Code and documentation for a updated release to the Omic Kriging R package. Focused primarily on improving efficiency, improving ease of use, and reducing dependencies.
This package provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y. More updated versions can be found here
Authors and Contributors:
- Hae Kyung Im
- Heather E. Wheeler
- Keston Aquino Michaels
- Vassily Trubetskoy
Please cite: H. E. Wheeler, K. Aquino-Michaels, E. R. Gamazon, V. V. Trubetskoy, M. E. Dolan, R. S. Huang, N. J. Cox, and H. K. Im, “Poly-Omic Prediction of Complex Traits: OmicKriging.,” Genetic epidemiology, vol. 38, no. 5, pp. 402–415, May 2014. http://onlinelibrary.wiley.com/doi/10.1002/gepi.21808/abstract