MARS: Leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

References

MARS: leveraging allelic heterogeneity to increase power of association testing. Genome Biol 22, 128 (2021). [Links]

Installation

• The C++ library for GNU GSL is required.
• makesigmasemiPDRcppGSL is required.
• marsR works only on *nix (Linux, Unix such as macOS) system. please check .Platform$OS.type function.
• We currently only support R 3.5+.*

install.packages("data.table")
install.packages("Rcpp")
install.packages("RcppGSL")
install.packages("RcppArmadillo")
install.packages("devtools")
devtools::install_github("junghyunJJ/makesigmasemiPDRcppGSL")
devtools::install_github("junghyunJJ/marsR")

Example

library(marsR)
> data(testdata)
> mars(testdata$stat, testdata$geno, threshold = 5e-08)
[2020-04-07 09:08:40] - calculating MARS alt LRT
[2020-04-07 09:08:40] - make null data
[2020-04-07 09:08:40] - calculating MARS null LRT
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s  
Total analysis time: 10.97 secs  
$alt
       LRT       uni
1 15985.79 1.968e-07

$results
  LRT_pvalue univariate_pvalue threshold_pvalue threshold_UNI significance_LRT significance_UNI
1          0        0.00099551       0.00099551         5e-08             TRUE            FALSE