Funding
Research Grants
Genome Dynamics: Evolution, Organization and Function
NIH 5P50GM076468 Gary Churchill
Our preliminary studies using inbred mice show that mammalian genomes contain extensive, regional domains of functionally related elements that coalesced over evolutionary time to promote the coinheritance and survival of compatible sets of alleles at functionally related genes, and that domains on separate chromosomes interact in a distinctly non-random manner, forming scale-free networks. In effect, the mammalian genome is a dynamic system which varies spatially in its organization and expression and temporally in its evolution and inheritance. Using a team of computational biologists, molecular biologists and geneticists, we propose extending our studies of genome dynamics as an integrated system from an evolutionary perspective. This requires using computational approaches on large data sets we will generate to describe the interactions between genome organization, gene expression, phenotype determination and the impact of recombination hotspots in determining inheritance of co-adapted sets of alleles. By developing detailed maps of these interactions we can evaluate the underlying principles. We will develop training and outreach programs to make our information and tools available to the research community at large and initiate efforts to promote the development of this research area, taking advantage of The Jackson Laboratory's sustained programs in related efforts. To promote the long-term growth of this field of endeavor, in addition to the established faculty that have come together to pursue this program, we are recruiting several younger faculty so that their related efforts can flourish and develop over time. Our projects are designed to enhance our research capabilities for discovering the molecular mechanisms underlying human health and disease. Recent advances in genomic sciences have made it clear that reaching these understandings is a powerful step forward in developing improved prevention and therapy for our most common ills, including cancer, heart disease, and disorders of the immune and neurological systems.
QTL Analysis in Combined Inbred Line Crosses
NIH 5R01GM070683 Gary Churchill
Rodent inbred line crosses are widely used to map genetic loci associated with complex traits. This approach has proven to be powerful for detecting quantitative trait loci (QTL) however the resolution of QTL locations is often not sufficient to identify the gene (or genes) that are involved. We propose to develop analytical methods to combine information available in two or more inbred line crosses that will help to narrow conventional confidence intervals for QTL regions. Furthermore, by exploiting high-density single nucleotide polymorphism (SNP) data that are currently being generated for the common strains of inbred mice, we expect to achieve gene level resolution of QTLs by combining data from multiple crosses. Our strategy is based on the hypothesis that the common mouse strains are derived from a limited ancestral gene pool and thus QTLs detected in multiple crosses are likely to represent shared ancestral polymorphisms.
Experimental Design & Analysis for Expression Microarray
NIH 1R21CA088327 Gary Churchill
This first phase of this project will develop statistical methods for the design and analysis of gene expression microarray experiments based on statistical principles. Our experimental designs will allow a scientist to carry out classical (ANOVA style) experiments using microarray technology. Designed experiments offer quality control, normalization and a method of analysis that takes multiple sources of variation into account. New analysis tools will be developed based on the experimental designs and on our experience with array data generated in this phase of the project. We will establish the basic infrastructure, including informatics tools, for microarray analysis at the Jackson Laboratory. We will develop a detailed strategy, that is both robust and efficient, for a large scale survey of gene expression in tissue samples obtained from mouse models of mammary and ovarian cancers. The second phase of our project will involve carrying out this survey with the goal of classifying and charactering tumor types based on expression data. The expression data will be analyzed together with available information on the pathology of the tumor and the genetic background in which the tumor type arises. The ultimate goal of this project is to provide a sound statistical basis for the routine practice of expression profiling of mouse tumors. It is our premise that experimental design and design-based analysis tools are essential to obtaining high quality expression information at a reasonable cost.
Use of Diversity Outbred Mice to rapidly identify drug toxicity loci
1RC1CA145504-0110 Gary Churchill
Variation in the efficacy and toxicity of chemotherapeutic agents is consistently observed across human populations with severe systemic toxicity and unpredictable efficacy being hallmarks of cancer therapies. Adverse drug reactions can be difficult to study in humans due to issues such as environmental variability and the availability of relevant populations. Consequently, animal model systems are needed to fully understand the genetic basis of drug response.
Genetically defined mouse models offer a tractable experimental system for mapping susceptibility genes and for examining their function in the context of a complex, living organism. The Jackson Laboratory is developing a new variety of mice that are designed to maximize allelic diversity (Diversity Outbred Mice). Each individual animal within this population will be genetically unique and, as a whole, the population approximates the genetic diversity observed in human populations. Diversity Outbred Mice can be subjected to sophisticated physiological and molecular phenotyping beyond what is possible in human subjects. Their population structure will allow high resolution genetic mapping analogous to human genome-wide association studies.
We propose to use the Diversity Outbred Mice in a proof of concept study to establish their utility in rapidly identifying the genetic contribution to drug toxicity. This study can be executed quickly and will provide the most information possible while containing cost. We propose to identify genes responsible for the myelosuppressive effects of three cancer chemotherapeutic drugs, doxorubicin, cyclophosphamide, and docetaxel. We have chosen myelosuppression as the physiological response because this adverse effect is a major contributor to morbidity, mortality and costs associated with cancer treatment; mice are known to respond to these agents in a manner similar to humans; and the response can be measured using standard clinical hematological methods.
Glenn Award for Research in Biological Mechanisms of Aging
Shirng-Wern Tsaih
Causal Factors in Mouse Models of Aging
New Scholar Award in Aging, Ellison Medical Foundation AG-NS-0421 Shirng-Wern Tsaih
This project will develop novel methods for causal inference utilizing multiple age-related phenotypes collected on a panel of mouse inbred stains across a range of ages at the Jackson Laboratory Integrative Center for Genetic Regulation of Aging. This unique study of mammalian aging will accelerate the pace of aging research. Graphical models are a rapidly emerging area of statistical inference that enable us to gain insights into causal relationships among multiple variables. Establishing causal relationship is especially important in aging studies to help distinguish primary causes for secondary reactions to biological changes that occur with age. By combining graphical modeling techniques with traditional longitudinal analysis, we will be able to elucidate the dynamic relationship over time. We will apply these techniques to study relationships among metabolic, body composition and blood chemical traits to identify those factors most likely to have direct effects on aging and age-related pathology. In particular, we hope to identify measurable traits that can serve as reliable early indicators of longevity and age related pathologies. By including both gene expression and genetic variation data in the graphical modeling techniques, we will be able to place measurements in a pathway affecting lifespan and to state which measurements are correlated with lifespan but are not causal. Hence this study will provide a unique and powerful statistical approach to identify those causal factors that have direct effect on aging and lifespan.
Educational Grants
Course on Mathematical Analysis of Complex Phenotypes
NIH 5R25GM061364 Gary Churchill
We are currently in the midst of a genetics revolution that promises to push "breakthrough" biomedical research to new levels. A large part of this revolution depends upon the analysis of the relationships between genes and their effects on complex biological systems and phenotypes. Investigators must become increasingly skilled in the use of mathematical, computational and statistical tools to address a variety of important biological questions. Similarly, scientists with mathematical or computational backgrounds who wish to apply their skills to some of these complex problems will need to develop a deeper understanding of the biological principles involved. This course was initially offered in 2000 and has continued to focus on current approaches used to address the analysis of complex genetic traits both in humans and in other model systems. It is designed for biologists requiting training in the use of mathematical, computational and statistical tools as well for scientists with mathematical or computational backgrounds that wish to develop a deeper understanding of contemporary biological problems in genetics. The overall goal of this course is to train new scientists and re-train established investigators in the use of mathematical tools for the analysis of complex phenotypes and systems. Those completing the course will acquire a critical working knowledge of experimental approaches to: (1) mapping genes in inbred lines, (2) linkage analysis and mapping genes in pedigrees, (3) association analysis and mapping genes in populations, and (4) bio-informatics and software tools for database "mining". These aims have been, and will continue to be, accomplished by an intensive 7-day course offered in the fall of each year at The Jackson Laboratory in Bar Harbor Maine. Students will be chosen for their outstanding research potential in fields relevant to the course and will interact with a group of prominent computational biologists, bio-informaticists, biologists and geneticists both from The Jackson Laboratory and from other institutions. Student enrollment is kept deliberately small (30) to achieve a desirable level of student-faculty interaction. Didactic sessions will be held in the mornings, while the afternoon and evening sessions will be reserved for hands-on training workshops and case analysis of on-going projects in complex trait analysis.
Postdoctoral Fellowships
Bayesian Dynamic Genome Scale Modeling of HDL Cholesterol Transport
NIH 1F32HL095240 Rachael Hageman
Atherosclerosis, the primary cause of coronary artery disease (CAD), remains the number one cause of death in the United States and other industrialized nations. A major approach to the prevention and treatment of atherosclerosis is to target genes, mechanisms and pathways that are involved in high density lipoprotein (HDL) metabolism, and the process of reverse cholesterol transport (RCT). Therapeutic interventions of this nature exploit the atherogenic properties of HDL and promote the efflux of cholesterol from the body. Interventions that serve to alter the regulatory pathways of HDL metabolism often have off target system-wide consequences that are poorly understood and cannot be measured experimentally. A Bayesian methodology will be applied to analyze a genetically based dynamic computational model of HDL metabolism. The model will be described by a large system of ordinary differential equations that obeys physiological principles, existing data, enzyme kinetics, laws of mass balance and thermodynamic constraints. In the Bayesian modeling approach, the unknown parameters are related to the data through conditional and marginal probability density functions. In this framework, additional a priori information about the system is brought into the simulations to offset the lack of sufficient data, and allow for more physiological predictions. Monte Carlo sampling techniques will be applied to explore these densities and collect a family of suitable models that reflect the inherent variability of the underlying population. The sample of representative models will be simulated for the purpose of system-wide: (1) dynamic predictions of both measurable and immeasurable quantities, and (2) steady state and dynamic sensitivity studies to establish quantitative and qualitative relationships between system components and pathways. Features of the methodology will be applied to predict how the metabolic pathways of different genetic populations function under variable conditions, e.g., environment, medicine, diet and disease. A model of this type can be applied to assess the system-wide effects of drug targets, and to validate and design future experiments.
Assessing Variation in mRNA Transcript Levels in Mice Using Microarray Data
NIH 1F32GM087849 Peter Vedell
Variation in mRNA transcript levels in mice will be assessed using data from three microarray experiments that involve tissue samples from laboratory mice and are designed to quantify sources of variation within and across laboratory mouse strains and tissues. We will also develop variance estimation methods that improve upon existing methods. Microarray experiments can be designed to test a wide range of hypotheses, but the inferential power of subsequent analysis may not be fully realized without an understanding of variation in transcript abundance within and between individual mice of the same strain, between different strains, and between different tissues. Further study of variation in transcript abundance through experiments to study these sources of variation has great potential since important disease-related phenotypes have been shown to vary in inbred mice due to genetics, epigenetics, and biological factors
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