Alencar Xavier1, Shizhong Xu2, William M Muir3, Katy Martin Rainey1. 1. Department of Agronomy and. 2. Department of Plant Science, University of California, Riverside, CA 92521, USA. 3. Department of Animal Science, Purdue University, West Lafayette, IN 47907 and.
Abstract
MOTIVATION: Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm. RESULTS: Here we introduce NAM, an R package that allows user to take into account prior information regarding population stratification to relax the linkage phase assumption of current methods. It allows markers to be treated as a random effect to increase the resolution, and uses a sliding-window strategy to increase power and avoid double fitting markers into the model. AVAILABILITY AND IMPLEMENTATION: NAM is an R package available in the CRAN repository. It can be installed in R by typing install.packages ('NAM'). CONTACT: krainey@purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary date are available at Bioinformatics online.
MOTIVATION: Mixed linear models provide important techniques for performing genome-wide association studies. However, current models have pitfalls associated with their strong assumptions. Here, we propose a new implementation designed to overcome some of these pitfalls using an empirical Bayes algorithm. RESULTS: Here we introduce NAM, an R package that allows user to take into account prior information regarding population stratification to relax the linkage phase assumption of current methods. It allows markers to be treated as a random effect to increase the resolution, and uses a sliding-window strategy to increase power and avoid double fitting markers into the model. AVAILABILITY AND IMPLEMENTATION:NAM is an R package available in the CRAN repository. It can be installed in R by typing install.packages ('NAM'). CONTACT: krainey@purdue.edu. SUPPLEMENTARY INFORMATION: Supplementary date are available at Bioinformatics online.
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