Steven Munger, Ph.D., uses a systems genetics approach and advanced computational methods to study development and complex disease in advanced mouse populations.
The genetic origins of most developmental disorders and diseases are complex and remain poorly understood. Rather than a single deleterious mutation, genetic susceptibility to common diseases is conferred by many variants that individually assert subtle effects but in certain combinations perturb cellular networks sufficiently to bias them to a disordered/diseased state. Classic single-gene experimental approaches fail to elucidate these interactions, but new integrative genomic and genetic approaches from the emerging field of “Systems Genetics” hold considerable promise.
The Munger lab combines experimental and computational methods with advanced mouse mapping populations to solve these complex genetic puzzles.
We examine the natural genetic variation driving individual differences in 1) susceptibility to sex reversal during primary sex determination of the gonad, 2) disease severity in a mouse model of Cornelia de Lange Syndrome, and 3) transcript and protein expression in the adult liver. We apply our genetic and genomics toolkit to predict causal variants and genetic interactions underlying phenotypic variability, validate these predictions at the bench with functional genomics methods, and ultimately seek to extend these results to inform patient diagnosis and treatment.
Chick JM*, Munger SC*, Simecek P, Huttlin EL, Choi KB, Gatti DM, Raghupathy N, Svenson KL, Churchill GA§, and Gygi SP§. Defining the consequences of genetic variation on a proteome-wide scale. Nature. 2016, In Press.
Munger SC, Natarajan A, Looger LL, Ohler U, Capel B. Fine time course expression analysis identifies cascades of activation and repression and maps a putative regulator of mammalian sex determination. PLoS Genet. 2013;9(7):e1003630.
Musser MA, Munger SC, Gunn TM. Meeting report of the 26th International Mammalian Genome Conference. Mamm Genome. 2013 Jun;24(5-6):179-89.
Munger SC, Raghupathy N, Choi K, Simons AK, Gatti DM, Hinerfeld DA, Svenson KL, Keller MP, Attie AD, Hibbs MA, Graber JH, Chesler EJ, Churchill GA. RNA-Seq alignment to individualized genomes improves transcript abundance estimates in multiparent populations. Genetics. 2014 Sep;198(1):59-73.
A generous gift from Ray Sproats will support research into using human stem cells to treat age-related eye disease.
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