Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

中文翻译：

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多性状全基因组关联研究揭示了与玉米花序和叶片结构相关的基因座。

玉米花序是一个复杂的表型，涉及多种性状的物理和发育相互作用。鉴于有证据表明基因可以多效性地促成其中一些性状，我们使用了公开的玉米数据来评估多变量全基因组关联研究（GWAS）方法识别多效性定量性状基因座（pQTL）的能力。我们分析了n 个多样性小组中23个可公开获得的花序和叶片相关性状=用376,336个标记进行基因分型的281个玉米品系表明，我们测试的两种多元GWAS方法能够鉴定基因组区域中的pQTL，与先前研究中发现的相似关联相吻合。然后，我们对同一个体进行了并行模拟研究，结果表明，对于所有评估的模拟设置，多变量GWAS方法比可比较的单变量方法产生更高的真-阳性定量性状核苷酸（QTN）检测率，除了相关的模拟性状时的遗传力为0.9。