Sample Size and Power for Association Studies Involving Mitochondrial DNA Haplogroups.

mthapower

Calculate sample size and power for association studies involving mitochondrial DNA haplogroups - Based on Samuels et al. AJHG, 2006. 78(4):713-720. DOI:10.1086/502682

Installation

• From CRAN:

install.packages("mthapower")

• From GitHub:

# install.packages("devtools")
devtools::install_github("aurora-mareviv/mthapower")

Shiny app

• Run in Shinyapps.io: mtDNA_power_calc
• Run locally from Gist:

# install.packages("shiny")
shiny::runGist('5895082')

Examples

Sample size estimation

• Determine the minimum number of cases (Ncmin), required to detect: either a change from p0 (haplogroup frequency in controls) to p1 (haplogroup frequency in cases), or a given OR, with a predefined confidence interval, in a study with Nh haplogroups.

library(mthapower)
library(dplyr)
mydata <- mthacases(p0=0.445, Nh=11,
                    OR.cas.ctrl=c(2), power=80,
                    sig.level=0.05) # Baudouin study
mydata <- mthacases(p0=0.445, Nh=11,
                    OR.cas.ctrl=c(1.25,1.5,1.75,2,2.25,2.5,2.75,3),
                    power=80, sig.level=0.05)
mydata <- mydata[c(2,6)]
mydata %>%
  knitr::kable()

plot(mydata)

Power estimation

• For a given study size, determine the minimum effect size that can be detected with the desired power and significance level, in a study with Nh haplogroups.

# Example 2a:
# library(mthapower)
pow <- mthapower(n.cases=203, p0=0.443, Nh=13, OR.cas.ctrl=2.33, sig.level=0.05)
pow %>%
  knitr::kable()

# Example 2b:
# Create data frames
pow.H150 <- mthapower(n.cases=seq(50,1000,by=50), p0=0.433, Nh=11,
                      OR.cas.ctrl=1.5, sig.level=0.05)
pow.H175 <- mthapower(n.cases=seq(50,1000,by=50), p0=0.433, Nh=11,
                      OR.cas.ctrl=1.75, sig.level=0.05)
pow.H200 <- mthapower(n.cases=seq(50,1000,by=50), p0=0.433, Nh=11,
                      OR.cas.ctrl=2, sig.level=0.05)
pow.H250 <- mthapower(n.cases=seq(50,1000,by=50), p0=0.433, Nh=11,
                      OR.cas.ctrl=2.5, sig.level=0.05)

# Bind the three data frames:
bindata <- rbind(pow.H150,pow.H175,pow.H200,pow.H250)
# Adds column OR to binded data frame:
bindata$OR <- rep(factor(c(1.50,1.75,2,2.5)),
              times = c(nrow(pow.H150),
                        nrow(pow.H175),
                        nrow(pow.H200),
                        nrow(pow.H250)))
# Create plot:
# install.packages("car")
library(car)
scatterplot(power~ncases | OR, regLine=FALSE,
            smooth=FALSE,
            boxplots=FALSE,  by.groups=TRUE,
            data=bindata)