Search Results (10)

Grain number per spike (GNS) is a key component of wheat yield, yet its genetic architecture remains incompletely understood. This study phenotyped 610 wheat accessions for GNS in four environments and genotyped them using 429,721 single nucleotide polymorphisms (SNPs). The phenotypes were associated with the SNPs using a three-variance multi-locus random-SNP-effect mixed linear model (3VmrMLM) to identify quantitative trait nucleotides (QTNs), as well as QTN-by-environment (QEI) and QTN-by-QTN (QQI) interactions. These genetic components and residual error explained approximately 18%, 31%, 28%, and 23% of the phenotypic variance, respectively. Two and one previously reported genes were found around QTNs and QEIs, respectively. Bioinformatics and haplotype analyses subsequently yielded 25 candidate genes, 22 gene-by-environment interactions (GEIs), and 24 gene-by-gene interactions (GGIs) around the QTNs, QEIs, and QQIs, respectively. Notably, TraesCS1D01G280000, the wheat homolog of OsRopGEF10, was located near a major QTN explaining over 10% of the total phenotypic variation. A gene interaction network constructed from all identified genes highlighted the central role of GGIs in GNS regulation. Environmental variation may reshape the regulatory network through GEIs. Furthermore, superior haplotypes of 12 candidate genes were identified, providing valuable targets for improving wheat yield. Overall, this study dissects the genetic architecture of GNS and offers practical resources for wheat molecular breeding. Full article

Grain protein content (GPC) is a key quality trait in wheat, determining both nutritional value and end-use functionality, yet its genetic architecture is complex and highly influenced by the environment. In this study, a diverse panel of 327 wheat accessions was evaluated for GPC across multiple environments. Significant phenotypic variation was observed, with best linear unbiased estimates (BLUEs) ranging from 12.80% to 18.79%, and a moderate broad-sense heritability (h² = 0.52) was estimated. Genotype-by-environment interactions were highly significant. Genome-wide association analysis using the FarmCPU model identified seven stable quantitative trait nucleotides (QTNs) associated with GPC on chromosomes 1A, 1B, 2A, 2D, 3B, 5A, and 6A. Among these, QGpc.yzu-2A was consistently detected in three environments. Further analysis of the QGpc.yzu-2A region identified 26 annotated genes, 8 of which were expressed in grains. One gene, TraesCS2A02G473000 (RNA-binding protein), exhibited high nucleotide diversity and is a strong candidate for functional validation. Additionally, QGpc.yzu-6A co-localized with the known TaNAM-6A gene, reinforcing the role of this region in GPC regulation. This study provides valuable insights into the genetic basis of GPC in wheat and offers molecular markers and candidate genes for marker-assisted selection to improve grain protein content in breeding programs. Full article

Chickpea is a staple crop for many nations worldwide. Modeling genotype-by-environment interactions and assessing the genotype’s ability to contribute adaptive alleles are crucial for chickpea breeding. In this study, we evaluated 12 agronomically important traits of 159 accessions from the N.I. Vavilov All Russian Institute for Plant Genetic Resources collection. These included 145 landraces and 13 cultivars grown in different climatic conditions in Kuban (45°18′ N and 40°52′ E) in both 2016 and 2022, as well as in Astrakhan (46°06′ N and 48°04′ E) in 2022. Using the IIIVmrMLM model in multi-environmental mode, we identified 161 quantitative trait nucleotides (QTNs) with stable genetic effects across different environments. Furthermore, we have observed 254 QTN-by-environment interactions with distinct environment-specific effects. Notably, five of these interactions manifested large effects, with R² values exceeding 10%, while the highest R² value for stable QTNs was 4.7%. Within the protein-coding genes and their 1 Kb flanking regions, we have discerned 22 QTNs and 45 QTN-by-environment interactions, most likely tagging the candidate causal genes. The landraces obtained from the N.I Vavilov All Russian Institute for Plant Genetic Resources collection exhibit numerous favorable alleles at quantitative trait nucleotide loci, showing stable effects in the Kuban and Astrakhan regions. Additionally, they possessed a significantly higher number of Kuban-specific favorable alleles of the QTN-by-environment interaction loci compared to the Astrakhan-specific ones. The environment-specific alleles found at the QTN-by-environment interaction loci have the potential to enhance chickpea adaptation to specific climatic conditions. Full article

Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype. Full article

Soybean hundred seed weight (HSW) is a complex quantitative trait affected by multiple genes and environmental factors. To date, a large number of quantitative trait nucleotides (QTNs) have been reported, but less information on QTN-by-environment interactions (QEIs) and QTN-QTN interaction (QQIs) for soybean HSW is available. Mapping without QEIs and QQIs result in missing some important QTNs that are significantly related to HSW. Therefore, the present study conducted genome-wide association analysis to map main QTNs, QEIs and QQIs for HSW in a panel with 573 diverse soybean lines tested in three independent environments (E1, E2 and E3) with Mean- and best linear unbiased value (BLUP)- phenotype. In all, 147 main effect QTNs, 11 QEIs, and 24 pairs of QQIs were detected in the Mean-phenotype, and 138 main effect QTNs, 13 QEIs, and 27 pairs of QQIs in the BLUP-phenotype. The total phenotypic variation explained by the main effect QTNs, QEIs, and QQIs were 35.31–39.71, 8.52–8.89 and 34.77–35.09%, respectively, indicating an important role of non-additive effects on HSW. Out of these, 33 QTNs were considered as stable with 23 colocalized with previously known loci, while 10 were novel QTNs. In addition, 10 pairs stable QQIs were simultaneously detected in the two phenotypes. Based on homolog search in Arabidopsis thaliana and in silico transcriptome data, seven genes (Glyma13g42310, Glyma13g42320, Glyma08g19580, Glyma13g44020, Glyma13g43800, Glyma17g16620 and Glyma07g08950) from some main-QTNs and two genes (Glyma06g19000 and Glyma17g09110) of QQIs were identified as potential candidate genes, however their functional role warrant further screening and functional validation. Our results shed light on the involvement of QEIs and QQIs in regulating HSW in soybean, and these together with candidate genes identified would be valuable genomic resources in developing soybean cultivars with desirable seed weight. Full article

Plant architecture is one of the key factors affecting maize yield formation and can be divided into secondary traits, such as plant height (PH), ear height (EH), and leaf number (LN). It is a viable approach for exploiting genetic resources to improve plant density. In this study, one natural panel of 226 inbred lines and 150 family lines derived from the offspring of T32 crossed with Qi319 were genotyped by using the MaizeSNP50 chip and the genotyping by sequence (GBS) method and phenotyped under three different environments. Based on the results, a genome-wide association study (GWAS) and linkage mapping were analyzed by using the MLM and ICIM models, respectively. The results showed that 120 QTNs (quantitative trait nucleotides) and 32 QTL (quantitative trait loci) related to plant architecture were identified, including four QTL and 40 QTNs of PH, eight QTL and 41 QTNs of EH, and 20 QTL and 39 QTNs of LN. One dominant QTL, qLN7-2, was identified in the Zhangye environment. Six QTNs were commonly identified to be related to PH, EH, and LN in different environments. The candidate gene analysis revealed that Zm00001d021574 was involved in regulating plant architecture traits through the autophagy pathway, and Zm00001d044730 was predicted to interact with the male sterility-related gene ms26. These results provide abundant genetic resources for improving maize plant architecture traits by using approaches to biological breeding. Full article

Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels. Full article

Bacterial leaf streak (BLS) is a devastating rice disease caused by the bacterial pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), which can result in severe damage to rice production worldwide. Based on a total of 510 rice accessions, trialed in two seasons and using six different multi-locus GWAS methods (mrMLM, ISIS EM-BLASSO, pLARmEB, FASTmrMLM, FASTmrEMMA and pKWmEB), 79 quantitative trait nucleotides (QTNs) reflecting 69 QTLs for BLS resistance were identified (LOD > 3). The QTNs were distributed on all chromosomes, with the most distributed on chromosome 11, followed by chromosomes 1 and 5. Each QTN had an additive effect of 0.20 (cm) and explained, on average, 2.44% of the phenotypic variance, varying from 0.00–0.92 (cm) and from 0.00–9.86%, respectively. Twenty-five QTNs were detected by at least two methods. Among them, qnBLS11.17 was detected by as many as five methods. Most of the QTNs showed a significant interaction with their environment, but no QTNs were detected in both seasons. By defining the QTL range for each QTN according to the LD half-decay distance, a total of 848 candidate genes were found for nine top QTNs. Among them, more than 10% were annotated to be related to biotic stress resistance, and five showed a significant response to Xoc infection. Our results could facilitate the in-depth study and marker-assisted improvement of rice resistance to BLS. Full article

Chlorogenic acid (CGA) plays a crucial role in defense response, immune regulation, and the response to abiotic stress in plants. However, the genetic regulatory network of CGA biosynthesis pathways in perennial plants remains unclear. Here, we investigated the genetic architecture for CGA biosynthesis using a metabolite-based genome-wide association study (mGWAS) and expression quantitative trait nucleotide (eQTN) mapping in a population of 300 accessions of Populus tomentosa. In total, we investigated 204 SNPs which were significantly associated with 11 metabolic traits, corresponding to 206 genes, and were mainly involved in metabolism and cell growth processes of P. tomentosa. We identified 874 eQTNs representing 1066 genes, in which the expression and interaction of causal genes affected phenotypic variation. Of these, 102 genes showed significant signatures of selection in three geographical populations, which provided insights into the adaptation of CGA biosynthesis to the local environment. Finally, we constructed a genetic network of six causal genes that coordinately regulate CGA biosynthesis, revealing the multiple regulatory patterns affecting CGA accumulation in P. tomentosa. Our study provides a multiomics strategy for understanding the genetic basis underlying the natural variation in the CGA biosynthetic metabolites of Populus, which will enhance the genetic development of abiotic-resistance varieties in forest trees. Full article

Selecting wheat with favorable spike characteristics has been a popular breeding strategy to improve sink capacity and yield potential. In the present study, quantitative trait loci (QTLs) for yield potential traits were identified using a recombinant inbred line (RIL) population derived from the cross between Taejoong and Keumkang, two Korean wheat cultivars. A linkage map encompassing a total genetic length of 6544.8 cM was constructed using 838 single nucleotide polymorphisms from the 35K Axiom Wheat Breeder’s Array. We detected eight QTLs for four yield potential traits that are consistently identified in at least two of the three environments, that is, one for days to heading date (QDHD-1 on chromosome 7B), three for spike length (QSL-1, QSL-2, and QSL-3 on chromosomes 1D, 5A, and 6A, respectively), one for tiller number (QTN-1 on chromosome 5B), and three for length of center rachis (QLCR-1, QLCR-2, and QLCR-3 on chromosomes 1B, 5B, and 6A, respectively). Notably, Taejoong contributed the alleles for long spike at all three spike length QTLs with the additive effects of 0.6 cm, 0.6 cm, and 0.9 cm at QSL-1, QSL-2, and QSL-3, respectively. No significant two-way or three-way interaction was observed among QSL-1, QSL-2, and QSL-3, indicating that pyramiding the Taejoong alleles at the three QTLs can increase spike length additively. While the Taejoong alleles at QSL-1, QSL-2, and QSL-3 were associated with increased days to heading date, more kernels per spike, and reduced tiller number per plant, the extent of the pleiotropic effects were different among the three QTLs. Due to the limited number of molecular markers and mapping resolution, further work is required to narrow down the identified QTLs and characterize their effects more precisely. Our results would provide useful information for modulating spike characteristics and improving yield potential in wheat breeding programs. Full article