Orthologous comparisons of the Hd1 region across genera reveal Hd1 gene lability within diploid Oryza species and disruptions to microsynteny in Sorghum.

来自 PUBMED

作者:

Sanyal AAmmiraju JSLu FYu YRambo TCurrie JKollura KKim HRChen JMa JSan Miguel PMingsheng CWing RAJackson SA

展开

摘要:

Heading date is one of the most important quantitative traits responsible for the domestication of rice. We compared a 155-kb reference segment of the Oryza sativa ssp. japonica cv. Nipponbare genome surrounding Hd1, a major heading date gene in rice, with orthologous regions from nine diploid Oryza species that diverged over a relatively short time frame (∼16 My) to study sequence evolution around a domestication locus. The orthologous Hd1 region from Sorghum bicolor was included to compare and contrast the evolution in a more distant relative of rice. Consistent with other observations at the adh1/adh2, monoculm1, and sh2/a1 loci in grass species, we found high gene colinearity in the Hd1 region amidst size differences that were lineage specific and long terminal repeat retrotransposon driven. Unexpectedly, the Hd1 gene was deleted in O. glaberrima, whereas the O. rufipogon and O. punctata copies had degenerative mutations, suggesting that other heading date loci might compensate for the loss or nonfunctionality of Hd1 in these species. Compared with the japonica Hd1 region, the orthologous region in sorghum exhibited micro-rearrangements including gene translocations, seven additional genes, and a gene triplication and truncation event predating the divergence from Oryza.

收起

展开

DOI:

10.1093/molbev/msq133

被引量:

13

年份:

1970

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(138)

参考文献(0)

引证文献(13)

来源期刊

-

影响因子:暂无数据

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读