Numerous Quantitative Trait Loci Research Articles

Genomic investigation of 18,421 lines reveals the genetic architecture of rice.

Understanding how numerous quantitative trait loci (QTL) shape phenotypic variation is an important question in genetics. To address this, we established a permanent population of 18,421 (18K) rice lines with reduced population structure. We generated reference-level genome assemblies of the founders and genotyped all 18K-rice lines through whole-genome sequencing. Through high-resolution mapping, 96 high-quality candidate genes contributing to variation in 16 traits were identified, including OsMADS22 and OsFTL1 verified as causal genes for panicle number and heading date, respectively. We identified epistatic QTL pairs and constructed a genetic interaction network with 19 genes serving as hubs. Overall, 170 masking epistasis pairs were characterized, serving as an important factor contributing to genetic background effects across diverse varieties. The work provides a basis to guide grain yield and quality improvements in rice.

Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population

BackgroundCarcass weight (HCW) and marbling (MARB) are critical for meat quality and market value in beef cattle. In composite breeds like Brangus, which meld the genetics of Angus and Brahman, SNP-based analyses have illuminated some genetic influences on these traits, but they fall short in fully capturing the nuanced effects of breed of origin alleles (BOA) on these traits. Focus on the impacts of BOA on phenotypic features within Brangus populations can result in a more profound understanding of the specific influences of Angus and Brahman genetics. Moreover, the consideration of BOA becomes particularly significant when evaluating dominance effects contributing to heterosis in crossbred populations. BOA provides a more comprehensive measure of heterosis due to its ability to differentiate the distinct genetic contributions originating from each parent breed. This detailed understanding of genetic effects is essential for making informed breeding decisions to optimize the benefits of heterosis in composite breeds like Brangus.ObjectiveThis study aims to identify quantitative trait loci (QTL) influencing HCW and MARB by utilizing SNP and BOA information, incorporating additive, dominance, and overdominance effects within a multi-generational Brangus commercial herd.MethodsWe analyzed phenotypic data from 1,066 genotyped Brangus steers. BOA inference was performed using LAMP-LD software using Angus and Brahman reference sets. SNP-based and BOA-based GWAS were then conducted considering additive, dominance, and overdominance models.ResultsThe study identified numerous QTLs for HCW and MARB. A notable QTL for HCW was associated to the SGCB gene, pivotal for muscle growth, and was identified solely in the BOA GWAS. Several BOA GWAS QTLs exhibited a dominance effect underscoring their importance in estimating heterosis.ConclusionsOur findings demonstrate that SNP-based methods may not detect all genetic variation affecting economically important traits in composite breeds. BOA inclusion in genomic evaluations is crucial for identifying genetic regions contributing to trait variation and for understanding the dominance value underpinning heterosis. By considering BOA, we gain a deeper understanding of genetic interactions and heterosis, which is integral to advancing breeding programs. The incorporation of BOA is recommended for comprehensive genomic evaluations to optimize trait improvements in crossbred cattle populations.

Quantitative trait loci mapping for survival of virus infection and virus levels in honey bees

Israeli acute paralysis virus (IAPV) is a highly virulent, Varroa-vectored virus that is of global concern for honey bee health. Little is known about the genetic basis of honey bees to withstand infection with IAPV or other viruses. We set up and analyzed a backcross between preselected honey bee colonies of low and high IAPV susceptibility to identify quantitative trait loci (QTL) associated with IAPV susceptibility. Experimentally inoculated adult worker bees were surveyed for survival and selectively sampled for QTL analysis based on SNPs identified by whole-genome resequencing and composite interval mapping. Additionally, natural titers of other viruses were quantified in the abdomen of these workers via qPCR and also used for QTL mapping. In addition to the full dataset, we analyzed distinct subpopulations of susceptible and non-susceptible workers separately. These subpopulations are distinguished by a single, suggestive QTL on chromosome 6, but we identified numerous other QTL for different abdominal virus titers, particularly in the subpopulation that was not susceptible to IAPV. The pronounced QTL differences between the susceptible and non-susceptible subpopulations indicate either an interaction between IAPV infection and the bees' interaction with other viruses or heterogeneity among workers of a single cohort that manifests itself as IAPV susceptibility and results in distinct subgroups that differ in their interaction with other viruses. Furthermore, our results indicate that low susceptibility of honey bees to viruses can be caused by both, virus tolerance and virus resistance. QTL were partially overlapping among different viruses, indicating a mixture of shared and specific processes that control viruses. Some functional candidate genes are located in the QTL intervals, but their genomic co-localization with numerous genes of unknown function delegates any definite characterization of the underlying molecular mechanisms to future studies.

Surveying the genomic landscape of silage-quality traits in maize (Zea mays L.)

Despite the longstanding importance of silage as a critical feed source for ruminants, its quality improvement has been largely overlooked. Although numerous quantitative trait loci (QTL) and genes affecting silage quality in maize have been reported, only a few have been effectively incorporated into breeding programs. Addressing this gap, the present study undertook a comprehensive meta-QTL (MQTL) analysis involving 523 QTL associated with silage-quality traits collected from 14 published studies. Of the 523 QTL, 405 were projected onto a consensus map comprising 62,424 genetic markers, resulting in the identification of 60 MQTL and eight singletons. The average confidence interval (CI) of the MQTL was 3.9-fold smaller than that of the source QTL. Nine of the 60 identified MQTL were classified as breeder’s MQTL owing to their small CIs, involvement of more QTL, and large contribution to phenotypic variation. One-third of the MQTL co-localized with DNA marker-trait associations identified in previous genome-wide association mapping studies. A set of 78 high-confidence candidate genes influencing silage quality were identified in the MQTL regions. These genes and associated markers may advance marker-assisted breeding for maize silage quality.

High-throughput phenotyping and deep learning to analyze dynamic panicle growth and dissect the genetic architecture of yield formation

The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R2 was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines.

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Interaction and association analysis of malting related traits in barley.

Barley is considered as a foundation of the brewing and malting industry. Varieties with superior malt quality traits are required for efficient brewing and distillation processes. Among these, the Diastatic Power (DP), wort-Viscosity (VIS), β-glucan content (BG), Malt Extract (ME) and Alpha-Amylase (AA) are controlled by several genes linked to numerous quantitative trait loci (QTL), identified for barley malting quality. One of the well-known QTL, QTL2, associated with barley malting trait present on chromosome 4H harbours a key gene, called as HvTLP8 that has been identified for influencing the barley malting quality through its interaction with β-glucan in a redox-dependent manner. In this study, we examined to develop a functional molecular marker for HvTLP8 in the selection of superior malting cultivars. We first examined the expression of HvTLP8 and HvTLP17 containing carbohydrate binding domains in barley malt and feed varieties. The higher expression of HvTLP8 prompted us to further investigate its role as a marker for malting trait. By exploring the 1000 bp downstream 3' UTR region of HvTLP8, we found single nucleotide polymorphism (SNP) in between Steptoe (feed variety) and Morex (malt variety), which was further validated by Cleaved Amplified Polymorphic Sequence (CAPS) marker assay. Analysis of 91 individuals from the Steptoe x Morex doubled haploid (DH) mapping population revealed CAPS polymorphism in HvTLP8. Highly significant (p<0.001) correlations among ME, AA and DP malting traits were observed. The correlation coefficient (r) between these traits ranged from 0.53 to 0.65. However, the polymorphism in HvTLP8 did not correlate effectively with ME, AA, and DP. Altogether, these findings will help us to further design the experiment regarding the HvTLP8 variation and its association with other desirable traits.

Application of SolCAP Genotyping in Potato (Solanum tuberosum L.) Association Mapping.

Potato variety development entails a number of breeding steps, as well as testing and, finally, commercialization. Historically, phenotypic assesment were carried out to select and germplasm development. The US Department of Agriculture (USDA) funded the Solanaceae Coordinated Agricultural Project (SolCAP) to decode genomic resources into tools that breeders and geneticists can use. This project resulted in the creation of a genome-wide single-nucleotide polymorphism (SNP) array that can be used to evaluate elite potato-breeding germplasm. This array was used to genotype a diverse panel of Solanum species, as well as numerous biparental, diploid, and tetraploid populations. It has high marker density to generate genetic maps that can be used to identify numerous quantitative trait loci (QTLs) for agronomic, quality, biotic, and abiotic resistance traits. Up to now, numerous QTLs for important traits have been identified using new diploid and tetraploid genetic maps. SNP markers were used to assess germplasm relationships and fingerprint varieties and identify candidate genes. The Infinium 8303 SolCAP Potato array offers a common set of SNP markers that can be used for mapping, germplasm assessment, and fingerprinting with confidence. This array has also been helpful in furthering our understanding of the potato genome. Furthermore, some other Infinium potato arrays (i.e., 12K, 20K, and 25K) have been genotyped, and breeders can map quantitative trait loci (QTLs) across multiple populations to improve our understanding of economically important traits and lead to marker-assisted selection (MAS) and breeding and, ultimately, improved varieties.

High-resolution genome-wide association study of a large Czech collection of sweet cherry (Prunus avium L.) on fruit maturity and quality traits.

In sweet cherry (Prunus avium L.), quantitative trait loci have been identified for fruit maturity, colour, firmness, and size to develop markers for marker-assisted selection. However, resolution is usually too low in those analyses to directly target candidate genes, and some associations are missed. In contrast, genome-wide association studies are performed on broad collections of accessions, and assemblies of reference sequences from Tieton and Satonishiki cultivars enable identification of single nucleotide polymorphisms after whole-genome sequencing, providing high marker density. Two hundred and thirty-five sweet cherry accessions were sequenced and phenotyped for harvest time and fruit colour, firmness, and size. Genome-wide association studies were used to identify single nucleotide polymorphisms associated with each trait, which were verified in breeding material consisting of 64 additional accessions. A total of 1 767 106 single nucleotide polymorphisms were identified. At that density, significant single nucleotide polymorphisms could be linked to co-inherited haplotype blocks (median size ~10kb). Thus, markers were tightly associated with respective phenotypes, and individual allelic combinations of particular single nucleotide polymorphisms provided links to distinct phenotypes. In addition, yellow-fruit accessions were sequenced, and a ~ 90-kb-deletion on chromosome 3 that included five MYB10 transcription factors was associated with the phenotype. Overall, the study confirmed numerous quantitative trait loci from bi-parental populations using high-diversity accession populations, identified novel associations, and genome-wide association studies reduced the size of trait-associated loci from megabases to kilobases and to a few candidate genes per locus. Thus, a framework is provided to develop molecular markers and evaluate and characterize genes underlying important agronomic traits.

Meta-QTL analysis for mining of candidate genes and constitutive gene network development for fungal disease resistance in maize (Zea mays L.)

The development of resistant maize cultivars is the most effective and sustainable approach to combat fungal diseases. Over the last three decades, many quantitative trait loci (QTL) mapping studies reported numerous QTL for fungal disease resistance (FDR) in maize. However, different genetic backgrounds of germplasm and differing QTL analysis algorithms limit the use of identified QTL for comparative studies. The meta-QTL (MQTL) analysis is the meta-analysis of multiple QTL experiments, which entails broader allelic coverage and helps in the combined analysis of diverse QTL mapping studies revealing common genomic regions for target traits. In the present study, 128 (33.59%) out of 381 reported QTL (from 82 studies) for FDR could be projected on the maize genome through MQTL analysis. It revealed 38 MQTL for FDR (12 diseases) on all chromosomes except chromosome 10. Five MQTL namely 1_4, 2_4, 3_2, 3_4, and 5_4 were linked with multiple FDR. Total of 1910 candidate genes were identified for all the MQTL regions, with protein kinase gene families, TFs, pathogenesis-related, and disease-responsive proteins directly or indirectly associated with FDR. The comparison of physical positions of marker-traits association (MTAs) from genome-wide association studies with genes underlying MQTL interval verified the presence of QTL/candidate genes for particular diseases. The linked markers to MQTL and putative candidate genes underlying identified MQTL can be further validated in the germplasm through marker screening and expression studies. The study also attempted to unravel the underlying mechanism for FDR resistance by analyzing the constitutive gene network, which will be a useful resource to understand the molecular mechanism of defense-response of a particular disease and multiple FDR in maize.

Identifying QTLs for Grain Size in a Colossal Grain Rice (Oryza sativa L.) Line, and Analysis of Additive Effects of QTLs.

Grain size is an important component of quality and harvest traits in the field of rice breeding. Although numerous quantitative trait loci (QTLs) of grain size in rice have been reported, the molecular mechanisms of these QTLs remain poorly understood, and further research on QTL observation and candidate gene identification is warranted. In our research, we developed a suite of F2 intercross populations from a cross of 9311 and CG. These primary populations were used to map QTLs conferring grain size, evaluated across three environments, and then subjected to bulked-segregant analysis-seq (BSA-seq). In total, 4, 11, 12 and 14 QTLs for grain length (GL), grain width (GW), 1000-grain weight (TGW), and length/width ratio (LWR), respectively, were detected on the basis of a single-environment analysis. In particular, over 200 splicing-related sites were identified by whole-genome sequencing, including one splicing-site mutation with G>A at the beginning of intron 4 on Os03g0841800 (qGL3.3), producing a smaller open reading frame, without the third and fourth exons. A previous study revealed that the loss-of-function allele caused by this splicing site can negatively regulate rice grain length. Furthermore, qTGW2.1 and qGW2.3 were new QTLs for grain width. We used the near-isogenic lines (NILs) of these GW QTLs to study their genetic effects on individuals and pyramiding, and found that they have additive effects on GW. In summary, these discoveries provide a valuable genetic resource, which will facilitate further study of the genetic polymorphism of new rice varieties in rice breeding.

EAR APICAL DEGENERATION1 regulates maize ear development by maintaining malate supply for apical inflorescence.

Ear length (EL) is a key trait that contributes greatly to grain yield in maize (Zea mays). While numerous quantitative trait loci for EL have been identified, few causal genes have been studied in detail. Here we report the characterization of ear apical degeneration1 (ead1) exhibiting strikingly shorter ears and the map-based cloning of the casual gene EAD1. EAD1 is preferentially expressed in the xylem of immature ears and encodes an aluminum-activated malate transporter localizing to the plasma membrane. We show that EAD1 is a malate efflux transporter and loss of EAD1 leads to lower malate contents in the apical part of developing inflorescences. Exogenous injections of malate rescued the shortened ears of ead1. These results demonstrate that EAD1 plays essential roles in regulating maize ear development by delivering malate through xylem vessels to the apical part of the immature ear. Overexpression of EAD1 led to greater EL and kernel number per row and the EAD1 genotype showed a positive association with EL in two different genetic segregating populations. Our work elucidates the critical role of EAD1 in malate-mediated female inflorescence development and provides a promising genetic resource for enhancing maize grain yield.

New insights into flowering date in Prunus: fine mapping of a major QTL in sweet cherry.

Flowering date is an important trait in Prunus fruit species, especially for their adaptation in a global warming context. Numerous quantitative trait loci (QTLs) have been identified and a major one was previously located on LG4. The objectives of this study were to fine-map this QTL in sweet cherry, to identify robust candidate genes by using the new sweet cherry genome sequence of the cultivar “Regina” and to define markers usable in marker-assisted selection (MAS). We performed QTL analyses on two populations derived from crosses using cultivars “Regina” and “Garnet” as parents. The first one (n = 117) was phenotyped over ten years, while the second one (n = 1386) was evaluated during three years. Kompetitive allele specific PCR (KASP) markers located within the QTL region on LG4 were developed and mapped within this region, consisting in the first fine mapping in sweet cherry. The QTL interval was narrowed from 380 kb to 68 kb and candidate genes were identified by using the genome sequence of “Regina”. Their expression was analyzed from bud dormancy period to flowering in cultivars “Regina” and “Garnet”. Several genes, such as PavBOI-E3, PavSR45a and PavSAUR71, were differentially expressed in these two cultivars and could be then considered as promising candidate genes. Two KASP markers were validated using a population derived from a cross between cultivars “Regina” and “Lapins” and two collections, including landraces and modern cultivars. Thanks to the high synteny within the Prunus genus, these results give new insights into the control of flowering date in Prunus species and pave the way for the development of molecular breeding strategies.

Detection of drought tolerance-related QTL in the Plainsman V./Cappelle Desprez doubled haploid wheat population

Drought stress is one of the major abiotic factors that significantly reduces wheat grain yield. Improving drought tolerance is a challenge that plant breeders are facing nowadays. In this study, our goal was to identify quantitative trait loci (QTL) in the Plainsman V./Cappelle Desprez doubled haploid (DH) population under drought induced as decreased irrigation (ds) and well-watered (ww) conditions in glasshouse. In total, 54 QTL were detected across the three years in two water regimes linked to 10 drought tolerance-related agronomic traits. Out of the detected QTL regions several have been previously reported. The QTL on chromosome 1A (wPt-744613-wPt-8016) related to thousand grain weight was detected in both ds and ww conditions, explaining the 12.7–17.4% of the phenotypic variance. QTL for grain yield was detected on chromosomes 1A, and 6B in the ds treatment. Numerous QTL was identified under both irrigation levels.

Overexpression of a Cytochrome P450 Monooxygenase Involved in Orobanchol Biosynthesis Increases Susceptibility to Fusarium Head Blight

Fusarium Head Blight (FHB) is a cereal disease caused primarily by the ascomycete fungus Fusarium graminearum with public health issues due to the production of mycotoxins including deoxynivalenol (DON). Genetic resistance is an efficient protection means and numerous quantitative trait loci have been identified, some of them related to the production of resistance metabolites. In this study, we have functionally characterized the Brachypodium distachyon BdCYP711A29 gene encoding a cytochrome P450 monooxygenase (CYP). We showed that BdCYP711A29 belongs to an oligogenic family of five members. However, following infection by F. graminearum, BdCYP711A29 is the only copy strongly transcriptionally induced in a DON-dependent manner. The BdCYP711A29 protein is homologous to the Arabidopsis thaliana MAX1 and Oryza sativa MAX1-like CYPs representing key components of the strigolactone biosynthesis. We show that BdCYP711A29 is likely involved in orobanchol biosynthesis. Alteration of the BdCYP711A29 sequence or expression alone does not modify plant architecture, most likely because of functional redundancy with the other copies. B. distachyon lines overexpressing BdCYP711A29 exhibit an increased susceptibility to F. graminearum, although no significant changes in defense gene expression were detected. We demonstrate that both orobanchol and exudates of Bd711A29 overexpressing lines stimulate the germination of F. graminearum macroconidia. We therefore hypothesize that orobanchol is a susceptibility factor to FHB.

Modeling first order additive × additive epistasis improves accuracy of genomic prediction for sclerotinia stem rot resistance in canola.

The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies indicating the highly polygenic nature of sclerotinia stem rot resistance. Overall, our results highlight the use of simple epistasis terms for prediction of breeding values and total genetic values for a complex disease resistance phenotype in canola.

Integration of high-density genetic mapping with transcriptome analysis uncovers numerous agronomic QTL and reveals candidate genes for the control of tillering in sorghum.

Phenotypes such as branching, photoperiod sensitivity, and height were modified during plant domestication and crop improvement. Here, we perform quantitative trait locus (QTL) mapping of these and other agronomic traits in a recombinant inbred line (RIL) population derived from an interspecific cross between Sorghum propinquum and Sorghum bicolor inbred Tx7000. Using low-coverage Illumina sequencing and a bin-mapping approach, we generated ∼1920 bin markers spanning ∼875 cM. Phenotyping data were collected and analyzed from two field locations and one greenhouse experiment for six agronomic traits, thereby identifying a total of 30 QTL. Many of these QTL were penetrant across environments and co-mapped with major QTL identified in other studies. Other QTL uncovered new genomic regions associated with these traits, and some of these were environment-specific in their action. To further dissect the genetic underpinnings of tillering, we complemented QTL analysis with transcriptomics, identifying 6189 genes that were differentially expressed during tiller bud elongation. We identified genes such as Dormancy Associated Protein 1 (DRM1) in addition to various transcription factors that are differentially expressed in comparisons of dormant to elongating tiller buds and lie within tillering QTL, suggesting that these genes are key regulators of tiller elongation in sorghum. Our study demonstrates the usefulness of this RIL population in detecting domestication and improvement-associated genes in sorghum, thus providing a valuable resource for genetic investigation and improvement to the sorghum community.

Quantitative Trait Locus (QTLs) Mapping for Quality Traits of Wheat Based on High Density Genetic Map Combined With Bulked Segregant Analysis RNA-seq (BSR-Seq) Indicates That the Basic 7S Globulin Gene Is Related to Falling Number.

Numerous quantitative trait loci (QTLs) have been identified for wheat quality; however, most are confined to low-density genetic maps. In this study, based on specific-locus amplified fragment sequencing (SLAF-seq), a high-density genetic map was constructed with 193 recombinant inbred lines derived from Chuanmai 42 and Chuanmai 39. In total, 30 QTLs with phenotypic variance explained (PVE) up to 47.99% were identified for falling number (FN), grain protein content (GPC), grain hardness (GH), and starch pasting properties across three environments. Five NAM genes closely adjacent to QGPC.cib-4A probably have effects on GPC. QGH.cib-5D was the only one detected for GH with high PVE of 33.31–47.99% across the three environments and was assumed to be related to the nearest pina-D1 and pinb-D1genes. Three QTLs were identified for FN in at least two environments, of which QFN.cib-3D had relatively higher PVE of 16.58–25.74%. The positive effect of QFN.cib-3D for high FN was verified in a double-haploid population derived from Chuanmai 42 × Kechengmai 4. The combination of these QTLs has a considerable effect on increasing FN. The transcript levels of Basic 7S globulin and Basic 7S globulin 2 in QFN.cib-3D were significantly different between low FN and high FN bulks, as observed through bulk segregant RNA-seq (BSR). These QTLs and candidate genes based on the high-density genetic map would be beneficial for further understanding of the genetic mechanism of quality traits and molecular breeding of wheat.

Identification and validation of Fusarium head blight resistance QTL in the U.S. soft red winter wheat cultivar ‘Jamestown’

AbstractUse of genetic resistance is one of the most important strategies to manage the devastating disease Fusarium head blight (FHB) in wheat. Numerous quantitative trait loci (QTL) having varying effects on reducing FHB and the mycotoxin deoxynivalenol (DON) accumulation have been reported from Asian, European, or distant sources such as wild relatives of wheat (Triticum aestivum L.). However, coming from nonadapted backgrounds, the incorporation of such QTL into regional breeding programs has often resulted in the simultaneous transfer of other undesirable traits. Therefore, it is important to identify, characterize, and deploy sources of genetic resistance that do not suffer from poor adaptability and/or linkage drag. In the present work, QTL associated with FHB resistance in a high‐yielding, moderately resistant soft red winter wheat cultivar ‘Jamestown’ were mapped and validated. The QTL mapping was done using a recombinant inbred line (RIL) population of Pioneer ‘25R47’ × Jamestown having 186 individuals. Phenotyping over 2 yr at three locations, and genotyping using the 90K single nucleotide polymorphism (SNP) platform identified two new QTL, named QFHB.vt‐1B.1 and QFHB.vt‐1B.2, on the chromosome 1B long arm. The QTL contributed to FHB incidence, FHB severity, Fusarium‐damaged kernels, and DON content. Independent mapping of these QTL using two additional RIL populations of FG95195 × Jamestown (170 RILs) and Jamestown × LA97113UC‐124 (77 RILs) validated their stability and effectiveness in different genetic backgrounds. Kompetitive allele specific polymerase chain reaction (KASP) assays were developed using linked SNPs for marker‐assisted selection of the QTL. These QTL are being used in breeding programs to develop FHB‐resistant, high‐yielding varieties.

A Two-Stage Method for Improving the Prediction Accuracy of Complex Traits by Incorporating Genotype by Environment Interactions inBrassica napus

Improving the prediction accuracy of a complex trait of interest is key to performing genomic selection (GS) for crop breeding. For the complex trait measured in multiple environments, this paper proposes a two-stage method to solve a linear model that jointly models the genetic effects and the genotype × environment interaction (G × E) effects. In the first stage, the least absolute shrinkage and selection operator (LASSO) penalized method was utilized to identify quantitative trait loci (QTL). Then, the ordinary least squares (OLS) approach was used in the second stage to reestimate the QTL effects. As a case study, this approach was used to improve the prediction accuracies of flowering time (FT), oil content (OC), and seed yield per plant (SY) inBrassica napus(B. napus). The results showed that theG × Eeffects reduced the mean squared error (MSE) significantly. Numerous QTL were environment-specific and presented minor effects. On average, the two-stage method, named OLS post-LASSO, offers the highest prediction accuracies (correlations are 0.8789, 0.9045, and 0.5507 for FT, OC, and SY, respectively). It was followed by the marker × environment interaction (M × E) genomic best linear unbiased prediction (GBLUP) model (correlations are 0.8347, 0.8205, and 0.4005 for FT, OC, and SY, respectively), the LASSO method (correlations are 0.7583, 0.7755, and 0.2718 for FT, OC, and SY, respectively), and the stratified GBLUP model (correlations are 0.6789, 0.6361, and 0.2860 for FT, OC, and SY, respectively). The two-stage method showed an obvious improvement in the prediction accuracy, and this study will provide methods and reference to improve GS of breeding.