PDF(Pubmed)
Abstract：
Unknown genetic architecture makes it difficult to characterize the genetic basis of traits and associated molecular markers because of the complexity of small effect quantitative trait loci (QTLs), environmental effects, and difficulty in phenotyping. Seedling emergence of wheat (Triticum aestivum L.) from deep planting, has a poorly understood genetic architecture, is a vital factor affecting stand establishment and grain yield, and is historically correlated with coleoptile length. This study aimed to dissect the genetic architecture of seedling emergence while accounting for correlated traits using one multi-trait genome-wide association study (MT-GWAS) model and three single-trait GWAS (ST-GWAS) models. The ST-GWAS models included one single-locus model [mixed-linear model (MLM)] and two multi-locus models [fixed and random model circulating probability unification (FarmCPU) and Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK)]. We conducted GWAS using two populations. The first population consisted of 473 varieties from a diverse association mapping panel phenotyped from 2015 to 2019. The second population consisted of 279 breeding lines phenotyped in 2015 in Lind, WA, with 40,368 markers. We also compared the inclusion of coleoptile length and markers associated with reduced height as covariates in our ST-GWAS models. ST-GWAS found 107 significant markers across 19 chromosomes, while MT-GWAS found 82 significant markers across 14 chromosomes. The FarmCPU and BLINK models, including covariates, were able to identify many small effect markers while identifying large effect markers on chromosome 5A. By using multi-locus model breeding, programs can uncover the complex nature of traits to help identify candidate genes and the underlying architecture of a trait, such as seedling emergence.
摘要：
未知的遗传结构使得难以表征性状和相关分子标记的遗传基础，因为小效应数量性状基因座（QTLs）的复杂性，环境影响,和表型困难。深植小麦（TriticumaestivumL.）的幼苗出苗，有一个知之甚少的基因结构，是影响林分建立和粮食产量的重要因素，历史上与胚芽鞘长度相关。本研究旨在使用一个多性状全基因组关联研究（MT-GWAS）模型和三个单性状GWAS（ST-GWAS）模型来剖析幼苗出苗的遗传结构，同时考虑相关性状。ST-GWAS模型包括一个单基因座模型[混合线性模型（MLM）]和两个多基因座模型[固定和随机模型循环概率统一（FarmCPU）以及贝叶斯信息和连锁不平衡迭代嵌套键槽（BLINK）]。我们使用两个群体进行GWAS。第一个种群由473个品种组成，这些品种来自2015年至2019年的不同关联映射面板表型。第二个种群由2015年在林德表型的279个育种系组成，WA,有40,368个标记。在我们的ST-GWAS模型中，我们还比较了胚芽鞘长度和与身高降低相关的标记作为协变量。ST-GWAS在19条染色体上发现了107个重要标记，而MT-GWAS在14条染色体上发现了82个显著的标记。FarmCPU和BLINK模型，包括协变量，能够识别许多小效应标记，同时识别5A染色体上的大效应标记。通过使用多基因座模型育种，程序可以揭示性状的复杂性质，以帮助识别候选基因和性状的潜在结构，如幼苗出苗。
