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

Author:

Rohde Palle Duun1ORCID,Fourie Sørensen Izel2,Sørensen Peter2

Affiliation:

1. Genomic Medicine, Department of Health Science and Technology, Aalborg University , 9260 Gistrup, Denmark

2. Center for Quantitative Genetics and Genomics, Aarhus University , 8000 Aarhus, Denmark

Abstract

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/.

Funder

Novo Nordisk Foundation initiative Open Discovery Innovation Network

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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