Philipp Norman, Weise Stephan, Oppermann Markus, Börner Andreas, Graner Andreas, Keilwagen Jens, Kilian Benjamin, Zhao Yusheng, Reif Jochen C, Schulthess Albert W
Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
Department of Genebank, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany.
Front Plant Sci. 2018 May 8;9:609. doi: 10.3389/fpls.2018.00609. eCollection 2018.
Genebanks are a rich source of genetic variation. Most of this variation is absent in breeding programs but may be useful for further crop plant improvement. However, the lack of phenotypic information forms a major obstacle for the educated choice of genebank accessions for research and breeding. A promising approach to fill this information gap is to exploit historical information gathered routinely during seed regeneration cycles. Still, this data is characterized by a high non-orthogonality hampering their analysis. By examining historical data records for flowering time, plant height, and thousand grain weight collected during 70 years of regeneration of 6,207 winter wheat ( L.) accessions at the German Federal Genebank, we aimed to elaborate a strategy to analyze and validate non-orthogonal historical data in order to charge genebank information platforms with high quality ready-to-use phenotypic information. First, a three-step quality control assessment considering the plausibility of trait values and a standard as well as a weather parameter index based outlier detection was implemented, resulting in heritability estimates above 0.90 for all three traits. Then, the data was analyzed by estimating best linear unbiased estimations (BLUEs) applying a linear mixed-model approach. An resampling study mimicking different missing data patterns revealed that accessions should be regenerated in a random fashion and not blocked by origin or acquisition date in order to minimize estimation biases in historical data sets. Validation data was obtained from multi-environmental orthogonal field trials considering a random subsample of 3,083 accessions. Correlations above 0.84 between BLUEs estimated for historical data and validation trials outperformed previous approaches and confirmed the robustness of our strategy as well as the high quality of the historical data. The results indicate that the IPK winter wheat collection reveals an extraordinary high phenotypic diversity compared to other collections. The quality checked ready-to-use phenotypic information resulting from this study is the first brick to extend traditional, conservation driven genebanks into bio-digital resource centers.
基因库是丰富的遗传变异来源。育种计划中大多不存在这种变异，但它可能有助于进一步改良作物。然而，缺乏表型信息成为明智选择用于研究和育种的基因库种质的主要障碍。填补这一信息空白的一个有前景的方法是利用种子更新周期中定期收集的历史信息。尽管如此，这些数据具有高度非正交性，妨碍了对它们的分析。通过检查德国联邦基因库对6207份冬小麦种质进行70年更新期间收集的开花时间、株高和千粒重的历史数据记录，我们旨在制定一种分析和验证非正交历史数据的策略，以便为基因库信息平台提供高质量的现成表型信息。首先，实施了一个三步质量控制评估，考虑性状值的合理性以及基于标准和天气参数指数的异常值检测，所有三个性状的遗传力估计值均高于0.90。然后，采用线性混合模型方法估计最佳线性无偏估计值（BLUEs）对数据进行分析。一项模拟不同缺失数据模式的重采样研究表明，种质应以随机方式更新，而不应按来源或获取日期进行分组，以尽量减少历史数据集中的估计偏差。验证数据来自多环境正交田间试验，试验对象为3083份种质的随机子样本。历史数据估计的BLUEs与验证试验之间的相关性高于0.84，优于以前的方法，证实了我们策略的稳健性以及历史数据的高质量。结果表明，与其他种质库相比，IPK冬小麦种质库具有极高的表型多样性。本研究产生的经过质量检查的现成表型信息是将传统的、以保护为导向的基因库扩展为生物数字资源中心的第一块基石。
