Matching Items (30)
Description
Bridging social capital describes the diffusion of information across networks built between individuals of different social identities. This project aims to understand if the bridging ties of economic connectedness (EC), measured by data from Facebook friends and calculated as the average share of high socioeconomic status friends that an individual from a low socioeconomic status has, can be a predictor of variations in COVID-19 infection risk across Arizona ZIP code tabulation areas (ZCTAs). Economic connectedness values across Arizona ZCTAs was examined in addition to the correlation of EC to various social and demographic factors such as age, sex, race and ethnicity, educational background, income, and health insurance coverage. A multiple linear regression model was conducted to examine the association of EC to biweekly COVID-19 growth rate from October 2020 to November 2021, and to examine the longitudinal trends in the association between these two factors. The study found that the bridging ties of economic connectedness has a significant effect size comparable to that of other demographic features, and has implications in being used to identify vulnerabilities and health disparities in communities during the pandemic.
ContributorsBoby, Maria (Author) / Oh, Hyunsung (Thesis director) / Marsiglia, Flavio (Committee member) / Liu, Li (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution & Social Change (Contributor) / School of Social Work (Contributor)
Created2023-05
Description
Computational and systems biology are rapidly growing fields of academic study, but unfamiliar researchers are impeded by a lack of accessible, programming-optional, modelling tools. To address this gap, I developed BioSSA, a web framework built on JavaScript and D3.js which allows users to explore a small library of curated biophysical models as well as create and simulate their own reaction network. The mathematical foundation of BioSSA is the Stochastic Gillespie Algorithm, which is widely used in mathematical modeling and biology to represent chemical reaction systems. SGA is particularly well-suited as an introductory modelling tool because of its flexibility, broad applicability, and its ability to numerically approximate systems when analytical solutions are not available. BioSSA is freely available to the community and further improvements are planned.
ContributorsRamirez, Daniel (Author) / Ghasemzadeh, Hassan (Thesis director) / Liu, Li (Committee member) / Lu, Mingyang (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2023-05
Description
Background: Dyslexia is a neurodevelopmental impacting reading and writing ability present in around 5 to 9 percent of the population. The etiology of the condition is not currently well understood.
Purpose: To identify new genes of interest regarding the etiology of dyslexia, describe the interaction of those genes within known gene networks, and discuss potential relationships between their expression in the early developing brain and phenotypic outcomes.
Method: With informed consent, participants’ phenotypic and exome data were collected. Phenotypic data were collected using assessments measuring reading and spelling ability. Exome data were collected via saliva samples and processed at the UW-CRDR. Exome data were then filtering using Seqr and compared across participant families. Certain genes with identical variations were visually validated using the Integrated Genome Viewer, and then investigated using STRING Network Analysis and the Human Brain Transcriptome.
Results: Three genes were identified: BCL6, DNAH1, and DNAH12. Protein-protein interactions were confirmed between DNAH1 and DNAH12 via STRING Network Analysis. BLC6 and DNAH1 experience higher postnatal expression in the cerebellar cortex. DNAH12 experiences higher prenatal expression in the hippocampus.
Discussion: The findings appear to be consistent with a heterogenous and polygenic model of dyslexia. The correlation between the participants’ genotypes and phenotypes is not strong enough to draw significant conclusions regarding genotype/phenotype connections. A larger participant sample size and analysis of a large pool of shared genes may reveal a clearer relationship.
ContributorsBanta, Claire (Author) / Peter, Beate (Thesis director) / Liu, Li (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Sanford School of Social and Family Dynamics (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2024-05
Description
Speech sound disorders (SSDs) are the most prevalent type of communication disorder in children. Clinically, speech-language pathologists (SLPs) rely on behavioral methods for assessing and treating SSDs. Though clients typically experience improved speech outcomes as a result of therapy, there is evidence that underlying deficits may persist even in individuals who have completed treatment for surface-level speech behaviors. Advances in the field of genetics have created the opportunity to investigate the contribution of genes to human communication. Due to the heterogeneity of many communication disorders, the manner in which specific genetic changes influence neural mechanisms, and thereby behavioral phenotypes, remains largely unknown. The purpose of this study was to identify genotype-phenotype associations, along with perceptual, and motor-related biomarkers within families displaying SSDs. Five parent-child trios participated in genetic testing, and five families participated in a combination of genetic and behavioral testing to help elucidate biomarkers related to SSDs. All of the affected individuals had a history of childhood apraxia of speech (CAS) except for one family that displayed a phonological disorder. Genetic investigation yielded several genes of interest relevant for an SSD phenotype: CNTNAP2, CYFIP1, GPR56, HERC1, KIAA0556, LAMA5, LAMB1, MDGA2, MECP2, NBEA, SHANK3, TENM3, and ZNF142. All of these genes showed at least some expression in the developing brain. Gene ontology analysis yielded terms supporting a genetic influence on central nervous system development. Behavioral testing revealed evidence of a sequential processing biomarker for all individuals with CAS, with many showing deficits in sequential motor skills in addition to speech deficits. In some families, participants also showed evidence of a co-occurring perceptual processing biomarker. The family displaying a phonological phenotype showed milder sequential processing deficits compared to CAS families. Overall, this study supports the presence of a sequential processing biomarker for CAS and shows that relevant genes of interest may be influencing a CAS phenotype via sequential processing. Knowledge of these biomarkers can help strengthen precision of clinical assessment and motivate development of novel interventions for individuals with SSDs.
ContributorsBruce, Laurel (Author) / Peter, Beate (Thesis advisor) / Daliri, Ayoub (Committee member) / Liu, Li (Committee member) / Scherer, Nancy (Committee member) / Weinhold, Juliet (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021
Description
All biological processes like cell growth, cell differentiation, development, and aging requires a series of steps which are characterized by gene regulation. Studies have shown that gene regulation is the key to various traits and diseases. Various factors affect the gene regulation which includes genetic signals, epigenetic tracks, genetic variants, etc. Deciphering and cataloging these functional genetic elements in the non-coding regions of the genome is one of the biggest challenges in precision medicine and genetic research. This thesis presents two different approaches to identifying these elements: TreeMap and DeepCORE. The first approach involves identifying putative causal genetic variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. TreeMap performs an organized search for individual and multiple causal variants using a tree guided nested machine learning method. DeepCORE on the other hand explores novel deep learning techniques that models the relationship between genetic, epigenetic and transcriptional patterns across tissues and cell lines and identifies co-operative regulatory elements that affect gene regulation. These two methods are believed to be the link for genotype-phenotype association and a necessary step to explaining various complex diseases and missing heritability.
ContributorsChandrashekar, Pramod Bharadwaj (Author) / Liu, Li (Thesis advisor) / Runger, George C. (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2020
Description
The severity of the health and economic devastation resulting from outbreaks of viruses such as Zika, Ebola, SARS-CoV-1 and, most recently, SARS-CoV-2 underscores the need for tools which aim to delineate critical disease dynamical features underlying observed patterns of infectious disease spread. The growing emphasis placed on genome sequencing to support pathogen outbreak response highlights the need to adapt traditional epidemiological metrics to leverage this increasingly rich data stream. Further, the rapidity with which pathogen molecular sequence data is now generated, coupled with advent of sophisticated, Bayesian statistical techniques for pathogen molecular sequence analysis, creates an unprecedented opportunity to disrupt and innovate public health surveillance using 21st century tools. Bayesian phylogeography is a modeling framework which assumes discrete traits -- such as age, location of sampling, or species -- evolve according to a continuous-time Markov chain process along a phylogenetic tree topology which is inferred from molecular sequence data.
While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance.
While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance.
ContributorsVaiente, Matteo (Author) / Scotch, Matthew (Thesis advisor) / Mubayi, Anuj (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2020
Description
Obesity is one of the most challenging health conditions of our time, characterized by complex interactions between behavioral, environmental, and genetic factors. These interactions lead to a distinctive obese phenotype. Twenty years ago, the gut microbiota (GM) was postulated as a significant factor contributing to the obese phenotype and associated metabolic disturbances. Exercise had shown to improve and revert the metabolic abnormalities in obese individuals. Also, genistein has a suggested potential anti-obesogenic effect. Studying the dynamic interaction of the GM with relevant organs in metabolic homeostasis is crucial for the design of new long-term therapies to treat obesity. The purpose of this experimental study is to examine exercise (Exe), genistein (Gen), and their combined intervention (Exe + Gen) effects on GM composition and musculoskeletal mitochondrial oxidative function in diet-induced obese mice. Also, this study aims to explore the association between gut microbial diversity and mitochondrial oxidative capacity. 132 adult male (n=63) and female (n= 69) C57BL/6 mice were randomized to one of five interventions for twelve weeks: control (n= 27), high fat diet (HFD; n=26), HFD + Exe (n=28), HFD + Gen (n=27), or HFD + Exe + Gen (n=24). All HFD drinking water was supplemented with 42g sugar/L. Fecal pellets were collected, DNA extracted, and measured the microbial composition by sequencing the V4 of the 16S rRNA gene with Illumina. The mitochondrial oxidative capacity was assessed by measuring the enzymatic kinetic activity of the citrate synthase (CS) of forty-nine mice. This study found that Exe groups had a significantly higher bacterial richness compared to HFD + Gen or HFD group. Exe + Gen showed the synergistic effect to drive the GM towards the control group´s GM composition as we found Ruminococcus significantly more abundant in the HFD + Exe + Gen than the rest of the HFD groups. The study did not find preventive capacity in either of the interventions on the CS activity. Therefore, further research is needed to confirm the synergistic effect of Exe, Exe, and Gen on the gut bacterial richness and the capacity to prevent HFD-induced deleterious effect on GM and mitochondrial oxidative capacity.
ContributorsOrtega Santos, Carmen Patricia (Author) / Whisner, Corrie M (Thesis advisor) / Dickinson, Jared M (Committee member) / Katsanos, Christos (Committee member) / Gu, Haiwei (Committee member) / Liu, Li (Committee member) / Al-Nakkash, Layla (Committee member) / Arizona State University (Publisher)
Created2021
Description
Circular RNAs (circRNAs) are a class of endogenous, non-coding RNAs that are formed when exons back-splice to each other and represent a new area of transcriptomics research. Numerous RNA sequencing (RNAseq) studies since 2012 have revealed that circRNAs are pervasively expressed in eukaryotes, especially in the mammalian brain. While their functional role and impact remains to be clarified, circRNAs have been found to regulate micro-RNAs (miRNAs) as well as parental gene transcription and may thus have key roles in transcriptional regulation. Although circRNAs have continued to gain attention, our understanding of their expression in a cell-, tissue- , and brain region-specific context remains limited. Further, computational algorithms produce varied results in terms of what circRNAs are detected. This thesis aims to advance current knowledge of circRNA expression in a region specific context focusing on the human brain, as well as address computational challenges.
The overarching goal of my research unfolds over three aims: (i) evaluating circRNAs and their predicted impact on transcriptional regulatory networks in cell-specific RNAseq data; (ii) developing a novel solution for de novo detection of full length circRNAs as well as in silico validation of selected circRNA junctions using assembly; and (iii) application of these assembly based detection and validation workflows, and integrating existing tools, to systematically identify and characterize circRNAs in functionally distinct human brain regions. To this end, I have developed novel bioinformatics workflows that are applicable to non-polyA selected RNAseq datasets and can be used to characterize circRNA expression across various sample types and diseases. Further, I establish a reference dataset of circRNA expression profiles and regulatory networks in a brain region-specific manner. This resource along with existing databases such as circBase will be invaluable in advancing circRNA research as well as improving our understanding of their role in transcriptional regulation and various neurological conditions.
The overarching goal of my research unfolds over three aims: (i) evaluating circRNAs and their predicted impact on transcriptional regulatory networks in cell-specific RNAseq data; (ii) developing a novel solution for de novo detection of full length circRNAs as well as in silico validation of selected circRNA junctions using assembly; and (iii) application of these assembly based detection and validation workflows, and integrating existing tools, to systematically identify and characterize circRNAs in functionally distinct human brain regions. To this end, I have developed novel bioinformatics workflows that are applicable to non-polyA selected RNAseq datasets and can be used to characterize circRNA expression across various sample types and diseases. Further, I establish a reference dataset of circRNA expression profiles and regulatory networks in a brain region-specific manner. This resource along with existing databases such as circBase will be invaluable in advancing circRNA research as well as improving our understanding of their role in transcriptional regulation and various neurological conditions.
ContributorsSekar, Shobana (Author) / Liang, Winnie S (Thesis advisor) / Dinu, Valentin (Thesis advisor) / Craig, David (Committee member) / Liu, Li (Committee member) / Arizona State University (Publisher)
Created2018
Description
Childhood Apraxia of Speech (CAS) is a severe motor speech disorder that is difficult to diagnose as there is currently no gold-standard measurement to differentiate between CAS and other speech disorders. In the present study, we investigate underlying biomarkers associated with CAS in addition to enhanced phenotyping through behavioral testing. Cortical electrophysiological measures were utilized to investigate differences in neural activation in response to native and non-native vowel contrasts between children with CAS and typically developing peers. Genetic analysis included full exome sequencing of a child with CAS and his unaffected parents in order to uncover underlying genetic variation that may be causal to the child’s severely impaired speech and language. Enhanced phenotyping was completed through extensive behavioral testing, including speech, language, reading, spelling, phonological awareness, gross/fine motor, and oral and hand motor tasks. Results from cortical electrophysiological measures are consistent with previous evidence of a heightened neural response to non-native sounds in CAS, potentially indicating over specified phonological representations in this population. Results of exome sequencing suggest multiple genetic variations contributing to the severely affected phenotype in the child and provide further evidence of heterogeneous genomic pathways associated with CAS. Finally, results of behavioral testing demonstrate significant impairments evident across tasks in CAS, suggesting underlying sequential processing deficits in multiple domains. Overall, these results have the potential to delineate functional pathways from genetic variations to the brain to observable behavioral phenotypes and motivate the development of preventative and targeted treatment approaches.
ContributorsVose, Caitlin (Author) / Peter, Beate (Thesis advisor) / Liu, Li (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2018