FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm

• DOI: 10.3389/fgene.2021.721600
• Publication Date: 2021-11-18T09:58:44Z
• Authors: Wenwu Xu; Xiaodong Liu; Mingfu Liao; Shijun Xiao; Min Zheng; Tianxiong Yao; Zuoquan Chen; Lusheng Huang; Zhiyan Zhang
• Keywords: Genomic Selection

Genomic selection is an approach to select elite breeding stock based on the use of dense genetic markers and that has led development various models derive a predictive equation. However, current genomic software faces several issues such as low prediction accuracy, computational efficiency, or inability handle large-scale sample data. We report model named FMixFN with four zero-mean normal distributions prior optimize ability computing efficiency. The variance in our precisely determined F2 population, estimated values (GEBV) can be obtained accurately quickly combination iterative conditional expectation algorithm. demonstrated improves efficiency compared other methods, GBLUP, SSgblup, MIX, BayesR, BayesA, BayesB. Most importantly, may data, thus should able meet needs large companies combined schedules. Our study developed Bayes called FMixFN, which combines stable high big data-oriented potential future. method freely accessed at https://zenodo.org/record/5560913 (DOI: 10.5281/zenodo.5560913).