RESEARCH

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 fetal origins, 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. Significant advances in quantitative proteomics (e.g. Orbitrap MS3 mass spectrometry) and sample multiplexing (e.g. TMT isobaric labelling) within the past five years 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.

 

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, and most trainees contribute to more than one project while gaining expertise at both the bench and the terminal window. I (Steve) strive to provide an open, collaborative, diverse, and supportive environment where both people and research can flourish.

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 applies cutting edge genetic and genomic resources with advanced computational methods to explore the genetic basis of pluripotency, cell differentiation, organogenesis, tissue homeostasis, developmental disorder, and disease.

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 exploitation, of genetic diversity 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 genetic background (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, reductionism can be misleading in genetic studies. 

The common thread connecting all projects in the Munger Lab is the embrace, and exploitation, of genetic diversity to reveal novel mechanisms and insights about gene regulation, function, and disease processes.
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Advanced mouse models and methods for genetic dissection of gene regulatory networks.

A. The DO stock and CC strains derive from eight inbred founder strains. B. Tmem68 is located on Chr 4. Transcript abundance is affected by local genetic variation (top panel), but protein abundance is controlled by genetic variation on Chr 13 (middle). Mediation analysis identifies NNT protein as the causal intermediate conferring this effect on TMEM68 protein abundance (bottom). C. Network representation of the interaction between NNT and TMEM68.

© 2017 Steven Munger