Our lab is actively applying a systems approach to study the genetics of health and disease, incorporating new statistical methods for the investigation of complex disease-related traits in the mouse. We are developing new methods and software that will improve the power of quantitative trait loci mapping and microarray analysis, as well as graphical models which aim to intuitively and precisely characterize the genetic architecture of disease.
Mathematical modeling is an essential tool for understanding complex biological systems. Often biological parameters cannot be measured experimentally, either because of expense, ethical issues or technological limitations. However, we can use probability theory to bridge this gap in our knowledge in an informed manner. Ideally we want to represent the complexity of the system as compactly as possible and graph theory enables us to provide a simple visual representation for communicating the structure of complex data relationships, for example, by representing interacting genes by a series of interconnected nodes. Modeling of biological systems is expected to be an iterative process. The models provide the basis for hypothesis testing by experimentalists, which in turn should lead to further refinement of the models.
In collaboration with scientists from Gene Network Systems (GNS), we are developing a genetically based Bayesian dynamic model of HDL metabolism for the purpose of performing system wide sensitivity studies of genetic perturbations in inbred mouse strains. This methodology will be applied to predict how the metabolic pathways function under variable conditions, e.g., environment, medicine, diet and disease.
We have applied graphical modeling techniques to study body composition and bone density in mouse populations. We were able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research.
QTL Methods & Applications
Quantitative Trait Loci (QTL) analysis is the identification of genes or regions of the genome that influence complex, or quantitative, traits. Unlike discrete traits (e.g. eye color, or the presence or absence of disease), quantitative traits (e.g. blood pressure, weight) vary continuously over a range of distribution in a population and are influenced by multiple genes as well as gene-gene and gene-environment interactions.
We aim to improve the way QTL analysis is performed. We have developed methods for combining data from multiple inbred line crosses, selectively phenotyping crosses, quantitative analysis of information content in genetic mapping study designs, Bayesian methods for mapping epistatic loci, mapping of multi-trait epistatic networks, mapping on the X chromosome, permutation testing in novel cross designs, and we have proposed new experimental strategies for gene identification. We introduced a major advance in multi-trait analysis that uses genetic loci to anchor and direct causal pathways and applied it to body composition traits.
We analyze crosses spanning a wide range of traits including HDL-cholesterol, depression, blood pressure, albuminuria and kidney function, anemia, bone density, asthma, developmental abnormalities, diabetes, and digestion. Several of these studies provided evidence to support specific candidate genes or have narrowed the plausible candidates to a small number of genes.
The QTL Archive at The Jackson Laboratory is a growing resource that provides access to raw data from QTL studies using rodent crosses. Submissions by the community are encouraged and the data are curated by members of the Churchill group. Sharing raw QTL cross data not only protects that data from loss, but also allows new advances in QTL analysis to be applied to previously generated crosses.
Microarray Design & Analysis
Microarray analysis is the characterization of DNA or RNA from a sample of interest. Using microarrays it is possible to measure the biological signature of a sample to determine which genes are expressed.
The Churchill Group strives to improve the way microarray experiments are designed and analyzed. We have demonstrated that thoughtful experimental design is critical, for example, the importance of randomization in obtaining reproducible results. Recognizing that a range of technologies is available to investigators, we have performed cross platform comparisons to determine the consequences of using different microarrays. Our group has also developed novel analysis methods, for example, for three- and four-color microarray systems, and small microarray experiments.
We perform microarray experiments to investigate a wide range of traits including bone differentiation and mass, body composition, digestion, kidney disease, breast cancer and sleep. These studies provide evidence to support specific candidate genes and reveal the network of gene-gene and/or gene-environment interactions underlying these traits.
The Center for Genome Dynamics
The Center for Genome Dynamics is one of ten National Centers for Systems Biology supported by the National Institute of General Medical Sciences (NIGMS). The primary mission of these Centers is to promote institutional development of multidisciplinary research, training, and outreach programs that focus on systems-level studies of biomedical phenomena within the NIGMS mission.
Investigators within the Center for Genome Dynamics previously used inbred mice to show that mammalian genomes contain extensive, regional domains of functionally related elements that coalesced over evolutionary time. In effect, the mammalian genome is a dynamic system that varies spatially in its organization and expression and temporally in its evolution and inheritance.
Using a team of computational biologists, molecular biologists and geneticists, the Center is extending these studies of genome dynamics as an integrated system from an evolutionary perspective. This requires using computational approaches on large data sets generated by the Center to describe the interactions between genome organization, gene expression, phenotype determination and the impact of recombination hotspots in determining inheritance of co-adapted sets of alleles. By developing detailed maps of these interactions we can evaluate the underlying principles. The Center includes investigators at The University of North Carolina, The University of Wisconsin-Madison and Oakridge National Laboratory. Gary Churchill is the Principal Investigator of the Center.
As part of the Center for Genome Dynamics and in collaboration with Dr. Fernando Pardo-Manuel de Villena at the University of North Carolina at Chapel Hill, we have designed a new microarray, the Mouse Diversity Genotyping Array. The array is the most advanced high-density mouse genotyping microarray currently available. The Jackson Laboratory now offers a Mouse Diversity Genotyping Array Service utilizing this innovative genotyping microarray. The completion of the design and production of the Mouse Diversity Genotyping Array represents a starting point for new directions of inquiry. We are currently performing exhaustive genotyping of existing mouse strains, which will form the basis of a complete characterization of the laboratory mouse genome. We are also exploring novel applications of the array, including copy number variation, allele specific gene expression and differential methylation analysis.
The Collaborative Cross
Genetically defined mouse models offer a tractable experimental system for mapping the genes underlying disease and for examining their function in the context of a complex, living organism. However, the current paradigm of using a small number of genetic backgrounds does not address the important role of genetic variation as a determinant of individual response and fails to provide strong mapping resolution. What is needed is a new mouse resource that has genetic diversity comparable to or greater than the common human genetic variants; that, unlike fully inbred strains, has a genetic constitution more similar to humans; and provides an ability to map genetic loci at level of resolution that can identify individual genes.
The Collaborative Cross (CC) is a large panel of new inbred mouse strains that are currently being developed through a community effort that was partially conceived and developed by Gary Churchill. The CC was designed to address some of the perceived shortcoming of available mouse strain resources, including small numbers of strains, limited genetic diversity, and a complex history that results in unknown confounding relationships among common inbred strains. The CC strains are derived from an eight way cross using a set of founder strains that include three wild-derived strains. The wild strains contribute 75% of the genetic diversity of the CC. Without relevant genetic diversity, forward genetic approaches cannot make discoveries. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems.
As a further advance in gene mapping resources, The Jackson Laboratory is developing a new variety of mice that are designed to maximize allelic diversity. The Diversity Outbred (DO) mice are derived from the same eight strains as the CC population. Motivation for creating the DO was derived from the success of fine mapping in other multi-way cross populations. The DO population is designed to produce and maintain a balanced mixture of the founder genomes. DO mice are maintained by random breeding with avoidance of sib mating. Simulations showed that with a population of at least 100 breeding pairs, the DO could be maintained for up to 300 generations with negligible losses of allelic diversity.
The CC strains are being inbred to produce stable clones. The DO mice, on the other hand, are being maintained as an outbred stock. Advantages of outbreeding include, normal levels of heterozygosity, similar to the human genetic condition, and substantially increased mapping resolution. A drawback of the DO is that each animal is genetically unique and thus not reproducible. Combinations of genetic loci that are discovered in the DO mice can be replicated in CC strains or in their reproducible F1 progeny. In this regard, the CC and DO populations are complementary. Together these new resources will revolutionize our understanding, treatment and ultimately, prevention of pervasive human diseases.
For many scientists, statistical methods are inaccessible until they are implemented as software tools. We believe it is critical to put powerful software tools into the hands of data generating scientists so that they can fully utilize their data.
We have contributed to the design and implementation of the R/qtl software package in collaboration with Dr Karl Broman at the University of Wisconsin Madison. R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental populations derived from inbred lines. It is implemented as an add-on package for the freely available statistical software, R.
We have also created software tools for the analysis of microarray data. R/maanova (MicroArray ANalysis Of VAriance) provides a complete workflow for microarray data analysis including: data quality checks and visualization. R/maanova is an extensible, interactive environment for microarray analysis implemented as an add-on package for R.
We believe that new statistical methods must be accessible to the broader scientific community. This requires user-friendly interfaces to facilitate, such as our J/qtl software, that provides an intuitive point and click interface to R/qtl and is structured to guide an analyst through the appropriate sequence of steps. Similarly motivated by a desire for accessibility, we have developed J/maanova to help people without programming skills to analyze their microarray data.
Education and Outreach Program
In a unique experiment in high school education, we have introduced students to genetics and bioinformatics through a series of synthetic and real data problems. In the GeniQuest program students are trained to carry out genetic studies using simulated cross data from the genomes of "dragons." Once students have mastered the basics, they can analyze QTL data from our QTL Archive. GeniQuest is a team effort combining the talents of The Jackson Laboratory, the Maine Mathematics and Science Alliance, and the Concord Consortium to develop classroom modules to teach computational biology. The project aims to introduce students to genetics, quantitative trait loci analysis, and the relationship between phenotypes and genotypes.
Principal Investigator: Gary A. Churchill, Ph.D., Professor
Research Programs Manager: Imogen Hurley, Ph.D.
Postdoctoral Associates: Narayanan Raghupathy, Steven Munger, KB Choi, Long-yang Wu.
Bioinformatics Analyst II: Dan Gatti, Ph.D.
Educational Outreach Coordinator: Susan McClatchy, M.S.
Visiting Scientists: Karl Broman, Ph.D., Johns Hopkins University; Elissa Chesler, Ph.D., Oak Ridge National Laboratory; Cathy Laurie, Ph.D., University of Washington; Kenneth Manly, Ph.D., University of Buffalo; Michael Nachman, Ph.D., University of Arizona; Fernando Pardo Manuel de Villena, Ph.D., University of North Carolina; Frans Schalekamp
Research Administrative Assistant: Jessica CN Seavey, M.S.
Aljakna A, Choi S, Savage H, Hageman Blair R, Gu T, Svenson KL, Churchill GA, Hibbs M, Korstanje R. 2012. Pla2g12b and Hpn are genes identified by mouse ENU mutagenesis that affect HDL cholesterol. PLoS One 7(8):e43139. PMC3422231
Blair RH, Kliebenstein DJ, Churchill GA. 2012. What can causal networks tell us about metabolic pathways?What can causal networks tell us about metabolic pathways? PLoS Comput Biol 8(4):e1002458. PMC3320578
Churchill GA, Gatti DM, Munger SC, Svenson KL. 2012. The diversity outbred mouse population. Mamm Genome 23(9-10):713-718. PMC3524832
Collaborative Cross Consortium. 2012. The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population. Genetics 190(2):389-401. PMC3276630
Didion JP, Yang H, Sheppard K, Fu CP, McMillan L, Pardo-Manuel de Villena F, Churchill GA. 2012. Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias. BMC Genomics 13(1):34. PMC3305361
Kelada SNP, Aylor DL, Peck BCE, Ryan JF, Tavarez U, Buus RJ, Miller DR, Chesler EJ, Threadgill DW, Churchill GA, Pardo-Manuel de Villena F, Collins FS. 2012. Genetic analysis of hematological parameters in incipient lines of the collaborative cross. G3 (Bethesda) 2(2):157-165. PMC3284323
Leduc MS, Blair RH, Verdugo RA, Tsaih SW, Walsh K, Churchill GA, Paigen B. 2012. Using bioinformatics and systems genetics to dissect HDL cholesterol levels in an MRL/MpJ x SM/J intercross. J Lipid Res 5(6):1163-1175. PMC3351823
Lenarcic AB, Svenson KL, Churchill GA, Valdar W. 2012. A general Bayesian approach to analyzing diallel crosses of inbred strains. Genetics 190(2):413-435. PMC3276624
Pajer K, Andrus BM, Gardner W, Lourie A, Strange B, Campo J, Bridge J, Blizinsky K, Dennis K, Vedell P, Churchill GA, Redei EE. 2012. Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression. Transl Psychiatry (2)e101. PMC3337072
Phifer-Rixey M, Bonhomme F, Boursot P, Churchill GA, Piálek J, Tucker PK, Nachman MW. 2012. Adaptive evolution and effective population size in wild house mice. Mol Biol Evol Apr 22:[Epub ahead of print].
Svenson KL, Gatti DM, Valdar W, Welsh CE, Cheng R, Chesler EJ, Palmer AA, McMillan L, Churchill GA. 2012. High-Resolution Genetic Mapping Using the Mouse Diversity Outbred Population. Genetics 190(2):437-447. PMC3276626
Thaisz J, Tsaih SW, Feng M, Philip VM, Zhang Y, Yanas L, Sheehan S, Xu L, Miller DR, Paigen B, Chesler EJ, Churchill GA, Dipetrillo K. 2012. Genetic analysis of albuminuria in collaborative cross and multiple mouse intercross populations. Am J Physiol Renal Physiol 303(7):F972-9781. PMC3469684
Threadgill DW, Churchill GA. 2012. Ten Years of the Collaborative Cross. G3 2(2):153-156. PMC3284322
Wang JR, Pardo-Manuel de Villena F, Lawson HA, Cheverud JM, Churchill GA, McMillan L. 2012. Imputation of single-nucleotide polymorphisms in inbred mice using local phylogeny. Genetics 190(2):449-458. PMC3276610
Zhang W, Korstanje R, Thaisz J, Staedtler F, Harttman N, Xu L, Feng M, Yanas L, Yang H, Valdar W, Churchill GA, DiPetrillo K. 2012. Genome-Wide Association Mapping of Quantitative Traits in Outbred Mice. G3 2(2):167-174. PMC328432
Aitman TJ, Boone C, Churchill GA, Hengartner MO, Mackay TF, Stemple DL. 2011. The future of model organisms in human disease research. Nat Rev Genet 12(8):575-582.
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, Calaway JD, Didion JD, Hua K, Nehrenberg DL, Powell CL, Steigerwalt J, Xie Y, Kelada SNP, Collins F, Yang IV, Schwartz DA, Branstetter LA, Chesler EJ, Miller DR, Spence J, Lui 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, de Villena FP, Churchill GA. 2011. Genetic analysis of complex traits in the emerging collaborative cross. Genome Res 21(8):1213-1222. PMC3149489
Berndt A, Leme AS, Williams LK, Von Smith R, Savage HS, Stearns TM, Tsaih SW, Shapiro SD, Peters LL, Paigen B, Svenson KL. 2011. Comparison of unrestrained plethysmography and forced oscillation for identifying genetic variability of airway responsiveness in inbred mice. Physiol Genomics 43(1):1-11. PMC3026348
Hageman RS, Leduc MS, Caputo CR, Tsaih SW, Churchill GA, Korstanje R. 2011. Uncovering genes and regulatory pathways related to urinary albumin excretion. J Am Soc Nephrol 22(1):73-81. PMC3014036
Hageman RS, Ludec M, Paigen B, Korstanje R, Churchill GA. 2011. A bayesian framework for inference of the genotype-phenotype map for segregating populations. Genetics 187(4):1163-1170. PMC3070524
Kelada SN, Aylor D, Tavarez U, Kubalanza K, Carpenter D, Miller D, Chesler E, Churchill G, Villena FP, Schwartz DA, Collins FS. 2011. Identification of Genetic Loci in Mice That Mediate Allergen-Induced Airway Hyperresponsiveness, Inflammation, and Serum IgG1. Proc Am Thorac Soc 8(2):203.
Leduc MS, Hageman RS, Verdugo RA, Tsaih SW, Walsh K, Churchill GA, Paigen B. 2011. Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice. J Lipid Res 52(9):1672-1682. PMC3151687
Mathes WF, Aylor DL, Miller DR, Churchill GA, Chesler EJ, de Villena FP, Threadgill DW, Pomp D. 2011. Architecture of energy balance traits in emerging lines of the Collaborative Cross. Am J Physiol Endocrinol Metab. 300(6):E1124-1134. PMC3118585
Philip VM, Sokoloff G, Ackert-Bicknell CL, Striz M, Branstetter L, Beckmann MA, Spence JS, Jackson BL, Galloway LD, Barker P, Wymore AM, Hunsicker PR, Durtschi DC, Shaw GS, Shinpock S, Manly KF, Miller DR, Donohue KD, Culiat CT, Churchill GA, Lariviere WR, Palmer AA, O'Hara BF, Voy BH, Chesler EJ. 2011. Genetic analysis in the Collaborative Cross breeding population. Genome Res 21(8):1223-1238. PMC3149490
Threadgill DW, Miller DR, Churchill GA, de Villena FP. 2011. The collaborative cross: a recombinant inbred mouse population for the systems genetic era. ILAR J 52(1):24-31.
Vedell PT, Svenson KL, Churchill GA. 2011. Stochastic variation of transcript abundance in C57BL/6J mice. BMC Genomics 12:167. PMC3082245
Yang H, Wang JR, Didion JP, Buus RJ, Bell TA, Welsh CE, Bonhomme F, Harr B, Yu, AH, Nachman MW, Pialek J, Tucker P, Boursot P, McMillan L, Churchill GA, de Villena FP. 2011. Subspecific origin and haplotype diversity in the laboratory mouse. Nat Genet 43(7):648-655. PMCID: PMC3125408
Ackert-Bicknell CL, Karasik D, Li Q, Smith RV, Hsu YH, Churchill GA, Paigen BJ, Tsaih SW. 2010. Mouse BMD quantitative trait loci show improved concordance with human genome wide association loci when recalculated on a new, common mouse genetic map. J Bone Miner Res 25(8):1808-1825. PMC3153351
Andrus BM, Blizinsky K, Vedell PT, Dennis K, Shukla PK, Schaffer DJ, Radulovic J, Churchill GA, Redei EE. 2010. Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models. Mol Psychiatry Nov 16:[Epub ahead of print]. PMC3117129
Hageman RS, Wagener A, Hantschel C, Svenson KL, Churchill GA, Brockmann GA. 2010. High fat diet leads to tissue specific changes reflecting risk factors for diseases in DBA/2J mice. Physiol Genomics 42(1):55-66. PMC2888560
Hutchins LN, Ding Y, Szatkiewicz JP, Von Smith R, Yang H, de Villena FP, Churchill GA, Graber JH. 2010. CGDSNPdb: a database resource for error-checked and imputed mouse SNPs. Database (July 6)Baq008. PMC2911843
Leduc MS, Hageman RS, Meng Q, Verdugo RA, Tsaih SW, Churchill GA, Paigen B, Yuan R. 2010. Identification of genetic determinants of IGF-1 levels and longevity among mouse inbred strains. Aging Cell 9(5):823-836. PMC3025299
Li Y, Tesson BM, Churchill GA, Jansen RC. 2010. Critical reasoning on causal inference in genome-wide linkage and association studies. Trends in Genetics 26(12): 493-498. PMC2991400
Li RH, Churchill GA. 2010. Epistasis contributes to the genetic buffering of plasma HDL cholesterol in mice. Physiol Genomics 42A(4):228-234. PMC3008368
Peters LL, Shavit JA, Lambert AJ, Tsaih SW, Li Q, Su Z, Leduc MS, Paigen B, Churchill, GA, Ginsburg D, Brugnara C. 2010. Sequence variation at multiple loci influences red cell hemoglobin concentration. Blood 116(25): e139-149. PMC3031420
Woo YH, Walker M, Churchill GA. 2010. Coordinated expression domains in Mammalian genomes. PloS One 5(8):pii: e12158. PMC2923606
Ackert-Bicknell CL, Shockley KR, Horton LG, Lecka-Czernik B, Churchill GA, Rosen CJ. 2009. Strain-specific effects of rosiglitazone on bone mass, body composition, and serum insulin-like growth factor-I. Endocrinology 150(3):1330-1340. PMC2654751
Brockmann GA, Tsaih SW, Neuschl C, Churchill GA, Li R. 2009. Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both. Physiol Genomics 36(2):114-126. PMC2636925
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. 2009. A new standard genetic map for the mouse. Genetics 182(4):1335-1340. PMC2728870
Leiter EH, Reifsnyder PC, Wallace R, Li R, King B, Churchill G. 2009. NOD x 129.H2(g7) backcross delineates 129S1/SvlmJ-derived genomic regions modulating type 1 diabetes development in mice. Diabetes 58(7): 1700-1703. PMC2699846
Llamas B, Verdugo RA, Churchill GA, Deschepper CF. 2009. Chromosome Y variants from different inbred mouse strains are linked to differences in the morphologic and molecular responses of cardiac cells to postpubertal testosterone. BMC Genomics 10:150. PMC2679052
Mackiewicz M, Zimmerman JE, Shockley KR, Churchill GA, Pack AI. 2009. What are microarrays teaching us about sleep? Trends Mol Med 15(2):79-87.
Rodriguez MR, Lundgren A, Sebastian P, Li Q, Churchill G, Brown MG. 2009. A Cmv2 QTL on chromosome X affects MCMV resistance in New Zealand male mice. Mamm Genome 20:414-423. PMC2767104
Shockley KR, Lazarenko OP, Czernik PJ, Rosen CJ, Churchill GA, Lecka-Czernik B. 2009. PPARgamma2 nuclear receptor controls multiple regulatory pathways of osteoblast differentiation from marrow mesenchymal stem cells. J Cell Biochem 106(2):232-246. PMC2631627
Shockley KR, Witmer D, Burgess-Herbert SL, Paigen, B, Churchill GA. 2009. The effects of atherogenic diet on hepatic gene expression across mouse strains. Physiol Genomics 39(3):172-182. PMC2789673
Solberg Woods LC, Ahmadiyeh N, Baum A, Shimomura K, Li Q, Steiner DF, Turek FW, Takahashi JS, Churchill GA, Redei EE. 2009. Identification of genetic loci involved in diabetes using a rat model of depression. Mamm Genome 20(8):486-497. PMC2775460
Su Z, Ishimori N, Chen Y, Leiter EH, Churchill GA, Paigen B, Stylianou IM. 2009. Four additional mouse crosses improve the lipid QTL landscape and identify Lipg as a QTL gene. J Lipid Res 50:2083-2094. PMC2739753
Verdugo RA, Deschepper CF, Munoz G, Pomp D, Churchill GA. 2009. Importance of randomization in microarray experimental designs with illumina platforms. Nucleic Acids Res 37(17):5610-5618. PMC2761262
Yang H, Ding Y, Hutchins LN, Szatkiewicz J, Bell TA, Paigen BJ, Graber JH, de Villena FP, Churchill GA. 2009. A customized and versatile high-density genotyping array for the mouse. Nat Methods 6(9):663-666. PMC2735580
Yuan R, Tsaih SW, Petkova SB, Marin de Evsikova C, Xing S, Marion MA, Bogue MA, Mills KD, Peters LL, Bult CJ, Rosen CJ, Sundberg JP, Harrison DE, Churchill GA, Paigen B. 2009. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8(3):277-287. PMC2768517
Ackert-Bicknell CL, Demissie S, Marφn de Evsikova C, Hsu YH, DeMambro VE, Karasik D, Cupples LA, Ordovas JM, Tucker KL, Cho K, Canalis E, Paigen B, Churchill GA, Forejt J, Beamer WG, Ferrari S, Bouxsein ML, Kiel DP, Rosen CJ. 2008. PPARG by dietary fat interaction influences bone mass in mice and humans. J Bone Miner Res 23(9):1398-1408. PMC2683155
Bailey JS, Grabowski-Boase L, Steffy BM, Wiltshire T, Churchill GA, Tarantino LM. 2008. Identification of QTL for locomotor activation and anxiety using closely-related inbred strains. Genes Brain Behav (Epub ahead of print).
Chesler EJ, Miller DR, Branstetter LR, Galloway LD, Jackson BL, Philip VM, Voy BH, Culiat CT, Threadgill DW, Williams RW, Churchill GA, Johnson DK, Manly KF. 2008. The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics. Mamm Genome 19(6):382-389. PMC274091
Churchill GA, Doerge RW. 2008. Naive application of permutation testing leads to inflated type I error rates. Genetics 178(1):609-610. PMC2206111
Doorenbos C, Tsaih SW, Sheehan S, Ishimori N, Navis G, Churchill G, Dipetrillo K, Korstanje R. 2008. Quantitative trait loci for urinary albumin in crosses between C57BL/6J and A/J inbred mice in the presence and absence of Apoe. Genetics 179(1):693-699. PMC2390645
Iraqi FA, Churchill G, Mott R. 2008. The Collaborative Cross, developing a resource for mammalian systems genetics: A status report of the Wellcome Trust cohort. Mamm Genome 19(6):379-381.
Ishimori N, Stylianou IM, Korstanje R, Marion MA, Li R, Donahue LR, Rosen CJ, Beamer WG, Paigen B, Churchill GA. 2008. Quantitative trait loci for BMD in an SM/J by NZB/BlNJ intercross population and identification of Trps1 as a probable candidate gene. J Bone Miner Res 23(9):1529-1537. PMC2586053
Kumar KG, Byerley LO, Volaufova J, Drucker DJ, Churchill GA, Li R, York B, Zuberi A, Richards BK. 2008. Genetic variation in Glp1r expression influences the rate of gastric emptying in mice. Am J Physiol Regul Integr Comp Physiol 294(2):R362-R371.
Li R, Svenson KL, Donahue LR, Peters LL, Churchill GA. 2008. Relationships of dietary fat, body composition, and bone mineral density in inbred mouse strain panels. Physiol Genomics 33(1):26-32.
Mrug M, Zhou J, Woo Y, Cui X, Szalai AJ, Novak J, Churchill GA, Guay-Woodford LM. 2008. Overexpression of innate immune response genes in a model of recessive polycystic kidney disease. Kidney Int 73(1):63-76.
Stylianou IM, Affourtit JP, Shockley KR, Wilpan RY, Abdi FA, Bhardwaj S, Rollins J, Churchill GA, Paigen B. 2008. Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification. Genetics 178(3):1795-1805. PMC2278051
Szatkiewicz JP, Beane GL, Ding Y, Hutchins L, Pardo-Manuel de Villena F, Churchill GA. 2008. An imputed genotype resource for the laboratory mouse. Mamm Genome 19(3):199-208. PMC2725522
Yang H, Harrington CA, Vartanian K, Coldren CD, Hall R, Churchill GA. 2008. Randomization in laboratory procedure is key to obtaining reproducible microarray results. PLoS ONE 3(11):e3724. PMC2579585
Churchill GA. 2007. Recombinant inbred strain panels: a tool for systems genetics. Physiol Genomics 31(2):174-175.
Davidson CE, Li Q, Churchill GA, Osborne LR, McDermid HE. 2007. Modifier locus for exencephaly in Cecr2 mutant mice is syntenic to the 10q25.3 region associated with neural tube defects in humans. Physiol Genomics 31(2):244-251.
Herschkowitz JI, Simin K, Weigman VJ, Mikaelian I, Usary J, Hu Z, Rasmussen KE, Jones LP, Assefnia S, Chandrasekharan S, Backlund MG, Yin Y, Khramtsov AI, Bastein R, Quackenbush J, Glazer RI, Brown PH, Green JE, Kopelovich L, Furth PA, Palazzo JP, Olopade OI, Bernard PS, Churchill GA, Van Dyke T, Perou CM. 2007. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol 8(5):R76.
Kiernan AE, Li R, Hawes NL, Churchill GA, Gridley T. 2007. Genetic background modifies inner ear and eye phenotypes of jag1 heterozygous mice. Genetics 177(1):307-311.
Lecka-Czernik B, Ackert-Bicknell C, Adamo ML, Marmolejos V, Churchill GA, Shockley KR, Reid IR, Grey A, Rosen CJ. 2007. Activation of Peroxisome Proliferator-activated Receptor Gamma (PPARgamma) by Rosiglitazone Suppresses Components of the IGF Regulatory System in vitro and in vivo. Endocrinology 148(2):903-911.
Mackiewicz M, Shockley KR, Romer MA, Galante RJ, Zimmerman JE, Naidoo N, Baldwin DA, Jensen ST, Churchill GA, Pack AI. 2007. Macromolecule biosynthesis: a key function of sleep. Physiol Genomics 31(3):441-457.
Nishihara E, Tsaih SW, Tsukahara C, Langley S, Sheehan S, Dipetrillo K, Kunita S, Yagami K, Churchill GA, Paigen B, Sugiyama F. 2007. Quantitative trait loci associated with blood pressure of metabolic syndrome in the progeny of NZO/HILtJ x C3H/HeJ intercrosses. Mamm Genome 18(8):573-583.
Peters LL, Robledo RF, Bult CJ, Churchill GA, Paigen BJ, Svenson KL. 2007. The mouse as a model for human biology: a resource guide for complex trait analysis. Nat Rev Genet 8(1):58-69.
Petkov PM, Graber JH, Churchill GA, DiPetrillo K, King BL, Paigen K. 2007. Evidence of a large-scale functional organization of mammalian chromosomes. PLoS Biology 5(5):e127.
Satagopan JM, Sen S, Churchill GA. 2007. Sequential Quantitative Trait Locus Mapping in Experimental Crosses. Statistical Applications in Genetics and Molecular Biology 6:Article 12.
Schughart K, Churchill G. 2007. 6th annual meeting of the Complex Trait Consortium. Mamm Genome 18(10):683-685.
Sen S, Satagopan JM, Broman KW, Churchill GA. 2007. R/qtlDesign: inbred line cross experimental design. Mamm Genome 18:87-93.
Sheehan S, Tsaih SW, King BL, Stanton C, Churchill GA, Paigen B, DiPetrillo K. 2007. Genetic analysis of albuminuria in a cross between C57BL/6J and DBA/2J mice. Am J Physiol Renal Physiol 293(5):F1649-F1656.
Shockley KR, Rosen CJ, Churchill GA, Lecka-Czernik B. 2007. PPARgamma2 Regulates a Molecular Signature of Marrow Mesenchymal Stem Cells. PPAR Res 2007:81219.
Svenson KL, Von Smith R, Magnani PA, Suetin HR, Paigen B, Naggert JK, Li R, Churchill GA, Peters LL. 2007. Multiple trait measurements in 43 inbred mouse strains capture the phenotypic diversity characteristic of human populations. J Appl Physiol 102(6):2369-2378.
The International Stem Cell Initiative. 2007. Characterization of human embryonic stem cell lines by the International Stem Cell Initiative. Nat Biotechnol 25(7):803-816.
Yang H, Bell TA, Churchill GA, Pardo-Manuel de Villena F. 2007. On the subspecific origin of the laboratory mouse. Nat Genet 39(9):1100-1107.
Yang H, Churchill G. 2007. Estimating p-values in small microarray experiments. Bioinformatics 23(1):38-43.
Baum AE, Solberg LC, Churchill GA, Ahmadiyegh N, Takahashi JS, Redei EE. 2006. Test-and behavior-specific genetic factors affect WKY hypoactivity in tests of emotionality. Behav Brain Res 169:220-230.
Broman KW, Sen S, Owens SE, Manichaikul A, Southard-Smith EM, Churchill GA. 2006. The X chromosome in quantitative trait locus mapping. Genetics 174:2151-2158.
Churchill GA. 2006. The genetics of gene expression. Mamm Genome 17:465.
Cui X, Affourtit J, Shockley KR, Woo Y, Churchill GA. 2006. Inheritance patterns of transcript levels in F1 hybrid mice. Genetics 174(2):627-637.
Graber JH, Churchill GA, Dipetrillo KJ, King BL, Petkov PM, Paigen K. 2006. Patterns and mechanisms of genome organization in the mouse. J Exp Zool 305A(9):683-688.
Ishimori N, Li R, Walsh KA, Korstanje R, Rollins JA, Petkov P, Pletcher MT, Wiltshire T, Donahue LR, Rosen CJ, Beamer WG, Churchill GA, Paigen B. 2006. Quantitative trait loci that determine BMD in C57BL/6J and 129S1/SvImJ inbred mice. J Bone Miner Res 21(1):105-12.
Li R, Tsaih S-W, Shockley K, Stylianou IM, Wergedal J, Paigen B, Churchill GA. 2006. Structural model analysis of multiple quantitative traits. PLoS Genetics 2(7):e114.
Peters LL, Lambert AJ, Zhang W, Churchill GA, Brugnara C, Platt OS. 2006. Quantitative trait loci for baseline erythroid traits. Mamm Genome 17:298-309.
Reifsnyder PC, Li R, Silveira PA, Churchill G, Serreze DV, Leiter EH. 2006. Conditioning the genome identifies additional diabetes resistance loci in Type 1 diabetes-resistant NOR/Lt mice. Genes Immun 7:184.
Shockley KR, Churchill GA. 2006. Gene expression analysis of mouse chromosome substitution strains. Mamm Genome 17:598-614.
Solberg LC, Baum AE, Ahmadiyeh N, Shimomura K, Li R, Turek FW, Takahashi JS, Churchill GA, Redei EE. 2006. Genetic analysis of the stress-responsive adrenocortical axis. Physiol Genomics 27(3):362-369.
Stylianou IM, Korstanje R, Li R, Sheehan S, Paigen B, Churchill GA. 2006. Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci. Mamm Genome 17:22-36.
Stylianou IM, Tsaih SW, Dipetrillo K, Ishimori N, Li R, Paigen B, Churchill G. 2006. Complex genetic architecture revealed by analysis of high-density lipoprotein cholesterol in chromosome substitution strains and f2 crosses. Genetics 174(2):999-1007.
Wergedal JE, Ackert-Bicknell CL, Tsaih S-W, Sheng M H-C, Li R, Mohan S, Beamer WG, Churchill GA, Baylink DJ. 2006. Femur mechanical properties in the F2 Progeny of an NZB/B1NJ x RF/J cross are regulated predominantly by genetic loci that regulate bone geometry. J Bone Miner Res 21(8):1256-1266.
Wittenburg H, Lyons MA, Li R, Kurtz U, Wang X, Mossner J, Churchill GA, Carey MC, Paigen B. 2006. QTL mapping for genetic determinants of lipoprotein cholesterol levels in combined crosses of inbred mouse strains. J Lipid Res 47:1780-1790.
Zimmerman JE, Rizzo W, Shockley KR, Raizen DM, Naidoo N, Mackiewicz M, Churchill GA, Pack AI. 2006. Multiple mechanisms limit the duration of wakefulness in Drosophila brain. Physiol Genomics 27(3):337-350.