Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers

Abstract

Biofortification is gaining importance globally to improve human nutrition through enhancing the micronutrient content, such as vitamin A, iron, and zinc, in staple food crops. The present study aims to identify the chromosomal regions governing the grain iron concentration (GFeC), grain zinc concentration (GZnC), and thousand kernel weight (TKW) using recombinant inbred lines (RILs) in wheat, developed from a cross between HD3086 and HI1500. The experiment was conducted in four different production conditions at Delhi viz., control, drought, heat, and combined heat and drought stress and at Indore under drought stress. Grain iron and zinc content increased under heat and combined stress conditions, while thousand kernel weight decreased. Medium to high heritability with a moderate correlation between grain iron and zinc was observed. Out of 4,106 polymorphic markers between the parents, 3,407 SNP markers were used for linkage map construction which spanned over a length of 14791.18 cm. QTL analysis identified a total of 32 chromosomal regions governing the traits under study, which includes 9, 11, and 12 QTLs for GFeC, GZnC, and TKW, respectively. A QTL hotspot was identified on chromosome 4B which is associated with grain iron, grain zinc, and thousand kernel weight explaining the phenotypic variance of 29.28, 10.98, and 17.53%, respectively. Similarly, common loci were identified on chromosomes 4B and 4D for grain iron, zinc, and thousand kernel weight. In silico analysis of these chromosomal regions identified putative candidate genes that code for proteins such as Inositol 1,3,4-trisphosphate 5/6-kinase, P-loop containing nucleoside triphosphate hydrolase, Pleckstrin homology (PH) domains, Serine-threonine/tyrosine-protein kinase and F-box-like domain superfamily proteins which play role in many important biochemical or physiological process. The identified markers linked to QTLs can be used in MAS once successfully validated.

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat

“…The genetic linkage map spanned a length of 6,769.45 cM with the marker density ranging from 2.28 cM/marker in 1A to 14.21 cM/marker in 5D, with an average marker density of 5.52 cM/marker ( Puttamadanayaka et al, 2020 ; Khaled et al, 2022 ; Manjunath et al, 2023 ). The variation in marker density across chromosomes reflects differences in recombination rates and genetic distances between the markers ( Zhang et al, 2019 ).…”

“…The variation in marker density across chromosomes reflects differences in recombination rates and genetic distances between the markers ( Zhang et al, 2019 ). Genome B displays the highest number of markers, followed by genomes A and D ( Yang et al, 2017 ; Gutierrez-Gonzalez et al, 2019 ; Manjunath et al, 2023 ). This trend can be attributed to the greater diversity in A and B genomes than in the D genome.…”

“…The agronomic traits such as plant height, spike length, number of spikelets, grain weight per spike, and thousand-grain weight can be measured relatively easily, and hence these traits can be targeted for developing more stress-tolerant crop varieties ( Devate et al, 2022 ). The identification of quantitative trait loci (QTLs) associated with intricate traits furnishes invaluable insights into the chromosomal regions regulating these characteristics ( Devate et al, 2022 ; Manjunath et al, 2023 ). Genomics tools enable the identification of potential candidate genes within these regions ( Shavrukov, 2016 ).…”

Multivariate analysis and genetic dissection of staygreen and stem reserve mobilisation under combined drought and heat stress in wheat (Triticum aestivum L.)

“…The linkage map generated by Manjunath et al (2023) for the same population was used for QTL mapping. A total of 4,106 markers were reported to be polymorphic between the parents .…”

“…Mobilisation related traits have also been reported by previous workers such as SRM ( Salem et al, 2007 ; Yang et al, 2007 ), and water-soluble carbohydrates ( Bennett et al, 2012 ) using different mapping populations. However, only a few QTLs have been identified with high phenotypic variance ( Manjunath et al, 2023 ). Furthermore, multivariate data is very common in biological experiments.…”