Identifying Parameters for Defining "Essentially Derived Varieties" of Maize Inbred Lines Using High-Throughput Genome-Wide SNP Markers

文献类型： 外文期刊

第一作者： Yan, Yuanyuan

作者： Yan, Yuanyuan;Sun, Shanqiu;Xing, Ruixia;Jiang, Haiyang;Cheng, Beijiu;Yan, Yuanyuan

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关键词： high-throughput genotyping; essentially derived variety; residual heterozygosity; genetic drift; SNP chip array

期刊名称：PLANTS-BASEL （ 影响因子：4.658； 五年影响因子：4.827 ）

ISSN：

年卷期： 2022 年 11 卷 15 期

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收录情况： SCI

摘要： Well-developed maize reference genomes and genotyping technology along with fast decreasing detection costs have enabled the chance of shifting essentially derived varieties (EDV) identification to high-throughput SNP genotyping technology. However, attempts of using high-throughput technologies such as SNP array on EDV identification and the essential baseline parameters such as genetic homozygosity and/or stability in EDV practices have not been characterized. Here, we selected 28 accessions of 21 classical maize inbreds, which definitely form a pedigree network from initial founders to derivatives that had made huge contribution to corn production, to demonstrate these fundamental analyses. Our data showed that average residual heterozygosity (RH) rate of these 28 accessions across genome was about 1.03%. However, the RH rate of some accessions was higher than 3%. In addition, some inbreds were found to have an overall RH rate lower than 2% but over 8% level at certain chromosomes. Genetic drift (GD) between two accessions from different years or breeding programs varied from 0.13% to 13.16%. Accessions with low GD level showed cluster distribution pattern and compared with RH distributions indicated that RH was not the only resource of GD. Both RH and GD data suggested that genetic purity analysis is an essential procedure before determining EDV. Eleven derivative lines were characterized with regard to their genome compositions and were inferred as their breeding histories. The backcross, bi-parental recycling, and mutation breeding records could be identified. The data provide insights of underlining fundamental parameters for defining EDV threshold and the results demonstrate the EDV identification process.

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