Search

Background: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches. [ABSTRACT FROM AUTHOR]

Micronutrient deficiency is a widespread food-related health problem around the world. The present study was conducted to evaluate a set of 63 advanced breeding lines of bread wheat (Triticum aestivum L.) for grain iron (GFe) and grain zinc (GZn) concentrations, and to characterise the germplasm set via simple sequence repeat (SSR) markers (both genic and random). Substantial variation was found for both micronutrients. GFe concentration ranged from 28.9 to 67.4 mg kg-1 and GZn from 26.3 to 56.6 mg kg-1.Molecular characterisation with six genic and 20 random SSR markers detected 168 alleles with an average of 3.170 alleles per locus. Analysis of genotypic data based on division into two subpopulations revealed 165 alleles with an average of 3.113 alleles per locus in the low GFe--GZn subpopulation, whereas in the high GFe--GZn subpopulation, 149 alleles with an average of 2.811 alleles per locus were detected. Genic SSRs detected a higher average number of alleles (3.273 alleles per locus) than random SSRs (3.143 alleles per locus). Hierarchical clustering using genic markers alone clustered the whole germplasm set into two distinct groups: one possessing low GFe--GZn genotypes, the other with high GFe--GZn genotypes. Study of marker--trait associations (MTAs) identified seven new MTAs (six for GZn and one for GFe) and validated one MTA for GZn concentration. The promising genotypes andMTAs identified during the study will prove useful in wheat bio-fortification programs in the future. [ABSTRACT FROM AUTHOR]