Institute for Genomic Diversity
Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, and.
G3 (Bethesda). 2019 Sep 4;9(9):3023-3033. doi: 10.1534/g3.119.400549.
Modern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The usefulness of endophenotypes for delineating the regulatory landscape of the genome and genetic dissection of complex trait variation remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299-genotype and seven-tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation acts through altered transcript abundance for maize kernel traits, including 30 grain carotenoid abundance traits, 20 grain tocochromanol abundance traits, and 22 field-measured agronomic traits. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the largely independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits than the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This not only improves the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.
现代农业物种复杂性状的改良依赖于将可遗传的分子变异与可观察的表型成功关联。从历史上看，这种研究主要基于易于测量的遗传标记。最近新技术的出现允许检测和量化生物中间产物（以下简称内表型），这些中间产物现在可以在不同个体中大规模地进行测量。内表型在植物中用于描绘基因组的调控景观和复杂性状变异的遗传剖析仍然没有得到充分探索。本文展示了利用大规模（299 个基因型和 7 种组织）基因表达资源来剖析多个层次生物组织的性状的效用。使用基于单组织和多组织的全转录组关联研究（TWAS），我们揭示了大约一半的功能变异是通过改变玉米籽粒性状的转录丰度来实现的，包括 30 种籽粒类胡萝卜素丰度性状、20 种籽粒生育酚丰度性状和 22 种田间农艺性状。将 TWAS 的功效与全基因组关联研究（GWAS）和一种结合了 GWAS 和 TWAS 的综合方法进行比较，我们证明了将 TWAS 与 GWAS 相结合的结果可以增加检测已知基因的能力，并有助于优先考虑可能的因果基因。在很大程度上独立的玉米嵌套关联作图（NAM）群体中使用方差分解方法，我们还表明，通过结合 GWAS 和 TWAS 鉴定的最相关基因解释了大多数性状的可遗传方差的比例高于随机基因和 GWAS 或 TWAS 单独鉴定的基因所捕获的可遗传方差。这不仅提高了将基因与表型联系起来的能力，还突出了植物中调控变异的表型后果。
