CAPE: An R Package for Combined Analysis of Pleiotropy and Epistasis

<div><p>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 <a href="http://cran.r-project.org/web/packages/cape" target="_blank">http://cran.r-project.org/web/packages/cape</a>.</p></div

Overview of coefficient reparametrization for two phenotypes.

<p>On the left, main effect and interaction parameters for two variants (<i>var1</i> and <i>var2</i>) are derived from pairwise regressions (). The interaction coefficients are reparametrized as and on the right, which describe variant-to-variant influences that fit both phenotypes via indirect associations. For the source variant is <i>var1</i> and the target variant is <i>var2</i>, with the source and target reversed for . The intercept and possible covariate terms are not shown. Note the main effects () are unchanged in the reparametrization.</p

Overview of R/cape workflow and visualization tools using example data [1].

<p>(A) Phenotypes are first decomposed into orthogonal eigentraits (ETs). Phenotype composition and global variance fraction are displayed for each ET, facilitating the selection of ETs for interaction analysis. In this study, the first two ETs were selected, which contained the correlated signal between all phenotypes and a divergence between phenotypes, respectively. (B) Pair-wise linear regression is next performed on each ET. Symmetric matrices of all marker pair interaction terms are displayed in matrix form, with gray and white bars along the axes to mark chromosome boundaries. The first two ETs for this study are shown. (C) Regression parameters are next reparametrized (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003270#pcbi-1003270-g001" target="_blank">Figure 1</a>) to derive models of directed interactions between markers and from markers to phenotypes. In the adjacency matrix view (left), markers are designated as sources or targets of directed interactions, and marker-to-phenotype influences are in the rightmost columns. Only variants with significant main effect or interaction are shown, and gray dots mark pairs that were not included in the model due to linkage disequilibrium. In the network view (right), arrows are directed from source to target marker positions across all chromosomes. Red arrows indicate suppressive (negative) interactions. Main effects are represented below the variants with green indicating an effect that increases phenotype and gray indicating no significant main effect on phenotype.</p

Restaging Patients with Rectal Cancer Following Neoadjuvant Chemoradiation: A Systematic Review

In the USA, most patients with clinical stage II/III rectal cancer receive neoadjuvant chemoradiation (chemo/XRT) over 5-6 weeks followed by a 6-10-week break before proctectomy. As chemotherapy is delivered at radio-sensitizing doses, there is essentially a 3-month window during which potential systemic disease is untreated. Evidence regarding the utility of restaging patients prior to proctectomy is limited. PubMed, Scopus, Web of Science, and the Cochrane Library were searched for studies evaluating the utility of restaging patients with rectal cancer after completion of long-course chemo/XRT, and reporting associated changes in management. Studies that were non-English, included <50 patients, or examining the diagnostic accuracy of imaging modalities were excluded. Study quality was evaluated using the modified Newcastle Ottawa Scale. Eight studies were identified including a total of 1251 patients restaged between completion of chemo/XRT and proctectomy. All studies were retrospective. Restaging identified new metastatic disease in 72 (6.0%) patients, with 4 studies reporting specific sites: liver (n = 28), lung (n = 8), adrenal (n = 1), bone (n = 1), and multiple sites (n = 7). Overall progression (distant or local) was detected in 88 (7.0%) patients and resulted in a change in management in 77 (87.5%) of these patients. Tumor-related prognostic characteristics were inconsistently reported among studies, precluding meta-analysis. Although restaging between completion of neoadjuvant chemo/XRT and proctectomy detects disease progression in only a small percentage of patients, findings alter the treatment plan in the vast majority of these patients. Multi-institutional collaboration with analysis of well-defined prognostic variables may better identify patients most likely to benefit from restaging

Proteomic analysis of the oil palm fruit mesocarp reveals elevated oxidative phosphorylation activity is critical for increased storage oil production

10.1021/pr400606hJournal of Proteome Research12115096-5109JPRO