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

Abstract

AbstractSummaryHere, we present an expanded utility of the R package qgg for quantitative genetic and genomic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes five different Bayesian Linear Regression (BLR) models, 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 (GWAS) summary statistics. To showcase some of the novel implementations, we analysed two quantitative trait phenotypes, body mass index and standing height from United Kingdom Biobank (UKB). We compared genomic prediction accuracies from single and multiple trait models, showed accurate estimation of genomic parameters, illustrate how a BLR model can be used to fine map potential causal loci, and finally, provide an extension of gene set enrichment analyses based on the BLR framework. 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 tools and data formats.AvailabilityOur methodology is implemented in the publicly available R software package qgg using fast and memory efficient algorithms in C++ and is available from 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 from our accompanied homepage https://qganalytics.com/.Contactpalledr@hst.aau.dk and pso@qgg.au.dkSupplementary informationSupplementary data are available online.