Studies of multiple measures of a quantitative trait can have greater precision and thus statistical power compared to single measure studies, but this has rarely been studied in the relation to quantitative trait measurement error models in genetic association studies. Using estimated glomerular filtration rate (eGFR), a quantitative measure of kidney function, as an example we constructed measurement error models of a quantitative trait with systematic and random error components. We then examined the effects on precision of the parameter estimate between genetic loci and eGFR resulting from varying the correlation and contribution of the error components. We also compared the empirical results from 3 genome-wide association studies (GWAS) of kidney function in 9049 European Americans: a single measure, a 3-measure model of the same biomarker of kidney function, and a 6-measure model of different biomarkers of kidney function. Simulations showed that given the same amount of overall errors, inclusion of measures with less correlated systematic errors led to greater gain in precision. The empirical GWAS results confirmed that both the 3- and 6-measure models detected more eGFR-associated genomic loci with stronger statistical association than the single-measure model despite some heterogeneity among the measures. Multiple measures of a quantitative trait can increase the statistical power of a study without additional participant recruitment. However, careful attention must be paid to the correlation of systematic errors and inconsistent associations when different biomarkers or methods are used to measure the quantitative trait
