Corteva Agrisciences, 8305 NW 62nd Ave. Johnston IA, and
Purdue University, 915 W State St. West Lafayette IN.
G3 (Bethesda). 2019 Nov 5;9(11):3855-3866. doi: 10.1534/g3.119.400728.
The evaluation of prediction machines is an important step for a successful implementation of genomic-enabled selection in plant breeding. Computation time and predictive ability constitute key metrics to determine the methodology utilized for the consolidation of genomic prediction pipeline. This study introduces two methods designed to couple high prediction accuracy with efficient computational performance: 1) a non-MCMC method to estimate marker effects with a Laplace prior; and 2) an iterative framework that allows solving whole-genome regression within mixed models with replicated observations in a single-stage. The investigation provides insights on predictive ability and marker effect estimates. Various genomic prediction techniques are compared based on cross-validation, assessing predictions across and within family. Properties of quantitative trait loci detection and single-stage method were evaluated on simulated plot-level data from unbalanced data structures. Estimation of marker effects by the new model is compared to a genome-wide association analysis and whole-genome regression methods. The single-stage approach is compared to a GBLUP fitted via restricted maximum likelihood, and a two-stages approaches where genetic values fit a whole-genome regression. The proposed framework provided high computational efficiency, robust prediction across datasets, and accurate estimation of marker effects.
预测机的评估是在植物育种中成功实施基因组选择的重要步骤。计算时间和预测能力是确定用于整合基因组预测管道的方法的关键指标。本研究提出了两种方法，旨在结合高预测准确性和高效的计算性能：1）使用拉普拉斯先验估计标记效应的非 MCMC 方法；2）一种迭代框架，允许在具有重复观测值的混合模型中解决全基因组回归问题。该研究提供了关于预测能力和标记效应估计的见解。基于跨验证，比较了各种基因组预测技术，评估了跨家庭和家庭内的预测。在不平衡数据结构的模拟图级数据上评估了数量性状位点检测和单阶段方法的特性。新模型的标记效应估计与全基因组关联分析和全基因组回归方法进行了比较。将单阶段方法与通过限制最大似然拟合的 GBLUP 进行了比较，还与通过全基因组回归拟合遗传值的两阶段方法进行了比较。所提出的框架提供了高计算效率、跨数据集稳健的预测和准确的标记效应估计。
