An R
package for Quantitative Genetic and Genomic analyses
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:
fitting linear mixed models
construction of genomic relationship matrices
estimating genetic parameters (heritability and correlation)
genomic prediction
single marker association analysis
gene set enrichment analysis
qgg handles large-scale data by taking advantage
of:
multithreaded matrix operations implemented in BLAS libraries
(e.g. OpenBLAS, ATLAS or MKL)
fast and memory-efficient batch processing of genotype data stored
in binary files (e.g. PLINK bedfiles)
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.
Install
You can install qgg from CRAN with:
install.packages("qgg")
The most recent version of qgg can be obtained from
github:
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
Notes
Below is a set of notes for the quantitative genetic theory,
statistical models and methods implemented in the qgg package:
Edwards SM, Thomsen B, Madsen P, Sørensen P. 2015. Partitioning of
genomic variance reveals biological pathways associated with udder
health and milk production traits in dairy cattle. Genet Sel
Evol 47:60. doi:10.1186/s12711-015-0132-6
Edwards SM, Sørensen IF, Sarup P, Mackay TFC, Sørensen P. 2016.
Genomic prediction for quantitative traits is improved by mapping
variants to gene ontology categories in Drosophila
melanogaster. Genetics 203:1871–1883. doi:10.1534/genetics.116.187161
Ehsani A, Janss L, Pomp D, Sørensen P. 2015. Decomposing genomic
variance using information from GWA, GWE and eQTL analysis. Anim
Genet 47:165–173. doi:10.1111/age.12396
Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, Lund MS, Sørensen P.
2017. Exploring the genetic architecture and improving genomic
prediction accuracy for mastitis and milk production traits in dairy
cattle by mapping variants to hepatic transcriptomic regions responsive
to intra-mammary infection. Genet Sel Evol 49:1–18. doi:10.1186/s12711-017-0319-0
Fang L, Sahana G, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. 2017.
Integrating sequence-based GWAS and RNA-seq provides novel insights into
the genetic basis of mastitis and milk production in dairy cattle.
Sci Rep 7:45560. doi:10.1038/srep45560
Fang L, Sørensen P, Sahana G, Panitz F, Su G, Zhang S, Yu Y, Li B,
Ma L, Liu G, Lund MS, Thomsen B. 2018. MicroRNA-guided prioritization of
genome-wide association signals reveals the importance of
microRNA-target gene networks for complex traits in cattle. Sci
Rep 8:1–14. doi:10.1038/s41598-018-27729-y
Ørsted M, Rohde PD, Hoffmann AA, Sørensen P, Kristensen TN. 2017.
Environmental variation partitioned into separate heritable components.
Evolution (N Y) 72:136–152. doi:10.1111/evo.13391
Ørsted M, Hoffmann AA, Rohde PD, Sørensen P, Kristensen TN. 2018.
Strong impact of thermal environment on the quantitative genetic basis
of a key stress tolerance trait. Heredity (Edinb). doi:10.1038/s41437-018-0117-7
Rohde PD, Krag K, Loeschcke V, Overgaard J, Sørensen P, Kristensen
TN. 2016. A quantitative genomic approach for analysis of fitness and
stress related traits in a Drosophila melanogaster model
population. Int J Genomics 2016:1–11.
Rohde PD, Demontis D, Cuyabano BCD, The GEMS Group, Børglum AD,
Sørensen P. 2016. Covariance Association Test (CVAT) identify genetic
markers associated with schizophrenia in functionally associated
biological processes. Genetics 203:1901–1913. doi:10.1534/genetics.116.189498
Sarup P, Jensen J, Ostersen T, Henryon M, Sørensen P. 2016.
Increased prediction accuracy using a genomic feature model including
prior information on quantitative trait locus regions in purebred Danish
Duroc pigs. BMC Genet 17:11. doi:10.1186/s12863-015-0322-9
Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D.
2015. Genetic control of environmental variation of two quantitative
traits of Drosophila melanogaster revealed by whole-genome
sequencing. Genetics 201:487–497. doi:10.1534/genetics.115.180273
Sørensen IF, Edwards SM, Rohde PD, Sørensen P. 2017. Multiple trait
covariance association test identifies gene ontology categories
associated with chill coma recovery time in Drosophila
melanogaster. Sci Rep 7:2413. doi:10.1038/s41598-017-02281-3
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