GAUSS: A comprehensive R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation and TWAS analysis of cosmopolitan cohorts

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 access to 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 . To further assist users, we provided illustrative use-case scenarios that are conveniently found at . Contact leed13{at}miamioh.edu Supplementary information Supplementary data are available at Bioinformatics online. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Miami University start-up fund (to D.L.) and Shelter Diabetes Research Award (to D.L.) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All the GWAS summary statistics used in the manuscript were downloaded from I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at