Research output: Contribution to journal › Article › peer-review
Gene-based association tests using GWAS summary statistics. / Svishcheva, Gulnara R.; Belonogova, Nadezhda M.; Zorkoltseva, Irina V. et al.
In: Bioinformatics (Oxford, England), Vol. 35, No. 19, 01.10.2019, p. 3701-3708.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Gene-based association tests using GWAS summary statistics
AU - Svishcheva, Gulnara R.
AU - Belonogova, Nadezhda M.
AU - Zorkoltseva, Irina V.
AU - Kirichenko, Anatoly V.
AU - Axenovich, Tatiana I.
N1 - Publisher Copyright: © 2019 The Author(s). Published by Oxford University Press. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - RARE VARIANTS
KW - P-VALUES
KW - MULTIPLE SNPS
KW - METAANALYSIS
KW - POWERFUL
KW - ADJUSTMENT
KW - TRAITS
UR - http://www.scopus.com/inward/record.url?scp=85072716470&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btz172
DO - 10.1093/bioinformatics/btz172
M3 - Article
C2 - 30860568
AN - SCOPUS:85072716470
VL - 35
SP - 3701
EP - 3708
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 19
ER -
ID: 21699970