Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice.

CAPE

Introduction

An R package for the Combined Analysis of Epistasis and Pleiotropy

The CAPE R package implements a method, originally described in Carter et al. (2012), that infers directed interaction networks between genetic variants for predicting the influence of genetic perturbations on phenotypes. This method takes advantage of complementary information in partially pleiotropic genetic variants to resolve directional influences between variants that interact epistatically. CAPE can be applied to a variety of genetic variants, such as single nucleotide polymorphisms (SNPs), copy number variations (CNVs) or structural variations (SVs).

For detailed documentation about how to format data, load data, and analyze data, please see the CAPE vignette.

New Features!

• new run_cape() function runs the entire cape pipeline with one command
• read in data in multiple formats (R/qtl, R/qtl2, and PLINK)
• performs kinship correction using linear mixed models as described in Kang et al. (2008)
• Handles multi-parent populations
• R6 reformatting improves speed and handling of large data

Installation

CAPE requires R 3.6+ to run.

# Install the released version from CRAN
install.packages("cape")

# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("TheJacksonLaboratory/cape")

Demos

CAPE provides demo scripts, which you can run to verify that the installation was successful.

demo(package = "cape")

demo(demo_plink)

demo(demo_qtl)

demo(demo_qtl2)

To-Do:

• enable CAPE run in parallel

License

CAPE is licensed under GPL-3

References

Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. CAPE: an R package for combined analysis of pleiotropy and epistasis. PLoS Comput. Biol. 9, e1003270 (2013).

Kang, H. M. et al. Efficient control of population structure in model organism association mapping. Genetics 178, 1709–1723 (2008).

Related Publications

Carter, G. W. Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis. G3 (Bethesda) 3, 807–814 (2013).

Carter, G. W., Hays, M., Sherman, A. & Galitski, T. Use of pleiotropy to model genetic interactions in a population. PLoS Genet. 8, e1003010 (2012).

Tyler, A. L., McGarr, T. C., Beyer, B. J., Frankel, W. N. & Carter, G. W. A genetic interaction network model of a complex neurological disease. Genes, Brain and Behavior 13, 831–840 (2014).

Tyler, A. L. et al. Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice. Genetics 206, 621–639 (2017).

Tyler, A. L. et al. Epistatic networks jointly influence phenotypes related to metabolic disease and gene expression in diversity outbred mice. Genetics 206, 621–639 (2017).

Tyler, A. L., Donahue, L. R., Churchill, G. A. & Carter, G. W. Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross. PLoS Genet. 12, e1005805–22 (2016).