Overview
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.
Scientific report
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 and found over 250 disease-related QTL. These QTL are relatively large, on the order of 40 megabases (Mb), and contain hundreds of genes. 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. The statistical and bioinformatics methods that we are using to narrow QTL regions include a statistical technique for combining data from multiple mouse crosses, haplotype analysis, comparative genomics, and haplotype association mapping. Combining these tools often reduces the number of possible genes in a QTL region from several hundred to a manageable list of 5-15. Then we test each of these candidate genes by sequencing and expression tests to determine if the gene differs between the strains that gave the QTL. During the past two years, we have tentatively identified about 12 genes that affect HDL cholesterol.
Using the mouse to find human genes
For years, The Jackson Laboratory has maintained that discovering disease genes in the mouse can hasten their discovery in humans. This year, that 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 enormous 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.
The powerful and cost-effective strategy that we use to identify the genes underlying disease-related QTL is a mouse:human:mouse paradigm. We first search for candidate genes in the mouse using the approaches described above. When we have reduced the list of probable candidates to only 1-4 genes, we test those genes in a hypothesis-driven association study in humans. When we find a human association, we then return to the mouse for additional studies: first, to obtain definitive proof of the gene's involvement in the disease, using genetically engineered mice or other strategies, and second, to carry out functional studies to determine how the gene and its polymorphisms affect the disease phenotype.
Beyond HDL genes
These approaches to finding QTL genes have proven so powerful with HDL genes that we are extending them to other diseases in collaboration with others. The first is a study of lung disease, both asthma and emphysema. We are surveying a set of inbred strains for variation in susceptibility to these diseases, and then we will carry out our QTL analyses combining the new information with that in the literature, find genes for asthma and emphysema in the mouse, and test these in humans. Finally, we are heavily involved with a Jackson-wide study into the genetics of aging (see report on the Integrative Center for Genetics of Aging). We are surveying a large set of strains for differences in longevity and measuring their health and well-being in multiple ways every six months. We hope to gain insight into the genetic basis of longevity.
Lab staff
Principal Investigator: Beverly J. Paigen, Ph.D.
Research Scientist: Ron Korstanje, Ph.D.
Postdoctoral Fellow: Ujala Srivastava, Ph.D.
Predoctoral Associate: Seungbum Choi, B.S., M.S.
Research Laboratory Manager: Susan Sheehan, B.A., M.S.
Research Assistant III: Michael Marion, B.S.
Research Assistant II: Ken Walsh, B.S.
Research Assistant I: Aleksandra Aljakna, B.A., Christina Caputo, B.S.
Laboratory Technician IV: Rachel Elwell, Beverly Macy
Research Administrative Assistant: Patricia Cherry
Publication listings
(Selected from 2009 to present)
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 unrestained pleithysmography and forced oscillation for identifying genetic variability of airway responsiveness in inbred mice. Physiol Genomics 43(1):1-11.
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.
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.
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.
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.
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.
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.
Burgess-Herbert SL, Tsaih SW, Stylianou IM, Walsh K, Cox AJ, Paigen B. 2009. An experimental assessment of in silico haplotype association mapping in laboratory mice. BMC Genet 10:81-93. PMCID: PMC2797012
Cox A, Ackert-Bicknell C, Dumont BL, Ding Y, Tzenova Bell J, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih S-W, Churchill GA, Broman KW. 2009. A new standard genetic map for the laboratory mouse. Genetics 182:1-10. PMCID: PMC2728870
Shockley K, Witmer D, Burgess-Herbert SL, Paigen B, Churchill GA. 2009. The effects of atherogenic diet on hepatic gene expression across mouse strains. Physiol Genomics 39:172-182. PMCID: PMC2789673
Stylianou IM, Svenson KL, VanOrman SK, Langle Y, Millar JS, Paigen B, Rader DJ. 2009. Novel ENU-induced point mutation in scavenger receptor class B, member 1, results in liver specific loss of SCARB1 protein. PLoS One 4(8):e6521. PMCID: PMC2715880
Su Z, Cox A, Shen Y, Stylianou IM, Paigen B. 2009. Farp2 and Stk25 are candidate genes for the HDL cholesterol locus on mouse chromosome 1. Arterioscler Thromb Vasc Biol 29:107-113. PMCID: PMC2782635
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. PMCID: PMC2739753
Su Z, Wang X, Tsiah S-W, Zhang A, Cox A, Sheehan S, Paigen B. 2009. Genetic basis of HDl variation in 129/SvImJ and C57BL/6J mice: importance of testing candidate genes in targeted mutant mice. J Lipid Res 50:116-125.
PMCID: PMC2602865
Xing S, Tsaih SW, Yuan R, Svenson KL, Jorgenson LM, So M, Paigen BJ, Korstanje R. 2009. Genetic influence on electrocardiogram time intervals and heart rate in aging mice. Am J Physiol Heart Circ Physiol 296(6):H1907-H1913. PMCID: PMC2768517
Yang H, Ding Y, Hutchins LN, Szatkiewicz J, Bell TA, Paigen BJ, de Villena FP, Churchill GA. 2009. A customized and versatile high-density genotyping array for the mouse. Nat Methods 9(6):663-666. PMCID: PMC2735580
Yuan R, Tsaih SW, Petkova SB, Evsikova CM, Xing S, Marion MA, Bogue MA, Mills KD, Bult CL, Rosen CL, Sundberg JP, Harrison DE, Churchill GA, Peters L, Paigen B. 2009. Aging in inbred strains of mice: study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell 8:277-287. PMCID: PMC2768517
