Merrick Lance F, Burke Adrienne B, Zhang Zhiwu, Carter Arron H
Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States.
Front Plant Sci. 2022 Jan 28;12:772907. doi: 10.3389/fpls.2021.772907. eCollection 2021.
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 ( 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.
未知的遗传结构使得难以表征性状的遗传基础及相关分子标记，原因在于微效数量性状基因座（QTL）的复杂性、环境效应以及表型分型的难度。小麦（L.）深播时的幼苗出土情况，其遗传结构尚不清楚，是影响苗数和籽粒产量的关键因素，并且在历史上与胚芽鞘长度相关。本研究旨在通过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）模型来剖析幼苗出土的遗传结构，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法（FarmCPU）以及贝叶斯信息和连锁不平衡迭代嵌套关键法（BLINK）]。我们使用两个群体进行GWAS。第一个群体由来自一个多样化关联作图群体的473个品种组成，这些品种在2015年至2019年期间进行了表型分析。第二个群体由2015年在华盛顿州林德进行表型分析的279个育种系组成，具有40368个标记。我们还比较了在ST - GWAS模型中纳入胚芽鞘长度和与株高降低相关的标记作为协变量的情况。ST - GWAS在19条染色体上发现了107个显著标记，而MT - GWAS在14条染色体上发现了82个显著标记。包括协变量的FarmCPU和BLINK模型能够识别许多微效标记，同时在5A染色体上识别出大效应标记。通过使用多基因座模型育种，项目能够揭示性状的复杂本质，以帮助识别候选基因和性状的潜在结构，如幼苗出土情况。
