Data from: Optimum design of family structure and allocation of resources in association mapping with lines from multiple crosses

Family mapping is based on multiple segregating families and is becoming increasingly popular due to advantages over population mapping. Though much progress has been made recently, the optimum design and allocation of resources for family mapping remains unclear. Here, we addressed these issues using a simulation study, resample model averaging and cross-validation approaches. Our results show that in family mapping, the predictive power and the accuracy of QTL detection depend greatly on the population size and phenotyping intensity. With small population sizes or few test environments, QTL results become unreliable and are hampered by a large bias in the estimation of the proportion of genotypic variance explained by the detected QTL. In addition, we observed that even though quality results can be achieved with low marker densities, no plateau is reached with our full marker complement. This suggests that higher quality results could be achieved with greater mar ker densities or sequence data, which will be available in the near future for many species.

家系作图（Family mapping）基于多个分离家系（segregating families）开展，相较于群体作图（population mapping）具备诸多优势，因此正愈发受到研究者的青睐。尽管近期该领域已取得诸多进展，但家系作图的最优实验设计与资源分配方案仍未明晰。本研究借助模拟实验、重采样模型平均（resample model averaging）与交叉验证（cross-validation）方法，针对上述问题展开了系统探究。研究结果显示，在家系作图中，数量性状基因座（Quantitative Trait Locus, QTL）检测的预测能力与准确率，在很大程度上取决于群体规模与表型鉴定强度。当群体规模较小或测试环境数量有限时，QTL检测结果的可靠性会显著降低，且检测到的QTL所解释的遗传方差比例的估计值会存在较大偏差，进而影响结果的可信度。此外，本研究观察到，即便采用较低的分子标记密度即可获得优质分析结果，但使用完整标记组时并未出现预测性能趋于饱和的态势。这表明，通过提升标记密度或采用测序数据，可进一步获得质量更优的分析结果，而这类资源在未来将可用于多数物种的相关研究。