The qgg package was developed based on the hypothesis that certain regions on the genome, so-called genomic features, may be enriched for causal variants affecting the trait. Several genomic feature classes can be formed based on previous studies and different sources of information such as genes, chromosomes or biological pathways.
qgg provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for:
qgg handles large-scale data by taking advantage of:
The qgg package provides a range of genomic feature modeling approaches, including genomic feature best linear unbiased prediction (GFBLUP) models, implemented using likelihood or Bayesian methods. Multiple features and multiple traits can be included in these models and different genetic models (e.g. additive, dominance, gene by gene and gene by environment interactions) can be used. Further extensions include a weighted GFBLUP model using differential weighting of the individual genetic marker relationships. Marker set tests, which are computationally very fast, can be performed. These marker set tests allow the rapid analyses of different layers of genomic feature classes to discover genomic features potentially enriched for causal variants. Marker set tests can thus facilitate more accurate prediction models.
You can install qgg from CRAN with:
install.packages("qgg")
The most recent version of qgg
can be obtained from github:
library(devtools)
devtools::install_github("psoerensen/qgg")
Below is a set of tutorials used for the qgg package:
This tutorial provides a brief introduction to R package qgg using small simulated data examples. Practicals_brief_introduction
This tutorial provides an introduction to R package qgg using 1000G data. Practicals_1000G_tutorials
This tutorial provide a simple introduction to polygenic risk scoring (PRS) of complex traits and diseases using simulated data. The practical will be a mix of theoretical and practical exercises in R that are used for illustrating/applying the theory presented in the corresponding lecture notes on polygenic risk scoring. Practicals_human_example
In this tutorial we will be analysing quantitative traits observed in a mice population. The mouse data consist of phenotypes for traits related to growth and obesity (e.g. body weight, glucose levels in blood), pedigree information, and genetic marker data. Practicals_mouse_example
Below is a set of notes for the quantitative genetic theory, statistical models and methods implemented in the qgg package:
Estimation of Genetic Predisposition
Estimation of Genetic Parameters
Best Linear Unbiased Prediction Models
REstricted Maximum Likelihood Methods
Bayesian Linear Regression Models
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