Gregory Carter - US grants

DESCRIPTION (provided by applicant): This proposed grant award is designed to enable the principle investigator (PI), a theoretical physicist by training, to develop into an independent researcher in systems biology. The PI proposes a program aimed at constructing and testing quantitative models of genetic interaction. Dr. Timothy Galitski, an expert in microbiology, genetics, and genomics, will serve as mentor and Dr. Leroy Hood, an expert in genomics and systems biology, will serve as co-mentor. The training program includes laboratory training, course work, seminars, and conference attendance. The goal of the proposed research program is to carry out a systematic and quantitative analysis of genetic interactions in a model system. Genetic interaction analysis, in combination with molecular biology, has been successfully used to generate hypotheses of how genes and gene perturbations functionally interact to affect phenotypes. These techniques will be employed to study the differentiation of yeast cells from yeast- form growth to the pathogen-like invasive filamentous form, a major prototype in the study of cell differentiation. The research program is divided into three specific aims: (1) Develop predictive, quantitative gene-influence models and integrate with molecular-interaction and phenotype data;(2) Experimentally test model-derived predictions of genomic expression patterns and phenotypes;and (3) Extend modeling methods to crossbred populations. Experimental results will be incorporated into the initial datasets for subsequent rounds of analysis and refined modeling to develop computational methods for wider application. Understanding the effects of genetic diversity on human health and disease will require not only identifying trait genes, but also understanding how they functionally combine to affect a phenotype or clinical outcome. The proposed research aims to facilitate this understanding by developing models of genetic interaction integrated with physical interaction data. The goal of the modeling is to predict the effects of interacting genetic perturbations, allowing for the formulation and testing of polygenic hypotheses and identification of specific molecular candidates for targeted therapeutic intervention.

Project G: Using Multiple Phenotypes to Model Genetic Epistasis;Gregory W. Carter (Jackson) Systems Genetics seeks to infer polygenic regulatory models of complex traits by identifying key genes and mapping networks that give rise to epistasis and plelotropy. The simultaneous modeling of multiple related phenotypes enables greater resolution of the interacting genetic network by providing a large set of constraints on possible solutions. This project will develop methods to exploit multidimensional phenotype data in genetic modeling of disease.

PROJECT SUMMARY/ABSTRACT Formulating biological models from genetic studies with multidimensional phenotype data requires new analytical methods that capture the complexity of genetic systems while also providing verifiable hypotheses of variant activity. This challenge has become increasingly acute with the advent of genome-scale data resources designed to determine how genetic variation affects biological processes at molecular resolution. This proposal addresses this need by leveraging two complementary aspects of genetic complexity: pleiotropy, in which one variant affects multiple phenotypes; and epistasis, in which multiple variants interact to affect one phenotype. Although widely observed in model organisms and increasingly in human data, these phenomena are rarely distilled into concise models. Our method, called Combined Analysis of Pleiotropy and Epistasis (CAPE), integrates these aspects to mathematically constrain possible genetic models and determine a genetic network that best describes the multiple phenotypes. This is achieved through multivariate linear regression followed by a formal reparametrization that translates interaction coefficients into directed edges between variants, each representing genetic suppression or enhancement. CAPE has proven successful in model systems and we now aim to extend the approach to complex genetic systems that include greater allelic diversity, sex, and dietary differences. To this end, we will use the Diversity Outbred (DO) mouse population, the Genotype-Tissue Expression (GTEx), and the ENCODE and Roadmap projects to developing models of complex gene regulation. We will model regulatory interactions between genetic variants and epigenetic states to interpret genetic networks and uncover rules that govern gene expression. To facilitate collaborations and community use, we will develop open-source software tools. These tools will include an R-based software library to perform CAPE analysis in human and model populations, and a suite of visualization tools to facilitate researcher exploration and interpretation of results. Our overall goal is to derive new methods to discover complex and novel genetic mechanisms of gene regulation and disease risk and, in the course of this work, release analytical and visualization tools for use in complex trait research. This project is divided into three specific aims. Aim 1 is to develop methods to infer networks of genetic variants that influence high-dimensional quantitative traits and create analytic and visualization software for community use. Aim 2 is to apply our methods to model gene expression in DO mice and human tissues. Aim 3 is to integrate epigenetic and genetic data to model how genetic variation and chromatin modifications interactively affect gene expression.

PROJECT SUMMARY The long-term goal of the proposed project is to enable development of improved treatments for retinal pigment epithelium (RPE) diseases. The RPE plays a critical role in function and maintenance of the posterior eye, and RPE disruption can lead to severe vision impairment and loss. Many heritable retinal diseases have origins or contributions from the RPE, including monogenic diseases and age-related macular degeneration, a complex disease that is a leading cause of blindness in the western aging population. While gene augmentation therapy has previously been used to successfully treat a small number of affected individuals with RPE-associated diseases, currently there are no effective cures for the vast majority of these diseases. Hence, there is a significant need to develop generalized therapeutic approaches that would be effective in treating multiple RPE-related diseases. We have observed that numerous RPE disease phenotypes are shared among mouse models bearing mutations in cell-adhesion and extracellular matrix (ECM) molecules, suggesting that the underlying pathways/networks that are disrupted and lead to the observed pathologies might also be shared. The goal of this project is to identify the common RPE pathogenic pathways/networks that may serve as druggable targets. Identifying druggable targets that participate during the pre-symptomatic stage of the disease is particularly important, to enable development of therapies that can prevent, delay onset, or decrease the severity of RPE-associated diseases irrespective of the initial cause of the disease. Our approach is to use four mouse models with RPE-driven disease to generate comprehensive quantitative phenotypic data and global and single-cell gene-expression data, and to integrate and analyze these data using sophisticated computational methods to reveal the shared molecular and biological mechanisms associated with pre-clinical RPE pathology. This will be accomplished in three aims: 1) Assess pre-clinical and end-stage RPE-related phenotypes in the four models. 2) Identify the shared molecular and biological pathways underlying the RPE-related phenotypes in the four models. Global and single-cell gene expression analysis will be performed, and these data and the phenotype data from Aim 1 will be analyzed using computational methods to identify the shared pathways perturbed in the four models. The computational results will be verified in vivo via generation and analysis of mouse models of key shared misregulated molecules. These well-characterized models will be made available to the research community. 3) Further validate in vivo that the phenotypes and key molecules and pathways identified in Aims 1 and 2 are similarly perturbed in additional models bearing mutations in cell-adhesion and ECM molecules generated by the KOMP2 program sited at The Jackson Laboratory (JAX). Successful completion of these aims will identify common molecular and biological pathways underlying RPE-related disorders, revealing potential therapeutic targets that could be effective in a broad range of these diseases, regardless of the cause of the disease.

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the most common form of dementia and the sixth leading cause of death in the United States. Currently no treatments are available that prevent or slow the disease. Genetics is thought to account for up to 70% of risk for developing AD. Apolipoprotein E (APOE) is the greatest genetic risk factor with inheritance of the ?4 allele of APOE (APOE?4) contributing approximately 15-50% of late-onset AD (LOAD) genetic risk. More than twenty other genes have been consistently associated with AD through next generation sequencing and genome-wide association studies (GWAS). Therefore in most cases, AD is likely caused by interactions between combinations of genetic factors. However, for many genes, the causative risk variants have not been identified and the mechanisms by which these genes contribute to AD are not known. This knowledge gap makes it extremely difficult to predict risk for and develop new strategies for treating AD. To bridge this gap, we propose a systems genetics approach in mice to identify combinations of genetic factors that modulate risk for AD. In particular we will focus on identifying genetic interactors of APOE?4. We aim to identify genetic factors that modify APOE-dependent processes most relevant to AD including lipid and amyloid clearance, APP processing, synaptic maintenance, vascular health and immune cell activation. Previous attempts to model AD in mice have utilized only a tiny fraction of the available genetic diversity and we believe this is one of the main reasons why mouse models have failed to recapitulate key aspects of human AD contributing to the lack of success in clinical trials. At The Jackson Laboratory (JAX), we have access to mouse strains that capture as much genetic diversity as is present in the human population and the expertise to maximize their potential. Our approach incorporates four classical inbred strains (C57BL/6J (B6), WSB/EiJ, CAST/Ei and NZO/HILtJ) and a recently developed panel of recombinant inbred lines (The Collaborative Cross, CC). We will use a combination of cutting edge genetic, genomic and computational methods to formulate and validate predictions about how specific AD-relevant genes interact to affect AD- related phenotypes. In Aim 1, we will determine the extent by which diverse genetic contexts modulate APOE?4-dependent processes. To achieve this we have crossed APOE?4, APPswe and PS1de9 from C57BL/6J to WSB/EiJ, CAST/EiJ and NZO/HILtJ. Our data show these strains provide variation in AD-relevant outcomes including cognitive ability, immune cell activation, body composition and metabolism. In Aim 2, we will determine how known LOAD genes modify the effects of APOE?4. We have identified 10 CC lines that together harbor an allelic series for at least 12 GWAS genes such as TREM2, ABCA7, BIN1, CLU, PICALM and CD33. We will determine specific variants that, in combination with APOE?4, affect AD phenotypes. In Aim 3 we will validate specific combinations of variants in different genetic contexts to more precisely define the role of genetic interactors of APOE?4 in AD.