GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, gaussian imputation and TWAS analysis of cosmopolitan cohorts (2024)

Motivation

As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g., fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers.

Results

To address these challenges, we present GAUSS—a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including i) estimating ancestry proportion of study cohorts, ii) calculating ancestry-informed linkage disequilibrium, iii) imputing summary statistics of unobserved variants, iv) conducting transcriptome-wide association studies, and v) correcting for “Winner’s Curse” biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32,953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information.

Availability and implementation

The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text 1 from Supplementary Data.

Supplementary information

Supplementary data are available at Bioinformatics online.