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. 2023 Nov 1;39(11):btad656.
doi: 10.1093/bioinformatics/btad656.

Expanded utility of the R package, qgg, with applications within genomic medicine

Palle Duun Rohde  1 , Izel Fourie Sørensen  2 , Peter Sørensen  2
Affiliations

Expanded utility of the R package, qgg, with applications within genomic medicine

Palle Duun Rohde et al. Bioinformatics. .

Abstract

Summary: Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study summary data. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software and data formats.

Availability and implementation: The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available at our GitHub page https://psoerensen.github.io/qgg/.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Overview of the simple and streamlined workflow for genetic analysis of complex traits that takes PLINK files as input. The output from each function is designed to match the input format of downstream analyses.
See this image and copyright information in PMC

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