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Cancer Epidemiology, Biomarkers & Prevention
Cancer Epidemiology, Biomarkers & Prevention
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CEBP Focus: Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology

Improving Power to Detect Changes in Blood miRNA Expression by Accounting for Sources of Variability in Experimental Designs

Sarah I. Daniels, Fenna C.M. Sillé, Audrey Goldbaum, Brenda Yee, Ellen F. Key, Luoping Zhang, Martyn T. Smith and Reuben Thomas
Sarah I. Daniels
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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  • For correspondence: sdaniels@berkeley.edu
Fenna C.M. Sillé
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Audrey Goldbaum
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Brenda Yee
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Ellen F. Key
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Luoping Zhang
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Martyn T. Smith
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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Reuben Thomas
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California.
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DOI: 10.1158/1055-9965.EPI-14-0623 Published December 2014
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  • Figure 1.
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    Figure 1.

    qPCR measurements of sources of blood miRNA variability. A, proportions of interindividual, intraindividual, and technical variability were estimated for 12 subjects using a mixed-effects model of qPCR data from seven target miRNAs (miR16, miR342-3p, miR30d, miR185, let7d, miR130a, miR451), two endogenous control small RNAs (RNU48 and snRNA U6), and one exogenous spike-in (cel-39). Technical variability includes variability within- and between-extraction batches as well as plate-to-plate variability. B, interclass correlation (ICC) for each source of variability was calculated as the proportion of total variance for each miRNA.

  • Figure 2.
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    Figure 2.

    Cumulative distributions of minimum detectable fold changes in miRNAs for bootstrap procedure using repeated measures. Smallest fold changes detected for the 143 miRNAs (with 80% statistical power) are plotted under the seven experimental design conditions (for N = 75 vs. 75 subjects), which vary in proportion of repeated measures [(A) 20%, (B) 50%, and (C) 100%] and number of repeated measures per subject (n1 = 0, n1 = 1 or n1 = 4). Fold changes are reported with 80% power for simulations with 100 bootstraps for 4 unique subjects each with 3 time point measurements (25). The vertical line in each figure is for purposes of comparing distributions at a 2-fold change in miRNA expression.

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    Figure 3.

    Comparison of 50% repeated measure designs for detection of significant fold changes in miRNAs. Designs 0, 2A, and 2B were compared with each other to calculate the proportion of the 143 miRNAs for which two designs' CIs do not overlap at a given P value. (Designs differ by number of repeated measures for each subject.)

Tables

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  • Table 1.

    Summary table of experimental designs used for simulations of miRNA microarray data

    DesignN1N2Number of subjects with repeated measures (%)Number of repeated measures
    075750 (0)0
    1A757530 (20)1
    1B757530 (20)4
    2A757575 (50)1
    2B757575 (50)4
    3A7575150 (100)1
    3B7575150 (100)4
  • Table 2.

    Variance terms and P values for sources of variability

    Random effect
    qPCR replicateInterindividualSeasonalBetween batchesWithin batchResidual
    miRNA TargetVarianceP valueVarianceP valueVarianceP valueVarianceP valueVarianceP valueVarianceTotal
    miR130a0.826<0.0010.1490.3870.0001.0000.0001.0000.0001.0000.0891.064
    miR30d0.541<0.0010.0530.5600.0001.0000.0001.0000.0001.0000.1000.693
    miR1850.820<0.0010.2220.1530.0310.9100.0001.0000.1491.0000.1021.324
    let-7d0.441<0.0010.2340.1030.0001.0000.0360.7700.2710.6470.0401.021
    miR16 (TaqMan)0.499<0.0010.237<0.050.0450.7060.0360.8880.0001.0000.1130.929
    c.eleg-390.137<0.0010.0001.0000.0001.0000.0110.8150.0001.0000.0800.228
    RNU480.388<0.0010.0970.2640.0001.0000.0000.2130.0001.0000.0860.571
    SNU60.375<0.0010.0001.0000.0001.0000.0001.0000.0001.0000.4580.833
    miR16 (qScript)0.119<0.0010.129<0.0010.0001.0000.0001.0000.346<0.050.0140.607
    miR4510.308<0.0010.386<0.0010.401<0.050.0290.6940.4820.0730.0341.640
    miR342-3p0.164<0.0010.0090.5350.0001.0000.0061.0000.0581.0000.0330.271

    NOTE: The variability for each random-effect term in the model is reported, as well as P values based on ANOVA tests for each term of each modeled miRNA.

    • Table 3.

      Estimations of minimum detectable fold changes for 2 study designs

      miR451miR185
      Study 1Study 2Study 1Study 2
      Mean3.772.384.182.4
      SE0.950.531.20.67
      95% CI(1.75–5.16)(0.97–3.36)(2.67–6.46)(1.71–3.74)
      90% CI(1.88–5.11)(1.07–3.20)(3.69–6.37)(2.11–3.68)

      NOTE: The estimates of variability obtained from the empirical qPCR study of miR451 and miR185 were used to determine the minimum detectable fold change with 80% statistical power for a theoretical study (N = 75 vs. 75) of two miRNAs. The mean fold change, SE, 95% CI, and 90% CI are reported given no replicates (Study 1) versus a study given five extraction batches, five within-batch replicates, five time point replicates, and five qPCR replicates for 50% of the subjects (Study 2).

      Additional Files

      • Figures
      • Tables
      • Supplementary Data

        Files in this Data Supplement:

        • Supplementary Methods - Supplementary Methods for miR Extraction, qPCR, and statistical analysis
        • Supplementary Results - Supplementary Results for Figures S1-S4
        • Supplementary Legends - Supplementary Legends for Figures S1-S4
        • Supplementary Figure S1 - Supplementary Figure S1A and S1B: miR Extraction Methods Comparison
        • Supplementary Figure S2 - Supplementary Figure S2A and S2B: miR qPCR Assay Comparison
        • Supplementary Figure S3 - Supplementary Figure S3A-S3C: Cumulative Distributions including 95% Confidence Intervals
        • Supplementary Figure S4 - Supplementary Figure S4A and S4B: Comparison of Designs 0/1A/1B and Designs 0/3A/3B
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      Cancer Epidemiology Biomarkers & Prevention: 23 (12)
      December 2014
      Volume 23, Issue 12
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      Improving Power to Detect Changes in Blood miRNA Expression by Accounting for Sources of Variability in Experimental Designs
      Sarah I. Daniels, Fenna C.M. Sillé, Audrey Goldbaum, Brenda Yee, Ellen F. Key, Luoping Zhang, Martyn T. Smith and Reuben Thomas
      Cancer Epidemiol Biomarkers Prev December 1 2014 (23) (12) 2658-2666; DOI: 10.1158/1055-9965.EPI-14-0623

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      Improving Power to Detect Changes in Blood miRNA Expression by Accounting for Sources of Variability in Experimental Designs
      Sarah I. Daniels, Fenna C.M. Sillé, Audrey Goldbaum, Brenda Yee, Ellen F. Key, Luoping Zhang, Martyn T. Smith and Reuben Thomas
      Cancer Epidemiol Biomarkers Prev December 1 2014 (23) (12) 2658-2666; DOI: 10.1158/1055-9965.EPI-14-0623
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