The Carter Lab

Understanding the molecular regulation of meiosis by integrating multiple data types

Chromosomal crossover via homologous recombination is both a necessary step in mammalian meiosis and the method by which genetic variation is redistributed throughout a population. Genome-wide assays of chromatin states and gene expression are revealing molecular details of this process, which is initiated with site selection by the protein Prdm9. In collaboration with the Paigen and Handel labs, we are combining data on epigenetic states with transcript abundances to understand the molecular mechanisms that drive recombination and meiosis in the mouse testis. The aim of this work is a comprehensive model of when, where, and how molecules like Prdm9 act to guide germ cell development.

Complex genetics in the development of late-onset Alzheimer’s disease

There are few effective treatments for late-onset Alzheimer’s disease once the disease is diagnosed. The identification of early biomarkers and development of reliable model systems for therapeutic development are crucial for advancing potential treatments for this disease. In collaboration with the Howell Lab and the Genetic Resource Science group, we are studying hundreds of genome sequences to identify potential genetic factors and using advanced genome engineering technologies to create faithful mouse models for late-onset Alzheimer’s. Furthermore, we are studying aging mice to identify early molecular signatures of Alzheimer’s disease development, which might serve as biomarkers that can be detected decades before the neurodegenerative symptoms appear.

Polygenic models of breast cancer subtypes

The genetic heterogeneity and complexity of cancer have posed significant challenges to the design of effective therapeutic strategies. The characterization of mRNA-expression subtypes in breast cancer facilitates genomic and genetic studies to identify biological processes and pathways that drive distinct molecular subtypes and elucidates the potential feasibility of subtype-specific drug targets. However, such therapies tend to have limited efficacy, often due to unpredicted compensation in the network of mutations. To address this problem we are applying a multi-trait genetic interaction analysis to genetic and genomic data from The Cancer Genome Atlas (TCGA) breast cancer project.  We are discovering how somatic copy-number variations and other mutations in oncogenes and tumor suppressors interact to affect gene-expression modules that contribute to distinct breast cancer subtypes.

Revealing the genetics of molecular epigenetics

Recent initiatives such as the ENCODE project have mapped regions of the genome that are believed to regulate gene expression through histone modifications, DNA methylation, and proteins that bind DNA. These regions often harbor variants that have been linked to human disease in genome-wide association studies, suggesting that genetic variation modifies gene expression by changing the regulatory chromatin state. We are carrying out a systematic study of how genetic variation in laboratory mice affects chromatin states in response to environmental stimuli. This study is providing concrete evidence for genetic-epigenetic interactions that potentially underlie human disease.

Quantifying information in genetic networks

The study of molecular epistasis has been used for decades in mapping pathways of linear information flow from gene to gene. However, the genetic complexity inherent in many biological systems can confound this strategy when the system is viewed on a genomic scale. Instead of mapping linear pathways, large-scale networks of genetic interactions tend to feature tangled modules of genes that function together to carry out cellular processes. Furthermore, given the prevalence and diversity of genetic interactions, it is often unclear how to optimally define the rules of genetic interaction that form the links in these networks. We are developing methods based on information theory to measure the information content of networks. This quantitative measure of complexity can serve as scoring function to find the most informative network from a given genetic data set. From this work we hope to develop both practical tools for genetic analysis and fundamental insights into how networks encode information.