Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.
Plant J. 2023 Mar;113(6):1109-1121. doi: 10.1111/tpj.16123. Epub 2023 Feb 10.
Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.
玉米（Zea mays ssp. mays）群体表现出广泛的遗传和表型多样性。随着测序成本的降低，越来越多的项目试图使用全基因组重测序策略来衡量玉米群体之间和内部的遗传差异，鉴定出数以百万计的分离单核苷酸多态性（SNP）和插入/缺失（InDel）。与微阵列和测序基因分型等较旧的基因分型策略不同，重测序原则上应该经常识别和评分常见的遗传变异。然而，在实践中，不同的项目经常使用不同的分析管道，通常使用不同的参考基因组组装，并在研究群体中一致过滤最小等位基因频率。这限制了以新的方式利用和混合来自不同项目的遗传多样性数据来解决新的生物学问题的潜力。在这里，我们使用来自 1276 个先前发表的玉米样本和 239 个新重测序的玉米样本的重测序数据，生成了一个单一的统一标记集，其中包含大约 3.66 亿个分离变体和约 4600 万个在作物野生近缘种、地方品种以及来自不同育种时代的热带和温带系中评分的高置信变体。我们证明，新的变体集可用于使用先前发表的性状数据集来识别已知的开花时间基因，并且有可能跟踪现代玉米全球分布中功能不同等位基因频率的变化。
