Most common human diseases are complex, caused by many genes that interact with environmental risk factors. For examples, the genes that cause heart disease interact with a person's diet and exercise regimen, and the genes that cause high blood pressure interact with a person's salt intake and stress level. We use mouse models to study these diseases because mice are genetically well defined, relatively inexpensive to work with, reproduce quickly, are biologically very similar to humans, and their environments can be strictly regulated. Understanding the genetics of quantitative traits in mice helps us understand them in humans. In our laboratory, we are focusing on finding genes that control HDL-cholesterol levels, one of the many traits underlying heart disease risk. If we can identify some of these genes, we will gain insight into gene networks and systems biology. Additionally, we are applying similar techniques to studies of lung disease and the genetics of aging and longevity.
Finding the Genes for Common Diseases
Focus on HDL genes
Many common human diseases are complex, caused by multiple genes that interact with environmental risk factors. Our research centers on the use of mouse models to understand the genetics of human disease. Our current focus is on finding the genes that control high density lipoprotein (HDL)-cholesterol, the good cholesterol. Heart disease risk can be explained in part by the balance between HDL and low density lipoprotein (LDL)-cholesterol, the bad cholesterol. For example, an increase of only 1% in HDL levels decreases the risk of heart disease by 2-3%. However, while several good drugs capable of lowering LDL levels are currently available, therapies to raise HDL levels are quite limited. The most effective means now available for increasing HDL levels are exercise, cessation of smoking, moderate alcohol intake (a glass of wine daily), and consumption of healthy fats (olive oil, avocado, fish). The availability of drugs that could increase HDL levels would be a useful addition to the other therapies, especially for those who already have evidence of heart disease. We have two primary objectives in our focus on identifying genes that control HDL. First, we hope that some of these genes will be good drug targets and thus improve the range of treatment options for heart disease, the major cause of death in the United States. Second, we know very little about systems biology-how genes interact with each other and with the environment to cause the variation in disease risk observed among people. If we can identify some of the genes regulating HDL levels, one of many traits underlying heart disease risk, we will gain some insight into gene networks and systems biology.
Accelerating the pace of finding QTL genes
The first step in finding genes responsible for a quantitative disease is to identify quantitative trait loci (QTL) associated with the disease. Over the past decade, our laboratory has carried out over 25 mouse QTL crosses. Once a QTL is found, the next step is to narrow the QTL region in order to identify the particular gene or genes regulating the trait. This has traditionally been done using additional mouse crosses. However, the Human Genome Project, the sequencing of the mouse and human genome, and the resources put into developing other genetic resources and tools are changing the way we work. The field of genetics has become increasingly data-rich, with extensive databases and sophisticated tools for data mining and analysis, and we are using these resources to accelerate the pace of gene identification.
For years, The Jackson Laboratory has maintained that discovering disease genes in the mouse can hasten their discovery in humans. This dogma has moved much closer to reality, as we have used the power of the mouse model together with the advantages of the human system to identify disease genes. This approach is based on the observation that the QTL for a trait are found in homologous positions in human and mouse. This cross-species concordance has biological and economic consequences: finding disease genes in mice and then confirming them in humans is much simpler, faster, and less expensive than finding them in humans first.
Principal Investigator: Beverly J. Paigen, Ph.D.
Research Assistant II: Ken Walsh, B.S.
Research Assistant I: Aleksandra Aljakna, B.A.
Scientific Writer: Joanne Curier
Research Administrative Assistant: Patricia Cherry
(Selected from 2010 to present)
Paigen B, Marion MA, Stearns TM, Harper JM, Svenson KL. 2014. The effect of culling on health and physiology of mouse litters. Lab Anim [Epub ahead of print]
Ackert-Bicknell C, Paigen B, Korstanje R. 2013. Recalculation of 23 mouse HDL QTL datasets improves accuracy and allows for better candidate gene analysis. J Lipid Res 54(4):984+994. PMCID: PMC3606003
Himes BE, Sheppard K, Berndt A, Leme AS, Myers RA, Gignoux CR, Levin AM, Gauderman WJ, Yang JJ, Mathias RA, Romieu I, Torgerson DG, Roth LA, Huntsman S, Eng C, Klanderman B, Ziniti J, Senter-Sylvia J, Szefler SJ, Lemanske RF Jr, Zeiger RS, Strunk RC, Martinez FD, Boushey H, Chinchilli VM, Israel E, Mauger D, Koppelman GH, Postma DS, Nieuwenhuis MA, Vonk JM, Lima JJ, Irvin CG, Peters SP, Kubo M, Tamari M, Nakamura Y, Litonjua AA, Tantisira KG, Raby BA, Bleecker ER, Meyers DA, London SJ, Barnes KC, Gilliland FD, Williams LK, Burchard EG, Nicolae DL, Ober C, DeMeo DL, Silverman EK, Paigen B, Churchill G, Shapiro SD, Weiss ST. 2013. Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene. PLoS One. 2013;8(2):e56179. PMCID: PMC3572953
Hochrath K, Ehnert S, Ackert-Bicknell CL, Lau Y, Schmid A, Krawczyk M, Hengstler JG, Dunn J, Hiththetiya K, Rathkolb B, Micklich K, Hans W, Fuchs H, Gailus-Durner V, Wolf E, de Angelis MH, Dooley S, Paigen B, Wildemann B, Lammert F, Nüssler AK. 2013. Modeling hepatic osteodystrophy in Abcb4 deficient mice. Bone 55(2):501-511
Ackert-Bicknell CL, Demissie S, Tsaih SW, Beamer WG, Cupples LA, Paigen BJ, Hsu YH, Kiel DP, Karasik D. 2012. Genetic variation in TRPS1 may regulate hip geometry as well as bone mineral density. Bone 50(5):1188-1195. PMCID: PMC3322322
Leduc MS, Blair RH, Verdugo RA, Tsaih SW, Walsh K, Churchill GA, Paigen B. 2012. Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross. J Lipid Res 53(6):1163-1175. PMCID: PMC3351823
Yuan R, Meng Q, Nautiyal J, Flurkey K, Tsaih SW, Krier R, Parker MG, Harrison DE, Paigen B. 2012. Genetic coregulation of age of female sexual maturation and lifespan through circulating IGF1 among inbred mouse strains. Proc Natl Acad Sci 109(21):8224-8229. PMCID:PMC3361401
Berndt A, Cario CL, Silva KA, Kennedy VE, Harrison DE, Paigen B, Sundberg JP. 2011. Identification of fat4 and tsc22d1 as novel candidate genes for spontaneous pulmonary adenomas. Cancer Res 71: 5779-5791. PMCID: PMC3165088
Berndt A, Leme AS, Williams LK, Von Smith R, Savage HS, Stearns T, Tsaih SW, Shapiro SD, Peters LL, Paigen BJ, 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. PMCID: PMC3026348
Berndt A, Savage HS, Stearns TM, Paigen B. 2011. Genetic analysis of lung function in inbred mice suggests vitamin D receptor as a candidate gene. Mol Genet Genomics 286: 237-246. PMCID: PMC3175031
Hageman RS, Leduc MS, Korstanje R, Paigen B, Churchill GA. 2011. A Bayesian framework for inference of the genotype-phenotype map for segregating populations. Genetics 187(4):1163-1170. PMCID: PMC3070524
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. PMCID: PMC3151687
Leduc MS, Lyons M, Darvishi K, Walsh K, Sheehan S, Amend S, Cox A, Orho-Melander M, Kathiresan S, Paigen B, Korstanje R. 2011. The mouse QTL map helps interpret human genome-wide association studies for HDL cholesterol. J Lipid Res 52(6):1139-1149. PMCID: PMC3090235
Peng RD, Paigen B, Eggleston PA, Hagberg KA, Krevans M, Curtin-Brosnan J, Benson C, Shreffler WG, Matsui EC. 2011. Both the variability and level of mouse allergen exposure influence the phenotype of the immune response in workers at a mouse facility. J Allergy Clin Immunology 128(2):390-396. PMCID: PMC3149759
Yuan R, Peters LL, Paigen B. 2011. Mice as a mammalian model for research on the genetic of aging. ILAR J 52(1):4-15. PMCID: PMC3074346
Ackert-Bicknell CL, Karasik D, Li, Q, Smith RV, Hsu Y-H, Churchill GA, Paigen B, Tsaih S-W. 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):1808-1820. PMCID: PMC3153351
Cox A, Sheehan SM, Klöting I, Paigen B, Korstanje R. 2010. Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals. Heredity 105(5):426-432. PMCID: PMC2958246
Curtin-Brosnan J, Paigen B, Hagberg KA, Langley S, O'Neil EA, Krevans M, Eggleston PA, Matsui EC. 2010. Occupational mouse allergen exposure among non-mouse handlers. J Occup Environ Hyg 7(12):726-734. PMCID: PMC3143460
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. PMCID: PMC3025299
Leme AS, Berndt A, Williams LK, Tsaih SW, Szatkiewicz JP, Paigen B, Shapiro SD. 2010. A survey of airway responsiveness in 36 inbred mouse strains facilitates gene mapping studies and identification of quantitative trait loci. Mol Genet Genomics 283(4):317-326. PMCID: PMC2885868
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. PMCID: PMC3031420
Prevorsek Z, Gorjane G, Paigen B, Horvat S. 2010. Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci. Mamm Genome 21:172-185.
Su Z, Leduc MS, Korstanje R, Paigen B. 2010. Untangling HDL QTL on mouse chromosome 5 and identifying Scarb1 and Acads as the underlying genes. J Lipid Res 51(9):2706-2713. PMCID: PMC2918452