Abstract
Genetic association studies of complex traits often rely on standardised quantitative phenotypes, such as percentage of predicted forced expiratory volume and body mass index to measure an underlying trait of interest (eg lung function, obesity). These phenotypes are appealing because they provide an easy mechanism for comparing subjects, although such standardisations may not be the best way to control for confounders and other covariates. We recommend adjusting raw or standardised phenotypes within the study population via regression. We illustrate through simulation that optimal power in both population- and family-based association tests is attained by using the residuals from within-study adjustment as the complex trait phenotype. An application of family-based association analysis of forced expiratory volume in one second, and obesity in the Childhood Asthma Management Program data, illustrates that power is maintained or increased when adjusted phenotype residuals are used instead of typical standardised quantitative phenotypes.
Highlights
Failure to adjust for confounders and other covariates can greatly diminish the efficiency of genetic association studies
Covariate adjustment is so crucial for traits like body mass index (BMI) and percentage of predicted forced expiratory volume in one second (PPFEV) that they are standardised by definition
When the data were generated to resemble the Childhood Asthma Management Program (CAMP) study population as closely as possible, the most powerful approach used the residuals from within-study adjustment of FEV, followed by within-study adjusted PPFEV, PPFEV and FEV
Summary
Failure to adjust for confounders and other covariates can greatly diminish the efficiency of genetic association studies. Covariate adjustment is so crucial for traits like body mass index (BMI) and percentage of predicted forced expiratory volume in one second (PPFEV) that they are standardised by definition. BMI (instead of weight) is used as a measure of obesity because height contributes noise to the relationship between obesity and weight. PPFEV, the amount of air a person can blow out in one second divided by the expected amount, given the person’s sex, height and, sometimes, other covariates, is used as a measure of lung function instead of unadjusted forced expiratory volume (FEV) because sex, height and other covariates add noise to the relationship between lung function and FEV. To determine expected FEV, various equations have been proposed, each a regression equation fit to a specific study population.[4,5,6] Both BMI and PPFEV were developed to assess phenotypes in individuals when there are no population data available — for example, determining the severity of asthma or obesity during physical examination
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