Asthma, a chronic respiratory disease affecting over 20 million Americans and 300 million people worldwide, results from the complex interaction of multiple genetic and environmental factors. Many studies have searched for individual genetic variants that contribute to asthma susceptibility, but a thorough understanding of the genetic basis of asthma has not been achieved. Our main hypothesis is that the genetic architecture underlying asthma susceptibility can be better understood by considering multiple genes and incorporating multiple sources of genomic data, including human asthma and mouse airways hyperresponsiveness (AHR) data. This hypothesis will be addressed via specific aims in which we will: (1) identify genetic variants that predispose humans to asthma and modulate human AHR by mapping regions associated with AHR in inbred strains of mice to human genome-wide association data, (2) use gene expression data, known protein-protein interactions, and known functional pathways to enhance the search for asthma and AHR genetic variants in humans, and (3) integrate individual human genetic variants to create a multivariate predictive model of asthma. Novel variants identified by Specific Aims 1 and 2 will be validated by replication in independent human populations of asthmatics. The predictive model created in Specific Aim 3 will be validated through replication and prediction in independent human populations of asthmatics. By completing the specific aims, we hope to make progress towards the development of a comprehensive model of the genetics of asthma, particularly of asthma characterized by increased airways responsiveness.
By completing this proposal, we hope to identify genetic variants that modulate asthma risk. Identifying such variants could provide biological insights that may eventually lead to a better understanding of asthma. Additionally, we will create a predictive model of asthma, which could potentially lead to the development of a clinical prognostic test of who is at risk for developing asthma.
|McGeachie, Michael J; Wu, Ann C; Tse, Sze Man et al. (2015) CTNNA3 and SEMA3D: Promising loci for asthma exacerbation identified through multiple genome-wide association studies. J Allergy Clin Immunol 136:1503-10|
|Sharma, Sunita; Zhou, Xiaobo; Thibault, Derek M et al. (2014) A genome-wide survey of CD4(+) lymphocyte regulatory genetic variants identifies novel asthma genes. J Allergy Clin Immunol 134:1153-62|
|Klerman, Elizabeth B; Wang, Wei; Duffy, Jeanne F et al. (2013) Survival analysis indicates that age-related decline in sleep continuity occurs exclusively during NREM sleep. Neurobiol Aging 34:309-18|
|Himes, Blanca E; Sheppard, Keith; Berndt, Annerose et al. (2013) Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene. PLoS One 8:e56179|
|St Hilaire, Melissa A; Sullivan, Jason P; Anderson, Clare et al. (2013) Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing. Accid Anal Prev 50:992-1002|
|Himes, Blanca E; Qiu, Weiliang; Klanderman, Barbara et al. (2013) ITGB5 and AGFG1 variants are associated with severity of airway responsiveness. BMC Med Genet 14:86|
|McGeachie, Michael J; Stahl, Eli A; Himes, Blanca E et al. (2013) Polygenic heritability estimates in pharmacogenetics: focus on asthma and related phenotypes. Pharmacogenet Genomics 23:324-8|
|Phillips, A J K; Robinson, P A; Klerman, E B (2013) Arousal state feedback as a potential physiological generator of the ultradian REM/NREM sleep cycle. J Theor Biol 319:75-87|
|Klerman, Hadassa; St Hilaire, Melissa A; Kronauer, Richard E et al. (2012) Analysis method and experimental conditions affect computed circadian phase from melatonin data. PLoS One 7:e33836|
|Himes, Blanca E; Jiang, Xiaofeng; Hu, Ruoxi et al. (2012) Genome-wide association analysis in asthma subjects identifies SPATS2L as a novel bronchodilator response gene. PLoS Genet 8:e1002824|
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