EXCEED THE SPACE PROVIDED. Asthma is a common, chronic inflammatory disorder of the lungs influenced by genetic and environmental factors. As part of the CSGA, we have searched for genes that influence asthma susceptibility using clinical phenotypes. Numerous suggestive chromosomal areas have been found, though no clear linkages have been established. We hypothesize that asthma is a complex condition both with respect to its inheritance and pathophysiology, including the elements of atopy, airways hyperreactivity and inflammation. We have a unique resource comprised of large, multi-generation families (Minnesota Families), with which we can assume greater genetic homogeneity, improve phenotypes based upon the major physiological elements and provide more power to focus gene searches using multi-component phenotypes predisposing to the development of asthma. The overall goals of this proposal are to determine if asthma is a unique disease or a 'cluster' of distinct disorders and identify the genes responsible for their development. These goals will be pursued in the following specific aims. 1. Establish quantitative traits predisposing to asthma, and apply statistical cluster analyses to these traits among the Minnesota Families. 2. Identify the chromosomal regions responsible for these multi-component phenotypes, using multipoint linkage analyses. 3. Identify candidate genes in these mapped areas responsible for the components predisposing to asthma by using genetic fine mapping and DNA sequence analysis. This study will utilize the extensive data already collected on families through the CSGA and a novel approach to identify asthma susceptibility genes. This will be the first step in the identification of genes that can be used for predictive genetic analyses and development of drug targets for this common medical problem. PERFORMANCE SITE ========================================Section End===========================================

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL049609-13
Application #
6898238
Study Section
Mammalian Genetics Study Section (MGN)
Program Officer
Banks-Schlegel, Susan P
Project Start
1992-09-30
Project End
2007-05-31
Budget Start
2005-06-01
Budget End
2006-05-31
Support Year
13
Fiscal Year
2005
Total Cost
$786,463
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Lu, Meng; Lee, Hye-Seung; Hadley, David et al. (2014) Logistic Principal Component Analysis for Rare Variants in Gene-Environment Interaction Analysis. IEEE/ACM Trans Comput Biol Bioinform 11:1020-8
Li, Xin; Chen, Yixuan; Li, Jing (2010) Detecting genome-wide haplotype polymorphism by combined use of Mendelian constraints and local population structure. Pac Symp Biocomput :348-58
Chen, Yixuan; Li, Xin; Li, Jing (2010) A novel approach for haplotype-based association analysis using family data. BMC Bioinformatics 11 Suppl 1:S45
Koehler, Megan L; Bondell, Howard D; Tzeng, Jung-Ying (2010) Evaluating haplotype effects in case-control studies via penalized-likelihood approaches: prospective or retrospective analysis? Genet Epidemiol 34:892-911
Blumenthal, M N; Zhong, W; Miller, M et al. (2010) Serum metalloproteinase leukolysin (MMP-25/MT-6): a potential metabolic marker for atopy-associated inflammation. Clin Exp Allergy 40:859-66
Callegaro, Andrea; Lebrec, Jeremie J P; Houwing-Duistermaat, Jeanine J (2010) Testing for genetic association in the presence of linkage and gene-covariate interactions. Biom J 52:22-33
Schwarz, Daniel F; König, Inke R; Ziegler, Andreas (2010) On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics 26:1752-8
Liu, Youfang; Li, Yi-Ju; Satten, Glen A et al. (2009) A regression-based association test for case-control studies that uses inferred ancestral haplotype similarity. Ann Hum Genet 73:520-6
Chanda, Pritam; Sucheston, Lara; Liu, Song et al. (2009) Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits. BMC Genomics 10:509
Li, Xin; Li, Jing (2009) An almost linear time algorithm for a general haplotype solution on tree pedigrees with no recombination and its extensions. J Bioinform Comput Biol 7:521-45

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