NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica

Description

The description below was taken directly from the NCBI database of Genotypes and Phenotypes (dbGaP):

This study is part of the NHLBI Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Program. TOPMed is part of a broader Precision Medicine Initiative, which aims to provide disease treatments that are tailored to an individual's unique genes and environment. TOPMed will contribute to this initiative through the integration of whole-genome sequencing (WGS) and other -omics (e.g., metabolic profiles, protein and RNA expression patterns) data with molecular, behavioral, imaging, environmental, and clinical data. In doing so, this program aims to uncover factors that increase or decrease the risk of disease, to identify subtypes of disease, and to develop more targeted and personalized treatments. Two genotype call sets derived from WGS are now available, one called Freeze 4 (GRCh37) and another called Freeze 5b (GRCh38), with largely overlapping sample sets. Information about how to identify other TOPMed WGS accessions for cross-study analysis, as well as descriptions of TOPMed methods of data acquisition, data processing and quality control, are provided in the accompanying documents, "TOPMed Whole Genome Sequencing Project - Freeze 4, Phase 1" and "TOPMed Whole Genome Sequencing Project - Freeze 5b, Phases 1 and 2". Please check the study list at the top of each of these methods documents to determine whether it applies to this study accession.

This administrative supplement to the project, "The Genetic Epidemiology of Asthma in Costa Rica" (R37 HL066289) is in response to NOT-HL-14-029 to perform whole genome sequencing (WGS) on existing NHLBI populations. We focus on asthma because of its public health significance. Asthma affects 26 million U.S. children and adults, remains a major cause of morbidity (one-half million hospitalizations a year) and is the most common cause of school and work days lost. Asthma-related costs are estimated to be over $12.7 billion annually. The Asthma Probands for both the extended pedigrees and the trios utilized in this study were selected on the basis of a physician diagnosis of asthma; a history of recurrent asthma attacks or at least 2 respiratory symptoms; and either airway hyperresponsiveness to methacholine or significant response to bronchodilator (Albuterol) administration. These criteria are identical to the criteria used in the Childhood Asthma Management Program (CAMP).

The three primary goals of this project are to: (1) identify common and rare genetic variants that determine asthma and its associated phenotypes (height, weight, IgE level, lung function, bronchodilator response, steroid treatment response) through whole genome sequencing (WGS); (2) perform novel family based association analysis of our WGS data to identify novel genes for asthma; and (3) integrate epigenomic and transcriptomic data with our WGS data and determine the epistatic interactions present using systems genomics approaches. Identification of the molecular determinants of asthma remains an important priority in translational science. Genome-wide association studies (GWAS) have been successful in this regard, identifying at least 10 novel susceptibility genes for asthma. However, as with most complex traits, the variants identified by GWAS explain only a fraction of the estimated heritability of this disorder. Herein, we propose a novel family-based study design and state-of-the-art genome sequencing techniques to map a set of sequence variants for asthma and its associated phenotypes and assess the interrelationships of the identified genes and variants using systems genomics methods. We have assembled a team of investigators highly-skilled and expert in whole genome sequencing (Drs. Michael Cho and Benjamin Raby), genetic association analysis (Drs. Scott T. Weiss, Jessica Lasky-Su and Christoph Lange), integrative genomics (Drs. Raby, Kelan Tantisira, Augusto Litonjua and Dawn DeMeo), and systems genomics (Drs. Weiss, Amitabh Sharma, Lange and Raby) to address this important problem with both a novel study design and data set.

General information

phs#	Study abbreviation	Study type	Parent phs#
phs000988	CRA	Parent-Offspring Trios	N/A