Genome Dynamics: Evolution, Organization and Function
NIH 5P50GM076468 Gary Churchill
Our work is centered on advancing systems genetics approaches to study the genetic and environmental factors shaping dynamic, genome-wide processes of epigenetic modification, recombination, gene expression, and metabolism in a mammalian model system. Our approach is based on our previous work providing a detailed molecular understanding of the evolutionary origins of the laboratory mouse, which in turn led to the adoption of two novel populations of mice with extensive genomic diversity. The Collaborative Cross recombinant inbred strains provide a fixed number of reproducible genomes optimal for multiple testing, while Diversity Outbred mice provide high genetic mapping resolution and an endless supply of unique genomes. These populations share the same allelic compositions and are derived from the same set of eight progenitor strains. Together these mouse populations provide an integrating framework for connecting the multiple domains of genomic function we study, as well as complementary approaches for developing and validating predictive models of genetic and environmental effects. By pioneering the application of these resources, our Center aims to establish high community standards and new approaches for systems genetics studies.
The Center for Genome Dynamics will engage a group of scientists with diverse specialties in computational, statistical, and biological domains in a common collaborative work environment. The Center will provide mentorship for career development of new faculty and postdoctoral associates. Our unique education program will engage high school and undergraduate students in challenging computational biology research.
Our projects are designed to enhance our capabilities for discovering genetic and environmental causes of phenotypic diversity and for elucidating the molecular mechanisms underlying human health and disease. Using the premier mammalian model organism combined with high throughput molecular phenotyping technologies, physiological profiling, and computational modeling, we will develop predictive modeling and validation strategies that test the premises of personalized medicine. Our goal, using a variety of disease phenotypes, is to improve prediction and intervention strategies for complex diseases, with broad implication for multiple areas of human disease.
Shock Center for Aging Research at the Jackson Laboratory
NIH 3P30AG038070 Gary Churchill
Human genetic variation plays a significant role in regulating differences in longevity and changes in overall health and disease susceptibility with age. Understanding the links between genetic variation and the biology of aging promises to ultimately identify approaches to extend the human healthspan. However, healthspan is a complex trait, and determining the interacting polymorphic alleles and environmental factors that affect it is difficult. Meeting this challenge will require a systems approach to aging, utilizing an experimental organism that models the genetic and biological complexity of the human population.
The Jackson Shock Center (JSC) proposes to use its expertise in mouse models and complex traits to build on successes of the previous funding period and to develop the unique resources necessary to enable the aging community to elucidate the genetic underpinnings of healthspan. Specifically, JSC will provide:
1) Aging Mice & Tissues through a central core of large crosses and reference populations, including the Collaborative Cross lines, which offer unprecedented genetic variation; 2) Mouse populations genotyped and comprehensively characterized for physiological and behavioral traits relevant to aging and healthspan; 3) Novel Statistical Methods developed to enable researchers to identify correlations, narrow QTL, and to understand causal versus reactive relationships of aging related traits; and 4) Integrated Mouse and Human Aging Data assembled into an annotated genetic map of mouse and human aging loci to enable researchers to rapidly identify and validate genes implicated in human aging and to suggest translational interventions to extend healthspan. All JSC resources, methods, phenotypic and genetic data, and maps will be publically available through the Mouse Phenome Database (MPD), the JSC website, and a proposed web portal, which will integrate the resources and information of the Nathan Shock Centers (NSC).
JSC will provide unprecedented, coordinated aging resources and a vibrant intellectual environment to support 29 faculty and more than 20 independent, grant-funded research projects aimed at unraveling genetic control of human aging at The Jackson Laboratory (JAX). These resources will be broadly disseminated to support more than 20 existing collaborations as well as numerous external aging investigators, greatly expanding JSC's role as a center for national aging research. In the long term, JSC will continue to focus JAX expertise in genomics and biology on aging, leading to enhanced resources for the research community and a better understanding of the molecular mechanisms of lifespan and healthspan.
QTL Analysis in Combined Inbred Line Crosses
NIH 2R01GM070683 Gary Churchill
Forward genetics approaches using animal models are undergoing a period of change, in part in response to the changes that have occurred in human genetics in the past few years. New experimental designs, including the collaborative cross and advanced heterogeneous stock populations, have the potential to yield large populations of animals with a genetic constitution that more accurately reflects the human genetic state with regard to diversity and heterozygosity. In addition, there has been rapid development of inexpensive high- throughput phenotyping capabilities, notably with gene expression microarrays, but metabolite and protein profiling will soon cross thresholds of quality and affordability. These changes necessitate the development of new computational and statistical tools for interpreting data. Our aims are to develop statistical methods in anticipation of new experimental approaches, to develop and disseminate software and data resources, and to analyze and interpret new and historical data from forward genetics experiments in mice. Our focus will shift from the historical objectives which emphasized gene discovery to new model-based approaches that exploit high dimensional and cumulative data to model systemic responses to genetic and environmental perturbations. The timely development of statistical methods and software will be critical to the success of mouse genetics in the coming years.
Aging Research Using the Diversity Outbred Mice
Ellison Medical Foundation 2010 Senior Scholar in Aging Award Gary Churchill
Only a handful of interventions are known to extend lifespan in mammals. Among these, dietary restriction is the best established. In a recent study by Harrison and others, rapamycin was the first drug demonstrated to increase mammalian lifespan. However, all known interventions that affect aging are accompanied by detrimental side effects. Pharmaceutical interventions are most appealing but there is still insufficient knowledge of mammalian aging at the mechanistic and genetic level to identify effective drug targets. We will develop a comprehensive strategy for the discovery and validation of targets in a mammalian system that closely mimics the genetic state of humans. Our approach will utilize genetic variation in mice to identify the genes and pathways that can potentially regulate lifespan with minimal detrimental effects. Mice are remarkably similar to human beings: 99% of mouse genes have human counterparts. Yet, mice age 25 times faster than humans, greatly facilitating aging experiments. Genes identified in mouse studies can be evaluated in human populations by studying whether there is an association between people with one form of the gene and an increase in lifespan. If a gene proves to be important in human aging, researchers can return to the mouse to test methods of clinical intervention. We propose to use a powerful new genetic approach to study the mechanisms that regulate aging and healthy lifespan. The diversity outcross (DO) is a novel mouse population that mimics human genetic diversity. Traditional drug treatment studies use populations with no genetic variance and compare responses to an intervention. Our strategy represents a new approach to target discovery in gerontology by identifying genes that regulate differential response to interventions affecting the aging process. Specifically, this project will assay parameters of healthy life span and immune and metabolic aging in DO mice under control, diet restriction and rapamycin treatment conditions. We expect the DO mice to show variation at least as great as seen in the human population. We will determine the genetic regions that mediate the effects of treatment interventions. Our long-term objective is to identify targets for interventions that will increase healthy life span.
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.
Experimental Design & Analysis for Expression Microarray
NIH 1R21CA088327 Gary Churchill
NIH 4R33CA088327 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.
Glenn Award for Research in Biological Mechanisms of Aging Shirng-Wern Tsaih
Causal Factors in Mouse Models of Aging
Ellison Medical Foundation New Scholar Award in Aging 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.
Course on Mathematical Analysis of Complex Phenotypes
NIH 2R25GM061364 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, environment and the effects of these interactions 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 other model systems. It is designed for biologists requiring training in the use of mathematical, computational and statistical tools as well as 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) QTL analysis, (2) microarray analysis, (3) statistical methodology (Bayesian methods, causal inference), (4) gene expression networks and co-expression analysis, and (5) bio-informatics and software tools for database "mining". These aims will be, accomplished by an intensive 6-day course to be 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 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 special lectures.
Sensitized Screen in the Diversity Outcross Mouse Population
NIH 1F32HD074299 Steven Munger
An individual's genetic makeup influences their susceptibility to disease or disorder, and can also affect disease severity, prognosis, and even treatment options. Most genetic variants exert subtle effects in isolation, and are thus maintained in the population. However, certain combinations of variants are incompatible; yet it remains poorly understood how an accumulation of small genetic perturbations can compromise normal homeostasis and sensitize a tissue to disorder. The current project will characterize how natural genetic variation segregating in a sensitized population interacts to disrupt the buffering capacity of a transcription network during organ development. The proposed research will take a systems genetics approach to model the transcription network in the embryonic mouse gonad during sex determination. The gonad arises at mid-gestation competent to differentiate as a testis or ovary irrespective of sex chromosome constitution. This unique plasticity is conferred by a balanced transcriptome with features associated with both differentiated sexual fates. Failure to establish or maintain one sexual fate (e.g. testis) causes sex reversal to the alternative fate (e.g. ovary). Genetic background is known to affect susceptibility to sex reversal by unbalancing the underlying transcription network. The proposed sensitized screen will introduce a background-dependent sex-reversing mutation to sensitize a genetic mapping population to sex reversal. This highly diverse population of genetically unique individuals, the Diversity Outbred (DO) stock, is derived from the same eight founder strains as the emerging Collaborative Cross (CC) recombinant inbred strains, and captures genome-wide high levels of genetic variation and provides high mapping resolution. Aim 1 will characterize the expression of a subset of known sex determination genes in gonads from the DO panel, eight CC founder strains, and the sex-reversing Dax1/Nr0b1 mutant strain. This survey will provide a baseline measurement of expression variability in the DO population and enable the modeling of an undirected transcription network based on coexpression relationships. Aim 2 will derive a large population of DO embryos that are sensitized to sex reversal by the Dax1/Nr0b1 mutation. Regions of the genome that affect gene expression in the gonad (expression quantitative trait loci, or eQTL) will be identified from a combination of RNA-Seq expression and dense genotyping data. The resulting eQTL data will be used to develop a detailed predictive network model of sex determination.
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.
ANOVA-Based Approaches to Time-Series Microarray Data
NIH 5F32HG003968 Keith Shockley
There is a need to integrate statistical significance with biological relevance when considering dynamic changes in transcriptional response in biomedical research. Towards this end, methodologies based on analysis of variance (ANOVA) will be developed to detect differentially expressed genes in time course microarray data through focused contrasts and trend tests within and between treatment conditions. The efficiency of these ANOVA-based analyses will be compared with empirical Bayes methods and B-spline fitting analyses to synthesize new methodologies that incorporate the best features of the various approaches. Next, statistically relevant associations between gene lists derived from the ANOVA method and known biological processes will be used to uncover relationships important in studies of sleep deprivation in brain tissue sampled for flies and mice and factors influencing osteocyte and adipocyte differentiation in UAMS-33 mouse cell cultures. Finally, confirmatory real-time quantitative PCR assays will be designed and applied to the UAMS-33 mouse cell line experiments with higher frequency time point sampling in order to build graphical models that finely characterize the time dynamics of this system.
Variability in Gene Expression Experiments
NIH 5F32CA106233 Natalie Blades
Biological variation in gene expression appears to be present even among carefully matched, genetically uniform individuals (Oleksiak et al. 2002); however, there is also a component of technical variation that can be attributed to the material handling and measurement processes. In order to characterize the biological component of variability, an experiment must also provide a means to assess technical variance components (Churchill 2002). This work will enhance interpretation of future gene expression studies by use of statistical models to explore: 1. Gene expression assays on replicated tumor samples with homogeneous populations of cells: granulosa cell tumors. The variability due to inherent biological sources will be estimated from this experimental data. 2. Gene expression assays on replicate samples of a tumor type with a heterogeneous population of neoplastic cells: mammary tumors from mice transgenic with the c-myc oncogene. Statistical methods will be developed to identify the gene expression attributable to specific cell types within a heterogeneous tumor. 3. A set of calibration experiments performed on multiple platforms (cDNA microarrays, long oligo arrays, Affymetrix GeneChips, and Massively Parallel Signature Sequencing) to assess and compare the components of variance contributed by tissue type, cDNA library production, and platform.