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Transcriptome-Based Prediction of Complex Traits in Maize.
The Plant Cell ( IF 10.0 ) Pub Date : 2019-10-22 , DOI: 10.1105/tpc.19.00332 Christina B Azodi 1, 2 , Jeremy Pardo 1, 3 , Robert VanBuren 3, 4 , Gustavo de Los Campos 5 , Shin-Han Shiu 2, 6, 7
The Plant Cell ( IF 10.0 ) Pub Date : 2019-10-22 , DOI: 10.1105/tpc.19.00332 Christina B Azodi 1, 2 , Jeremy Pardo 1, 3 , Robert VanBuren 3, 4 , Gustavo de Los Campos 5 , Shin-Han Shiu 2, 6, 7
Affiliation
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The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.
中文翻译：
基于转录组的玉米复杂性状预测。
从全基因组序列信息预测性状的能力（即基因组预测）提高了我们对复杂性状遗传基础和改良育种实践的理解。转录组数据也可能对基因组预测有用。然而，目前尚不清楚转录水平如何预测性状，特别是在不同发育阶段对性状进行评分时。使用玉米（Zea mays）遗传标记和幼苗的转录水平来预测成熟植物性状，我们发现转录和遗传标记模型具有相似的性能。当这些模型中权重最大（即最重要）的转录本和遗传标记用于一个联合模型时，性能会提高。此外，对预测重要的遗传标记并不接近或被识别为重要转录本的调控变异。这些发现表明转录水平可用于预测性状，并且它们的预测能力不仅仅归因于转录基因组区域的遗传变异。最后，遗传标记模型仅识别出 14 个基准开花时间基因中的 1 个，而转录本模型识别出 5 个。这些数据强调，除了可用于基因组预测之外，转录组数据还可以提供性状和变异之间的联系，而这种联系是难以轻易获得的。在序列级别捕获。
更新日期：2020-01-11
中文翻译：
基于转录组的玉米复杂性状预测。
从全基因组序列信息预测性状的能力（即基因组预测）提高了我们对复杂性状遗传基础和改良育种实践的理解。转录组数据也可能对基因组预测有用。然而，目前尚不清楚转录水平如何预测性状，特别是在不同发育阶段对性状进行评分时。使用玉米（Zea mays）遗传标记和幼苗的转录水平来预测成熟植物性状，我们发现转录和遗传标记模型具有相似的性能。当这些模型中权重最大（即最重要）的转录本和遗传标记用于一个联合模型时，性能会提高。此外，对预测重要的遗传标记并不接近或被识别为重要转录本的调控变异。这些发现表明转录水平可用于预测性状，并且它们的预测能力不仅仅归因于转录基因组区域的遗传变异。最后，遗传标记模型仅识别出 14 个基准开花时间基因中的 1 个，而转录本模型识别出 5 个。这些数据强调，除了可用于基因组预测之外，转录组数据还可以提供性状和变异之间的联系，而这种联系是难以轻易获得的。在序列级别捕获。
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