association between increased gastrointestinal SCFAs and metabolic health, as observed in the observational study, is not paralleled when inducing acute plasma elevations.Second, we looked at other determinants affecting gut-brain interactions in metabolic health and central appetite signaling. Therefore, we studied the relation between the microbiota and central appetite regulation in identical twin pairs discordant for BMI. Second, we studied the relation between microbial composition and post-surgery gastrointestinal symptoms upon bariatric surgery. Third, we report the effects of increased protein intake on host microbiota composition and central regulation of appetite. Finally, we explored the effects of combination therapy with GLP-1 agonist exenatide and SGLT2 inhibitor dapagliflozin on brain responses to food stimuli
Computational Imaging for Phase Retrieval and Biomedical Applications
In conventional imaging, optimizing hardware is prioritized to enhance image quality directly. Digital signal processing is viewed as supplementary. Computational imaging intentionally distorts images through modulation schemes in illumination or sensing. Then its reconstruction algorithms extract desired object information from raw data afterwards. Co-designing hardware and algorithms reduces demands on hardware and achieves the same or even better image quality. Algorithm design is at the heart of computational imaging, with model-based inverse problem or data-driven deep learning methods as approaches. This thesis presents research work from both perspectives, with a primary focus on the phase retrieval issue in computational microscopy and the application of deep learning techniques to address biomedical imaging challenges.
The first half of the thesis begins with Fourier ptychography, which was employed to overcome chromatic aberration problems in multispectral imaging. Then, we proposed a novel computational coherent imaging modality based on Kramers-Kronig relations, aiming to replace Fourier ptychography as a non-iterative method. While this approach showed promise, it lacks certain essential characteristics of the original Fourier ptychography. To address this limitation, we introduced two additional algorithms to form a whole package scheme. Through comprehensive evaluation, we demonstrated that the combined scheme outperforms Fourier ptychography in achieving high-resolution, large field-of-view, aberration-free coherent imaging.
The second half of the thesis shifts focus to deep-learning-based methods. In one project, we optimized the scanning strategy and image processing pipeline of an epifluorescence microscope to address focus issues. Additionally, we leveraged deep-learning-based object detection models to automate cell analysis tasks. In another project, we predicted the polarity status of mouse embryos from bright field images using adapted deep learning models. These findings highlight the capability of computational imaging to automate labor-intensive processes, and even outperform humans in challenging tasks.</p
OBSERVATIONAL CAUSAL INFERENCE FOR NETWORK DATA SETTINGS
Observational causal inference (OCI) has shown significant promise in recent years, both as a tool for improving existing machine learning techniques and as an avenue to aid decision makers in applied areas, such as health and climate science. OCI relies on a key notion, identification, which links the counterfactual of interest to the observed data via a set of assumptions. Historically, OCI has relied on unrealistic assumptions, such as the âno latent confoundersâ assumption. To address this, Huang and Valtorta (2006) and Shpitser and Pearl (2006) provided sound and complete algorithms for identification of causal effects in causal directed acyclic graphs with latent variables. Nevertheless, these algorithms can only handle relatively simple causal queries.
In this dissertation, I will detail my contributions which generalize identification theory in key directions. I will describe theory which enables identification of causal effects when i) data do not satisfy the âindependent and identically distributedâ assumption, as in vaccine or social network data, and ii) the intervention of interest is a function of other model variables, as in off-line, off-policy learning, iii) when these two complicated settings intersect. Additionally, I will highlight some novel ways to conceive of interventions in networks. I will conclude with a discussion of future directions
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