Ron Korstanje & Beverly Paigen
1. Using in silico Mapping to Evaluate Clustering of Quantitative Trait Loci
Previous work by Center faculty has established two critical properties of quantitative trait loci (QTL). First, they represent only a sub-set of the totality of genes functionally required for a complex trait such as the regulation of blood pressure or relative susceptibility to atherosclerosis. And second, the same sub-set of genes determine QTLs across mammalian species. QTLs for a given trait are often located at homologous chromosomal locations in human, mouse and rat. A probable explanation for these findings is that QTLs represent key regulatory functions whose role has been conserved over the course of mammalian evolution.
Given that the QTLs for various phenotypes are responsible for most of the population variation on which evolution can act and that the identity of these QTL is an ancient property of mammals, genome dynamics predicts that the QTLs for fundamental physiological properties are likely to be genetically linked to promote the co-inheritance of favorable allelic combinations. The hypothesis of this project is that functional domains are a general feature of mammalian chromosomal organization and will be revealed by the clustering of QTLs affecting a phenotype. Rigorously testing this hypothesis requires that we efficiently locate QTLs at a resolution of a few Mb or less across the diversity of Mus subspecies.
We are performing the following activities to test this hypothesis:
- Further developing in silico QTL mapping with the goals of improving its statistical validity, genetic resolution and efficiency.
- Characterizing the large number of strains collected for the Population Genetics project for a series of phenotypes that are quantitatively robust, and easy to collect.
- Determining in silico QTLs for the phenotypes using the SNPs genotyped in the Population Genetics project.
- Testing whether the QTLs for a trait cluster physically or belong to the same LD network.
2. Pathway Modeling Through Systems Genetics Approaches
The vast genetic resources at The Jackson Laboratory combined with the methods and tools developed by The Center for Genome Dynamics allow us to try a new approach to pathway modeling. In this systems genetics approach we focus on one pathway, perturb the pathway by changing the genetic and environmental background and measure as many parameters within the pathway as possible. Combining this systematically collected high-quality quantitative data with the Bayesian models build by Rachael Hageman result in quantitative models that can predict the effect of specific perturbations (e.g. drugs, genetic mutations) on the pathway. We are currently focusing on two pathways:
The involvement of the vitamin D pathway in the cardio-renal axis
The cardio-renal axis is the association between cardiac and renal function resulting in mutual pathologies, so patients with chronic renal function are likely to develop left ventricular hypertrophy (LVH), and those with LVH, chronic renal function. We are particularly interested in the role of vitamin D in this important syndrome. Mice from eight different inbred strains are being fed normal chow, vitamin D deficient, and vitamin D enriched diets and many parameters of the vitamin D pathway and renal and cardiac phenotypes are measured
Perturbing the HDL cholesterol pathway with ENU mutants
The Jackson Laboratory’s ENU mutagenesis program has identified an excellent collection of 19 mutant mice, all in the C57BL/6J background, that either have suppressed or elevated HDL. These are a great resource for understanding HDL regulation and for discovering novel genes. So far, the mutation in two of these lines has been identified (Scarb1 and Ldlr), while gene discovery in the other 17 lines is ongoing. We currently focus on the liver and look at the differential gene expression between the mutants and C57BL/6J. The expression profiles of the different mutant lines allow us to identify the genetic networks involved in the regulation of HDL cholesterol levels. In addition, the hepatocytes of the different lines can provide a model system that for each line has a different perturbation and therefore will have a different outcome when challenged. Therefore, primary cell cultures are being set up and used for quantitative studies on metabolic fluxes when being treated with HDL.
Center related publications
Genetic analysis of complex traits in the emerging collaborative cross
Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA, Baric RS, Ferris MT, Frelinger JA, Heise M, Frieman MB, Gralinski LE, Bell TA, Didion JD, Hua K, Nehrenberg DL, Powell CL, Steigerwalt J, Xie Y, Kelada SN, Collins FS, Yang IV, Schwartz DA, Branstetter LA, Chesler EJ, Miller DR, Spence J, Liu EY, McMillan L, Sarkar A, Wang J, Wang W, Zhang Q, Broman KW, Korstanje R, Durrant C, Mott R, Iraqi FA, Pomp D, Threadgill D, Pardo-Manuel de Villena F, Churchill GA.
Genome Res. 2011 Aug;21(8):1223-38. PMCID: PMC3149489 [ Full Text ] [ Highlight in Nature Reviews Genetics ] [ datasets ]
A Bayesian Framework for Inference of the Genotype-Phenotype Map for Segregating Populations
Hageman RS, Leduc MS, Korstanje R, Paigen B, Churchill GA.
Genetics. 2011 Apr;187(4):1163-70. PMCID: PMC3070524 [ Full Text ] [ datasets 1 ] [ datasets 2 ]
An experimental assessment of in silico haplotype association mapping in laboratory mice
Burgess-Herbert SL, Tsaih SW, Stylianou IM, Walsh K, Cox AJ, Paigen B.
BMC Genet. 2009 Dec 9;10:81. PMCID: PMC2797012. [ Full Text ] [ datasets ]
A New Standard Genetic Map for the Mouse
Cox A, Ackert-Bicknell C, Dumont BL, Ding Y, Tzenova Bell J, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih SW, Churchill GA, Broman KW.
Genetics. 2009 Aug;182(4):1335-44. PMCID: PMC2728870. [ Full Text ]
The Effects of Atherogenic Diet on Hepatic Gene Expression Across Mouse Strains
Shockley KR, Witmer D, Burgess-Herbert SL, Paigen B, Churchill GA.
Physiol Genomics. 2009 Nov 6;39(3):172-82. PMCID: PMC2789673. [ Full Text ] [ datasets ]
A customized and versatile high-density genotyping array for the mouse
Yang H, Ding Y, Hutchins LN, Szatkiewicz J, Bell TA, Paigen BJ, Graber JH, de Villena FP, Churchill GA.
Nat Methods. 2009 Sep;6(9):663-6. PMCID: PMC2735580. [ Full Text ] [ datasets ]
The mouse as a model for human biology: a resource guide for complex trait analysis
Peters LL, Robledo RF, Bult CJ, Churchill GA, Paigen BJ, Svenson KL.
Nat Rev Genet. 2007 Jan;8(1):58-69.
Multiple trait measurements in 43 inbred mouse strains capture the phenotypic diversity characteristic of human populations
Svenson KL, Von Smith R, Magnani PA, Suetin HR, Paigen B, Naggert JK, Li R, Churchill GA, Peters LL.
J Appl Physiol. 2007 Jun;102(6):2369-78. [ Full Text ] [ datasets ]
Structural model analysis of multiple quantitative traits
Li R, Tsaih SW, Shockley K, Stylianou IM, Wergedal J, Paigen B, Churchill GA.
PLoS Genet. 2006 Jul;2(7):e114. PMCID: PMC1513264 [ Full Text ] [ datasets ]