LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

Takiy-Eddine Berrandou; David Balding; Doug Speed

doi:10.1016/j.ajhg.2022.11.010

LDAK-GBAT: Fast and powerful gene-based association testing using summary statistics

Am J Hum Genet. 2023 Jan 5;110(1):23-29. doi: 10.1016/j.ajhg.2022.11.010. Epub 2022 Dec 7.

Authors

Takiy-Eddine Berrandou¹, David Balding², Doug Speed³

Affiliations

¹ Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark. Electronic address: takiy.berrandou@qgg.au.dk.
² Melbourne Integrative Genomics, Melbourne University, Melbourne, VIC, Australia.
³ Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark. Electronic address: doug@qgg.au.dk.

Abstract

We present LDAK-GBAT, a tool for gene-based association testing using summary statistics from genome-wide association studies that is computationally efficient, produces well-calibrated p values, and is significantly more powerful than existing tools. LDAK-GBAT takes approximately 30 min to analyze imputed data (2.9M common, genic SNPs), requiring less than 10 Gb memory. It shows good control of type 1 error given an appropriate reference panel. Across 109 phenotypes (82 from the UK Biobank, 18 from the Million Veteran Program, and nine from the Psychiatric Genetics Consortium), LDAK-GBAT finds on average 19% (SE: 1%) more significant genes than the existing tool sumFREGAT-ACAT, with even greater gains in comparison with MAGMA, GCTA-fastBAT, sumFREGAT-SKAT-O, and sumFREGAT-PCA.

Keywords: UK Biobank; complex traits; gene-based association testing; genome-wide association study; statistical genetics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genetic Testing*
Genome-Wide Association Study*
Phenotype
Polymorphism, Single Nucleotide / genetics