Plant Genome最新文献

Fusarium head blight (FHB) of wheat (Triticum aestivum L.), caused by Fusarium graminearum, is a major wheat disease that causes significant yield and quality loss. The use of resistant cultivars is an effective tool for managing FHB; however, FHB resistance is a complex trait. The objectives of this study were to identify quantitative trait locus (QTL) associated with FHB resistance in the Triumph/25R51 doubled haploid (DH) population. The DH population was evaluated for FHB resistance across six field environments and an additional greenhouse study. The population was genotyped using single nucleotide polymorphism (SNP) markers, and a linkage map was constructed with a total length of 3018 cM across 26 linkage groups. QTL analysis identified multiple FHB resistance loci, including on chromosomes 2B, 2D, 3B, 5A, and 7D. The QTL on chromosome 3BS was the most important QTL for all FHB-related traits and explained 27%, 25%, 14%, and 13% of phenotypic variation for FHB severity, FHB index, Fusarium-damaged kernels (FDK) level, and deoxynivalenol (DON) content, respectively. Marker validation and haplotype analysis confirmed this QTL was due to Fhb1, with the resistance allele from 25R51 parent. FHB resistance QTL on 2D was collocated with both plant height and days to anthesis QTL at the Ppd-D1 locus. The population segregated for Rht-B1 locus, coincided with plant height QTL on 4B, but was not linked with FHB traits. This study showed that combining Fhb1 with additional resistance alleles enhances resistance to FHB, and the significant QTLs identified will be further useful for introgression in winter wheat breeding.

Sparse selection indices (SSIs) can be used to predict the genetic merit of selection candidates using high-dimensional phenotypes (e.g., crop imaging) measured on each of the candidates of selection. Unlike traditional selection indices, SSIs can perform variable selection, thus enabling borrowing of information from a subset of the measured phenotypes. Likewise, sparse genomic prediction (SGP) can be used to predict genetic merit by borrowing information from a subset of the training dataset. In this study, we introduce a framework for multi-trait/environment SGP (MT-SGP) that combines the features of SSI and SGP into a single model. For candidates of selection, an MT-SGP produces prediction equations that use subsets of the training data, borrowing information from correlated traits expressed in training genotypes that are genetically close to the candidates of selection. Along with the methodology, we present an R-package (sparse family and selection index) that provides functions to solve SSIs, SGP, and MT-SGP problems. After presenting simplified examples that illustrate the use of the functions included in the package, we provide extensive benchmarks (using three data sets covering three crops and 30 traits/environments). Our results suggest that MT-SGP either outperforms (with up to 15% gains in prediction accuracy) or performs similarly to MT-genomic best linear unbiased prediction. The benchmarks provide insight regarding the conditions (sample size, genetic correlation among traits, and trait heritability) under which the use of MT-SGP can lead to gains in prediction accuracy.

Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype-phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop- and trait-specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding.

Seed dormancy is regulated by a combination of developmental and environmental cues to ensure seedling survival in a changing environment. In barley (Hordeum vulgare L.), the SD1 and SD2 (where SD is standard deviation) loci regulate dormancy and pre-harvest sprouting (PHS), though their role in physiological development remains unclear. Malting barley production in Eastern Canada is currently limited due to the high potential for PHS in the region. To understand what genetic loci might be influencing dormancy in Eastern Canadian barley, we evaluated the LegCi biparental population, which was derived from the Léger variety. A quantitative trait loci close to the SD1 on chromosome 5 locus was identified as regulating germination in LegCi, suggesting that the alanine aminotransferase gene (AlaAT1), which underlies dormancy regulation at SD1, influences dormancy in LegCi. Alanine aminotransferases influence energy production in the cell, particularly during nitrogen limitation or oxygen deprivation. LegCi genotypes segregating for dormancy at the SD1 allele showed no differences in abscisic acid or GA-dependent gene expression during grain fill but varied for hypoxia-induced gene expression. Hypoxia suppressed germination in all genotypes but had a significantly higher impact on genotypes with the dormant AlaAT1 relative to genotypes with the non-dormant AlaAT1. This trend was not dependent on the presence of the hull, suggesting that signaling or metabolism inside the germinating seed is influencing hypoxia sensitivity. This work suggests that the non-dormant allele of SD1 is associated with reduced hypoxia stress sensitivity to promote germination. Further work is needed to determine if this trend extends to other barley genotypes.

Pulses are a valuable source of plant proteins for human and animal nutrition and have various industrial applications. Understanding the genetic basis for the relative abundance of different seed storage proteins is crucial for developing cultivars with improved protein quality and functional properties. In this study, we employed two complementary approaches, genome-wide association study (GWAS) and quantitative trait locus (QTL) mapping, to identify genetic loci underlying seed protein composition in pea (Pisum sativum L.). Sodium dodecyl sulfate-polyacrylamide gel electrophoresis was used to separate the seed proteins, and their relative abundance was quantified using densitometric analysis. For GWAS, we analyzed a diverse panel of 209 accessions genotyped with an 84,691 single-nucleotide polymorphism (SNP) array and identified genetic loci significantly associated with globulins, such as convicilin, vicilin, legumins, and non-globulins, including lipoxygenase, late embryogenesis abundant protein, and annexin-like protein. Additionally, using QTL mapping with 96 recombinant inbred lines, we mapped 11 QTL, including five that overlapped with regions identified by GWAS for the same proteins. Some of the significant SNPs were within or near the genes encoding seed proteins and other genes with predicted functions in protein biosynthesis, trafficking, and modification. This comprehensive genetic mapping study serves as a foundation for future breeding efforts to improve protein quality in pea and other legumes.

Roses (Rosa L.) are among the most economically important ornamentals worldwide, with ploidy ranging from diploid (2x) to hendecaploid (11x), though most cultivars are diploid (2x), triploid (3x), or tetraploid (4x). To enable large-scale analyses of ploidy and aneuploidy in roses using high-density single nucleotide polymorphism (SNP) array data, we developed Qploidy, an R package. Qploidy leverages tools for estimating allele dosage, adapts methods from human genetics for copy number estimation, and optimizes the standardization of allele intensity (R) and B allele frequency (BAF) for ploidy levels greater than 2x. With Qploidy, we analyzed a dataset of 1944 samples consisting of 588 samples from a germplasm collection and 1356 samples from 14 biparental mapping populations. The majority of genotypes in the germplasm collection were tetraploid (56%), followed by diploids (20%) and triploids (11%). The percentage of aneuploids was lower in the germplasm collection (2%) compared to biparental populations (16%). Reduced fitness likely explains the higher frequency of aneuploids in mapping populations compared to the germplasm collection, where stronger selective pressures normally act. In tetraploid biparental populations, pentasomy (65%) was significantly more common than trisomy (19%). Also, aneuploid states were predominantly transmitted through the female parent (87%), suggesting greater gametophyte sensitivity to chromosome number variation, particularly the loss of a chromosome. Since aneuploidy disturbs linkage and quantitative trait loci (QTL) analyses, Qploidy may also be used to guide the removal of aneuploid-affected data prior to downstream analysis. Besides roses, Qploidy can be used to study ploidy and aneuploidy in other polyploid species.

Leaf rust (LR) and stripe rust (YR), which are caused by Puccinia triticina and Puccinia striiformis, respectively, are among the most devastating wheat rusts worldwide. These diseases can be managed by using genetically resistant cultivars, an economical and environmentally safer alternative to fungicides. Over 100 and 80 Lr and Yr resistance genes have been discovered, respectively; however, rust pathogens are overcoming introduced resistance genes in the southeastern United States. Genome-wide association study has emerged as a valuable tool to identify new LR and YR resistance loci. In this study, a panel of 263 soft red winter wheat genotypes was evaluated for LR and YR severity in Plains, GA, and Williamson, GA, in a randomized complete block design of two replicates during 2019 and 2021-2023. Also, LR and YR infection types were assessed on seedlings at the three leaf stage in three greenhouse trials. A total of 26 significant quantitative trait loci (QTL) explaining 0.6%-30.8% phenotypic variance (PV) was detected by at least two of the five GAPIT models (BLINK, CMLM, FarmCPU, GLM, and MLM) tested. Nine major QTL included QLrYr-2A.1 linked to single-nucleotide polymorphism S2A_20855466, which had the highest overall PV (30.8%) for response to both rust pathogens in the field. Using the Chinese Spring Reference Genome Version 1.0, we detected 16 candidate genes, and four known R genes and QTL overlapped two major QTL. Of these QTL, 16 are likely novel genetic loci with potential for marker-assisted selection.

Cannabis (Cannabis sativa L.), once sidelined by decades of prohibition, has now gained recognition as a multifaceted and promising plant in both medical research and commercial applications following its recent legalization. This study leverages a genome-wide association study (GWAS) on 174 drug-type Cannabis accessions from the legal Canadian market, focusing on identifying quantitative trait loci (QTL) and candidate genes associated with eleven cannabinoid traits using 282K common single-nucleotide polymorphisms. This approach aims to transform our understanding of Cannabis genetics. We have pinpointed 33 significant markers that significantly influence cannabinoid production, promising to drive the development of Cannabis varieties with specific cannabinoid profiles. Among the notable findings is a massive haplotype of ∼60 Mb on chromosome 7 in Type I (i.e., tetrahydrocannabinol [THC]-dominant) accessions, highlighting a major genetic influence on cannabinoid profiles. These insights offer valuable guidance for Cannabis breeding programs, enabling the use of precise genetic markers to select and refine promising Cannabis varieties. This approach promises to speed up the breeding process, reduce costs significantly compared to traditional methods, and ensure that the resulting Cannabis varieties are optimized for specific medical and recreational needs. This study marks a significant stride toward fully integrating Cannabis into modern agricultural practices and genetic research, paving the way for future innovations.

Crown rust (Puccinia coronata f. sp. Avenae Erikss.) poses a significant threat to oat production worldwide. The most effective strategy for managing this disease involves identifying, mapping, and deploying resistance genes to develop cultivars with enhanced resistance. In this study, we conducted a meta-analysis of quantitative trait loci (QTLs) linked to crown rust resistance across diverse oat populations and environments. From 11 studies conducted between 2003 and 2024, we selected 167 QTLs, of which 127 were successfully mapped onto an oat consensus linkage map. These QTLs were mainly located on chromosomes of the D and C sub-genomes, showing considerable variation in genetic distances and marker associations. Based on the integration of these QTLs in a meta-QTL (MQTL) analysis, 23 MQTLs were identified for crown rust resistance in the oat genome. Gene mining within the MQTL intervals identified 1526 candidate genes, most of which were located in the D sub-genome. Functional analysis revealed that these genes play key roles in stress response, hormonal regulation, and polyamine metabolism, which are crucial for plant defense. Conserved regulatory elements (cis-acting regulatory element [CAREs]) were also identified in the promoter regions of key resistance genes, indicating their involvement in light response, stress regulation, and hormone signaling. This study represents a significant advancement in understanding the genetic architecture of crown rust resistance in oat and provides a valuable resource for breeding programs focused on improving disease resistance.