High-density mapping for gray leaf spot resistance using two related tropical maize recombinant inbred line populations

Author(s):  
Long Chen ◽  
Li Liu ◽  
Ziwei Li ◽  
Yudong Zhang ◽  
Manjit S. Kang ◽  
...  
2021 ◽  
Author(s):  
Long Chen ◽  
Li Liu ◽  
Ziwei Li ◽  
Yudong Zhang ◽  
Manjit S Kang ◽  
...  

Abstract The identification of QTL/genes to resist gray leaf spot (GLS) caused by Cercospora zeae-maydis or Cercospora Zeina plays an urgent role in improving GLS resistance in maize breeding practice. In our study, two groups of recombinant inbred line (RIL) populations derived from CML373×Ye107 (176 RILs) and Chang7-2×Ye107 (190 RILs) were generated and subjected to genotyping-by-sequencing (GBS). GBS technology was used for large-scale single nucleotide polymorphism (SNP) discovery and simultaneous genotyping of all F7 lines from two related RIL populations in order to identify quantitative trait loci (QTL) associated with GLS resistance under natural conditions of disease occurrence. A total of 1929222287 reads in CML373×Ye107 (RIL-YCML) and 2585728312 reads in Chang7-2×Ye107 (RIL-YChang), with an average of 10961490 (RIL-YCML) and 13609096 (RIL-YChang) reads per individual, were got, which was roughly equal to 0.70-fold and 0.87-fold coverage of the maize B73 RefGen_V4 genome for each F7 individual, respectively. 6418 and 5139 SNP markers were extracted to construct two high-density genetic maps. Comparative analysis using these physically mapped marker loci demonstrated a satisfactory colinear relationship with the reference genome. Eleven GLS-resistant QTL have been detected. The individual QTL accounted for 2.05-24.00% of the phenotypic variance explained (PVE). The new consensus QTL (qYCM-DS3-3/ qYCM-LT3-1/ qYCM-LT3-2) with the largest effect was located in chromosome bin 3.05, with an interval of 2.7 Mb, representing 13.08 to 24.00% of the PVE. Further gene annotation indicated that there were four candidate genes (GRMZM2G032384, GRMZM2G041415, GRMZM2G041544, and GRMZM2G035992) for qYCM-LT3-1, which may be related to GLS resistance.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12504
Author(s):  
Guan Li ◽  
Yichen Cheng ◽  
Man Yin ◽  
Jinyu Yang ◽  
Jiezheng Ying ◽  
...  

Background The panicle is the most important organ in rice, and all the panicle-related traits are correlated with rice grain yield. Understanding the underlying genetic mechanisms controlling panicle development is very important for improving rice production. Methods Nine panicle-related traits including heading date, panicle length, number of primary branches, number of secondary branches, number of grains per panicle, number of panicles per plant, number of filled grains per plant, seed-setting rate, and grain yield per plant were investigated. To map the quantitative trait loci (QTLs) for the nine panicle-related traits, a PCR-based genetic map with 208 markers (including 121 simple sequence repeats and 87 InDels) and a high-density linkage map with 18,194 single nucleotide polymorphism (SNP) markers were both used. Results Using a recombinant inbred line population derived from an indica variety Huanghuazhan and a japonica line Jizi 1560, a total of 110 and 112 QTLs were detected for panicle-related traits by PCR-based genetic map and by high-density linkage map, respectively. Most of the QTLs were clustered on chromosomes 1, 2, 3, 6, and 7 while no QTLs were detected on chromosome 10. Almost all the QTLs with LOD values of more than 5.0 were repeatedly detected, indicating the accuracy of the two methods and the stability of the QTL effects. No genes for panicle-related traits have been previously reported in most of these regions. QTLs found in JD1006–JD1007 and RM1148–RM5556 with high LOD and additive values deserved further research. The results of this study are beneficial for marker-assisted breeding and provide research foundation for further fine-mapping and cloning of these QTLs for panicle-related traits.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Fang ◽  
Xiuyi Fu ◽  
Hanqiu Ge ◽  
Aixia Zhang ◽  
Tingyu Shan ◽  
...  

Abstract Background Maize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding. Results In this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis. Conclusions Our results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.


PLoS ONE ◽  
2012 ◽  
Vol 7 (12) ◽  
pp. e52777 ◽  
Author(s):  
Qingchun Pan ◽  
Farhan Ali ◽  
Xiaohong Yang ◽  
Jiansheng Li ◽  
Jianbing Yan

Crop Science ◽  
2004 ◽  
Vol 44 (1) ◽  
pp. 5 ◽  
Author(s):  
A. Kahraman ◽  
I. Kusmenoglu ◽  
N. Aydin ◽  
A. Aydogan ◽  
W. Erskine ◽  
...  

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