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PLoS One. 2014 Jan 24;9(1):e87044. doi: 10.1371/journal.pone.0087044. eCollection 2014.

Impact of measurement error on testing genetic association with quantitative traits.

Author information

1
Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore ; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
2
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore ; Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.
3
Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore ; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore ; Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.
4
Centre for Vision Research, University of Sydney, Sydney, Australia.
5
Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore ; Division of Human Genetics, Genome Institute of Singapore, Singapore, Singapore.
6
Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore ; Department of Medicine, National University of Singapore and National University Health System, Singapore, Singapore ; Duke-NUS Graduate Medical School, Singapore, Singapore.
7
Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore ; Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore.
8
Department of Ophthalmology, National University of Singapore and National University Health System, Singapore, Singapore ; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore ; Saw Swee Hock School of Public Health, National University Health System, National University of Singapore, Singapore, Singapore ; Duke-NUS Graduate Medical School, Singapore, Singapore.

Abstract

Measurement error of a phenotypic trait reduces the power to detect genetic associations. We examined the impact of sample size, allele frequency and effect size in presence of measurement error for quantitative traits. The statistical power to detect genetic association with phenotype mean and variability was investigated analytically. The non-centrality parameter for a non-central F distribution was derived and verified using computer simulations. We obtained equivalent formulas for the cost of phenotype measurement error. Effects of differences in measurements were examined in a genome-wide association study (GWAS) of two grading scales for cataract and a replication study of genetic variants influencing blood pressure. The mean absolute difference between the analytic power and simulation power for comparison of phenotypic means and variances was less than 0.005, and the absolute difference did not exceed 0.02. To maintain the same power, a one standard deviation (SD) in measurement error of a standard normal distributed trait required a one-fold increase in sample size for comparison of means, and a three-fold increase in sample size for comparison of variances. GWAS results revealed almost no overlap in the significant SNPs (p<10(-5)) for the two cataract grading scales while replication results in genetic variants of blood pressure displayed no significant differences between averaged blood pressure measurements and single blood pressure measurements. We have developed a framework for researchers to quantify power in the presence of measurement error, which will be applicable to studies of phenotypes in which the measurement is highly variable.

PMID:
24475218
PMCID:
PMC3901720
DOI:
10.1371/journal.pone.0087044
[Indexed for MEDLINE]
Free PMC Article

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