Gijoo Lee   Minseok Kim Taegun Kim
Seri Park Wonseok OH Kyungim Kim
Jiwon Park, Yunjung Kim, Ilyong Jung, Seungmin Song
Myungho Kim 2018~2021 Sungmin Park 2019~2021
Sejin Park 2018~2019 Jooheon Choi 2018~2019 Seonyoung Hwang 2017~2018 Changwook Oh 2017~2018
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.
Overview of MARS. Here, we assume that we are testing an association between a locus of m variants and a trait. The leftmost panel shows the input of MARS; m number of summary statistics for the locus and an n×m matrix that contains genotypes of m SNPs for n samples. The next two panels on the bottom show the re-sampling process in which we sample the null statistics K times from an MVN distribution with a variance-covariance matrix of Σ that contains LD of the genotypes X. The rightmost panel shows the process by which we estimate LRTstats for the null panel from which we can compute a p-value for the data.
The full citation to our paper is: Hormozdiari, F., Jung, J., Eskin, E., Jong Wha J. Joo. MARS: leveraging allelic heterogeneity to increase power of association testing. Genome Biol 22, 128 (2021). https://doi.org/10.1186/s13059-021-02353-8