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On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis

Author

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  • Young Lee

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea
    Department of Applied Statistics, Chung-Ang University, Seoul 156-756, Korea)

  • Suyeon Park

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea
    Department of Applied Statistics, Chung-Ang University, Seoul 156-756, Korea)

  • Sanghoon Moon

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea)

  • Juyoung Lee

    (The Center for Genome Science, Korea National Institute of Health, KCDC, Osong 361-951, Korea)

  • Robert C. Elston

    (Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA)

  • Woojoo Lee

    (Department of Statistics, Inha University, Incheon 402-751, Korea)

  • Sungho Won

    (Department of Public Health Science, Seoul National University, Seoul 151-742, Korea)

Abstract

Longitudinal data enables detecting the effect of aging/time, and as a repeated measures design is statistically more efficient compared to cross-sectional data if the correlations between repeated measurements are not large. In particular, when genotyping cost is more expensive than phenotyping cost, the collection of longitudinal data can be an efficient strategy for genetic association analysis. However, in spite of these advantages, genome-wide association studies (GWAS) with longitudinal data have rarely been analyzed taking this into account. In this report, we calculate the required sample size to achieve 80% power at the genome-wide significance level for both longitudinal and cross-sectional data, and compare their statistical efficiency. Furthermore, we analyzed the GWAS of eight phenotypes with three observations on each individual in the Korean Association Resource (KARE). A linear mixed model allowing for the correlations between observations for each individual was applied to analyze the longitudinal data, and linear regression was used to analyze the first observation on each individual as cross-sectional data. We found 12 novel genome-wide significant disease susceptibility loci that were then confirmed in the Health Examination cohort, as well as some significant interactions between age/sex and SNPs.

Suggested Citation

  • Young Lee & Suyeon Park & Sanghoon Moon & Juyoung Lee & Robert C. Elston & Woojoo Lee & Sungho Won, 2014. "On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis," IJERPH, MDPI, vol. 11(12), pages 1-21, November.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:12:p:12283-12303:d:42878
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