Cross-validation in association mapping and its relevance for the estimation of QTL parameters of complex traits

Association mapping has become a widely applied genomic approach to identify quantitative trait loci (QTL) and dissect the genetic architecture of complex traits. However, approaches to assess the quality of the obtained QTL results are lacking. We therefore evaluated the potential of cross-validation in association mapping based on a large sugar beet data set. Our results show that the proportion of the population that should be used as estimation and validation sets, respectively, depends on the size of the mapping population. Generally, a fivefold cross-validation, that is, 20% of the lines as independent validation set, appears appropriate for commonly used population sizes. The predictive power for the proportion of genotypic variance explained by QTL was overestimated by on average 38% indicating a strong bias in the estimated QTL effects. The cross-validated predictive power ranged between 4 and 50%, which are more realistic estimates of this parameter for complex traits. In addi...关联作图（Association mapping）已成为一种广泛应用的基因组学研究手段，用于鉴定数量性状基因座（QTL）并解析复杂性状的遗传架构。然而，目前尚缺乏用于评估所得QTL结果质量的相关方法。因此，我们基于大型甜菜数据集，评估了交叉验证在关联作图中的应用潜力。研究结果表明，分别用作估计集与验证集的群体比例，取决于作图群体的规模。通常而言，对于常用规模的作图群体，五折交叉验证（即选取20%的品系作为独立验证集）是较为合适的选择。由QTL解释的基因型方差占比的预测能力被平均高估了38%，这表明估计的QTL效应存在显著偏差。经交叉验证的预测能力介于4%至50%之间，这一结果对于复杂性状而言是该参数更为贴合实际的估计值。此外……