Systems genetics approaches to understand development and disorder
Most common adult diseases are highly polygenic, and stem from the combinatorial interactions among many genes and the environment. Population variation in disease susceptibility and prognosis has been associated with numerous segregating common and rare genetic variants, however the effects of all but a few of these variants remain unknown. Further complicating efforts to elucidate disease etiology is the growing recognition that adult onset diseases may have their origins during fetal development, and that the observed association of genetic variants to adult disease may stem from their direct effects on cell differentiation or lineage specification in early embryogenesis. Finally, recent large-scale phenotyping efforts have convoluted the picture even more by revealing the wide extent of pleiotropy in the genome, where individual genes may play multiple roles – and the genetic variants affecting them may have multiple consequences – in different pathways and at different stages. This new
understanding has led some to propose an "omnigenic" model of common disease, and it is increasingly clear that advances in systems-level modeling approaches will need to be coupled with advanced cell, organ, and animal models to fully disentangle the complex causal chain linking genetic variants to disease.
Our ability to reconstruct a causal network that links the proximal consequences of genetic variation on gene regulation, cell specification and cellular function, to their distal effects on measures of disease, depends on our ability to accurately quantify molecular and physiological phenotypes. Until recently, technological limitations restricted our understanding of the genome-wide effects of genetic variation on gene regulation to the level of transcript abundance. Recent advances in quantitative proteomics (e.g. Orbitrap MS3 mass spectrometry) and sample multiplexing (e.g. TMT isobaric labelling) have overcome these barriers and extended our view to the proteome, and in so doing have revealed a surprising disconnect between mRNA and protein abundance – contradicting the simplistic but widely held assumption of transcriptional regulation of protein expression. These studies identified many genetic variants that significantly perturbed mRNA abundance (eQTLs) but appeared to have no – or even antagonistic – effects on protein levels, suggesting that one or more post-transcriptional mechanisms play a major role in regulating the proteome independent from the transcriptome. Indeed, just as our understanding of the genetic complexity of common disease is expanding, so is our understanding of the complexity of gene regulation and the increasing role for post-transcriptional mechanisms in regulating protein expression and function.
No gene is an island. Genes act within networks, and to fully understand a gene's regulation and function, we must understand these complex genetic interactions - and the influence of genetic variation on them.
The Munger Lab combines multi-scale “omics” technologies and advanced statistical methods in genetically diverse mice and stem cell models – an integrated approach termed “systems genetics” – to connect these molecular dots and decode how population-level genetic differences influence gene regulation, cell differentiation, and organ function (see figure below). We are a highly collaborative lab that fully embraces team science, and most trainees contribute to more than one project and interact with multiple investigators. I (Steve) strive to provide an open, collaborative, diverse, and supportive environment where both people and research can flourish.
The Munger Lab applies a systems genetics approach - integrating cutting edge genetic and genomic resources with advanced computational methods - to explore the genetic basis of pluripotency, cell differentiation, developmental disorders, and adult onset diseases. We are also interested in understanding how environmental exposures interact with the genome to affect these processes.
Genetic dissection of complex phenotypes
Biology is complex, yet reductionism is still the norm in biomedical research. The potential influence of genetic variation in the population is often not considered at all - and in the case of most animal model studies, actively avoided - in studies of gene function, gene regulation, and disease. The common thread connecting all projects in the Munger Lab is the embrace, and application, of genetic diversity as a tool to reveal novel mechanisms and insights about these processes. Given that most physiological and disease traits vary significantly in the population and have a genetic component (i.e., are heritable and due to genetic variation), it is reasonable to assume that genetic variation likely alters the regulation and/or function of genes underlying these phenotypes. Recent genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) screens have borne this out, and combined with large-scale genome sequencing efforts, it is now clear that the consequences of many (most?) disease-associated mutations depends on an individual's genome sequence. No gene is an island; genes act within networks, and to fully understand a gene's regulation and function, we must understand these complex genetic interactions - and the influence of genetic variation on them. Results or conclusions from reductionist studies may not reflect the full range of effects in the population. In short, biology is complex, and reductionism can mislead.
The common thread connecting all projects in the Munger Lab is the embrace--and application--of genetic diversity to reveal novel mechanisms and insights about gene regulation, function, and disease processes.
Embracing genetic diversity in biomedical research
My research program takes advantage of advanced mouse populations, including the Diversity Outbred (DO) heterogeneous stock and Collaborative Cross (CC) recombinant inbred lines, that were specifically designed to incorporate abundant genetic diversity and provide high mapping power and resolution. The DO stock is an outbred population derived from eight inbred founder strains that segregates abundant genetic diversity (40M+ SNPs, 2M+ indels) in a balanced population structure ideal for genetic mapping; the recombinant inbred CC lines derive from the same inbred founder strains and provide a complementary and renewable resource for validation studies. This high level of genetic variation has important consequences for studies of gene regulation, as nearly every gene harbors multiple variants that may alter its regulation or function, and the downstream effects of these natural perturbations can be measured and used to infer global gene regulatory networks.