Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; Jiangsu Key Laboratory for Horticultural Crop Genetic Improvement, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China.
Plant Commun. 2022 May 9;3(3):100319. doi: 10.1016/j.xplc.2022.100319. Epub 2022 Mar 25.
Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, G for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate G to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of G in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html, has been provided in the supplemental materials for the users' convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F population.
理论和应用研究表明，在 F 群体中通过 bulked segregant analysis (BSA) 检测数量性状的极度超显性和小效应基因具有一定的难度。为了解决这个问题，我们提出了一种通过 BSA 和全基因组测序相结合来定位数量性状的各种类型数量性状基因座 (QTL) 的综合策略。在该策略中，两个极端群体中标记等位基因的reads 计数的数量被用来预测标记基因型的 reads 计数的数量。这些观察到的和预测到的数量被用来构建一个新的统计量 G 来检测数量性状基因 (QTGs)，该方法被命名为 dQTG-seq1。该方法明显优于现有的 BSA 方法。如果目标是鉴定极度超显性和小效应基因，则从每个极端表型 F 植物中保留一个 DNA/RNA 样本进行测序，并用观察到的标记等位基因和基因型的数量来计算 G 以检测 QTGs；这种方法被命名为 dQTG-seq2。在模拟和真实水稻数据集分析中，dQTG-seq2 可以比 BSA 和 QTL 作图方法鉴定出更多的极度超显性和小效应基因。dQTG-seq2 可能会扩展到其他异质作图群体。本研究中 G 的显著性阈值是通过置换实验确定的。此外，为了方便用户，在补充材料中提供了一份有关 R 软件 dQTG.seq 的手册，该手册可在 https://cran.r-project.org/web/packages/dQTG.seq/index.html 上获得。本研究为在 F 群体中鉴定所有类型的数量性状 QTL 提供了一种新的策略。
