Daryani Parisa, Amirbakhtiar Nazanin, Soorni Jahad, Loni Fatemeh, Darzi Ramandi Hadi, Shobbar Zahra-Sadat
Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
National Plant Gene Bank of Iran, Seed and Plant Improvement Institute (SPII), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Rice (N Y). 2024 Jan 16;17(1):7. doi: 10.1186/s12284-024-00684-1.
The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7 cM. The average CI of the identified MQTLs (4.68 cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0 cM. The mean CI of the obtained MQTLs (2.33 cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1 cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.
产量这一复杂性状受多个数量性状基因座(QTL)控制。鉴于全球水资源短缺问题,培育适合非淹水栽培的水稻品种在育种计划中具有重要意义。强大的元QTL(MQTL)分析方法可用于复杂数量性状的遗传剖析。在本研究中,进行了全面的MQTL分析,以确定水稻在水分亏缺条件下与耐旱性和产量相关性状相关的一致QTL区域。分析共利用了2000年至2021年间发表的134个水稻群体中的1087个QTL。对相关性状进行独特的MQTL分析,鉴定出213个稳定的MQTL。检测到的MQTL的置信区间(CI)在0.12至19.7厘摩之间。所鉴定的MQTL的平均CI(4.68厘摩)比初始QTL的平均CI窄2.74倍。有趣的是,63个MQTL与水稻在水分亏缺条件下产量和耐旱相关性状的全基因组关联研究检测到的SNP峰值位置重合。考虑到位于QTL概述峰值和SNP峰值位置的基因,引入了19个新的候选基因,这些基因与干旱响应指数、株高、穗数、生物量和籽粒产量相关。此外,对所有性状进行了包容性MQTL分析,以获得“育种MQTL”。该分析鉴定出96个CI范围为0.01至9.0厘摩的MQTL。所获得的MQTL的平均CI(2.33厘摩)比原始QTL的平均CI小4.66倍。13个满足初始QTL超过10个、CI<1厘摩且平均表型变异解释大于10%标准的MQTL被指定为“育种MQTL”。这些发现有望帮助育种者提高干旱胁迫条件下的水稻产量。