Genetic analysis and candidate gene identification of salt tolerancerelated traits in maize

Soil salinization poses a threat to maize production worldwide,but the genetic mechanism of salt tolerance in maize is not well understood.Therefore,identifying the genetic components underlying salt tolerance in maize is of great importance.In the current study,a teosinte-maize BC2F7 population was used to investigate the genetic basis of 21 salt tolerance-related traits.In total,125 QTLs were detected using a high-density genetic bin map,with one to five QTLs explaining 6.05–32.02%of the phenotypic variation for each trait.The total phenotypic variation explained(PVE)by all detected QTLs ranged from 6.84 to 63.88%for each trait.Of all 125 QTLs,only three were major QTLs distributed in two genomic regions on chromosome 6,which were involved in three salt tolerance-related traits.In addition,10 pairs of epistatic QTLs with additive effects were detected for eight traits,explaining 0.9 to 4.44%of the phenotypic variation.Furthermore,18 QTL hotspots affecting 3–7 traits were identified.In one hotspot(L5),a gene cluster consisting of four genes(ZmNSA1,SAG6,ZmCLCg,and ZmHKT1;2)was found,suggesting the involvement of multiple pleiotropic genes.Finally,two important candidate genes,Zm00001d002090 and Zm00001d002391,were found to be associated with salt tolerance-related traits by a combination of linkage and marker-trait association analyses.Zm00001d002090 encodes a calcium-dependent lipid-binding(CaLB domain)family protein,which may function as a Ca^(2+)sensor for transmitting the salt stress signal downstream,while Zm00001d002391 encodes a ubiquitin-specific protease belonging to the C19-related subfamily.Our findings provide valuable insights into the genetic basis of salt tolerance-related traits in maize and a theoretical foundation for breeders to develop enhanced salt-tolerant maize varieties.

Genetic architecture of embryo size and related traits in maize

The embryo in maize has a critical role in controlling kernel nutrition components and grain yield.We measured five embryo weight and size traits,six kernel weight and size traits,and five embryo-tokernel ratio traits in a nested association mapping(NAM)population of 611 recombinant inbred lines(RILs)derived from four inbred lines including the high-oil,giant-embryo line BY815 as the common parent.Using three statistical methods,we identified 5–22 quantitative trait loci(QTL)for each trait,explaining 4.7%–46.7%of the phenotypic variation.The genetic architecture of maize embryo size and its related traits appeared to be dominated by multiple small-effect loci with little epistasis,and the genetic control underlying embryo size appeared to be distinct from that underlying kernel size.A trait–QTL association network included 205 nodes and 439 edges and revealed 28 key loci associated with at least three traits.Cloned maize genes including Zm Urb2,Emp12 and Zm BAM1 d,maize orthologs of known rice genes that control seed size including BG1,XIAO and GS9,and 11 maize orthologs of Arabidopsis EMBRYO-DEFECTIVE(EMB)genes were identified as underlying these key loci.Further,the phenotypic and genetic relationships between embryo size and kernel size were evaluated,and genetic patterns for identified loci that control embryo size and its related traits were proposed.Our findings reveal distinct genetic architectures for embryo size,kernel size,and embryo-to-kernel ratio traits and establish a foundation for the improvement of embryo-size-mediated kernel nutrition and grain yield.

Genetic dissection of carotenoids in maize kernels using high-density single nucleotide polymorphism markers in a recombinant inbred line population

Carotenoids are antioxidants and vitamin A precursors that have important roles in human health. Hence, improving the carotenoid contents in maize kernels is a priority objective for breeders in order to obtain nutritional biofortification outcomes. In the current study, the genetic architecture of carotenoids in maize kernels was explored using a recombinant inbred line(RIL) population derived from a cross between inbred lines By804 and B73. A total of 81 QTLs were detected by using a high-density bin map and a simple sequence repeat(SSR)-based linkage map, with one to seven QTLs for each trait explaining 4.21%–47.53% of the phenotypic variation. A comparison of the QTL mapping efficiency between the two linkage maps revealed that the high-density bin map had higher resolution. In the current study 46 additional QTLs were identified, with 16 being common with previous studies and14 newly identified. Among the results, 29.6%(24/81) of QTLs explained > 10% of the phenotypic variation in the RIL population, and 70.4%(57/81) explained ≤ 10%. These results suggest that a few large-effect QTLs, together with a variable number of minor-effect QTLs,contributed to most of the genetic components of carotenoids in maize kernels.

Integrated linkage mapping and genome-wide association study to dissect the genetic basis of zinc deficiency tolerance in maize at seedling stage

Zinc(Zn)deficiency is the most widespread micronutrient deficiency,affecting yield and quality of crops worldwide.Identifying genes associated with Zn-deficiency tolerance in maize is a basis for elucidating its genetic mechanism.A K22×CI7 recombinant inbred population consisting of 210 lines and an association panel of 508 lines were used to identify genetic loci influencing Zn-deficiency tolerance.Under-Zn and-Zn/CK conditions,15 quantitative trait loci(QTL)were detected,each explaining 5.7%-12.6%of phenotypic variation.Sixty-one significant single-nucleotide polymorphisms(SNPs)were identified at P<10^(-5)by genome-wide association study(GWAS),accounting for 5%-14%of phenotypic variation.Among respectively 198 and 183 candidate genes identified within the QTL regions and the 100-kb regions flanking these significant SNPs,12 were associated with Zn-deficiency tolerance.Among these candidate genes,four genes associated with hormone signaling in response to Zn-deficiency stress were co-localized with QTL or SNPs,including the genes involved in the auxin(ZmARF7),and ethylene(ZmETR5,ZmESR14,and ZmEIN2)signaling pathways.Three candidate genes were identified as being responsible for Zn transport,including ZmNAS3 detected by GWAS,ZmVIT and ZmYSL11 detected by QTL mapping.Expression of ZmYSL11 was up-regulated in Zn-deficient shoots.Four candidate genes that displayed different expression patterns in response to Zn deficiency were detected in the regions overlapping peak GWAS signals,and the haplotypes for each candidate gene were further analyzed.