Greg Carter


Ph.D. University of Minnesota 1997


Contemporary technologies such as high-throughput genome sequencing now enable the measurement of biological systems with unprecedented scale, power, and precision, creating the opportunity to decipher the genetics that underlie human diseases. The overall goal of our laboratory is to develop novel computational strategies that use these data to understand complex genetic systems in which multiple genes and environmental factors combine to affect biological outcomes. These methods aim to map complex genetic architecture and infer models that predict the outcomes of genetic and environmental variation. Our work involves deriving network models of interacting genes, integrating disparate phenotypic and molecular data types, critically evaluating models with experimental tests, and understanding how biological information is encoded in genetic networks and genomic data.

Modeling pleiotropy and epistasis

The observations that a gene can affect many traits (pleiotropy) and that a trait can be affected by multiple interacting genes (epistasis) imply that cellular behavior is often the result of a genetic network involving multiple biological processes. We aim to exploit patterns of pleiotropy and epistasis to infer models of how genes interact to influence multiple phenotypic measures. To this end, we are devising novel mathematical and statistical methods using model organisms as platforms for development. By addressing the complexities of pleiotropy and epistasis we hope to improve the predictive power of network models and better reveal underlying biological mechanisms. We have recently released a software package called CAPE (Combined Analysis of Pleiotropy and Epistasis) to use this approach with genetic data.

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

Once diagnosed, there are few effective treatments for late-onset Alzheimer’s disease. The development of early diagnostics and 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 whole genome sequences from the Alzheimer’s Disease Sequencing Project to identify potential genetic factors and using advanced genome engineering technologies to create faithful mouse models for late-onset Alzheimer’s. Furthermore, we studying the transcripts of 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. These genetic networks simultaneously model multiple phenotypes to identify direct genetic influences as well as influences mediated by genetic interactions. 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 like 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 in 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.


  • Johnson ECB, Carter EK, Dammer EB, Duong DM, Gerasimov ES, Liu Y, Liu J, Betarbet R, Ping L, Yin L, Serrano GE, Beach TG, Peng J, De Jager PL, Haroutunian V, Zhang B, Gaiteri C, Bennett DA, Gearing M, Wingo TS, Wingo AP, Lah JJ, Levey AI, Seyfried NT. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat Neurosci. 2022 Feb;25(2):213-225. doi: 10.1038/s41593-021-00999-y. Epub 2022 Feb 3.
  • Yang HS, Onos KD, Choi K, Keezer KJ, Skelly DA, Carter GW, Howell GR. Natural genetic variation determines microglia heterogeneity in wild-derived mouse models of Alzheimer’s disease. Cell Rep. 2021 Feb 9;34(6):108739. doi: 10.1016/j.celrep.2021.108739.
  • Preuss C, Pandey R, Piazza E, Fine A, Uyar A, Perumal T, Garceau D, Kotredes KP, Williams H, Mangravite LM, Lamb BT, Oblak AL, Howell GR, Sasner M, Logsdon BA; MODEL-AD Consortium, Carter GW. A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease. Mol Neurodegener. 2020 Nov 10;15(1):67. doi: 10.1186/s13024-020-00412-5.
  • Pandey RS, Graham L, Uyar A, Preuss C, Howell GR, Carter GW. Genetic perturbations of disease risk genes in mice capture transcriptomic signatures of late-onset Alzheimer’s disease. Mol Neurodegener. 2019 Dec 26;14(1):50. doi: 10.1186/s13024-019-0351-3.
  • Tyler AL, Carter GW. Genetic interactions improve models of quantitative traits. Nat Genet. 2017 Mar 30;49(4):486-488. doi: 10.1038/ng.3829.
  • Marnik EA, Wang X, Sproule TJ, Park G, Christianson GJ, Lane-Retiker SK, Carter GW, Morse III HC, Roopenian DC, 2017. Precocious Interleukin 21 Expression by CD4 T cells of Naïve Mice Identifies a Novel Stage of T follicular Helper Cell Development in Autoimmune Disease, Cell Reports, 21(1):208-221.
  • Wu JW, Preuss C, Wang SP, Yang H, Ji B, Carter GW, Gladdy R, Andelfinger G, Mitchell GA. 2017. Epistatic interaction between the lipase-encoding genes Pnpla2 and Lipe causes liposarcoma in mice, PLoS Genetics, 13(5):1007716.
  • Tyler AL, Ji B, Gatti DM, Munger SC, Churchill GA, Svenson KL, Carter GW. 2017. Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in Diversity Outbred mice, Genetics 206, 621-639.
  • Ball RL, Fujiwara Y, Sun F, Hu J, Hibbs, M, Handel MA, Carter GW. 2016. Regulatory complexity revealed by integrated cytological and RNA-seq analyses of meiotic substages in mouse spermatocytes, BMC Genomics, 17:628.
  • Tyler AL, Donahue LR, Churchill GA, Carter GW. 2016. Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross, PLoS Genetics, 12(2): e1005805.
  • Walker M, Billings T, Baker CL, Powers N, Tian H, Saxl RL, Choi K, Hibbs MA, Carter GW, Handel MA, Paigen K, Petkov PM. 2015. Affinity-seq detects genome-wide PRDM9 binding sites and reveals the impact of prior chromatin modifications on mammalian recombination hotspot usage, Epigenetics and Chromatin, 8(1):1-13.
  • Tyler AL, McGarr TC, Beyer BJ, Frankel WN, Carter GW. 2014. A Genetic Interaction Network Model of a Complex Neurological Disorder, Genes Brain & Behavior, 13(8):831-840.
  • Philip VM, Tyler AL, Carter GW†. 2014. Dissection of Complex Gene Expression Using the Combined Analysis of Pleiotropy and Epistasis, Pac Symp Biocomput., 19:200-211.
  • Jackson HM, Soto I, Graham LC, Carter GW, Howell GR. 2013. Clustering of transcriptional profiles identifies changes to insulin signaling as an early event in a mouse model of Alzheimer’s disease, BMC Genomics, 14(1):831.
  • Tyler AL, Lu W, Hendrick J, Philip V, Carter GW. 2013. CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis, PLoS Computational Biology, 9(10): e1003270.
  • Carter GW. 2013. Inferring Gene Function and Network Organization in Drosophila Signaling by Combined Analysis of Pleiotropy and Epistasis, G3 3(5):807-14.
  • Mirzaei H, Knijnenburg T, Kim B, Robinson M, Picotti P, Carter GW, Li S, Dilworth D, Eng J, Aitchison J, Shmulevich I, Galitski T, Aebersold R, and Ranish J. 2013. Systematic measurement of transcription factor-DNA interactions by SRM mass spectrometry identifies candidate gene regulatory proteins, PNAS 110(9):3645-3650.
  • Carter GW, Hays M, Sherman A, Galitski T. 2012. Use of Pleiotropy to Model Genetic Interactions in a Population, PLoS Genetics 8(10): e1003010.
  • Carter GW, Hays M, Li S, and Galitski T. 2012. Predicting the Effects of Copy-Number Variation in Double and Triple Mutant Combinations, Pac Symp Biocomput. 17:19-30.
  • Carter GW, Rush CG, Uygun F, Sakhanenko NA, Galas DJ, and Galitski T. 2010. A Systems Biology Approach to Modular Genetic Complexity, Chaos 20:026102.
  • Galas DJ, Nykter M, Carter GW, Price N, and Shmulevich I. 2010. Biological Information as Set-Based Complexity, IEEE Transactions on Information Theory 56(2):667-677, preprint arXiv:0801.4024.
  • Carter GW, Galas DJ, and Galitski, T. 2009. Maximal Extraction of Biological Information from Genetic Interaction Data, PLoS Computational Biology 5(4):e1000347.
  • Carter GW and Dudley, AM. 2009. Systems genetics of complex traits, in Robert, ed., “Encyclopedia of Complexity and Systems Science”, Springer, New York.
  • Killcoyne S, Carter GW, Smith J, and Boyle J. 2009. Cytoscape:  A Community-Based Framework for Network Modeling, Methods Mol Biology 563: 219-239.
  • Carter GW, Prinz S, Neou C, Shelby JP, Marzolf B, Thorsson V, and Galitski T. 2007 Prediction of phenotype and genomic expression for combinations of mutations, Molecular Systems Biology3:96.
  • Selinummi J, Niemistö A, Saleem R, Carter GW, Aitchison J, Yli-Harja O, Shmulevich I, and Boyle J. 2007. A case study on 3-D reconstruction and shape description of perioxisomes in yeast, Proceedings of the 2007 IEEE International Conference on Signal Processing and Communication (ICSPC 2007) 672-675.
  • Carter GW, Rupp S, Fink GR, and Galitski T. 2006. Disentangling information flow in the Ras-cAMP signaling network, Genome Research 16: 520-526.
  • Drees BL, *Thorsson V, *Carter GW, Rives AW, Raymond M, Avila-Campillo I, Shannon P, and Galitski T. 2005. Derivation of genetic interaction networks from quantitative phenotype data , Genome Biology 6: R38 (*Equal contribution).