Mapping of QTL for total spikelet number per spike on chromosome 2D in wheat using a high-density genetic map

Genetic Variations and Haplotype Diversity of the Wheat FRIZZY PANICLE (WFZP) Gene in 98 Aegilops tauschii Accessions

BACKGROUND

The wheat FRIZZY PANICLE (WFZP) gene is a regulatory hub that controls spikelet formation in bread wheat, WFZP-D, as a favorable gene for wheat yield improvement. The sequences of WFZP-D in bread wheat accessions are known to be highly conserved.

METHODS

In this study, re-sequencing of WFZP homoeologous genes from 98 widely distributed Aegilops tauschii (the donor of the wheat D genome) germplasms was carried out to identify natural variations at both the nucleotide and polypeptide levels.

CONCLUSIONS

WFZP homeolog exhibited high conservation with no functional variants in the key AP2/ERF domain. Haplotype characterization identified five haplotypes (Hap-D1 to Hap-D5) based on nine single-nucleotide polymorphisms, five of which induced single amino acid residue substitutions downstream of the AP2/ERF domain. Hap-D1 (identical to Triticum aestivum WFZP-D) and Hap-D2 are two most common. Hap-D1 is concentrated in Iran and Azerbaijan, primarily associated with ssp. strangulata, while Hap-D2 displays broad distribution across the range and primarily belongs to ssp. tauschii. The remaining haplotypes (Hap-D3/4/5) are identified in ssp. tauschii accessions. These findings suggest that strategic integration of ssp. tauschii into wheat-breeding programs could enhance genetic diversity. The identified natural variations provide potential haplotype resources for improving wheat yield potential.

Dissection of QTLs underlying the genetic basis of drought resistance in wheat: a meta-analysis

Wheat (Triticum aestivum L.) is one of the most important cereal crops, with its grain serving as a predominant staple food source on a global scale. However, there are many biotic and abiotic stresses challenging the stability of wheat production. Among the abiotic stresses, drought is recognized as a significant stress and poses a substantial threat to food production and quality throughout the world. Raising drought tolerance of wheat varieties through genetic regulation is therefore considered as one of the most effective ways to combat the challenges caused by drought stress. Meta-QTL analysis has demonstrated its effectiveness in identifying consensus QTL regions in wheat drought resistance in numerous instances. In this study, we present a comprehensive meta-analysis aimed at unraveling the drought tolerance genetic basis associated with agronomic traits in bread wheat. Extracting data from 34 previously published studies, we aggregated a corpus of 1291 Quantitative Trait Loci (QTL) pertinent to wheat drought tolerance. Then, the translation of the consensus genetic map yielded a comprehensive compendium of 49 distinct MQTLs, each associated with diverse agronomic traits. Prominently featured among the MQTLs were MQTLs 1.1, 1.7, 1.8 (1D), 4.1 (4A), 4.6 (4D), 5.2 (5B), 6.6 (6B), and 7.2 (7B), distinguished as pivotal MQTLs offering significant potential for application in marker-assisted breeding endeavors. Altogether, a total of 66 putative candidate genes (CGs)-related drought tolerance were identified. This work illustrates a translational research approach in transferring information from published mapping studies to genomic regions hosting major QTLs governing key agronomical traits in wheat.

Identification and fine mapping of a QTL-rich region for yield- and quality-related traits on chromosome 4BS in common wheat (Triticum aestivum L.)

Yield and quality are important for plant breeding. To better understand the genetic basis underlying yield- and quality-related traits in wheat (Triticum aestivum L.), we conducted the quantitative trait locus (QTL) analysis using recombinant inbred lines (RILs) and a high-density genetic linkage map with a 90 K array. In this study, a total of 117 QTLs were detected for spike number per area (SNPA), thousand grain weight (TGW), grain number per spike (GNS), plant height (PH), spike length (SL), total spikelet number (TSN), spikelet density (SD), grain protein content (GPC), and grain starch content (GSC). Among these QTLs, 30 environmentally stable QTLs for yield- and quality-related traits were detected. Notably, five QTL-rich regions (Qrr) for yield- and/or quality-related traits were identified, including the QTL-rich region on chromosome 4BS (QQrr.cau-4B) for eight traits (SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC). The stable QTL-rich region QQrr.cau-4B was delimited into a physical interval of approximately 2.47 Mb. Based on the annotation information of the Chinese spring wheat genome v1.0 and parental re-sequencing results, the interval included twelve genes with sequence variations. Taken together, these results contribute to further understanding of the genetic basis of SNPA, GNS, PH, SL, TSN, SD, GPC, and GSC, and fine mapping of QQrr.cau-4B will be beneficial for gene cloning and marker-assisted selection in the genetic improvement of wheat varieties.

Identification and validation of two major QTLs for spikelet number per spike in wheat (Triticum aestivum L.)

The total number of spikelets (TSPN) and the number of fertile spikelets (FSPN) affect the final number of grains per spikelet in wheat. This study constructed a high-density genetic map using 55K single nucleotide polymorphism (SNP) arrays from a population of 152 recombinant inbred lines (RIL) from crossing the wheat accessions 10-A and B39. Twenty-four quantitative trait loci (QTLs) for TSPN and 18 QTLs for FSPN were localized based on the phenotype in 10 environments in 2019-2021. Two major QTLs, QTSPN/QFSPN.sicau-2D.4 (34.43-47.43 Mb) and QTSPN/QFSPN.sicau-2D.5(32.97-34.43 Mb), explained 13.97%-45.90% of phenotypic variation. Linked kompetitive allele-specific PCR (KASP) markers further validated these two QTLs and revealed that QTSPN.sicau-2D.4 had less effect on TSPN than QTSPN.sicau-2D.5 in 10-A×BE89 (134 RILs) and 10-A×Chuannong 16 (192 RILs) populations, and one population of Sichuan wheat (233 accessions). The alleles combination haplotype 3 with the allele from 10-A of QTSPN/QFSPN.sicau-2D.5 and the allele from B39 of QTSPN.sicau-2D.4 resulted in the highest number of spikelets. In contrast, the allele from B39 for both loci resulted in the lowest number of spikelets. Using bulk-segregant analysis-exon capture sequencing, six SNP hot spots that included 31 candidate genes were identified in the two QTLs. We identified Ppd-D1a from B39 and Ppd-D1d from 10-A and further analyzed Ppd-D1 variation in wheat. These results identified loci and molecular markers with potential utility for wheat breeding and laid a foundation for further fine mapping and cloning of the two loci.

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.

Analysis of Genetic Regions Related to Field Grain Number per Spike From Chinese Wheat Founder Parent Linfen 5064

Wheat founder parents have been important in the development of new wheat cultivars. Understanding the effects of specific genome regions on yield-related traits in founder variety derivatives can enable more efficient use of these genetic resources through molecular breeding. In this study, the genetic regions related to field grain number per spike (GNS) from the founder parent Linfen 5064 were analyzed using a doubled haploid (DH) population developed from a cross between Linfen 5064 and Nongda 3338. Quantitative trait loci (QTL) for five spike-related traits over nine experimental locations/years were identified, namely, total spikelet number per spike (TSS), base sterile spikelet number per spike (BSSS), top sterile spikelet number per spike (TSSS), fertile spikelet number per spike (FSS), and GNS. A total of 13 stable QTL explaining 3.91-19.51% of the phenotypic variation were found. The effect of six of these QTL, Qtss.saw-2B.1, Qtss.saw-2B.2, Qtss.saw-3B, Qfss.saw-2B.2, Qbsss.saw-5A.1, and Qgns.saw-1A, were verified by another DH population (Linfen 5064/Jinmai 47), which showed extreme significance (P < 0.05) in more than three environments. No homologs of reported grain number-related from grass species were found in the physical regions of Qtss.saw-2B.1 and Qtss.saw-3B, that indicating both of them are novel QTL, or possess novel-related genes. The positive alleles of Qtss.saw-2B.2 from Linfen 5064 have the larger effect on TSS (3.30%, 0.62) and have 66.89% in Chinese cultivars under long-term artificial selection. This study revealed three key regions for GNS in Linfen 5064 and provides insights into molecular marker-assisted breeding.

Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis

BACKGROUND

Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed.

RESULTS

We observed major quantitative agronomic traits of wheat; the winter survival rate (WSR), days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles.

CONCLUSIONS

From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits.

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.

Identification and validation of stable quantitative trait loci for grain filling rate in common wheat (Triticum aestivum L.)

We identified and validated two stable grain filling rate (GFR) quantitative trait loci (QTL) in wheat that positively influenced several yield-related traits. Among them, QGfr.sicau-7D.1 was a novel GFR QTL. The grain filling rate (GFR) plays a crucial role in determining grain yield. To advance the current understanding of the genetic characteristics underlying the GFR in common wheat, three recombinant inbred line populations were used to map and validate GFR quantitative trait loci (QTL). Using a high-density genetic linkage map, 10 GFR QTL were detected. They were located on chromosomes 2D, 4A, 4B, 5B, 6D, 7A and 7D, explained 4.99-12.62% of the phenotypic variation. Two of them, QGfr.sicau-6D and QGfr.sicau-7D.1, were detected in all four environments tested and their genetic effect was validated by closely linked kompetitive allele specific PCR (KASP) markers in different genetic backgrounds. The effects of these two GFR QTL on other yield-related traits were also estimated. QGfr.sicau-6D had a significant positive influence (p < 0.01) on thousand kernel weight, kernel width, kernel volume, and kernel surface area. QGfr.sicau-7D.1 had a significant positive influence (p < 0.01) on thousand kernel weight and kernel length. Furthermore, QGfr.sicau-7D.1 was a completely novel QTL for GFR; several genes associated with grain growth and development were predicted in its physical interval. These results will facilitate molecular marker-assisted selection of wheat with high-confidence QTL for GFR and fine mapping of genes associated with GFR, thereby contributing to yield improvement.

Genetic architecture underpinning yield component traits in wheat

Genetic atlas, reliable QTL and candidate genes of yield component traits in wheat were figured out, laying concrete foundations for map-based gene cloning and dissection of regulatory mechanisms underlying yield. Mining genetic loci for yield is challenging due to the polygenic nature, large influence of environment and complex relationship among yield component traits (YCT). Many genetic loci related to wheat yield have been identified, but its genetic architecture and key genetic loci for selection are largely unknown. Wheat yield potential can be determined by three YCT, thousand kernel weight, kernel number per spike and spike number. Here, we summarized the genetic loci underpinning YCT from QTL mapping, association analysis and homology-based gene cloning. The major loci determining yield-associated agronomic traits, such as flowering time and plant height, were also included in comparative analyses with those for YCT. We integrated yield-related genetic loci onto chromosomes based on their physical locations. To identify the major stable loci for YCT, 58 QTL-rich clusters (QRC) were defined based on their distribution on chromosomes. Candidate genes in each QRC were predicted according to gene annotation of the wheat reference genome and previous information on validation of those genes in other species. Finally, a technological route was proposed to take full advantage of the resultant resources for gene cloning, molecular marker-assisted breeding and dissection of molecular regulatory mechanisms underlying wheat yield.