Gut Microbiome, Aging and Cardiometabolic Diseases in American Indians
https://reporter.nih.gov/project-details/10259707
Funding Agency: NIH
PI: Jinying Zhao, M.D., Ph.D.
The goal of this project is to identify key gut microbiome features associated with cardiometabolic traits in American Indians in the Strong Heart Study (SHS). We will collect fecal samples from 1500 participants scheduled to visit SHS Phase VII, conduct deep metagenomic sequencing and identify gut microbiome features associated with biological aging and age-related cardiometabolic diseases in this high-risk minority population.
Genome-Wide Mapping and Integrated Analysis of DNA 6mA Methylome in Human AD Brain
https://reporter.nih.gov/project-details/10172823
Funding Agency: NIH
PI: Jinying Zhao, M.D., Ph.D.
The objectives of this project are to generate the first detailed map of brain 6mA methylome and identify causative genes harboring aberrant 6mA alterations associated with quantitative neuropathological measures for early features of AD pathology (e.g., amyloid plaques, neurofibrillary tangles). To achieve this, we propose three specific aims: (1) Genome-wide mapping of brain DNA 6mA methylome to identify differentially methylated genes/regions harboring altered 6mA sites (D6AMRs) associated with AD pathology in 1,200 postmortem brain tissue samples collected by two large, community-based population cohorts of aging and dementia. (2) Integrated multiomics analysis to elucidate the potential mechanistic role of 6mA alteration in AD pathology; and (3) Functionally validation of top-ranked candidate genes in 3D brain organoids derived from human iPSCs.
DNA hydroxymethylation and AD
Funding Agency: NIH
PI: Jinying Zhao, M.D., Ph.D.
The goal of this project is to identify causative 5hmC alterations associated with quantitative neuropathological measures for early features of AD pathology (e.g., amyloid plaques, neurofibrillary tangles). To achieve this, we propose four specific aims: (1) Identify differentially hydroxymethylated regions associated with AD pathology by genome-wide profiling of 5hmC in 740 postmortem brains collected by two large, community-based population studies of aging and dementia: the Religious Order Study (discovery sample) and the Rush Memory and Aging Project (replication sample). As traditional methods cannot discriminate between 5mC and 5hmC, we will perform 5hmC-capture sequencing, followed by TET-assisted bisulfite sequencing using novel techniques developed by our group and collaborators. (2) Conduct targeted methylation sequencing to identify additional AD-associated 5mC alterations that may have been missed by our previous EWAS as a result of the limited resolution and genome coverage of the Illumina platform. (3) Functionally validate the putative genes identified in Aims 1 and 2 using existing RNA-seq data from the same brain cortex and a fly model for AD. (4) Perform integrative `omics’ analyses to test the joint and interactive effects of multi-layer `omics’ markers on AD pathology.
Integrated Multi-Omics of Myelodysplastic Syndrome
Funding Agency: UF Office of Research
PI: Jinying Zhao, M.D., Ph.D.
The objectives of this pilot study are to generate the first integrative map of 5mC, 5hmC, and gene expression in human bone marrow, and to identify functional epigenetic determinants for myelodysplastic syndrome (MDS).
The External Exposome and COVID-19 Severity among Individuals with Alzheimer’s Disease and Related Dementias
https://reporter.nih.gov/project-details/10284141
Funding: NIH
PI: Hui Hu, Ph.D.
Compared with the general population, the individuals with Alzheimer’s disease and related dementias (ADRD) are substantially different in their individual characteristics and environmental exposure (defined as the external exposome) profiles. This project aims to understand the relationships between environmental exposures and COVID-19 severity among ADRD individuals. We will: (1) create a synthetic longitudinal COVID-19 cohort of individuals with ADRD, (2) systematically evaluate statistical methods for external exposome-wide association study (ExWAS), specifically for COVID-19 outcomes among ADRD patients, and (3) through an external ExWAS, identify external exposome factors associated with COVID-19 severity and then develop predictive models of COVID-19 severity among individuals with ADRD.
A telescopic algorithm for two-dimensional hidden Markov models with application to genetic studies
Funding Agency: NSF Division of Mathematical Sciences (DMS)
PI: Xiang-Yang Lou, Ph.D.
This project is funded by the NSF Division of Mathematical Sciences (DMS). Multi-dimensional Markov processes, or more generally or more generally, Markov random fields, are ubiquitous in the real world; dependencies in interacting particle systems, images, videos, digitized documents, and gene transmission are all examples of multi-dimensional Markov processes. This project aims to develop a more computationally efficient algorithm for statistical inference of a two-dimensional hidden Markov model (2-D HMM), explore its application to genetic studies, and develop a freely available software package capable of implementing the proposed methods. The proposed work will potentially advance new knowledge, increase the computational ability to solve real-world problems, and make a positive impact on computational mathematics, engineering disciplines, geosciences, and biology. Given the fact that gene transmission from parent to child is a two-dimensional Markov process, the application of the proposed approach to genetics may revolutionize statistical analysis of family data, offering an innovative approach toward better identification of genes and genomic prediction.
As a general statistical methodological research, there will be many potential applications in diverse fields including complex diseases. Human disease are usually caused by the interaction of a multitude of nature and nurture influences including genes, environmental and lifestyle factors, and others, via a complex biological network. Thus, the proposed 2-D HMM-based statistical framework will provide a more powerful means to identify genetic and environmental risk factors for complex diseases such as diabetes, obesity, autism/mental disorders, and cancers, helping better understanding of etiological mechanisms underlying them and their prevention, early detection, and precision care. Further, the biological network with many hierarchical endpoints (omics data at the gene, transcript, protein levels) can be viewed as a hidden Markov random field and thus multi-dimensional hidden Markov approach that is allowed to model random noise and also can capture the intrinsic (causal) dependencies between units, represents a new methodological strategy to model the biological complexity and develop sophisticated and advanced statistical and bioinformatical tools such as integration of multi-omics data to interrogate the etiology of diseases.
Hypertensive Disorders of Pregnancy and Early Risk of Maternal CVD: Influence of the External Exposome
https://reporter.nih.gov/project-details/10214088
Funding: NIH
PI: Hui Hu, Ph.D.
Hypertensive disorders of pregnancy (HDP) are the most common pregnancy complication and are associated with an increased long-term risk of cardiovascular disease (CVD) which is also the leading cause of death for women in the US. Emerging evidence suggests that environmental exposure during pregnancy may play an important role in the development of HDP. This study builds on our prior work in the external exposome and leverages our access to the unique statewide linked electronic health records-birth records data from the OneFlorida Clinical Research Consortium to address multiple challenges in the field by: 1) determining the association between pregnancy external exposome and risk of HDP, considering the subtype, onset, and severity of HDP and accounting for pregnancy residential mobility in exposure measurements; 2) assessing early risk of maternal CVD after pregnancy associated with HDP and the external exposome, and 3) developing predictive models of HDP and early risk of maternal CVD after pregnancy.
The External Exposome and COVID-19 Severity
https://reporter.nih.gov/project-details/10240752
Funding: NIH
PI: Hui Hu, Ph.D.
Emerging evidence suggests that long-term environmental exposures may be important determinants of COVID-19 severity. Traditional epidemiological studies examine environmental factors separately, which can be a time-consuming and, moreover, does not account for confounding by co-exposures. In this project, we will use the ‘external exposome’ as an ideal framework to identify novel exposures associated with severe COVID-19. This study will fill important knowledge gaps concerning the relationship between the external exposome and severe COVID-19. Moreover, it will improve identification of severe COVID-19 in patients and inform the design of future interventions.
Dynamic Prediction Modeling to Improve Clinical Predictions
https://reporter.nih.gov/project-details/10367329
Funding: NIH
PI: Stephen E. Kimmel, M.D.
Formal prediction models are frequently used to enhance clinicians’ and researchers’ ability to quantify and communicate risk. Unfortunately, many prediction models in use today prove inaccurate when applied over time and to new populations, yielding not only inaccurate predictions but also a false level of confidence about the quality of their risk assessments. This commonly occurs because models are applied to patients with different clinical characteristics and risk of disease, to medical practices that differ from those used to develop the model, and to methods of care that constantly change over time. A potential solution to these problems is `Dynamic Prediction Modeling.’ Rather than using existing models in practice without accommodating their inevitable degradation in performance and, at best, infrequently developing new models with the same limitations, dynamic prediction modeling updates an existing prediction model continually as new data are accrued.
HIV Dynamic Modelling for Identification of Transmission Epicenters (HIV-DYNAMITE)
https://reporter.nih.gov/project-details/9638523
Funding: NIH
PI: Mattia Prosperi, Ph.D.
The overarching goal of this project is to develop a novel theoretical and technical framework able to model dynamically HIV transmission clusters over time by analyzing large, longitudinal sequence data. Our new tool for dynamic identification of transmission epicenters (HIV-DYNAMITE) will aid in monitoring, in real time, hyper-connected and/or long-lasting clusters fueling emerging HIV regional epidemics.
Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing Data
https://reporter.nih.gov/project-details/10166816
Funding: NIH
PI: Qing Lu, Ph.D.
Project Summary With advancements in next-generation sequencing technologies, sequencing studies has become increasingly used in substance dependence (SD) research. Although the ongoing sequencing studies hold great promise for unraveling novel variants that contribute to SD, the high-dimensional data, low frequent variants, complex SD etiology, and heterogeneous SD phenotypes create tremendous analytic and computational challenges. The goals of this application are to develop new methods and software for designing and analyzing population-based and family-based sequencing data with single or multiple phenotypes, and to use them in collaborative research to investigate genetic variants and gene-gene/gene-environment (G-G/G-E) interactions associated with SD.
Methods and Software for High-dimensional Risk Prediction Research
https://reporter.nih.gov/project-details/10170422
Funding: NIH
PI: Qing Lu, Ph.D.
The goal of this application is thus to complete the development of a random field (RF) framework and software for high-dimensional risk prediction research using omic data, and then apply the framework to Alzheimer’s disease (AD). The successful completion of this project should address analytical challenges faced by massive amounts of omic data, and advance the methodology and software development for high-dimensional risk prediction in general. The application of the new methods and software to large-scale AD datasets could also lead to novel AD risk prediction models that could be further replicated and investigated through collaborative research.
Novel Metabolic Predictors of Diabetes in American Indians
https://reporter.nih.gov/project-details/9775232
Funding: NIH
PI: Jinying Zhao, M.D., Ph.D.
Our goal here is to identify novel and sensitive T2D predictors that are specific to AIs beyond classical T2D indicators. To achieve this, we will repeatedly measure concentrations of over 500 metabolites, including BCAAs, carbohydrates, hydroxyl acids, lipids, as well as gut microbial-derived metabolites, in fasting plasma (~5 yr apart) from normoglycemic SHS participants followed >15 years. Findings of this study will greatly advance our understanding of T2D pathology, and hold promise for reducing or eliminating T2D disparity in AIs, an ethnically important but traditionally understudied minority group suffering from alarmingly high rates of T2D and obesity.
Genome-wide Profiling of Brain DNA Hydroxymethylome in Alzheimer’s disease
https://reporter.nih.gov/project-details/9411576
Funding: NIH
PI: Jinying Zhao, M.D., Ph.D.
Alzheimer’s disease (AD) is a fatal neurodegenerative disorder that affects over 5 million Americans. Despite substantial effort, the mechanisms underlying AD remains elusive. This project leverages the wealth of unique resources collected by two large, community-based longitudinal cohorts of aging and dementia with brain donation at the time of death to identify novel causative epigenetic determinants for AD pathology. Findings of this study will provide important mechanistic insights into disease etiology, and are highly likely to lead to the discovery of novel diagnostic and therapeutic tools against AD.