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Mol Plant:華中農(nóng)業(yè)大學(xué)嚴(yán)建兵發(fā)表玉米基因研究成果 已有3人參與
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2016年7月4日,國(guó)際著名學(xué)術(shù)雜志《Cell》子刊《Molecular Plant》雜志在線發(fā)表了華中農(nóng)業(yè)大學(xué)生命科學(xué)技術(shù)學(xué)院嚴(yán)建兵教授課題組的葉片研究論文,論文揭示出在玉米遙遠(yuǎn)的表達(dá)數(shù)量性狀位點(diǎn)(eQTL)和非編碼序列對(duì)調(diào)控基因表達(dá)和數(shù)量性狀變異中起至關(guān)重要的作用。嚴(yán)建兵課題組的劉海軍和羅欣為論文共同第一作者,嚴(yán)建兵教授為論文通訊作者。 研究人員通過(guò)結(jié)合簡(jiǎn)化基因組測(cè)序(GBS)、高密度陣列技術(shù),和以往來(lái)自368個(gè)不同玉米自交系的深度RNA測(cè)序數(shù)據(jù),鑒別出了次要等位基因頻率(MAF)大于0.05的1.25M SNPs。在較大且不同的自然樣本中平衡的等位基因頻率和分布,幫助鑒別出了與1.8萬(wàn)多個(gè)基因(63.4%的測(cè)試基因)相關(guān)的eQTLs。他們發(fā)現(xiàn)在整個(gè)基因組中遙遠(yuǎn)的eQTLs更常見(jiàn)(約75%的eQTLs)。利用這一新數(shù)據(jù)集,研究人員鑒別出了13個(gè)影響玉米粒油濃度的新相關(guān)位點(diǎn)。其中,一個(gè)基因位點(diǎn)通過(guò)控制其他三個(gè)已知的油相關(guān)基因的表達(dá)影響了粒油變化。研究為擴(kuò)展對(duì)玉米基因組內(nèi)轉(zhuǎn)錄組變異細(xì)胞調(diào)控機(jī)制和功能性變異景觀的了解,由此增進(jìn)對(duì)數(shù)量性狀變異的認(rèn)識(shí)提供了重要的資源。 原文鏈接: 原文摘要: A detailed understanding of the genetic architecture of mRNA expression by millions of genetic variants is important for studying quantitative trait variation. In this study, we identified 1.25M SNPs with a minor allele frequency (MAF) greater than 0.05 by combining reduced genome sequencing (GBS), high-density array technologies (600K), and previous deep RNA-sequencing data from 368 diverse inbred lines of maize. The balanced alleleic frequencies and distributions in a relatively large and diverse natural panel helped to identify eQTLs associated with more than 18,000 genes (63.4% of tested genes). We found that distant eQTLs were more frequent (~75% of all eQTLs) across the whole genome. Thirteen novel associated loci affecting the maize kernel oil concentration were identified using the new dataset. Among which, one intergenic locus could affect the kernel oil variation by controlling expression of three other known oil related genes. Altogether, this study provided resources for expanding our understanding of the cellular regulatory mechanisms of transcriptome variation and the landscape of functional variants within the maize genome, thereby enhancing the understanding of quantitative variations. |

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