Suppr超能文献

小麦高世代品系各种数量性状的多变量分析。

Multivariate analysis for various quantitative traits in wheat advanced lines.

作者信息

Ali Naushad, Hussain Izhar, Ali Sardar, Khan Naqib Ullah, Hussain Ijaz

机构信息

Department of Plant Breeding and Genetics, The University of Agriculture, Peshawar, Pakistan.

Department Plant Breeding and Genetics, The University of Haripur, Pakistan.

出版信息

Saudi J Biol Sci. 2021 Jan;28(1):347-352. doi: 10.1016/j.sjbs.2020.10.011. Epub 2020 Oct 15.

Abstract

Genetic diversity play key role in the germplasm improvement which is directly correlated with the crop production. Various statistical techniques have been used to study diversity among different genotypes. Among these techniques multivariate is most frequently used one for the genetic association of genotypes. In the present study a total of 64 advance lines included one check cultivar were evaluated under the field conditions of Cereal Crop Research Institute, Pirsabaq Nowshera, Pakistan during September 2017. Data were recorded for nine different parameters. Multivariate analysis divided the total 64 genotypes into four groups. The first five PCs with Eigen values > 1 contributed 86.95% of the variability amongst genotypes. Characters with maximum values in PC1 were Spikelets spike (SPPS) (0.732), spike length (SPL) (0.722) and biological yield (BY) (0.607), PC2 comprised of 100-grain weight (TGW) (0.605), grain yield (GY) (0.482) while days to heading (DH) (0.393), for PC3 major contributors were BY (0.550) and number of tillers meter square (NTPS) (0.289), the contribution of PC4 were flag leaf area (FLA) (0.716) and SPL (0.298) and the maximum values for various traits in PC5 were SPPS (0.732), SPL (0.722) and BY (0.607). From the findings of present study best performing lines can be directly recommended for general cultivation or to be used in future breeding programs.

摘要

遗传多样性在种质改良中起着关键作用,而种质改良与作物产量直接相关。人们已使用各种统计技术来研究不同基因型之间的多样性。在这些技术中,多变量分析是最常用于基因型遗传关联研究的方法。在本研究中,2017年9月,在巴基斯坦瑙谢拉皮尔萨巴克的谷物作物研究所的田间条件下,对总共64个先进品系(包括一个对照品种)进行了评估。记录了九个不同参数的数据。多变量分析将总共64个基因型分为四组。特征值大于1的前五个主成分贡献了基因型间86.95%的变异性。在主成分1中具有最大值的性状是小穗数(0.732)、穗长(0.722)和生物产量(0.607),主成分2包括百粒重(0.605)、籽粒产量(0.482),而抽穗天数(0.393)是主成分3的主要贡献性状,主成分4的贡献性状是旗叶面积(0.716)和穗长(0.298),主成分5中各种性状的最大值是小穗数(0.732)、穗长(0.722)和生物产量(0.607)。根据本研究的结果,可以直接推荐表现最佳的品系用于一般种植或未来的育种计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b0bc/7783652/90025f87e6af/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验