Data from: 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 addition, QTL frequency distributions can be used to assess the precision of QTL position estimates and the robustness of the detected QTL. In summary, cross-validation can be a valuable tool to assess the quality of QTL parameters in association mapping.关联作图（Association mapping）现已成为一类广泛应用的基因组学研究方法，可用于鉴定数量性状位点（quantitative trait loci, QTL）并解析复杂性状的遗传架构。然而，当前仍缺乏用于评估所获QTL分析结果质量的有效手段。为此，本研究基于大型甜菜数据集，评估了交叉验证（cross-validation）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占比，取决于作图群体的规模。总体而言，针对常规应用的群体规模，采用五折交叉验证（即选取20%的品系作为独立验证集）是较为合适的方案。针对QTL所解释的基因型方差占比的预测能力，平均被高估了38%，这表明所估算的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，该数值更贴合复杂性状相关参数的实际估算结果。此外，QTL频率分布可用于评估QTL位置估算的精准度，以及所检测到的QTL的稳健性。综上，交叉验证可作为评估关联作图中QTL参数质量的有效工具。