class: center, middle, inverse, title-slide # ShinyGPAS: Interactive genomic prediction accuracy simulator based on deterministic formulas ## NCERA-225 Meeting ### Gota Morota <br /><br /> <a href="http://morotalab.org/" class="uri">http://morotalab.org/</a> ### 2017/10/18 --- # Shiny - [https://shiny.rstudio.com/](https://shiny.rstudio.com/) - A web application framework for **interactive** visualization - Able to generate user friendly web interfaces - Built on a reactive programming model - Entirely extensible - custom inputs and outputs - CSS themes - JavaScript and D3.js - Example - [Collision Detection](https://bl.ocks.org/mbostock/raw/3231298/) --- # Shiny framework <img src="Shinyframework.png" height="300px" width="650px"/> **Template** ```r library(shiny) ui <- fluidPage() server <- function(input, output) {} shinyApp(ui = ui, server = server) ``` --- # Control widgets <img src="widgets.png" width=700 height=480> .center[[RStudio](https://shiny.rstudio.com/tutorial/written-tutorial/lesson3/)] --- # Spring 2017 - ASCI 431 / 831 My first time teaching undergraduate students. <img src="ASCI431.png" width=500 height=480> --- # Deterministic formulas (1) - Daetwyler et al. (2008; 2010) `\begin{align} r &= \sqrt{\frac{N h^2}{N h^2 + M_e} } \end{align}` - Goddard 2009 `\begin{align} r &= \sqrt{1 - \frac{\lambda}{2N\sqrt{\alpha}} \ln\left( \frac{1 + \alpha + 2\sqrt{\alpha}}{1 + \alpha - 2\sqrt{\alpha}}\right) } \end{align}` where `\(\lambda\)` is `\(M_e/(h^2\ln(2N_e))\)` and `\(\alpha\)` is `\(1 + 2(M_e/Nh^2\ln(2N_e))\)` - Goddard et al. 2011 `\begin{align} r &= \sqrt{b \frac{Nbh^2/M_e}{1 + Nbh^2/M_e}} \end{align}` where `\(b = M/(M + M_e)\)` --- # Deterministic formulas (2) - de los Campos et al. (2013) `\begin{align} r &= \sqrt{ [1 - (1 - b)^2] h^2 } \end{align}` - Karaman et al. (2016) `\begin{align} r &= \sqrt{ h^2_M \left[ \frac{N h^2_M}{N h^2_M + M_e} \right] } \end{align}` --- # Deterministic formulas (3) - Wientjes et al. (2016) `\begin{align} r = \sqrt{ \begin{bmatrix} b_{AC} r_{G_{AC}} \sqrt{\frac{h^2_A}{M_{e_{A,C}}} } & b_{BC} r_{G_{BC}} \sqrt{\frac{h^2_B}{M_{e_{B,C}}}} \end{bmatrix} \begin{bmatrix} \frac{h^2_A}{M_{e_{A,C}}} + \frac{1}{N_A} & r_{G_{AB}} \sqrt{\frac{h^2_A h^2_B}{M_{e_{A,C}} M_{e_{B,C}} } } \\ r_{G_{AB}} \sqrt{\frac{h^2_A h^2_B}{M_{e_{A,C}} M_{e_{B,C}} } } & \frac{h^2_B}{M_{e_{B,C}}} + \frac{1}{N_B} \end{bmatrix}^{-1} } \\ \times \sqrt{\begin{bmatrix} b_{AC} r_{G_{AC}} \sqrt{\frac{h^2_A}{M_{e_{A,C}}}} \\ b_{BC} r_{G_{BC}} \sqrt{\frac{h^2_B}{M_{e_{B,C}}}} \end{bmatrix}} \end{align}` Combines two populations A and B to predict prediction accuracy in population C. --- class: inverse, center, middle # Demonstration of ShinyGPAS [https://chikudaisei.shinyapps.io/shinygpas/](https://chikudaisei.shinyapps.io/shinygpas/) --- # Preprint <img src="biorxiv.png" height="420px" width="710px"/> --- class: inverse, left, middle # ShinyGPAS - Shiny Genomic Prediction Accuracy Simulator Can be used for - _interactive_ exploration of potential factors influencing prediction accuracy - simulation of achievable prediction accuracy - prior to genotyping individuals or performing CV - supporting in-class teaching - no knowlege of R, HTML, JavaScript, or CSS is required. R code encapsulated as a web-based Shiny application Available at [https://chikudaisei.shinyapps.io/shinygpas/](https://chikudaisei.shinyapps.io/shinygpas/) and [https://github.com/morota/ShinyGPAS](https://github.com/morota/ShinyGPAS)