Plant Genome最新文献

Passion fruit (Passiflora edulis) is a highly nutritious horticultural crop cultivated widely across tropical and subtropical regions. Despite decades of breeding efforts that have led to the release of a few high-yielding cultivars, on-farm productivity remains suboptimal, and several existing cultivars are showing signs of declining vigor. To ensure the development of cultivars with stable and enhanced yields under both optimal and stress-prone conditions, there is a growing impetus to improve breeding efficiency. Integrating advanced genomics technologies into conventional breeding pipelines offers a promising path forward. Over the past decade, substantial genomic resources have been developed, including genome-wide markers, marker-trait associations, reference genomes, and resequencing datasets. Some of these tools are already being deployed in breeding programs to enhance yield and consumer-preferred traits. Emerging approaches such as genomic selection, speed breeding, and high-throughput phenotyping hold further potential to accelerate genetic gains. Realizing the full benefits of these tools will require strategic utilization of diverse and targeted genetic resources, coupled with streamlined cultivar delivery systems. Addressing the technical and operational bottlenecks that hinder the translation of genomic advances to field-ready cultivars will be key to securing the future of passion fruit improvement.

Low-light (LL) stress caused by persistent cloud cover during the Kharif season significantly reduces rice (Oryza sativa L.) grain yield (GY) by limiting photosynthesis, impairing assimilate production, and affecting reproductive development. To dissect the genetic basis of LL tolerance, 192 diverse rice genotypes were evaluated across contrasting light environments (LL and normal light under Rabi and Kharif seasons) and genotyped using a high-density 44K single nucleotide polymorphism array. Integrating phenotypic and genomic data enabled a multi-tiered analysis from quantitative trait locus (QTL) discovery to gene identification and haplotype dissection. Genome-wide association analysis identified 305 QTLs associated with GY and 11 related traits, including 148 LL-specific and 32 stable QTLs expressed across both seasons. Forty-two candidate genes were localized within major QTL intervals, and 12 were identified as hub genes based on their key roles in photosynthesis, light perception, hormone signaling, and starch biosynthesis. These included Gn1a, OsPsbS1, OsAGPL2, OsLhcb1, OsAUX1, OsSBDCP1, OsNPF5.16, OsPHYA, OsPHYB, OsGIF1, HY5, and OsYUC11. Expression profiling confirmed stronger induction of OsPHYA (∼2.5-fold) and OsPsbS1 (∼2.8-fold) in LL-tolerant genotypes like Purnendu and Swarnaprabha compared to susceptible lines. Haplotype analysis revealed several superior alleles, such as PHYA-Hap2 and OsPsbS1-Hap3, that were consistently associated with higher spikelet fertility, greater grain number, increased biomass, and improved GY under LL, with top-performing haplotypes enhancing yield by 12%-18%. Genotypes carrying these haplotypes (e.g., Purnendu, Swarnaprabha, and Chamarmani) represent valuable breeding donors. Overall, this study provides the first genome-wide identification of LL-specific haplotypes in rice, together with biologically validated hub genes. These findings offer actionable genomic targets and donor resources for developing LL-resilient, high-yielding cultivars suited to changing climate and light-limited environments.

Integrating genomic and environmental information holds the potential for enhancing the predictive power of genomic prediction models when accounting for the genotype-by-environment interactions. Hence, incorporating environmental covariates (EC) into these models can significantly influence their predictive accuracy. In this study, we utilized 1379 genotypes from the SoyNAM dataset, evaluated across four environments and genotyped with 4611 single-nucleotide polymorphism markers, to compare models incorporating genotype-by-environment and genotype-by-environmental covariate interactions using different covariance matrices. We evaluated four approaches: summarizing EC by averaging (AVG), filtering ECs based on a coefficient of determination criterion (FILT), segmenting ECs by crop phenology (STG), and a naïve approach that utilized all available information (ALL). Predictive ability was assessed as the Pearson's correlation between the genomic estimated breeding values and the adjusted phenotypes considering 10 replicates of three cross-validation scenarios (CV2: predicting tested genotypes in observed environments; CV1: untested genotypes in observed environments; CV0: tested genotypes in novel environments). Incorporating EC information into the models increased average predictive ability from 0.42 to 0.56 for CV1 and CV2. In these cases, the predictive ability was lower when EC information was averaged to compute the environmental kinship matrix, with slight differences observed with respect to the other approaches. Regarding the CV0 scheme, the model incorporating only genotype-by-environment information performed better (0.33). The naïve method, which utilized all available EC information (ALL), proved to be a promising approach, as it effectively improved the results in these scenarios while eliminating the need for additional steps in selecting variables.

Wheat is a major global staple food affected by three diseases: leaf rust (LR), stem rust (SR), and stripe rust (YR), all of which can cause substantial yield losses. Identifying genotypes with broad-spectrum resistance to diverse pathotypes of all three rusts remains a major challenge. In this study, we examined the genomic basis of resistance to three rust diseases LR, SR, and YR in a diverse panel of 346 bread wheat (Triticum aestivum) accessions. The seedling stage phenotypic evaluation was performed for 2 years using prevalent and virulent pathotypes. Based on best linear unbiased estimators, LR and YR displayed right-skewed distributions, whereas SR showed a bimodal pattern. Genotyping with the 35K Axiom Wheat Breeders Array, followed by quality control, yielded 11,910 high-quality single nucleotide polymorphisms (SNPs). Population structure analysis revealed five subpopulations and a whole genome linkage disequilibrium decay of 3.49 Mb. Multi-trait genome-wide association studies identified 11 significant SNPs distributed on chromosomes 3A, 3B, 3D, and 7B, which were associated with 47 disease resistance genes, 22 of which were highly expressed in at least one condition. The haplotype analysis revealed eight different haplotypes, where H006 and H007 were superior in terms of multiple rust resistance (MRR). Note that 17 elite accessions, including IC427824 and HGP1-359, were selected using multi-trait genotype ideotype distance index analysis. Three key Kompetitive allele specific polymerase chain reaction (KASP) markers, AX94381808, AX94874313, and AX94807942 were developed and validated. This integrated genomic approach advances the identification process and can accelerate the breeding of wheat cultivars with durable MRR.

"Cytosine methylation plays an important role in the regulation of gene expression in plants." Some iteration of this statement can be found in most papers centered on plant epigenetics and has become a widely accepted textbook claim. However, our generalized understanding of how DNA methylation exerts control over transcription is now challenged by observations demonstrating that transcriptional levels of most genes are unresponsive to DNA methylation changes. On a genome-wide scale, associations between DNA methylation and transcription are usually statistically weak. Even when correlations are found, the cause and effect can be difficult to identify, as methylation changes sometimes follow rather than precede transcriptional changes. While a growing number of studies explore a possible connection between differentially expressed genes (DEGs) and differentially methylated genes (DMGs), we demonstrate here that DEG-DMG overlaps are often significantly smaller than what could be expected by chance. This indicates that, contrary to expectations, changes in DNA methylation and changes in transcription sometimes avoid one another. Here, we discuss such observations and their implications for the hypothesis of a widespread control of gene expression directly by DNA methylation. While there are well-documented examples where DNA methylation regulates transcription, we argue that such cases represent a minority of genes, and we opine that approaches of reverse epigenetics are therefore unlikely to find broad application in breeding.

Breeding programs conventionally evaluate cohorts in separate trials; however, environmental differences across testing areas can be confounded with genetic differences between cohorts, potentially reducing the accuracy of breeding value estimation. We test whether the conventional approach of restricting randomization of cohorts to within trials reduces genomic and conventional selection accuracy when compared to the complete randomization of all cohorts across a trial, using in silico simulation with marker data from University of Illinois winter wheat breeding lines. We evaluated selection accuracy for conventional best linear unbiased prediction (BLUP), genomic BLUP (GBLUP), and genomic-enabled sparse testing across a comprehensive simulation space spanning narrow-sense heritabilities of 0.2-0.8, genetic correlations between testing areas from 0.2 to 1.0, and three replication levels. Difference-in-differences (DiD) analysis established causal inference by comparing design performance as conditions deteriorated from an optimal baseline where both designs performed equivalently. Complete randomization improved BLUP accuracy by 11.7%, reaching 15.7% under low replication and low genetic correlation between areas. Genomic data largely eliminated this design effect, with GBLUP showing no significant DiD interaction effect. However, genomic-enabled sparse testing revealed a significant DiD effect and an improvement in selection accuracy of 1.5% that increased to a 5.5% advantage under challenging conditions. While heritability had the strongest main effect on selection accuracy, genetic correlation between areas showed the largest interaction with randomization scheme, with design performance diverging significantly only as this parameter decreased. Programs with genomic data and balanced phenotypic data can use either restricted or complete randomization, but those with other circumstances can benefit from complete randomization.

Poplar (Populus spp.) breeding programs increasingly prioritize the development of male varieties due to the environmental issues caused by female catkins. Therefore, there is an urgent need for a reliable technique for early sex identification in Populus. Genomic selection (GS), as an efficient predictive method, offers a promising solution for early sex identification in poplar. In this study, we conducted genomic prediction for sex in Populus deltoides. Using five full-sib families of P. deltoides as the reference population, we identified 801 sex-associated loci through GWAS and precisely localized the sex determination region at the telomeric end of chromosome 19. We evaluated 14 GS statistical models using fivefold cross-validation under six marker densities. The results showed significant differences in prediction accuracy (PA) among different statistical models, ranging from 0.19 to 0.79, with the gradient boosting decision tree exhibiting the highest accuracy and stability. Notably, single nucleotide polymorphisms selected through GWAS significantly improved PA compared to random markers, achieving a corrected accuracy of 0.999. Using the optimal model and markers, we predicted the sex of 505 progenies from 27 full-sib families, with over 90% of the predictions being accurate. Overall, this study achieved high-accuracy sex prediction in P. deltoides through genome prediction, providing a novel and efficient method for poplar sex identification.

Pawpaw (Asimina triloba) is the only fruit-producing tree of the soursop (custard apple) family Annonaceae that is native to temperate North America. Pawpaws are extensively cultivated in the northeast United States, but to date, there are few genetic resources and no publicly available genome assemblies. Here, we present the first high-quality genome assembly and annotation of pawpaw (cultivar Mango), derived from high-fidelity third-generation sequencing. The 851.7-Mbp assembly consists of 68 contigs with a scaffold N50 of 28.5 Mbp, guanine-cytosine content of 37%, and a 96.1% benchmarking universal single-copy ortholog completeness score (eudicots). We profiled agronomically relevant transcription factors in the transcription factor family with the DNA-binding WRKY amino acid domain and no apical meristem/Arabidopsis transcription activation factor/cup-shaped cotyledon transcription factor families, which have functions related to environmental and pathogen immunity responses and regulation of fruit traits. Our resource facilitates future genetic and breeding research for this culturally important fruiting tree, expanding its economic and commercial potential.

Genomic selection allows the prediction of genetic values using SNP markers distributed across the genome. Its effectiveness depends on factors such as trait heritability, genetic similarity between training and validation sets, and population structure. Although results in homogeneous populations have been promising, its application in diverse germplasm remains a challenge. This study evaluates the predictive capacity of genomic best linear unbiased prediction models applied to agronomic and biochemical-structural traits related to stover quality in two maize populations: a diversity panel and a multiparental advanced generation inter-cross (MAGIC) population. Higher heritability was observed in the panel, especially for flowering traits (h² ≥ 0.88), with high intra-population predictive abilities (PA = 0.15-0.75) for most traits, compared to MAGIC (PA = 0.14-0.37). However, when applying the models from one population to another (cross-population prediction), the predictive ability was drastically reduced for most traits (PA < 0.05), possibly due to differences in allele frequencies and phases of linkage disequilibrium. Combining both populations in a single training set did not improve prediction (PA = 0.13-0.74) and even reduced it in some cases. These results indicate that genetic heterogeneity and differences in linkage disequilibrium between populations compromise the stability of marker effects. Therefore, it is critical to optimize the training set composition by considering genetic relatedness and population structure to improve the efficiency of genomic selection in diverse germplasm.