ArticlePLoS computational biology2013
CAPE: an R package for combined analysis of pleiotropy and epistasis.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 33 citations in OpenAlex.
- Pretreatment social network characteristics relate to increased risk of dropout and unfavorable outcomes among women in a residential treatment setting for substance use.Journal of substance abuse treatment · 2020Trial
- Epistasis and pleiotropy-induced variation for plant breeding.Plant biotechnology journal · 2024Review
- Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla.BMC genomics · 2024Article
- High-Density Mapping of Quantitative Trait Loci Controlling Agronomically Important Traits in Quinoa (Frontiers in plant science · 2022Article
- Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations.G3 (Bethesda, Md.) · 2021Article
- Genetic Interactions Affect Lung Function in Patients with Systemic Sclerosis.G3 (Bethesda, Md.) · 2020Article
- Testing PleiotropyG3 (Bethesda, Md.) · 2019Article
- Effect of drought stress on the genetic architecture of photosynthate allocation and remobilization in pods of common bean (Phaseolus vulgaris L.), a key species for food security.BMC plant biology · 2019Article
- MatrixEpistasis: ultrafast, exhaustive epistasis scan for quantitative traits with covariate adjustment.Bioinformatics (Oxford, England) · 2018Article
- Genome-Wide Association Mapping Reveals That Specific and Pleiotropic Regulatory Mechanisms Fine-Tune Central Metabolism and Growth in Arabidopsis.The Plant cell · 2017Article
- Towards Precision Medicine for Hypertension: A Review of Genomic, Epigenomic, and Microbiomic Effects on Blood Pressure in Experimental Rat Models and Humans.Physiological reviews · 2017Review
- Epistasis: Searching for Interacting Genetic Variants Using Crosses.G3 (Bethesda, Md.) · 2017Article
- Review
- Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice.Genetics · 2017Article
- Systems Genetic Analysis in GeneNetwork.org.Current protocols in neuroscience · 2017Article
- Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs.PloS one · 2017Article
- Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross.PLoS genetics · 2016Article
- Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery.Current genetic medicine reports · 2015Article
- A genetic interaction network model of a complex neurological disease.Genes, brain, and behavior · 2014Article
- Unraveling genetic modifiers in the gria4 mouse model of absence epilepsy.PLoS genetics · 2014Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Contemporary genetic studies are revealing the genetic complexity of many traits in humans and model organisms. Two hallmarks of this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene affects multiple phenotypes. Understanding the genetic architecture of complex traits requires addressing these phenomena, but interpreting the biological significance of epistasis and pleiotropy is often difficult. While epistasis reveals dependencies between genetic variants, it is often unclear how the activity of one variant is specifically modifying the other. Epistasis found in one phenotypic context may disappear in another context, rendering the genetic interaction ambiguous. Pleiotropy can suggest either redundant phenotype measures or gene variants that affect multiple biological processes. Here we present an R package, R/cape, which addresses these interpretation ambiguities by implementing a novel method to generate predictive and interpretable genetic networks that influence quantitative phenotypes. R/cape integrates information from multiple related phenotypes to constrain models of epistasis, thereby enhancing the detection of interactions that simultaneously describe all phenotypes. The networks inferred by R/cape are readily interpretable in terms of directed influences that indicate suppressive and enhancing effects of individual genetic variants on other variants, which in turn account for the variance in quantitative traits. We demonstrate the utility of R/cape by analyzing a mouse backcross, thereby discovering novel epistatic interactions influencing phenotypes related to obesity and diabetes. R/cape is an easy-to-use, platform-independent R package and can be applied to data from both genetic screens and a variety of segregating populations including backcrosses, intercrosses, and natural populations. The package is freely available under the GPL-3 license at http://cran.r-project.org/web/packages/cape.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.