Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

Association mapping and genomic prediction for processing and end-use quality traits in wheat (Triticum aestivum L.)

End-use and processing traits in wheat (Triticum aestivum L.) are crucial for varietal development but are often evaluated only in the advanced stages of the breeding program due to the amount of grain needed and the labor-intensive phenotyping assays. Advances in genomic resources have provided new tools to address the selection for these complex traits earlier in the breeding process. We used association mapping to identify key variants underlying various end-use quality traits and evaluate the usefulness of genomic prediction for these traits in hard red spring wheat from the Northern United States. A panel of 383 advanced breeding lines and cultivars representing the diversity of the University of Minnesota wheat breeding program was genotyped using the Illumina 90K single nucleotide polymorphism array and evaluated in multilocation trials using standard assessments of end-use quality. Sixty-three associations for grain or flour characteristics, mixograph, farinograph, and baking traits were identified. The majority of these associations were mapped in the vicinity of glutenin/gliadin or other known loci. In addition, a putative novel multi-trait association was identified on chromosome 6AL, and candidate gene analysis revealed eight genes of interest. Further, genomic prediction had a high predictive ability (PA) for mixograph and farinograph traits, with PA up to 0.62 and 0.50 in cross-validation and forward prediction, respectively. The deployment of 46 markers from GWAS to predict dough-rheology traits yielded low to moderate PA for various traits. The results of this study suggest that genomic prediction for end-use traits in early generations can be effective for mixograph and farinograph assays but not baking assays.

Characterization of a stable QTL for quality-related traits and its effects on yields in wheat (Triticum aestivum L.)

KEY MESSAGE

A total of 6 major stable QTLs and 59 pairwise epistatic eQTLs for quality-related traits were identified, and the candidate genes underlying qDt-KJ 4B, a novel major and stable QTL for dough tractility, were identified Wheat quality traits are usually negatively correlated with yield traits, but they affect the processing quality and nutritional value of wheat. Therefore, identifying more wheat quantitative trait loci (QTLs) and elucidating their genetic basis are essential for cultivating new high-quality and high-yielding wheat varieties. In this study, QTL analysis for five quality-related traits was performed on a recombinant inbred line (RIL) mapping population, KJ-RIL, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). A total of 6 major stable QTLs and 59 pairwise epistatic eQTLs (eQTLs) for dough tractility (DT), kernel hardness (KH), Zeleny sedimentation value (ZEL), water absorption (WAR) and wet gluten content (WGC) were identified in multiple environments. The genetic effects and additive pyramiding effects of the major and stable QTLs of qDt-KJ-4B on quality- and yield-related traits were characterized. The DT phenotypic values of the KJ-RILs increased with the number of favourable QTLs. BAB (only qDt-KJ-5D did not harbour favourable alleles) and BBA (only qDt-KJ-4A did not harbour favourable alleles) were the best combination for improving both the quality and yield potential of qDt-KJ-4B, qDt-KJ-4A and qDt-KJ-5D. The candidate genes underlying qDt-KJ-4B were predicted on the basis of multiomics data, with TraesKN4B01HG03930 and TraesKN4B01HG03950 as the most likely candidate genes. Overall, our results are helpful for elucidating the genetic relationships between quality- and yield-related traits and will aid in future development of new high-quality and high-yield wheat varieties to meet diverse consumption needs.

Identification of quantitative trait nucleotides for grain quality in bread wheat under heat stress

Heat stress is a critical factor affecting global wheat production and productivity. In this study, out of 500 studied germplasm lines, a diverse panel of 126 wheat genotypes grown under twelve distinct environmental conditions was analyzed. Using 35 K single-nucleotide polymorphism (SNP) genotyping assays and trait data on five biochemical parameters, including grain protein content (GPC), grain amylose content (GAC), grain total soluble sugars (TSS), grain iron (Fe), and zinc (Zn) content, six multi-locus GWAS (ML-GWAS) models were employed for association analysis. This revealed 67 stable quantitative trait nucleotides (QTNs) linked to grain quality parameters, explaining phenotypic variations ranging from 3 to 44.5% under heat stress conditions. By considering the results in consensus to at least three GWAS models and three locations, the final QTNs were reduced to 16, with 12 being novel findings. Notably, two novel markers, AX-94461119 (chromosome 2A) and AX-95220192 (chromosome 7D), associated with grain Fe and Zn, respectively, were validated through Kompetitive Allele Specific Polymerase Chain Reaction (KASP) approach. Candidate genes, including the P-loop-containing nucleoside triphosphate hydrolases (NTPases), Bowman-Birk type proteinase inhibitors (BBI), and the NPSN13 protein, were identified within associated genomic regions. These genes could serve as potential targets for enhancing quality traits and heat tolerance in future wheat improvement programs.

Comparative genome-wide association study of single- and multi-locus models with ontology analysis for enhancing Hanwoo cow reproductive traits

The reproductive characteristics of Hanwoo play a significant role in farm profitability by decreasing the generation interval. This study analyzed 1015 primiparous and 916 multiparous cows using a genome-wide association study with both single-locus (GEMMA, GCTA) and multi-locus models (FarmCPU, BLINK). A significant marker for age at first service was identified across all methods. For age at first conception, GEMMA identified two markers, while FarmCPU and BLINK identified 14 and two markers, respectively. Regarding age at first calving, GEMMA identified two markers, and FarmCPU and BLINK found 15 and two markers, respectively. In multiparous cows, except for days open, one marker for gestation length and two markers for calving interval were identified in the multi-locus models (FarmCPU and BLINK). Additionally, one marker for the number of services per conception was identified using GEMMA. Key candidate genes included PLCB1 (maintaining pregnancy), MUC1 (fetal development), and ADCY5 (associated with fetal birth), while TXNL1 regulates embryo implantation timing. Gene ontology functions associated with embryo implantation and placental regulation were also confirmed (GO:0046875). Although the multi-locus models identified a greater number of markers and candidate genes, there was no overlap with the results from the single-locus models. The multi-locus models showed enhanced detection power, but slight inflation in test statistics (λ values) necessitates cautious interpretation to avoid false positives. Thus, a combination of both models is recommended to improve reproductive efficiency in cows, providing valuable insights into the genetic aspects of reproductive traits.

Genome-Wide Association Study and Genomic Prediction of Soft Wheat End-Use Quality Traits Under Post-Anthesis Heat-Stressed Conditions

Wheat end-use quality is an important component of a wheat breeding program. Heat stress during grain filling impacts wheat quality traits, making it crucial to understand the genetic basis of wheat quality traits under post-anthesis heat stress. This study aimed to identify the genomic regions associated with wheat quality traits using genome-wide association studies (GWASs) and evaluate the prediction accuracy of different genomic selection (GS) models. A panel of 236 soft red facultative wheat genotypes was evaluated for end-use quality traits across four heat-stressed environments over three years. Significant phenotypic variation was observed across environments for traits such as grain yield (GY), grain protein (GP), grain hardness (GH), and flour yield (AFY). Heritability estimates ranged from 0.52 (GY) to 0.91 (GH). The GWASs revealed 136 significant marker-trait associations (MTAs) across all 21 chromosomes, with several MTAs located within candidate genes involved in stress responses and quality traits. Genomic selection models showed prediction accuracy values up to 0.60, with within-environment prediction outperforming across-environment prediction. These results suggest that integrating GWAS and GS approaches can enhance the selection of wheat quality traits under heat stress, contributing to the development of heat-tolerant varieties.

Genome-Wide Association for Morphological and Agronomic Traits in Phaseolus vulgaris L. Accessions

Exploring genetic resources through genomic analyses has emerged as a powerful strategy to develop common bean (Phaseolus vulgaris L.) cultivars that are both productive and well-adapted to various environments. This study aimed to identify genomic regions linked to morpho-agronomic traits in Mesoamerican and Andean common bean accessions and to elucidate the proteins potentially involved in these traits. We evaluated 109 common bean accessions over three agricultural years, focusing on traits including the grain yield (YDSD), 100-seed weight (SW), number of seeds per pod (SDPD), number of pods per plant (PDPL), first pod insertion height (FPIH), plant height (PLHT), days to flowering (DF), and days to maturity (DPM). Using multilocus methods such as mrMLM, FASTmrMLM, FASTmrEMMA, ISIS EM-BLASSO, and pLARmEB, we identified 36 significant SNPs across all chromosomes (Pv01 to Pv11). Validating these SNPs and candidate genes in segregating populations is crucial for developing more productive common bean cultivars through marker-assisted selection.

Pleiotropic Quantitative Trait Loci (QTL) Mining for Regulating Wheat Processing Quality- and Yield-Related Traits

To investigate the genetic basis of processing quality- and yield-related traits in bread wheat (Triticum aestivum L., AABBDD), a systematic analysis of wheat processing quality- and yield-related traits based on genome-wide association studies (GWASs) of 285 regional test lines of wheat from Hebei province, China, was conducted. A total of 87 quantitative trait loci (QTL), including twenty-one for water absorption (WA), four for wet gluten content, eight for grain protein content, seventeen for dough stability time (DST), thirteen for extension area (EA), twelve for maximum resistance (MR), five for thousand-grain weight (TGW), one for grain length, and six for grain width were identified. These QTL harbored 188 significant single-nucleotide polymorphisms (SNPs). Twenty-five SNPs were simultaneously associated with multiple traits. Notably, the SNP AX-111015470 on chromosome 1A was associated with DST, EA, and MR. SNPs AX-111917292 and AX-109124553 on chromosome 5D were associated with wheat WA and TGW. Most processing quality-related QTL and seven grain yield-related QTL identified in this study were newly discovered. Among the surveyed accessions, 18 rare superior alleles were identified. This study identified significant QTL associated with quality-related and yield-related traits in wheat, and some of them showed pleiotropic effects. This study will facilitate molecular designs that seek to achieve synergistic improvements of wheat quality and yield.

Multi-locus genome-wide association study for phosphorus use efficiency in a tropical maize germplasm

Phosphorus (P) is an essential macronutrient for maize (Zea mays L.) growth and development. Therefore, generating cultivars with upgraded P use efficiency (PUE) represents one of the main strategies to reduce the global agriculture dependence on phosphate fertilizers. In this work, genome-wide association studies (GWAS) were performed to detect quantitative trait nucleotide (QTN) and potential PUE-related candidate genes and associated traits in greenhouse and field trials under contrasting P conditions. The PUE and other agronomy traits of 132 maize inbred lines were assessed in low and normal P supply through the greenhouse and field experiments and Multi-locus GWAS was used to map the associated QTNs. Wide genetic variability was observed among the maize inbred lines under low and normal P supply. In addition, we confirm the complex and quantitative nature of PUE. A total of 306 QTNs were associated with the 24 traits evaluated using different multi-locus GWAS methods. A total of 186 potential candidate genes were identified, mainly involved with transcription regulator, transporter, and transference activity. Further studies are still needed to elucidate the functions and relevance of these genes regarding PUE. Nevertheless, pyramiding the favorable alleles pinpointed in the present study can be considered an efficient strategy for molecular improvement to increase maize PUE.

Integrating RTM-GWAS and meta‑QTL data revealed genomic regions and candidate genes associated with the first fruit branch node and its height in upland cotton

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.

Genetic dissection of value-added quality traits and agronomic parameters through genome-wide association mapping in bread wheat (T. aestivum L.)

Bread wheat (T. aestivum) is one of the world's most widely consumed cereals. Since micronutrient deficiencies are becoming more common among people who primarily depend upon cereal-based diets, a need for better-quality wheat varieties has been felt. An association panel of 154 T. aestivum lines was evaluated for the following quality traits: grain appearance (GA) score, grain hardness (GH), phenol reaction (PR) score, protein percent, sodium dodecyl sulfate (SDS) sedimentation value, and test weight (TWt). In addition, the panel was also phenotyped for grain yield and related traits such as days to heading, days to maturity, plant height, and thousand kernel weight for the year 2017-18 at the Borlaug Institute for South Asia (BISA) Ludhiana and Jabalpur sites. We performed a genome-wide association analysis on this panel using 18,351 genotyping-by-sequencing (GBS) markers to find marker-trait associations for quality and grain yield-related traits. We detected 55 single nucleotide polymorphism (SNP) marker trait associations (MTAs) for quality-related traits on chromosomes 7B (10), 1A (9), 2A (8), 3B (6), 2B (5), 7A (4), and 1B (3), with 3A, 4A, and 6D, having two and the rest, 4B, 5A, 5B, and 1D, having one each. Additionally, 20 SNP MTAs were detected for yield-related traits based on a field experiment conducted in Ludhiana on 7D (4) and 4D (3) chromosomes, while 44 SNP MTAs were reported for Jabalpur on chromosomes 2D (6), 7A (5), 2A (4), and 4A (4). Utilizing these loci in marker-assisted selection will benefit from further validation studies for these loci to improve hexaploid wheat for better yield and grain quality.

A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds of baked goods. Superior end-use quality is a major breeding target; however, improving it is especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families and are repetitive, with gaps commonplace in several genome assemblies. To overcome these barriers and efficiently identify superior wheat SSP alleles, we developed "PanSK" (Pan-SSP k-mer) for genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each SSP gene at the pangenomic level to reveal untapped diversity across landraces and modern cultivars. Genome-wide association studies with k-mers identified 23 SSP genes associated with end-use quality that represent novel targets for improvement. We evaluated the effect of rye secalin genes on end-use quality and found that removal of ω-secalins from 1BL/1RS wheat translocation lines is associated with enhanced end-use quality. Finally, using machine-learning-based prediction inspired by PanSK, we predicted the quality phenotypes with high accuracy from genotypes alone. This study provides an effective approach for genome design based on SSP genes, enabling the breeding of wheat varieties with superior processing capabilities and improved end-use quality.

Genome-wide screening of meta-QTL and candidate genes controlling yield and yield-related traits in barley (Hordeum vulgare L.)

Increasing yield is an important goal of barley breeding. In this study, 54 papers published from 2001-2022 on QTL mapping for yield and yield-related traits in barley were collected, which contained 1080 QTLs mapped to the barley high-density consensus map for QTL meta-analysis. These initial QTLs were integrated into 85 meta-QTLs (MQTL) with a mean confidence interval (CI) of 2.76 cM, which was 7.86-fold narrower than the CI of the initial QTL. Among these 85 MQTLs, 68 MQTLs were validated in GWAS studies, and 25 breeder's MQTLs were screened from them. Seventeen barley orthologs of yield-related genes in rice and maize were identified within the hcMQTL region based on comparative genomics strategy and were presumed to be reliable candidates for controlling yield-related traits. The results of this study provide useful information for molecular marker-assisted breeding and candidate gene mining of yield-related traits in barley.

RNA-Seq transcriptome profiling of immature grain wheat is a technique for understanding comparative modeling of baking quality

Improving the baking quality is a primary challenge in the wheat flour production value chain, as baking quality represents a crucial factor in determining its overall value. In the present study, we conducted a comparative RNA-Seq analysis on the high baking quality mutant "O-64.1.10" genotype and its low baking quality wild type "Omid" cultivar to recognize potential genes associated with bread quality. The cDNA libraries were constructed from immature grains that were 15 days post-anthesis, with an average of 16.24 and 18.97 million paired-end short-read sequences in the mutant and wild-type, respectively. A total number of 733 transcripts with differential expression were identified, 585 genes up-regulated and 188 genes down-regulated in the "O-64.1.10" genotype compared to the "Omid". In addition, the families of HSF, bZIP, C2C2-Dof, B3-ARF, BES1, C3H, GRF, HB-HD-ZIP, PLATZ, MADS-MIKC, GARP-G2-like, NAC, OFP and TUB were appeared as the key transcription factors with specific expression in the "O-64.1.10" genotype. At the same time, pathways related to baking quality were identified through Kyoto Encyclopedia of Genes and Genomes. Collectively, we found that the endoplasmic network, metabolic pathways, secondary metabolite biosynthesis, hormone signaling pathway, B group vitamins, protein pathways, pathways associated with carbohydrate and fat metabolism, as well as the biosynthesis and metabolism of various amino acids, have a great deal of potential to play a significant role in the baking quality. Ultimately, the RNA-seq results were confirmed using quantitative Reverse Transcription PCR for some hub genes such as alpha-gliadin, low molecular weight glutenin subunit and terpene synthase (gibberellin) and as a resource for future study, 127 EST-SSR primers were generated using RNA-seq data.

GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment

Globally, over 2 billion people suffer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a field trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhibited a significant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identified highly significantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively, compared to the corresponding single-trait-based models. The identified significantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortification of micronutrients via marker-assisted (MAS) and genomic selection (GS) methods.

Novel Genetic Loci from Triticum timopheevii Associated with Gluten Content Revealed by GWAS in Wheat Breeding Lines

The content and quality of gluten in wheat grain is a distinctive characteristic that determines the final properties of wheat flour. In this study, a genome-wide association study (GWAS) was performed on a wheat panel consisting of bread wheat varieties and the introgression lines (ILs) obtained via hybridization with tetraploid wheat relatives. A total of 17 stable quantitative trait nucleotides (QTNs) located on chromosomes 1D, 2A, 2B, 3D, 5A, 6A, 7B, and 7D that explained up to 21% of the phenotypic variation were identified. Among them, the QTLs on chromosomes 2A and 7B were found to contain three and six linked SNP markers, respectively. Comparative analysis of wheat genotypes according to the composition of haplotypes for the three closely linked SNPs of chromosome 2A indicated that haplotype TT/AA/GG was characteristic of ten ILs containing introgressions from T. timopheevii. The gluten content in the plants with TT/AA/GG haplotype was significantly higher than in the varieties with haplotype GG/GG/AA. Having compared the newly obtained data with the previously reported quantitative trait loci (QTLs) we inferred that the locus on chromosome 2A inherited from T. timopheevii is potentially novel. The introgression lines containing the new locus can be used as sources of genetic factors to improve the quality traits of bread wheat.

Validation of genes affecting rice mesocotyl length through candidate association analysis and identification of the superior haplotypes

Mesocotyl is an essential organ of rice for pushing buds out of soil and plays a crucial role in seeding emergence and development in direct-seeding. Thus, identify the loci associated with mesocotyl length (ML) could accelerate breeding progresses for direct-seeding cultivation. Mesocotyl elongation was mainly regulated by plant hormones. Although several regions and candidate genes governing ML have been reported, the effects of them in diverse breeding populations were still indistinct. In this study, 281 genes related to plant hormones at the genomic regions associated with ML were selected and evaluated by single-locus mixed linear model (SL-MLM) and multi-locus random-SNP-effect mixed linear model (mr-MLM) in two breeding panels (Trop and Indx) originated from the 3K re-sequence project. Furthermore, superior haplotypes with longer mesocotyl were also identified for marker assisted selection (MAS) breeding. Totally, LOC_Os02g17680 (explained 7.1-8.9% phenotypic variations), LOC_Os04g56950 (8.0%), LOC_Os07g24190 (9.3%) and LOC_Os12g12720 (5.6-8.0%) were identified significantly associated with ML in Trop panel, whereas LOC_Os02g17680 (6.5-7.4%), LOC_Os04g56950 (5.5%), LOC_Os06g24850 (4.8%) and LOC_Os07g40240 (4.8-7.1%) were detected in Indx panel. Among these, LOC_Os02g17680 and LOC_Os04g56950 were identified in both panels. Haplotype analysis for the six significant genes indicated that haplotype distribution of the same gene varies at Trop and Indx panels. Totally, 8 (LOC_Os02g17680-Hap1 and Hap2, LOC_Os04g56950-Hap1, Hap2 and Hap8, LOC_Os07g24190-Hap3, LOC_Os12g12720-Hap3 and Hap6) and six superior haplotypes (LOC_Os02g17680-Hap2, Hap5 and Hap7, LOC_Os04g56950-Hap4, LOC_Os06g24850-Hap2 and LOC_Os07g40240-Hap3) with higher ML were identified in Trop and Indx panels, respectively. In addition, significant additive effects for ML with more superior haplotypes were identified in both panels. Overall, the 6 significantly associated genes and their superior haplotypes could be used to enhancing ML through MAS breeding and further promote direct-seedling cultivation.

The Landscapes of Gluten Regulatory Network in Elite Wheat Cultivars Contrasting in Gluten Strength

Yangmai-13 (YM13) is a wheat cultivar with weak gluten fractions. In contrast, Zhenmai-168 (ZM168) is an elite wheat cultivar known for its strong gluten fractions and has been widely used in a number of breeding programs. However, the genetic mechanisms underlying the gluten signatures of ZM168 remain largely unclear. To address this, we combined RNA-seq and PacBio full-length sequencing technology to unveil the potential mechanisms of ZM168 grain quality. A total of 44,709 transcripts were identified in Y13N (YM13 treated with nitrogen) and 51,942 transcripts in Z168N (ZM168 treated with nitrogen), including 28,016 and 28,626 novel isoforms in Y13N and Z168N, respectively. Five hundred and eighty-four differential alternative splicing (AS) events and 491 long noncoding RNAs (lncRNAs) were discovered. Incorporating the sodium-dodecyl-sulfate (SDS) sedimentation volume (SSV) trait, both weighted gene coexpression network analysis (WGCNA) and multiscale embedded gene coexpression network analysis (MEGENA) were employed for network construction and prediction of key drivers. Fifteen new candidates have emerged in association with SSV, including 4 transcription factors (TFs) and 11 transcripts that partake in the post-translational modification pathway. The transcriptome atlas provides new perspectives on wheat grain quality and would be beneficial for developing promising strategies for breeding programs.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Consensus genomic regions for grain quality traits in wheat revealed by Meta-QTL analysis and in silico transcriptome integration

Grain quality traits are the key factors that determine the economic value of wheat and are largely influenced by genetics and the environment. In this study, using a meta-analysis of quantitative trait loci (QTLs) and a comprehensive in silico transcriptome assessment, we identified key genomic regions and putative candidate genes for the grain quality traits protein content, gluten content, and test weight. A total of 508 original QTLs were collected from 41 articles on QTL mapping for the three quality traits in wheat published from 2003 to 2021. When these original QTLs were projected onto a high-density consensus map consisting of 14,548 markers, 313 QTLs resulted in the identification of 64 MQTLs distributed across 17 of the 21 chromosomes. Most of the meta-QTLs (MQTLs) were distributed on sub-genomes A and B. Compared with the original QTLs, the confidence interval (CI) of the MQTLs was smaller, with an average CI of 4.47 cM, while the projected QTLs CI was 11.13 cM (2.49-fold lower). The corresponding physical length of the MQTL ranged from 0.45 to 239.01 Mb. Thirty-one of these 64 MQTLs were validated in at least one genome-wide association study. In addition, five of the 64 MQTLs were selected and designated as core MQTLs. The 211 quality-related genes from rice were used to identify wheat homologs in MQTLs. In combination with transcriptional and omics analyses, 135 putative candidate genes were identified from 64 MQTL regions. The findings should contribute to a better understanding of the molecular genetic mechanisms underlying grain quality and the improvement of these traits in wheat breeding.

Identification of leaf rust resistance loci in a geographically diverse panel of wheat using genome-wide association analysis

Leaf rust, caused by Puccinia triticina (Pt) is among the most devastating diseases posing a significant threat to global wheat production. The continuously evolving virulent Pt races in North America calls for exploring new sources of leaf rust resistance. A diversity panel of 365 bread wheat accessions selected from a worldwide population of landraces and cultivars was evaluated at the seedling stage against four Pt races (TDBJQ, TBBGS, MNPSD and, TNBJS). A wide distribution of seedling responses against the four Pt races was observed. Majority of the genotypes displayed a susceptible response with only 28 (9.8%), 59 (13.5%), 45 (12.5%), and 29 (8.1%) wheat accessions exhibiting a highly resistant response to TDBJQ, TBBGS, MNPSD and, TNBJS, respectively. Further, we conducted a high-resolution multi-locus genome-wide association study (GWAS) using a set of 302,524 high-quality single nucleotide polymorphisms (SNPs). The GWAS analysis identified 27 marker-trait associations (MTAs) for leaf rust resistance on different wheat chromosomes of which 20 MTAs were found in the vicinity of known Lr genes, MTAs, or quantitative traits loci (QTLs) identified in previous studies. The remaining seven significant MTAs identified represent genomic regions that harbor potentially novel genes for leaf rust resistance. Furthermore, the candidate gene analysis for the significant MTAs identified various genes of interest that may be involved in disease resistance. The identified resistant lines and SNPs linked to the QTLs in this study will serve as valuable resources in wheat rust resistance breeding programs.

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities

Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.

Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.)

We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi‑wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

Uncovering the genetic basis of gluten aggregation parameters by genome-wide association analysis in wheat (Triticum aestivum L.) using GlutoPeak

BACKGROUND

Numerous studies have shown that gluten aggregation properties directly affect the processing quality of wheat, however, the genetic basis of gluten aggregation properties were rarely reported.

RESULTS

To explore the genetic basis of gluten aggregation properties in wheat, an association population consisted with 207 wheat genotypes were constructed for evaluating nine parameters of aggregation properties on GlutoPeak across three-year planting seasons. A total of 940 significant SNPs were detected for 9 GlutoPeak parameters through genome-wide association analysis (GWAS). Finally, these SNPs were integrated to 68 non-redundant QTL distributed on 20 chromosomes and 54 QTL was assigned as pleiotropic loci which accounting for multiple parameters of gluten aggregation property. Furthermore, the peak SNPs representing 54 QTL domonstrated additive effect on all the traits. There was a significant positive correlation between the number of favorable alleles and the phenotypic values of each parameter. Peak SNPs of two novel QTL, q3AL.2 and q4DL, which contributing to both PMT (peak maximum time) and A3 (area from the first minimum to torque 15 s before the maximum torque) parameters, were selected for KASP (Kompetitive Allele Specific PCR) markers development and the KASP markers can be used for effectively evaluating the quality of gluten aggregation properties in the association population.

CONCLUSION

The rapid and efficient GlutoPeak method for gluten measurement can be used for early selection of wheat breeding. This study revealed the genetic loci related to GlutoPeak parameters in association population, which would be helpful to develop wheat elite lines with improved gluten aggregation through molecular marker-assisted breeding.

Amino acid transporter (AAT) gene family in Tartary buckwheat (Fagopyrum tataricum L. Gaertn.): Characterization, expression analysis and functional prediction

Tartary buckwheat (Fagopyrum tataricum L. Gaertn., TB) is an ancient minor crop and an important food source for humans to supplement nutrients such as flavonoids and essential amino acids. Amino acid transporters (AATs) play critical roles in plant growth and development through the transport of amino acids. In this study, 104 AATs were identified in TB genome and divided into 11 subfamilies by phylogenetic relationships. Tandem and segmental duplications promoted the expansion of FtAAT gene family, and the variations of gene sequence, protein structure and expression pattern were the main reasons for the functional differentiation of FtAATs. Based on RNA-seq and qRT-PCR, the expression patterns of FtAATs in different tissues and under different abiotic stresses were analyzed, and several candidate FtAATs that might affect grain development and response to abiotic stresses were identified, such as FtAAP12 and FtCAT7. Finally, combined with the previous studies, the expression patterns and phylogenetic relationships of AATs in multiple species, the functions of multiple high-confidence FtAAT genes were predicted, and the schematic diagram of FtAATs in TB was initially drawn. Overall, this work provided a framework for further functional analysis of FtAAT genes and important clues for the improvement of TB quality and stress resistance.

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci

Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2 cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as 2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55 cM with an average of 40.35 cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

Genome-wide Association Study for Starch Pasting Properties in Chinese Spring Wheat

In order to understand the genetic basis of starch pasting viscosity characteristics of Chinese spring wheat, we assessed the genetic variation of RVA parameters determined by the Rapid Visco Analyser in a panel of 192 Chinese spring wheat accessions grown in Er'shi, Shihezi and Zhaosu during 2012 and 2013 cropping seasons. A genome-wide association study with 47,362 single nucleotide polymorphism (SNP) markers was conducted to detect marker-trait associations using mixed linear model. Phenotypic variations of RVA parameters ranged from 1.6 to 30.7% and broad-sense heritabilities ranged from 0.62 to 0.91. Forty-one SNP markers at 25 loci were significantly associated with seven RVA traits in at least two environments; among these, 20 SNPs were located in coding sequences (CDS) of 18 annotation genes, which can lead to discovering novel genes underpinning starch gelatinization in spring wheat. Haplotype analysis revealed one block for breakdown (BD) on chromosome 3B and two blocks for pasting temperature (T) on chromosome 7B. Cultivars with superior haplotypes at these loci showed better starch pasting viscosity than the average of all cultivars surveyed. The identified loci and associated markers provide valuable sources for future functional characterization and genetic improvement of starch quality in wheat.

Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat

Soft white wheat is a wheat class used in foreign and domestic markets to make various end products requiring specific quality attributes. Due to associated cost, time, and amount of seed needed, phenotyping for the end-use quality trait is delayed until later generations. Previously, we explored the potential of using genomic selection (GS) for selecting superior genotypes earlier in the breeding program. Breeders typically measure multiple traits across various locations, and it opens up the avenue for exploring multi-trait-based GS models. This study's main objective was to explore the potential of using multi-trait GS models for predicting seven different end-use quality traits using cross-validation, independent prediction, and across-location predictions in a wheat breeding program. The population used consisted of 666 soft white wheat genotypes planted for 5 years at two locations in Washington, United States. We optimized and compared the performances of four uni-trait- and multi-trait-based GS models, namely, Bayes B, genomic best linear unbiased prediction (GBLUP), multilayer perceptron (MLP), and random forests. The prediction accuracies for multi-trait GS models were 5.5 and 7.9% superior to uni-trait models for the within-environment and across-location predictions. Multi-trait machine and deep learning models performed superior to GBLUP and Bayes B for across-location predictions, but their advantages diminished when the genotype by environment component was included in the model. The highest improvement in prediction accuracy, that is, 35% was obtained for flour protein content with the multi-trait MLP model. This study showed the potential of using multi-trait-based GS models to enhance prediction accuracy by using information from previously phenotyped traits. It would assist in speeding up the breeding cycle time in a cost-friendly manner.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

Genome-wide association studies (GWAS) are effectively applied to detect the marker trait associations (MTAs) using whole genome-wide variants for complex quantitative traits in different crop species. GWAS has been applied in wheat for different quality, biotic and abiotic stresses, and agronomic and yield-related traits. Predictions for marker-trait associations are controlled with the development of better statistical models taking population structure and familial relatedness into account. In this review, we have provided a detailed overview of the importance of association mapping, population design, high-throughput genotyping and phenotyping platforms, advancements in statistical models and multiple threshold comparisons, and recent GWA studies conducted in wheat. The information about MTAs utilized for gene characterization and adopted in breeding programs is also provided. In the literature that we surveyed, as many as 86,122 wheat lines have been studied under various GWA studies reporting 46,940 loci. However, further utilization of these is largely limited. The future breakthroughs in area of genomic selection, multi-omics-based approaches, machine, and deep learning models in wheat breeding after exploring the complex genetic structure with the GWAS are also discussed. This is a most comprehensive study of a large number of reports on wheat GWAS and gives a comparison and timeline of technological developments in this area. This will be useful to new researchers or groups who wish to invest in GWAS.

GWAS Case Studies in Wheat

With the advancements in next-generation sequencing technologies, leading to millions of single nucleotide polymorphisms in all crop species including wheat, genome-wide association study (GWAS) has become a leading approach for trait dissection. In wheat, GWAS has been conducted for a plethora of traits and more and more studies are being conducted and reported in journals. While application of GWAS has become a routine in wheat using the standardized approaches, there has been a great leap forward using newer models and combination of GWAS with other sets of data. This chapter has reviewed all these latest advancements in GWAS in wheat by citing the most important studies and their outputs. Specially, we have focused on studies that conducted meta-GWAS, multilocus GWAS, haplotype-based GWAS, Environmental- and Eigen-GWAS, and/or GWAS combined with gene regulatory network and pathway analyses or epistatic interactions analyses; all these have taken the association mapping approach to new heights in wheat.

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments

Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F_5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m⁻² and increased test weight by 2.14 and 3.47 kg m⁻³ with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

Identification and Validation of Stable Quantitative Trait Loci for SDS-Sedimentation Volume in Common Wheat (Triticum aestivum L.)

Sodium dodecyl sulfate-sedimentation volume is an important index to evaluate the gluten strength of common wheat and is closely related to baking quality. In this study, a total of 15 quantitative trait locus (QTL) for sodium dodecyl sulfate (SDS)-sedimentation volume (SSV) were identified by using a high-density genetic map including 2,474 single-nucleotide polymorphism (SNP) markers, which was constructed with a doubled haploid (DH) population derived from the cross between Non-gda3753 (ND3753) and Liangxing99 (LX99). Importantly, four environmentally stable QTLs were detected on chromosomes 1A, 2D, and 5D, respectively. Among them, the one with the largest effect was identified on chromosome 1A (designated as QSsv.cau-1A.1) explaining up to 39.67% of the phenotypic variance. Subsequently, QSsv.cau-1A.1 was dissected into two QTLs named as QSsv.cau-1A.1.1 and QSsv.cau-1A.1.2 by saturating the genetic linkage map of the chromosome 1A. Interestedly, favorable alleles of these two loci were from different parents. Due to the favorable allele of QSsv.cau-1A.1.1 was from the high-value parents ND3753 and revealed higher genetic effect, which explained 25.07% of the phenotypic variation, mapping of this locus was conducted by using BC3F1 and BC3F2 populations. By comparing the CS reference sequence, the physical interval of QSsv.cau-1A.1.1 was delimited into 14.9 Mb, with 89 putative high-confidence annotated genes. SSVs of different recombinants between QSsv.cau-1A.1.1 and QSsv.cau-1A.1 detected from DH and BC3F2 populations showed that these two loci had an obvious additive effect, of which the combination of two favorable loci had the high SSV, whereas recombinants with unfavorable loci had the lowest. These results provide further insight into the genetic basis of SSV and QSsv.cau-1A.1.1 will be an ideal target for positional cloning and wheat breeding programs.

A Genome-Wide Association Study Pinpoints Quantitative Trait Genes for Plant Height, Heading Date, Grain Quality, and Yield in Rye (Secale cereale L.)

Rye is the only cross-pollinating Triticeae crop species. Knowledge of rye genes controlling complex-inherited traits is scarce, which, currently, largely disables the genomics assisted introgression of untapped genetic variation from self-incompatible germplasm collections in elite inbred lines for hybrid breeding. We report on the first genome-wide association study (GWAS) in rye based on the phenotypic evaluation of 526 experimental hybrids for plant height, heading date, grain quality, and yield in 2 years and up to 19 environments. We established a cross-validated NIRS calibration model as a fast, effective, and robust analytical method to determine grain quality parameters. We observed phenotypic plasticity in plant height and tiller number as a resource use strategy of rye under drought and identified increased grain arabinoxylan content as a striking phenotype in osmotically stressed rye. We used DArTseq™ as a genotyping-by-sequencing technology to reduce the complexity of the rye genome. We established a novel high-density genetic linkage map that describes the position of almost 19k markers and that allowed us to estimate a low genome-wide LD based on the assessed genetic diversity in elite germplasm. We analyzed the relationship between plant height, heading date, agronomic, as well as grain quality traits, and genotype based on 20k novel single-nucleotide polymorphism markers. In addition, we integrated the DArTseq™ markers in the recently established 'Lo7' reference genome assembly. We identified cross-validated SNPs in 'Lo7' protein-coding genes associated with all traits studied. These include associations of the WUSCHEL-related homeobox transcription factor DWT1 and grain yield, the DELLA protein gene SLR1 and heading date, the Ethylene overproducer 1-like protein gene ETOL1 and thousand-grain weight, protein and starch content, as well as the Lectin receptor kinase SIT2 and plant height. A Leucine-rich repeat receptor protein kinase and a Xyloglucan alpha-1,6-xylosyltransferase count among the cross-validated genes associated with water-extractable arabinoxylan content. This study demonstrates the power of GWAS, hybrid breeding, and the reference genome sequence in rye genetics research to dissect and identify the function of genes shaping genetic diversity in agronomic and grain quality traits of rye. The described links between genetic causes and phenotypic variation will accelerate genomics-enabled rye improvement.

Genome-wide association study for morphological, phenological, quality, and yield traits in einkorn (Triticum monococcum L. subsp. monococcum)

Einkorn (Triticum monococcum L. subsp. monococcum, 2n = 2× = 14, AmAm) is a diploid wheat whose cultivation was widespread in the Mediterranean and European area till the Bronze Age, before it was replaced by the more productive durum and bread wheats. Although scarcely cultivated nowadays, it has gained renewed interest due to its relevant nutritional properties and as source of genetic diversity for crop breeding. However, the molecular basis of many traits of interest in einkorn remain still unknown. A panel of 160 einkorn landraces, from different parts of the distribution area, was characterized for several phenotypic traits related to morphology, phenology, quality, and yield for 4 years in two locations. An approach based on co-linearity with the A genome of bread wheat, supported also by that with Triticum urartu genome, was exploited to perform association mapping, even without an einkorn anchored genome. The association mapping approach uncovered numerous marker-trait associations; for 37 of these, a physical position was inferred by homology with the bread wheat genome. Moreover, numerous associated regions were also assigned to the available T. monococcum contigs. Among the intervals detected in this work, three overlapped with regions previously described as involved in the same trait, while four other regions were localized in proximity of loci previously described and presumably refer to the same gene/QTL. The remaining associated regions identified in this work could represent a novel and useful starting point for breeding approaches to improve the investigated traits in this neglected species.

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

The population growth trend in recent decades has resulted in continuing efforts to guarantee food security in which leguminous plants, such as the common bean (Phaseolus vulgaris L.), play a particularly important role as they are relatively cheap and have high nutritional value. To meet this demand for food, the main target for genetic improvement programs is to increase productivity, which is a complex quantitative trait influenced by many component traits. This research aims to identify Quantitative Trait Nucleotides (QTNs) associated with productivity and its components using multi-locus genome-wide association studies. Ten morpho-agronomic traits [plant height (PH), first pod insertion height (FPIH), number of nodules (NN), pod length (PL), total number of pods per plant (NPP), number of locules per pod (LP), number of seeds per pod (SP), total seed weight per plant (TSW), 100-seed weight (W100), and grain yield (YLD)] were evaluated in four environments for 178 Mesoamerican common bean domesticated accessions belonging to the Brazilian Diversity Panel. In order to identify stable QTNs, only those identified by multiple methods (mrMLM, FASTmrMLM, pLARmEB, and ISIS EM-BLASSO) or in multiple environments were selected. Among the identified QTNs, 64 were detected at least thrice by different methods or in different environments, and 39 showed significant phenotypic differences between their corresponding alleles. The alleles that positively increased the corresponding traits, except PH (for which lower values are desired), were considered favorable alleles. The most influenced trait by the accumulation of favorable alleles was PH, showing a 51.7% reduction, while NN, TSW, YLD, FPIH, and NPP increased between 18 and 34%. Identifying QTNs in several environments (four environments and overall adjusted mean) and by multiple methods reinforces the reliability of the associations obtained and the importance of conducting these studies in multiple environments. Using these QTNs through molecular techniques for genetic improvement, such as marker-assisted selection or genomic selection, can be a strategy to increase common bean production.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat

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.

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year's dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

Rapid Identification of QTL for Mesocotyl Length in Rice Through Combining QTL-seq and Genome-Wide Association Analysis

Mesocotyl is a crucial organ for pushing buds out of soil, which plays a vital role in seedling emergence and establishment in direct-seeded rice. Thus, the identification of quantitative trait loci (QTL) associated with mesocotyl length (ML) could accelerate genetic improvement of rice for direct seeding cultivation. In this study, QTL sequencing (QTL-seq) applied to 12 F₂ populations identified 14 QTL for ML, which were distributed on chromosomes 1, 3, 4, 5, 6, 7, and 9 based on the Δ(SNP-index) or G-value statistics. Besides, a genome-wide association study (GWAS) using two diverse panels identified five unique QTL on chromosomes 1, 8, 9, and 12 (2), respectively, explaining 5.3–14.6% of the phenotypic variations. Among these QTL, seven were in the regions harboring known genes or QTLs, whereas the other 10 were potentially novel. Six of the QTL were stable across two or more populations. Eight high-confidence candidate genes related to ML were identified for the stable loci based on annotation and expression analyses. Association analysis revealed that two PCR gel-based markers for the loci co-located by QTL-seq and GWAS, Indel-Chr1:18932318 and Indel-Chr7:15404166 for loci qML1.3 and qML7.2 respectively, were significantly associated with ML in a collection of 140 accessions and could be used as breeder-friendly markers in further breeding.

Amino acid transporter (AAT) gene family in foxtail millet (Setaria italica L.): widespread family expansion, functional differentiation, roles in quality formation and response to abiotic stresses

Background

Amino acid transporters (AATs) plays an essential roles in growth and development of plants, including amino acids long-range transport, seed germination, quality formation, responsiveness to pathogenic bacteria and abiotic stress by modulating the transmembrane transfer of amino acids. In this study, we performed a genome-wide screening to analyze the AAT genes in foxtail millet (Setaria italica L.), especially those associated with quality formation and abiotic stresses response.

Results

A total number of 94 AAT genes were identified and divided into 12 subfamilies by their sequence characteristics and phylogenetic relationship. A large number (58/94, 62%) of AAT genes in foxtail millet were expanded via gene duplication, involving 13 tandem and 12 segmental duplication events. Tandemly duplicated genes had a significant impact on their functional differentiation via sequence variation, structural variation and expression variation. Further comparison in multiple species showed that in addition to paralogous genes, the expression variations of the orthologous AAT genes also contributed to their functional differentiation. The transcriptomic comparison of two millet cultivars verified the direct contribution of the AAT genes such as SiAAP1, SiAAP8, and SiAUX2 in the formation of grain quality. In addition, the qRT-PCR analysis suggested that several AAT genes continuously responded to diverse abiotic stresses, such as SiATLb1, SiANT1. Finally, combined with the previous studies and analysis on sequence characteristics and expression patterns of AAT genes, the possible functions of the foxtail millet AAT genes were predicted.

Conclusion

This study for the first time reported the evolutionary features, functional differentiation, roles in the quality formation and response to abiotic stresses of foxtail millet AAT gene family, thus providing a framework for further functional analysis of SiAAT genes, and also contributing to the applications of AAT genes in improving the quality and resistance to abiotic stresses of foxtail millet, and other cereal crops.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07779-9.

Genome-Wide Association Study on Reproduction-Related Body-Shape Traits of Chinese Holstein Cows

Reproduction is an important production activity for dairy cows, and their reproductive performance can directly affect the level of farmers' income. To better understand the genomic regions and biological pathways of reproduction-related traits of dairy cows, in the present study, three body shape traits-Loin Strength (LS), Rump Angle (RA), and Pin Width (PW)-were selected as indicators of the reproductive ability of cows, and we conducted genome-wide association analyses on them. The heritability of these three traits was medium, ranging from 0.20 to 0.38. A total of 11 significant single-nucleotide polymorphisms (SNPs) were detected associated with these three traits. Bioinformatics analysis was performed on genes close to the significant SNPs (within 200 Kb) of LS, RA, and PW, and we found that these genes were totally enriched in 20 gene ontology terms and six KEGG signaling pathways. Finally, the five genes CDH12, TARP, PCDH9, DTHD1, and ARAP2 were selected as candidate genes that might affect LS. The six genes LOC781835, FSTL4, ATG4C, SH3BP4, DMP1, and DSPP were selected as candidate genes that might affect RA. The five genes USP6NL, CNTN3, LOC101907665, UPF2, and ECHDC3 were selected as candidate genes that might affect the PW of Chinese Holstein cows. Our results could provide useful biological information for the improvement of body shape traits and contribute to the genomic selection of Chinese Holstein cows.