A genome-wide association study (GWAS) combined with genomic prediction was conducted on four-plant architecture-related traits in a structured population of 206 inbred lines, providing a foundation for further understanding the genetic control of plant architecture traits in maize.
Optimizing leaf morphology is essential for improving maize plant architecture, plant density tolerance, and yield. Leaf length is a key agronomic trait controlled by complex genetic mechanisms. In this study, upper leaf length (ULL), ear leaf length (ELL) and lower leaf length (DLL) were evaluated across two environments using a multiparent RIL population derived from crosses between the temperate inbred line Ye107 and three tropical inbred lines. Combined with high-density GBS markers, genome-wide association study (GWAS) and QTL mapping were integrated to dissect the genetic basis of leaf length.
Leaf length traits exhibited high heritability (54.85%–76.85%). A total of 133 significant SNPs were identified by GWAS, and 13 candidate regions were detected by QTL localization, among which
ql1-17
explained 12.10% of the phenotypic variation. Joint analysis revealed multiple colocalized regions on chromosomes 1, 6 and 8, resulting in the identification of three key candidate genes
ZmHUA2 (Zm00001d029223)
,
ZmCAMTA5 (Zm00001d025235)
, and
ZmPAT16 (Zm00001d038367)
. Haplotype and qRT-PCR analyses showed that favorable haplotypes from tropical parents significantly increased leaf length, and these genes exhibited high expression specificity in the leaf elongation region.
This study elucidates the complex genetic architecture of leaf length in temperate × tropical hybrid populations. Through integrated analyses, we identified key genomic loci and prioritized three candidate genes, particularly
ZmHUA2 (Zm00001d029223)
and
ZmCAMTA5 (Zm00001d025235)
. These findings provide valuable genomic resources and favorable alleles for marker-assisted selection, establishing a robust theoretical and practical foundation for maize ideotype breeding aimed at optimizing leaf morphology to enhance yield potential under high-density planting conditions.
Haoran Lyu, F. Jiang, Yuxiang Luo et al.· BMC Plant Biology· 0 citations
Sweet corn is a globally important dual-purpose crop for both food and fresh vegetables. The plant architecture and ear-related traits directly determine its yield potential and field ecological adaptability. To elucidate the genetic architecture of these traits and identify superior alleles for breeding, we conducted a genome-wide association study (GWAS) on 11 agronomic traits using 30,597 high-quality SNP markers in a panel of 101 elite sweet corn inbred lines. Population genetic structure was analyzed using sparse non-negative matrix factorization (sNMF) and discriminant analysis of principal components (DAPC) algorithms, revealing three main clusters and six subpopulations. The clustering pattern was highly consistent with germplasm origin. Association mapping with the fixed and random Circulating Probability Unification (FarmCPU) model identified 16 significant marker–trait associations (MTAs), distributed across seven target agronomic traits. The phenotypic variance explained (PVE) by individual loci ranged from 8.0% to 16.0%. Among these, five stable MTAs across environments, a novel ERN locus (SNP25518) specific to sweet corn, and most association intervals overlapped with previously reported quantitative trait loci (QTLs). Within the ±0.15 Mb (defined by LD decay) flanking windows around the significant SNP loci, a total of 236 candidate genes were annotated, which are primarily involved in hormone signaling, carbon and nitrogen metabolism, cell division, and plant growth and development. In summary, this study dissected the genetic basis of key agronomic traits in sweet corn and provides a foundation for marker-assisted selection and functional validation.
Yan-Chao Du, Jing-Wen Xu, Huiming Li et al.· Plants· 0 citations
The genetic architecture and core candidate genes for shoot length (SL) and root length (RL) at the germination stage are dissected and seven core candidate genes for SL and 13 for RL are identified, including three pleiotropic genes regulating both traits.
Plant height is a key agricultural trait for lodging resistance in maize. To explore key genes regulating plant height, a genome-wide association study (GWAS) was conducted using a large population of 1149 inbred lines. Plant height (PH), ear height (EH), and ear height coefficient (EH/PH) in this population followed a standard normal distribution, with heritability of 86%, 85%, and 75%, respectively. The GWAS identified two significant SNPs (Chr3:163963128 and Chr3:163963155) associated with all three traits. Within a 100-kb genomic region spanning upstream and downstream of the two SNPs, four genes were examined by real-time PCR, and among them, ZmPH1 showed a significant difference between taller lines and shorter lines. ZmPH1 encodes a pentatricopeptide repeat protein. ZmPH1 was also found to colocalize with a mapped PH QTL (qPH3-1) and an EH QTL (qEH3-1) identified in our F2 mapping population. The inbred lines were classified into Hap1 and Hap2 based on the genotype of ZmPH1. Hap1 lines have significantly taller PH and EH than Hap2 lines. A KASP marker designed based on one SNP successfully distinguished tall and short inbred lines after PCR amplification. Subcellular localization analysis showed that ZmPH1 is a chloroplast-localized protein. This study presents a potential PPR gene involved in the regulation of plant height and provides potential genetic resources for maize plant height breeding.
Xiaobo Zhu, Yanyan Wang, Shiyan Liu et al.· Plant physiology and biochem...· 0 citations
Sugarcane (Saccharum spp.) is an important crop for food and energy security. Identifying SNPs and genes associated with sugarcane yield and related traits is crucial for developing high - yielding sugarcane cultivars through molecular breeding. Here, we measured nine phenotypic traits across 160 sugarcane genotypes and employed multiple statistical models (namely MLM, CMLM, MLMM, FarmCPU and SUPER) in GWAS to identify stable and pleiotropic loci. A total of 200 SNPs corresponding to 137 QTLs were detected to be significantly associated with nine traits using multiple statistical models, among which 18 QTLs were consistently identified by two or more models. Notably, the SNP S9A_47793177 on chromosome 9A showed the strongest association with phenotypic variation in aboveground biomass, with a phenotypic explanation rate of 70.54%. Additionally, several QTLs significantly associated with tillering - related traits were identified, suggesting that these QTLs may play crucial roles in the regulation of tillering. The QTLs and SNPs identified in this study provide a significant foundation for molecular marker - assisted breeding in sugarcane. This advancement can significantly enhance the efficiency of genetic improvement for sugarcane yield and tillering - related traits.
L. Zhang, C. Xu, J. Li et al.· Plant biology· 0 citations
Background/Objectives: Soybean first pod insertion height (FPIH) is a key trait established during plant development, but its genetic architecture in Eurasian germplasm remains largely unknown. Methods: We performed a GWAS and fine-mapping for FPIH using 180 Eurasian varieties (SoySNP50K array, imputed to ~4M SNPs) phenotyped in four Russian environments (2021–2022), using two models: a residual-based and a covariate-adjusted. SuSiE fine-mapping was applied to refine candidate loci. Results: The covariate-adjusted model demonstrated better control of genomic inflation (λ = 1.08 vs. 1.16) and higher SNP heritability (0.381 vs. 0.014); the lower heritability in the residual-based model was expected, as this model removes environmental main effects and the genetic variance associated with them. Thus, the models are complementary: one captures stable genetic effects, the other highlights environment-dependent signals. SuSiE refined three loci. On chromosome 13, two stable independent signals (Gm13_30553403, Gm13_30909346; PIP ≥ 0.99) were identified near MYB83 and BEN1, consistent across both models. On chromosome 4, the signal shifted to Gm04_36933515 (PIP = 0.9999) near COBL4/IRX6, although model dependency was observed, and this locus is not currently recommended for marker development. On chromosome 5, the original GWAS SNP was not causal; two tightly linked SNPs (Gm05_38427309 and Gm05_38710046, r2 = 0.714, PIP ≥ 0.99) formed a haplotype block located near ARR1/ARR2 and a B-box/CCT domain gene. Crucially, this signal was absent in the residual-based model (max PIP = 0.33), suggesting that the chromosome 5 locus may modulate developmental plasticity rather than exerting a direct main effect on FPIH. However, as we did not perform formal G × E testing, we present this as a hypothesis requiring further validation. The environment-dependent behavior of this locus indicates that it should be used with caution in breeding programs and validated under specific target environments. Conclusions: Chromosome 13 SNPs provide stable genetic associations across environments, whereas the chromosome 5 block requires environment-specific validation. This work provides the first fine-mapped GWAS for FPIH in Russian soybean germplasm and highlights how model choice can uncover or obscure environment-dependent genetic loci affecting plant development.
I. Zorkoltseva, A. Kirichenko, D. Potapov et al.· Genes· 0 citations
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