Asthma is one of the most common, chronic diseases. Its pathogenesis reflects complex interactions between genetic and environmental factors. We propose that the genes that influence asthma-associated quantitative traits (QTs) have sex-specific effects on risk for disease. In this application, we propose to continue our studies of 8 QTs, 6 with sex-specific genetic architecture, that are associated with asthma in the Hutterites, a founder population of European origins that lives communally. The overall objectives of our studies are to use state-of- the art, integrative approaches to characterize the genetic architecture of asthma and ultimately to identify asthma risk alleles. We propose 3 specific aims: 1) Obtain the full genome sequence for 96 relatively unrelated Hutterites selected to be most closely related to the other ~1200 Hutterites in our sample, and impute the discovered variation to the other individuals using the haplotype and known identity by descent structure of all chromosomes. We expect a significant number of variants that are rare in European populations will be enriched in the Hutterites and allow us to study the effects of rare vs. common variants on asthma risk. 2) Study sex-specific gene regulation in male and female Hutterites using RNA sequencing (RNAseq) to measure transcript levels in lymphoblastoid cell lines (LCLs) from 500 Hutterites that have been evaluated for asthma and phenotyped for 8 asthma-associated QTs: IgE, lymphocyte count, eosinophils, YKL-40, fractional exhaled nitric oxide (eNO), %predicted FEV1, FEV1/FVC ratio, and bronchial responsiveness index (BRI). We propose to identify genes that are differentially expressed in Hutterites with high vs. low QT values or correlated with QT values in all individuals, in males only, and in females only, and then map eQTLs (using Affymetrix genotypes as well as variants discovered in Aim 1) for those genes to identify variants that are eQTLs in the combined sample, in males only, and in females only. 3) Integrate genetic, genomic, and physiologic and disease phenotypes to discover asthma genes. In this aim we will move toward a comprehensive systems approach to fully integrate genetic, genomic, and phenotypic variation that is relevant to asthma. We will focus these studies on the 500 Hutterites with gene expression data and consider the genetic variation characterized in Aims 1 and 2, in addition to Affymetrix genotypes, physiologic QTs measured during the previous grant period, and asthma status.
Identifying genes for common diseases and disease-associated quantitative phenotypes with complex genetic architectures remains challenging. In this application we propose to further characterize variation in ~1200 members of a population isolate, the Hutterites, at the genome, transcriptome and phenome levels and to apply systems biology approaches to thoroughly dissect the genetic architecture of asthma. The discovery of risk alleles for common diseases could significantly impact public health and health care delivery.
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|Papachristou, Charalampos; Ober, Carole; Abney, Mark (2016) A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data. BMC Proc 10:221-226|
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|Bønnelykke, Klaus; Ober, Carole (2016) Leveraging gene-environment interactions and endotypes for asthma gene discovery. J Allergy Clin Immunol 137:667-79|
|Çal??kan, Minal; Baker, Samuel W; Gilad, Yoav et al. (2015) Host genetic variation influences gene expression response to rhinovirus infection. PLoS Genet 11:e1005111|
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