Gulnara R Svishcheva1,2, Nadezhda M Belonogova1, Irina V Zorkoltseva1, Anatoly V Kirichenko1, Tatiana I Axenovich1,3,4. 1. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia. 2. Vavilov Institute of General Genetics, the Russian Academy of Sciences, Moscow, Russia. 3. Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia. 4. Department of Biotechnology, L.K. Ernst Federal Center for Animal Husbandry, Dubrovitsy, Russia.
Abstract
MOTIVATION: A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. RESULTS: We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. AVAILABILITY AND IMPLEMENTATION: The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: A huge number of genome-wide association studies (GWAS) summary statistics freely available in databases provide a new material for gene-based association analysis aimed at identifying rare genetic variants. Only a few of the many popular gene-based methods developed for individual genotype and phenotype data are adapted for the practical use of the GWAS summary statistics as input. RESULTS: We analytically prove and numerically illustrate that all popular powerful methods developed for gene-based association analysis of individual phenotype and genotype data can be modified to utilize GWAS summary statistics. We have modified and implemented all of the popular methods, including burden and kernel machine-based tests, multiple and functional linear regression, principal components analysis and others, in the R package sumFREGAT. Using real summary statistics for coronary artery disease, we show that the new package is able to detect genes not found by the existing packages. AVAILABILITY AND IMPLEMENTATION: The R package sumFREGAT is freely and publicly available at: https://CRAN.R-project.org/package=sumFREGAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Nadezhda M Belonogova; Gulnara R Svishcheva; Anatoly V Kirichenko; Irina V Zorkoltseva; Yakov A Tsepilov; Tatiana I Axenovich Journal: PLoS Comput Biol Date: 2022-06-02 Impact factor: 4.779