QTL for spike-layer uniformity and their influence on yield-related traits in wheat

Characterization of a wheat stable QTL for spike length and its genetic effects on yield-related traits

Spike length (SL) is one of the most important agronomic traits affecting yield potential and stability in wheat. In this study, a major stable quantitative trait locus (QTL) for SL, i.e., qSl-2B, was detected in multiple environments in a recombinant inbred line (RIL) mapping population, KJ-RILs, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). The qSl-2B QTL was mapped to the 60.06-73.06 Mb region on chromosome 2B and could be identified in multiple mapping populations. An InDel molecular marker in the target region was developed based on a sequence analysis of the two parents. To further clarify the breeding use potential of qSl-2B, we analyzed its genetic effects and breeding selection effect using both the KJ-RIL population and a natural mapping population, which consisted of 316 breeding varieties/advanced lines. The results showed that the qSl-2B alleles from KN9204 showed inconsistent genetic effects on SL in the two mapping populations. Moreover, in the KJ-RILs population, the additive effects analysis of qSl-2B showed that additive effect was higher when both qSl-2D and qSl-5A harbor negative alleles under LN and HN. In China, a moderate selection utilization rate for qSl-2B was found in the Huanghuai winter wheat area and the selective utilization rate for qSl-2B continues to increase. The above findings provided a foundation for the genetic improvement of wheat SL in the future via molecular breeding strategies.

Two QTL regions for spike length showing pleiotropic effects on Fusarium head blight resistance and thousand-grain weight in bread wheat (Triticum aestivum L.)

Spike length (SL) plays an important role in the yield improvement of wheat and is significantly associated with other traits. Here, we used a recombinant inbred line (RIL) population derived from a cross between Yangmai 12 (YM12) and Yanzhan 1 (YZ1) to construct a genetic linkage map and identify quantitative trait loci (QTL) for SL. A total of 5 QTL were identified for SL, among which QSl.yaas-3A and QSl.yaas-5B are two novel QTL for SL. The YZ1 alleles at QSl.yaas-2D and QSl.yaas-5A, and the YM12 alleles at QSl.yaas-2A, QSl.yaas-3A, and QSl.yaas-5B conferred increasing SL effects. Two major QTL QSl.yaas-5A and QSl.yaas-5B explained 9.11-15.85% and 9.01-12.85% of the phenotypic variations, respectively. Moreover, the positive alleles of QSl.yaas-5A and QSl.yaas-5B could significantly increase Fusarium head blight (FHB) resistance (soil surface inoculation and spray inoculation were used) and thousand-grain weight (TGW) in the RIL population. Kompetitive allele-specific PCR (KASP) markers for QSl.yaas-5A and QSl.yaas-5B were developed and validated in an additional panel of 180 wheat cultivars/lines. The cultivars/lines harboring both the positive alleles of QSl.yaas-5A and QSl.yaas-5B accounted for only 28.33% of the validation populations and had the longest SL, best FHB resistance (using spray inoculation), and highest TGW. A total of 358 and 200 high-confidence annotated genes in QSl.yaas-5A and QSl.yaas-5B were identified, respectively. Some of the genes in these two regions were involved in cell development, disease resistance, and so on. The results of this study will provide a basis for directional breeding of longer SL, higher TGW, and better FHB resistance varieties and a solid foundation for fine-mapping QSl.yaas-5A and QSl.yaas-5B in future.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01427-8.

Fine mapping and characterization of a major QTL for plant height on chromosome 5A in wheat

KEY MESSAGE

We precisely mapped QPH.caas-5AL for plant height in wheat, predicted candidate genes and confirmed genetic effects in a panel of wheat cultivars. Plant height is an important agronomic trait, and appropriately reduced height can improve yield potential and stability in wheat, usually combined with sufficient water and fertilizer. We previously detected a stable major-effect quantitative trait locus QPH.caas-5AL for plant height on chromosome 5A in a recombinant inbred line population of the cross 'Doumai × Shi 4185' using the wheat 90 K SNP assay. Here , QPH.caas-5AL was confirmed using new phenotypic data in additional environment and new-developed markers. We identified nine heterozygous recombinant plants for fine mapping of QPH.caas-5AL and developed 14 breeder-friendly kompetitive allele-specific PCR markers in the region of QPH.caas-5AL based on the genome re-sequencing data of parents. Phenotyping and genotyping analyses of secondary populations derived from the self-pollinated heterozygous recombinant plants delimited QPH.caas-5AL into an approximate 3.0 Mb physical region (521.0-524.0 Mb) according to the Chinese Spring reference genome. This region contains 45 annotated genes, and six of them were predicted as the candidates of QPH.caas-5AL based on genome and transcriptome sequencing analyses. We further validated that QPH.caas-5AL has significant effects on plant height but not yield component traits in a diverse panel of wheat cultivars; its dwarfing allele is frequently used in modern wheat cultivars. These findings lay a solid foundation for the map-based cloning of QPH.caas-5AL and also provide a breeding-applicable tool for its marker-assisted selection. Keymessage We precisely mapped QPH.caas-5AL for plant height in wheat, predicted candidate genes and confirmed genetic effects in a panel of wheat cultivars.

Genetic basis of geographical differentiation and breeding selection for wheat plant architecture traits

BACKGROUND

Plant architecture associated with increased grain yield and adaptation to the local environments is selected during wheat (Triticum aestivum) breeding. The internode length of individual stems and tiller length of individual plants are important for the determination of plant architecture. However, few studies have explored the genetic basis of these traits.

RESULTS

Here, we conduct a genome-wide association study (GWAS) to dissect the genetic basis of geographical differentiation of these traits in 306 worldwide wheat accessions including both landraces and traditional varieties. We determine the changes of haplotypes for the associated genomic regions in frequency in 831 wheat accessions that are either introduced from other countries or developed in China from last two decades. We identify 83 loci that are associated with one trait, while the remaining 247 loci are pleiotropic. We also find 163 associated loci are under strong selective sweep. GWAS results demonstrate independent regulation of internode length of individual stems and consistent regulation of tiller length of individual plants. This makes it possible to obtain ideal haplotype combinations of the length of four internodes. We also find that the geographical distribution of the haplotypes explains the observed differences in internode length among the worldwide wheat accessions.

CONCLUSION

This study provides insights into the genetic basis of plant architecture. It will facilitate gene functional analysis and molecular design of plant architecture for breeding.

Identification and Validation of Quantitative Trait Loci Mapping for Spike-Layer Uniformity in Wheat

Spike-layer uniformity (SLU), the consistency of the spike distribution in the vertical space, is an important trait. It directly affects the yield potential and appearance. Revealing the genetic basis of SLU will provide new insights into wheat improvement. To map the SLU-related quantitative trait loci (QTL), 300 recombinant inbred lines (RILs) that were derived from a cross between H461 and Chinese Spring were used in this study. The RILs and parents were tested in fields from two continuous years from two different pilots. Phenotypic analysis showed that H461 was more consistent in the vertical spatial distribution of the spike layer than in Chinese Spring. Based on inclusive composite interval mapping, four QTL were identified for SLU. There were two major QTL on chromosomes 2BL and 2DL and two minor QTL on chromosomes 1BS and 2BL that were identified. The additive effects of QSlu.sicau-1B, Qslu.sicau-2B-2, and QSlu.sicau-2D were all from the parent, H461. The major QTL, QSlu.sicau-2B-2 and QSlu.sicau-2D, were detected in each of the conducted trials. Based on the best linear unbiased prediction values, the two loci explained 23.97% and 15.98% of the phenotypic variation, respectively. Compared with previous studies, the two major loci were potentially novel and the two minor loci were overlapped. Based on the kompetitive allele-specific PCR (KASP) marker, the genetic effects for QSlu.sicau-2B-2 were validated in an additional RIL population. The genetic effects ranged from 26.65% to 32.56%, with an average value of 30.40%. In addition, QSlu.sicau-2B-2 showed a significant (p < 0.01) and positive influence on the spike length, spikelet number, and thousand kernel weight. The identified QTL and the developed KASP marker will be helpful for fine-mapping these loci, finally contributing to wheat breeding programs in a marker-assisted selection way.

Identification and validation of two major QTLs for spike compactness and length in bread wheat (Triticum aestivum L.) showing pleiotropic effects on yield-related traits

Two major and stable QTLs for spike compactness and length were detected and validated in multiple genetic backgrounds and environments, and their pleiotropic effects on yield-related traits were analyzed. Spike compactness (SC) and length (SL) are greatly associated with wheat (Triticum aestivum L.) grain yield. To detect quantitative trait loci (QTL) associated with SC and SL, two biparental populations derived from crosses of Chuanmai42/Kechengmai1 and Chuanmai42/Chuannong16 were employed to perform QTL mapping in five environments. A total of 34 QTLs were identified, in which six major QTLs were repeatedly detected in more than four environments and the best linear unbiased prediction datasets, explaining 7.13-33.6% of phenotypic variation. These major QTLs were co-located in two genomic regions on chromosome 5A and 6A, namely QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. By developing kompetitive allele-specific PCR (KASP) markers that linked to them, the two loci were validated in different genetic backgrounds, and their interactions were also analyzed. Comparison analysis showed that QSc/Sl.cib-5A was not Vrn-A1 and Q, and QSc/Sl.cib-6A was likely a new locus for SC and SL. Both QSc/Sl.cib-5A and QSc/Sl.cib-6A had pleiotropic effects on other yield-related traits including plant height, thousand grain weight and grain length. Therefore, the two loci combined with the developed KASP markers might be potentially applicable in wheat breeding. Furthermore, based on the spatiotemporal expression patterns, gene annotation, orthologous search and sequence differences, TraesCS5A01G301400 and TraesCS6A01G090300 were considered as potential candidates for QSc/Sl.cib-5A and QSc/Sl.cib-6A, respectively. These results provided valuable information for fine mapping and cloning of the two loci in the future.

Multi-locus genome-wide association mapping for spike-related traits in bread wheat (Triticum aestivum L.)

BACKGROUND

Bread wheat (Triticum aestivum L.) is one of the most important cereal food crops for the global population. Spike-layer uniformity (the consistency of the spike distribution in the vertical space)-related traits (SLURTs) are quantitative and have been shown to directly affect yield potential by modifying the plant architecture. Therefore, these parameters are important breeding targets for wheat improvement. The present study is the first genome-wide association study (GWAS) targeting SLURTs in wheat. In this study, a set of 225 diverse spring wheat accessions were used for multi-locus GWAS to evaluate SLURTs, including the number of spikes per plant (NSPP), spike length (SL), number of spikelets per spike (NSPS), grain weight per spike (GWPS), lowest tiller height (LTH), spike-layer thickness (SLT), spike-layer number (SLN) and spike-layer uniformity (SLU).

RESULTS

In total, 136 significant marker trait associations (MTAs) were identified when the analysis was both performed individually and combined for two environments. Twenty-nine MTAs were detected in environment one, 48 MTAs were discovered in environment two and 59 MTAs were detected using combined data from the two environments. Altogether, 15 significant MTAs were found for five traits in one of the two environments, and four significant MTAs were detected for the two traits, LTH and SLU, in both environments i.e. E1, E2 and also in combined data from the two environments. In total, 279 candidate genes (CGs) were identified, including Chaperone DnaJ, ABC transporter-like, AP2/ERF, SWEET sugar transporter, as well as genes that have previously been associated with wheat spike development, seed development and grain yield.

CONCLUSIONS

The MTAs detected through multi-locus GWAS will be useful for improving SLURTs and thus yield in wheat production through marker-assisted and genomic selection.

Metabolomics analysis and metabolite-agronomic trait associations using kernels of wheat (Triticum aestivum) recombinant inbred lines

Plants produce numerous metabolites that are important for their development and growth. However, the genetic architecture of the wheat metabolome has not been well studied. Here, utilizing a high-density genetic map, we conducted a comprehensive metabolome study via widely targeted LC-MS/MS to analyze the wheat kernel metabolism. We further combined agronomic traits and dissected the genetic relationship between metabolites and agronomic traits. In total, 1260 metabolic features were detected. Using linkage analysis, 1005 metabolic quantitative trait loci (mQTLs) were found distributed unevenly across the genome. Twenty-four candidate genes were found to modulate the levels of different metabolites, of which two were functionally annotated by in vitro analysis to be involved in the synthesis and modification of flavonoids. Combining the correlation analysis of metabolite-agronomic traits with the co-localization of methylation quantitative trait locus (mQTL) and phenotypic QTL (pQTL), genetic relationships between the metabolites and agronomic traits were uncovered. For example, a candidate was identified using correlation and co-localization analysis that may manage auxin accumulation, thereby affecting number of grains per spike (NGPS). Furthermore, metabolomics data were used to predict the performance of wheat agronomic traits, with metabolites being found that provide strong predictive power for NGPS and plant height. This study used metabolomics and association analysis to better understand the genetic basis of the wheat metabolism which will ultimately assist in wheat breeding.