A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Asthma Prevalence and Severity in Arab American Communities in the Detroit Area, Michigan

Immigrant populations provide a unique intersection of cultural and environmental risk factors implicated in asthma etiology. This study focuses on asthma prevalence and severity in 600 Arab American households in metro Detroit, the largest immigrant reception zone for Arab Americans in North America. The survey method introduced a number of novel features: (a) a ranking scheme for the key environmental risk factors for asthma was used to derive an aggregated environmental risk index (ERI) for each household, and (b) an aggregate measure of asthma severity based on symptom frequency and intensity. Environmental risk factors and surrogates for socioeconomic status (SES) were found to be stronger predictors of asthma prevalence than asthma severity, while demographic variables such as English fluency and birth in the United States were better predictors of asthma severity than asthma prevalence. These results suggest that SES variables may be more reflective of environmental exposures in communities involved in this study, while English fluency and birth in the United States may be linked to health care access and utilization behavior that can influence the asthma management. We also found a significant relationship between asthma prevalence and degree of acculturation. Asthma prevalence was highest among moderately acculturated immigrants compared with new immigrants and those who were well acculturated, suggesting that among Arab Americans in the Detroit area, risk factors associated with new immigrant status are replaced by “western” risk factors as the population becomes more acculturated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44945/1/10903_2005_Article_3673.pd

An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations

Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but they do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying new associations and evidence for allelic heterogeneity. We also show how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large data sets (n > 10,000) practicable