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Key Message: Two genomic regions associated with FFBN and HFFBN and a potential regulatory gene (GhE6) of HFFBN were identified through the integration of RTM-GWAS and meta‑QTL analyses. Abstract The first fruit branch node (FFBN) and the height of the first fruit branch node (HFFBN) are two important traits that are related to plant architecture and early maturation in upland cotton. Several studies have been conducted to elucidate the genetic basis of these traits in cotton using biparental and natural populations. In this study, by using 9,244 SNP linkage disequilibrium block (SNPLDB) loci from 315 upland cotton accessions, we carried out restricted two-stage multilocus and multiallele genome-wide association studies (RTM-GWASs) and identified promising haplotypes/alleles of the four stable and true major SNPLDB loci that were significantly associated with FFBN and HFFBN. Additionally, a meta-quantitative trait locus (MQTL) analysis was conducted on 274 original QTLs that were reported in 27 studies, and 40 MQTLs associated with FFBN and HFFBN were identified. Through the integration of the RTM-GWAS and meta‑QTL analyses, two stable and true major SNPLDBs (LDB_5_15144433 and LDB_16_37952328) that were distributed in the two MQTLs were identified. Ultimately, 142 genes in the two genomic regions were annotated, and three candidate genes associated with FFBN and HFFBN were identified in the genomic region (A05:14.64-15.64 Mb) via RNA-Seq and qRT‒PCR. The results of virus-induced gene silencing (VIGS) experiments indicated that GhE6 was a key gene related to HFFBN and that GhDRM1 and GhGES were important genes associated with early flowering in upland cotton. These findings will aid in the future identification of molecular markers and genetic resources for developing elite early-maturing cultivars with ideal plant characteristics., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: This study identified a significant number of QTL that are associated with FHB disease resistance in NMBU spring wheat panel by conducting genome-wide association study. Fusarium head blight (FHB) is a widely known devastating disease of wheat caused by Fusarium graminearum and other Fusarium species. FHB resistance is quantitative, highly complex and divided into several resistance types. Quantitative trait loci (QTL) that are effective against several of the resistance types give valuable contributions to resistance breeding. A spring wheat panel of 300 cultivars and breeding lines of Nordic and exotic origins was tested in artificially inoculated field trials and subjected to visual FHB assessment in the years 2013-2015, 2019 and 2020. Deoxynivalenol (DON) content was measured on harvested grain samples, and anther extrusion (AE) was assessed in separate trials. Principal component analysis based on 35 and 25 K SNP arrays revealed the existence of two subgroups, dividing the panel into European and exotic lines. We employed a genome-wide association study to detect QTL associated with FHB traits and identify marker-trait associations that consistently influenced FHB resistance. A total of thirteen QTL were identified showing consistent effects across FHB resistance traits and environments. Haplotype analysis revealed a highly significant QTL on 7A, Qfhb.nmbu.7A.2, which was further validated on an independent set of breeding lines. Breeder-friendly KASP markers were developed for this QTL that can be used in marker-assisted selection. The lines in the wheat panel harbored from zero to five resistance alleles, and allele stacking showed that resistance can be significantly increased by combining several of these resistance alleles. This information enhances breeders´ possibilities for genomic prediction and to breed cultivars with improved FHB resistance., (© 2022. The Author(s).)

Key Message: A novel major QTL for FHB resistance was mapped to a 6.8 Mb region on chromosome 2D in a Chinese wheat cultivar Ji5265, and diagnostic KASP markers were developed for detecting it in a worldwide wheat collection. Fusarium head blight (FHB) is a serious disease in wheat (Triticum aestivum L.) and causes significant reductions in grain yield and quality worldwide. Breeding for FHB resistance is the most effective strategy to minimize the losses caused by FHB; therefore, identification of major quantitative trait loci (QTLs) conferring FHB resistance and development of diagnostic markers for the QTLs are prerequisites for marker-assisted selection (MAS). Ji5265 is a Chinese wheat cultivar resistant to FHB in multiple environments. An F 6 population of 179 recombinant inbred lines (RILs) was developed from Ji5265 × Wheaton. The population was genotyped by genotyping-by-sequencing (GBS) and phenotyped for FHB Type II resistance in greenhouses. A major QTL, designated as QFhb-2DL, was mapped in a 6.8 Mb region between the markers GBS10238 and GBS12056 on the long arm of chromosome 2D in Ji5265 and explained ~ 30% of the phenotypic variation for FHB resistance. The effect of QFhb-2DL on FHB resistance was validated using near-isogenic lines (NILs) derived from residual heterozygotes from an F 6 RIL of Ji5265 × Wheaton. The two flanking markers were converted into Kompetitive allele-specific PCR (KASP) markers (KASP10238 and KASP12056) and validated to be diagnostic in a collection of 2,065 wheat accessions. These results indicate that QFhb-2DL is a novel major QTL for resistance to FHB spread within a spike (Type II) and the two KASP markers can be used for MAS to improve wheat FHB resistance in wheat breeding programs., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Key Message: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Key Message: Based on the large-scale integration of meta-QTL and Genome-Wide  Association Study, 76 high-confidence MQTL regions and 237 candidate genes that affected wheat yield and yield-related traits were discovered. Improving yield and yield-related traits are key goals in wheat breeding program. The integration of accumulated wheat genetic resources provides an opportunity to uncover important genomic regions and candidate genes that affect wheat yield. Here, a comprehensive meta-QTL analysis was conducted on 2230 QTL of yield-related traits obtained from 119 QTL studies. These QTL were refined into 145 meta-QTL (MQTL), and 89 MQTL were verified by GWAS with different natural populations. The average confidence interval (CI) of these MQTL was 2.92 times less than that of the initial QTL. Furthermore, 76 core MQTL regions with a physical distance less than 25 Mb were detected. Based on the homology analysis and expression patterns, 237 candidate genes in the MQTL involved in photoperiod response, grain development, multiple plant growth regulator pathways, carbon and nitrogen metabolism and spike and flower organ development were determined. A novel candidate gene TaKAO-4A was confirmed to be significantly associated with grain size, and a CAPS marker was developed based on its dominant haplotype. In summary, this study clarified a method based on the integration of meta-QTL, GWAS and homology comparison to reveal the genomic regions and candidate genes that affect important yield-related traits in wheat. This work will help to lay a foundation for the identification, transfer and aggregation of these important QTL or candidate genes in wheat high-yield breeding., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Key Message: Linkage and genome-wide association analyses using high-throughput SNP genotyping revealed different loci controlling resistance to different isolates of race 65 of Colletotrichum lindemuthianum in common bean. Development of varieties with durable resistance to anthracnose is a major challenge in common bean breeding programs because of the extensive virulence diversity of Colletotrichum lindemuthianum fungus. We used linkage and genome-wide association analyses to tap the genomic regions associated with resistance to different isolates of race 65. Linkage mapping was done using an F 2 population derived from the cross between the Mesoamerican common beans BRS Estilo x Ouro Vermelho, inoculated with two different isolates of race 65. Association genetics relied on a diversity common bean panel containing 189 common bean accessions inoculated with five different isolates of race 65 as an attempt to validate the linkage analysis findings and, eventually, identify other genomic regions associated with resistance to race 65. The F 2 population and diversity panel were genotyped with the BARCBean6K_3 Illumina BeadChip containing 5398 SNP markers. Both linkage and genome-wide association analyses identified different loci controlling resistance to different isolates of race 65 on linkage group Pv04. Genome-wide association analysis also detected loci on Pv05, Pv10 and Pv11 associated with resistance to race 65. These findings indicate that resistance to race 65 can be overcome by the virulence diversity among different isolates of the same race and could lead to the loss of resistance after cultivar release. We identified 25 resistant common bean cultivars to all five isolates of race 65 in the diversity panel. The accessions should be useful to develop cultivars combining different resistance genes that favor durable resistance to anthracnose in common bean.

Fusarium head blight (FHB), or scab, for its devastating nature to wheat production and food security, has stimulated worldwide attention. Multidisciplinary efforts have been made to fight against FHB for a long time, but the great progress has been achieved only in the genomics era of the past 20 years, particularly in the areas of resistance gene/QTL discovery, resistance mechanism elucidation and molecular breeding for better resistance. This review includes the following nine main sections, (1) FHB incidence, epidemic and impact, (2) causal Fusarium species, distribution and virulence, (3) types of host resistance to FHB, (4) germplasm exploitation for FHB resistance, (5) genetic control of FHB resistance, (6) fine mapping of Fhb1, Fhb2, Fhb4 and Fhb5, (7) cloning of Fhb1, (8) omics-based gene discovery and resistance mechanism study and (9) breeding for better FHB resistance. The advancements that have been made are outstanding and exciting; however, judged by the complicated nature of resistance to hemi-biotrophic pathogens like Fusarium species and lack of immune germplasm, it is still a long way to go to overcome FHB.

Colletotrichum crown rot (CCR) is an important disease of strawberry (Fragaria ×ananassa) throughout the Southeastern US and in subtropical climates around the world, where hot and humid conditions facilitate rapid disease development. Yet no resistance loci have been described to date, as genetic studies have been historically difficult in allo-octoploid (2n = 8x = 56) strawberry. In the present study, we investigate the genetic architecture of resistance to CCR. Four population sets from the University of Florida were inoculated in four different seasons from 2013-2014 to 2016-2017. Two large, multiparental discovery population sets were used for QTL discovery, and two validation sets of cultivars and advanced selections representing the parent pool of the breeding program were also assessed. Subgenome-specific single-nucleotide polymorphism (SNP) markers were mapped, and FlexQTL™ software was utilized to perform a Bayesian, pedigree-based QTL analysis. A quantitative trait locus on linkage group 6B, which we name FaRCg1, accounts for most of the genetic variation for resistance in the discovery sets (26.8-29.8% in 2013-2014 and 17% in 2015-2016). High-throughput marker assays were developed for the most significant SNPs which correlated with the mode of the QTL region. The discovery and characterization of the FaRCg1 locus and the molecular tools developed from it will be utilized to achieve increased genetic gains for resistance.

Key Message: The number of SNPs required for QTL discovery is justified by the distance at which linkage disequilibrium has decayed. Simulations and real potato SNP data showed how to estimate and interpret LD decay. The magnitude of linkage disequilibrium (LD) and its decay with genetic distance determine the resolution of association mapping, and are useful for assessing the desired numbers of SNPs on arrays. To study LD and LD decay in tetraploid potato, we simulated autotetraploid genotypes and used it to explore the dependence on: (1) the number of haplotypes in the population (the amount of genetic variation) and (2) the percentage of haplotype specific SNPs (hs-SNPs). Several estimators for short-range LD were explored, such as the average r 2 , median r 2 , and other percentiles of r 2 (80, 90, and 95 %). For LD decay, we looked at LD ½,90 , the distance at which the short-range LD is halved when using the 90 % percentile of r 2 at short range, as estimator for LD. Simulations showed that the performance of various estimators for LD decay strongly depended on the number of haplotypes, although the real value of LD decay was not influenced very much by this number. The estimator LD ½,90 was chosen to evaluate LD decay in 537 tetraploid varieties. LD ½,90 values were 1.5 Mb for varieties released before 1945 and 0.6 Mb in varieties released after 2005. LD ½,90 values within three different subpopulations ranged from 0.7 to 0.9 Mb. LD ½,90 was 2.5 Mb for introgressed regions, indicating large haplotype blocks. In pericentromeric heterochromatin, LD decay was negligible. This study demonstrates that several related factors influencing LD decay could be disentangled, that no universal approach can be suggested, and that the estimation of LD decay has to be performed with great care and knowledge of the sampled material., Competing Interests: The authors declare that they have no conflict of interest.

Key Message: We enhance power and accuracy of QTL mapping in multiple related families, by clustering the founders of the families on their local genomic similarity. MCQTL is a linkage mapping software application that allows the joint QTL mapping of multiple related families. In its current implementation, QTLs are modeled with one or two parameters for each parent that is a founder of the multi-cross design. The higher the number of parents, the higher the number of model parameters which can impact the power and the accuracy of the mapping. We propose to make use of the availability of denser and denser genotyping information on the founders to lessen the number of MCQTL parameters and thus boost the QTL discovery. We developed clusthaplo, an R package ( http://cran.r-project.org/web/packages/clusthaplo/index.html ), which aims to cluster haplotypes using a genomic similarity that reflects the probability of sharing the same ancestral allele. Computed in a sliding window along the genome and followed by a clustering method, the genomic similarity allows the local clustering of the parent haplotypes. Our assumption is that the haplotypes belonging to the same class transmit the same ancestral allele. So their putative QTL allelic effects can be modeled with the same parameter, leading to a parsimonious model, that is plugged in MCQTL. Intensive simulations using three maize data sets showed the significant gain in power and in accuracy of the QTL mapping with the ancestral allele model compared to the classical MCQTL model. MCQTL_LD (clusthaplo outputs plug in MCQTL) is a versatile and powerful tool for QTL mapping in multiple related families that makes use of linkage and linkage disequilibrium (web site http://carlit.toulouse.inra.fr/MCQTL/ ).

A major objective for geneticists is to decipher genetic architecture of traits associated with agronomic importance. However, a majority of such traits are complex, and their genetic dissection has been traditionally hampered not only by the number of minor-effect quantitative trait loci (QTL) but also by genome-wide interacting loci with little or no individual effect. Soybean (Glycine max [L.] Merr.) seed isoflavonoids display a broad range of variation, even in genetically stabilized lines that grow in a fixed environment, because their synthesis and accumulation are affected by many biotic and abiotic factors. Due to this complexity, isoflavone QTL mapping has often produced conflicting results especially with variable growing conditions. Herein, we comparatively mapped soybean seed isoflavones genistein, daidzein, and glycitein by using several of the most commonly used mapping approaches: interval mapping, composite interval mapping, multiple interval mapping and a mixed-model based composite interval mapping. In total, 26 QTLs, including many novel regions, were found bearing additive main effects in a population of RILs derived from the cross between Essex and PI 437654. Our comparative approach demonstrates that statistical mapping methodologies are crucial for QTL discovery in complex traits. Despite a previous understanding of the influence of additive QTL on isoflavone production, the role of epistasis is not well established. Results indicate that epistasis, although largely dependent on the environment, is a very important genetic component underlying seed isoflavone content, and suggest epistasis as a key factor causing the observed phenotypic variability of these traits in diverse environments.

Advanced backcross QTL analysis is proposed as a method of combining QTL analysis with variety development. It is tailored for the discovery and transfer of valuable QTL alleles from unadapted donor lines (e.g., land races, wild species) into established elite inbred lines. Following this strategy, QTL analysis is delayed until the BC2 or BC3 generation and, during the development of these populations, negative selection is exercised to reduce the frequency of deleterious donor alleles. Simulations suggest that advanced backcross QTL analysis will be effective in detecting additive, dominant, partially dominant, or overdominant QTLs. Epistatic QTLs or QTLs with gene actions ranging from recessive to additive will be detected with less power than in selfing generations. QTL-NILs can be derived from advanced backcross populations in one or two additional generations and utilized to verify QTL activity. These same QTL-NILs also represent commercial inbreds improved (over the original recurrent inbred line) for one or more quantitative traits. The time lapse from QTL discovery to construction and testing of improved QTL-NILs is minimal (1-2 years). If successfully employed, advanced backcross QTL analysis can open the door to exploiting unadapted and exotic germplasm for the quantitative trait improvement of a number of crop plants.