%\VignetteIndexEntry{INPower Vignette}
%\VignettePackage{INPower}
%\VigetteDepends{INPower}

\documentclass[a4paper]{article}


\begin{document}

\title{INPower Package}
\maketitle

\section*{Introduction}
An R package to estimate the number of susceptibility loci and the distribution
 of their effect sizes for a trait on the basis of discoveries from existing 
 genome-wide association studies (GWASs). 

<<start>>=
library(INPower)
@

\section*{Height example}

Get the path to the data which contains a known set of susceptibility SNPs 
for height. 
<<data file>>=
datafile <- system.file("sampleData", "data.rda", package="INPower")
@

Load and print the data frame.
<<load data>>=
load(datafile)
data
@

For each known susceptibility SNP, we will need the MAF, effect size and power 
 of detection.
<<SNPs>>=
MAFs  <- data[, "MAF"]
betas <- data[, "Beta"]
pow   <- data[, "Power"] 
@

Suppose that a one-stage design with a genome-wide significant level of 1e-7 
 is considered for a future study. It is known that the heritability of height
 is ~0.8. It is of interest to predict the expected number of discoveries with
 sample sizes from 25,000 to 125,000 with an increment of 25,000.
 In addition, for the given sample sizes,
 it is also of interest to find power to detect at least k loci, with k ranging 
 from 25 to 125 with an increment of 25.  
<<call1>>=
INPower(MAFs, betas, pow, span=0.5, binary.outcome=FALSE, 
        sample.size=seq(25000,125000,by=25000), 
        signif.lvl=10^(-7), tgv=0.8, k=seq(25,125,by=25))
@

The function call below shows the same results as the above, 
 but without the tgv (total genetic variance) argument. As a result, the genetic
 variance explained is expressed as a percentage of the total variance of the 
 outcome, not of the total genetic variance.
<<call2>>=
INPower(MAFs, betas, pow, span=0.5, binary.outcome=FALSE, 
        sample.size=seq(25000,125000,by=25000), 
        signif.lvl=10^(-7), k=seq(25,125,by=25))
@

Now a two-stage study is considered with all the other conditions remaining the 
 same (including the estimate of total heritability). 
 In addition, the selection criterion for SNPs
 taken toward the second stage is 5e-5 and 30% of the given sample size is 
 assigned to the first stage (and hence 70% to the second stage). 
<<call3>>=
INPower(MAFs, betas, pow, span=0.5, binary.outcome=FALSE,
        sample.size=seq(25000,125000,by=25000), 
        signif.lvl=10^(-7), multi.stage.option=list(al=5*10^(-5), pi=0.3),
        tgv=0.8 , k=seq(25,125,by=25))
@

\section*{Session Information}
<<sessionInfo>>=
sessionInfo()
@ 

\end{document}


