Combined Genomic and Genetic Data Integration of Major Agronomical Traits in Bread Wheat (Triticum aestivum L.)

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.

Detection of consensus genomic regions and candidate genes for quality traits in barley using QTL meta-analysis

Improving barley grain quality is a major goal in barley breeding. In this study, a total of 35 papers focusing on quantitative trait loci (QTLs) mapping for barley quality traits published since 2000 were collected. Among the 454 QTLs identified in these studies, 349 of them were mapped onto high-density consensus maps, which were used for QTL meta-analysis. Through QTL meta-analysis, the initial QTLs were integrated into 41 meta-QTLs (MQTLs) with an average confidence interval (CI) of 1. 66 cM, which is 88.9% narrower than that of the initial QTLs. Among the 41 identified MQTLs, 25 were subsequently validated in publications using genome-wide association study (GWAS). From these 25 validated MQTLs, ten breeder's MQTLs were selected. Synteny analysis comparing barley and wheat MQTLs revealed orthologous relationships between eight breeder's MQTLs and 45 wheat MQTLs. Additionally, 17 barley homologs associated with rice quality traits were identified within the regions of the breeder's MQTLs through comparative analysis. The findings of this study provide valuable insights for molecular marker-assisted breeding and the identification of candidate genes related to quality traits in barley.

Meta-QTL analysis in wheat: progress, challenges and opportunities

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

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.)

A meta-analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto "PigeonPea_ConsensusMap_2022," saturated with 10,522 markers, which resulted in the prediction of 34 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54 cM) was 3.37 times lower than the CI of the initial QTLs (8.56 cM). Of the 34 MQTLs, 12 high-confidence MQTLs with CI (≤5 cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker-trait associations or significant SNPs identified in previous genome-wide association studies. Furthermore, synteny and ortho-MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL-assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

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 quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

BACKGROUND

Kernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes.

RESULTS

The analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61-0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%-14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites.

CONCLUSIONS

Major genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

Genome-wide meta-analysis of QTL for morphological related traits of flag leaf in bread wheat

Flag leaf is an important organ for photosynthesis of wheat plants, and a key factor affecting wheat yield. In this study, quantitative trait loci (QTL) for flag leaf morphological traits in wheat reported since 2010 were collected to investigate the genetic mechanism of these traits. Integration of 304 QTLs from various mapping populations into a high-density consensus map composed of various types of molecular markers as well as QTL meta-analysis discovered 55 meta-QTLs (MQTL) controlling morphological traits of flag leaves, of which 10 MQTLs were confirmed by GWAS. Four high-confidence MQTLs (MQTL-1, MQTL-11, MQTL-13, and MQTL-52) were screened out from 55 MQTLs, with an average confidence interval of 0.82 cM and a physical distance of 9.4 Mb, according to the definition of hcMQTL. Ten wheat orthologs from rice (7) and Arabidopsis (3) that regulated leaf angle, development and morphogenesis traits were identified in the hcMQTL region using comparative genomics, and were speculated to be potential candidate genes regulating flag leaf morphological traits in wheat. The results from this study provides valuable information for fine mapping and molecular markers assisted selection to improve morphological characters in wheat flag leaf.

Consensus genomic regions associated with grain protein content in hexaploid and tetraploid wheat

A meta-analysis of QTLs associated with grain protein content (GPC) was conducted in hexaploid and tetraploid wheat to identify robust and stable meta-QTLs (MQTLs). For this purpose, as many as 459 GPC-related QTLs retrieved from 48 linkage-based QTL mapping studies were projected onto the newly developed wheat consensus map. The analysis resulted in the prediction of 57 MQTLs and 7 QTL hotspots located on all wheat chromosomes (except chromosomes 1D and 4D) and the average confidence interval reduced 2.71-fold in the MQTLs and QTL hotspots compared to the initial QTLs. The physical regions occupied by the MQTLs ranged from 140 bp to 224.02 Mb with an average of 15.2 Mb, whereas the physical regions occupied by QTL hotspots ranged from 1.81 Mb to 36.03 Mb with a mean of 8.82 Mb. Nineteen MQTLs and two QTL hotspots were also found to be co-localized with 45 significant SNPs identified in 16 previously published genome-wide association studies in wheat. Candidate gene (CG) investigation within some selected MQTLs led to the identification of 705 gene models which also included 96 high-confidence CGs showing significant expressions in different grain-related tissues and having probable roles in GPC regulation. These significantly expressed CGs mainly involved the genes/gene families encoding for the following proteins: aminotransferases, early nodulin 93, glutamine synthetases, invertase/pectin methylesterase inhibitors, protein BIG GRAIN 1-like, cytochrome P450, glycosyl transferases, hexokinases, small GTPases, UDP-glucuronosyl/UDP-glucosyltransferases, and EamA, SANT/Myb, GNAT, thioredoxin, phytocyanin, and homeobox domains containing proteins. Further, eight genes including GPC-B1, Glu-B1-1b, Glu-1By9, TaBiP1, GSr, TaNAC019-A, TaNAC019-D, and bZIP-TF SPA already known to be associated with GPC were also detected within some of the MQTL regions confirming the efficacy of MQTLs predicted during the current study.

Capturing Wheat Phenotypes at the Genome Level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

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.

Meta-QTLs, ortho-MetaQTLs and candidate genes for grain Fe and Zn contents in wheat (Triticum aestivum L.)

Majority of cereals are deficient in essential micronutrients including grain iron (GFe) and grain zinc (GZn), which are therefore the subject of research involving biofortification. In the present study, 11 meta-QTLs (MQTLs) including nine novel MQTLs for GFe and GZn contents were identified in wheat. Eight of these 11 MQTLs controlled both GFe and GZn. The confidence intervals of the MQTLs were narrower (0.51-15.75 cM) relative to those of the corresponding QTLs (0.6 to 55.1 cM). Two ortho-MQTLs involving three cereals (wheat, rice and maize) were also identified. Results of MQTLs were also compared with the results of earlier genome wide association studies (GWAS). As many as 101 candidate genes (CGs) underlying MQTLs were also identified. Twelve of these CGs were prioritized; these CGs encoded proteins with important domains (zinc finger, RING/FYVE/PHD type, flavin adenine dinucleotide linked oxidase, etc.) that are involved in metal ion binding, heme binding, iron binding, etc. qRT-PCR analysis was conducted for four of these 12 prioritized CGs using genotypes which have differed for GFe and GZn. Significant differential expression in these genotypes was observed at 14 and 28 days after anthesis. The MQTLs/CGs identified in the present study may be utilized in marker-assisted selection (MAS) for improvement of GFe/GZn contents and also for understanding the molecular basis of GFe/GZn homeostasis in wheat.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-022-01149-9.

Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.)

In wheat, 2852 major QTLs of 8998 QTLs available for yield and related traits were used for meta-analysis; 141 meta-QTLs were identified, which included 13 breeder's MQTLs and 24 ortho-MQTLs; 1202 candidate genes and 50 homologues of genes for yield from other cereals were also identified. Meta-QTL analysis was conducted using 2852 of the 8998 known QTLs, retrieved from 230 reports published during 1999-2020 (including 19 studies on tetraploid wheat) for grain yield (GY) and the following ten component traits: (i) grain weight (GWei), (ii) grain morphology-related traits (GMRTs), (iii) grain number (GN), (iv) spikes-related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M), and (x) grain filling duration (GFD). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.4 cM as against a CI of > 12.1 cM (8.8 fold reduction) in the QTLs that were used. The corresponding physical length of CI ranged from 0.01 Mb to 661.9 Mb (mean, 31.5 Mb). Seventy-seven (77) of these 141 MQTLs overlapped marker-trait associations (MTAs) reported in genome-wide association studies. Also, 63 MQTLs (each based on at least 10 QTLs) were considered stable and robust, with 13 MQTLs described as breeder's MQTLs (selected based on small CI, large LOD, and high level of phenotypic variation explained). Thirty-five yield-related genes from rice, barley, and maize were also utilized to identify 50 wheat homologues in MQTLs. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize.

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

Delineating meta-quantitative trait loci for anthracnose resistance in common bean (Phaseolus vulgaris L.)

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, is one of the devastating disease affecting common bean production and productivity worldwide. Several quantitative trait loci (QTLs) for anthracnose resistance have been identified. In order to make use of these QTLs in common bean breeding programs, a detailed meta-QTL (MQTL) analysis has been conducted. For the MQTL analysis, 92 QTLs related to anthracnose disease reported in 18 different earlier studies involving 16 mapping populations were compiled and projected on to the consensus map. This meta-analysis led to the identification of 11 MQTLs (each involving QTLs from at least two different studies) on 06 bean chromosomes and 10 QTL hotspots each involving multiple QTLs from an individual study on 07 chromosomes. The confidence interval (CI) of the identified MQTLs was found 3.51 times lower than the CI of initial QTLs. Marker-trait associations (MTAs) reported in published genome-wide association studies (GWAS) were used to validate nine of the 11 identified MQTLs, with MQTL4.1 overlapping with as many as 40 MTAs. Functional annotation of the 11 MQTL regions revealed 1,251 genes including several R genes (such as those encoding for NBS-LRR domain-containing proteins, protein kinases, etc.) and other defense related genes. The MQTLs, QTL hotspots and the potential candidate genes identified during the present study will prove useful in common bean marker-assisted breeding programs and in basic studies involving fine mapping and cloning of genomic regions associated with anthracnose resistance in common beans.

Wheat Proteins: A Valuable Resources to Improve Nutritional Value of Bread

Triticum aestivum, commonly known as bread wheat, is one of the most cultivated crops globally. Due to its increasing demand, wheat is the source of many nutritious products including bread, pasta, and noodles containing different types of seed storage proteins. Wheat seed storage proteins largely control the type and quality of any wheat product. Among various unique wheat products, bread is the most consumed product around the world due to its fast availability as compared to other traditional food commodities. The production of highly nutritious and superior quality bread is always a matter of concern because of its increasing industrial demand. Therefore, new and more advanced technologies are currently being applied to improve and enrich the bread, having increased fortified nutrients, gluten-free, highly stable with enhanced shelf-life, and long-lasting. This review focused on bread proteins with improving wheat qualities and nutritional properties using modern technologies. We also describe the recent innovations in processing technologies to improve various quality traits of wheat bread. We also highlight some modern forms of bread that are utilized in different industries for various purposes and future directions.

Meta-QTLs, ortho-MQTLs, and candidate genes for thermotolerance in wheat (Triticum aestivum L.)

Meta-QTL analysis for thermotolerance in wheat was conducted to identify robust meta-QTLs (MQTLs). In this study, 441 QTLs related to 31 heat-responsive traits were projected on the consensus map with 50,310 markers. This exercise resulted in the identification of 85 MQTLs with confidence interval (CI) ranging from 0.11 to 34.9 cM with an average of 5.6 cM. This amounted to a 2.96-fold reduction relative to the mean CI (16.5 cM) of the QTLs used. Seventy-seven (77) of these MQTLs were also compared and verified with the results of recent genome-wide association studies (GWAS). The 85 MQTLs included seven MQTLs that are particularly useful for breeding purposes (we called them breeders' MQTLs). Seven ortho-MQTLs between wheat and rice genomes were also identified using synteny and collinearity. The MQTLs were used for the identification of 1,704 candidate genes (CGs). In silico expression analysis of these CGs permitted identification of 182 differentially expressed genes (DEGs), which included 36 high confidence CGs with known functions previously reported to be important for thermotolerance. These high confidence CGs encoded proteins belonging to the following families: protein kinase, WD40 repeat, glycosyltransferase, ribosomal protein, SNARE associated Golgi protein, GDSL lipase/esterase, SANT/Myb domain, K homology domain, etc. Thus, the present study resulted in the identification of MQTLs (including breeders' MQTLs), ortho-MQTLs, and underlying CGs, which could prove useful not only for molecular breeding for the development of thermotolerant wheat cultivars but also for future studies focused on understanding the molecular basis of thermotolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01264-7.

Comparative Genomic Analysis of Quantitative Trait Loci Associated With Micronutrient Contents, Grain Quality, and Agronomic Traits in Wheat (Triticum aestivum L.)

Comparative genomics and meta-quantitative trait loci (MQTLs) analysis are important tools for the identification of reliable and stable QTLs and functional genes controlling quantitative traits. We conducted a meta-analysis to identify the most stable QTLs for grain yield (GY), grain quality traits, and micronutrient contents in wheat. A total of 735 QTLs retrieved from 27 independent mapping populations reported in the last 13 years were used for the meta-analysis. The results showed that 449 QTLs were successfully projected onto the genetic consensus map which condensed to 100 MQTLs distributed on wheat chromosomes. This consolidation of MQTLs resulted in a three-fold reduction in the confidence interval (CI) compared with the CI for the initial QTLs. Projection of QTLs revealed that the majority of QTLs and MQTLs were in the non-telomeric regions of chromosomes. The majority of micronutrient MQTLs were located on the A and D genomes. The QTLs of thousand kernel weight (TKW) were frequently associated with QTLs for GY and grain protein content (GPC) with co-localization occurring at 55 and 63%, respectively. The co- localization of QTLs for GY and grain Fe was found to be 52% and for QTLs of grain Fe and Zn, it was found to be 66%. The genomic collinearity within Poaceae allowed us to identify 16 orthologous MQTLs (OrMQTLs) in wheat, rice, and maize. Annotation of promising candidate genes (CGs) located in the genomic intervals of the stable MQTLs indicated that several CGs (e.g., TraesCS2A02G141400, TraesCS3B02G040900, TraesCS4D02G323700, TraesCS3B02G077100, and TraesCS4D02G290900) had effects on micronutrients contents, yield, and yield-related traits. The mapping refinements leading to the identification of these CGs provide an opportunity to understand the genetic mechanisms driving quantitative variation for these traits and apply this information for crop improvement programs.

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.

Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

The genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.

Evidence for the Accumulation of Nonsynonymous Mutations and Favorable Pleiotropic Alleles During Wheat Breeding

Plant breeding leads to the genetic improvement of target traits by selecting a small number of genotypes from among typically large numbers of candidate genotypes after careful evaluation. In this study, we first investigated how mutations at conserved nucleotide sites normally viewed as deleterious, such as nonsynonymous sites, accumulated in a wheat, Triticum aestivum, breeding lineage. By comparing a 150 year old ancestral and modern cultivar, we found recent nucleotide polymorphisms altered amino acids and occurred within conserved genes at frequencies expected in the absence of purifying selection. Mutations that are deleterious in other contexts likely had very small or no effects on target traits within the breeding lineage. Second, we investigated if breeders selected alleles with favorable effects on some traits and unfavorable effects on others and used different alleles to compensate for the latter. An analysis of a segregating population derived from the ancestral and modern parents provided one example of this phenomenon. The recent cultivar contains the Rht-B1b green revolution semi-dwarfing allele and compensatory alleles that reduce its negative effects. However, improvements in traits other than plant height were due to pleiotropic loci with favorable effects on traits and to favorable loci with no detectable pleiotropic effects. Wheat breeding appears to tolerate mutations at conserved nucleotide sites and to only select for alleles with both favorable and unfavorable effects on traits in exceptional situations.

Dissection and validation of a QTL cluster linked to Rht-B1 locus controlling grain weight in common wheat (Triticum aestivum L.) using near-isogenic lines

This study dissected and validated a QTL cluster associated with thousand grain weight on chromosome 4B using multiple near-isogenic lines in common wheat. Grain size and weight are crucial components of wheat yield. Previously, we identified a QTL cluster for thousand grain weight (TGW) on chromosome 4B using the Nongda3338 (ND3338)/Jingdong6 (JD6) doubled haploid population. Here, near-isogenic lines (NILs) in the ND3338 background were developed to dissect and validate the QTL cluster. Based on six independent BC₃F_3:4 heterogeneous inbred families, the 4B QTL cluster was divided into two linked QTL intervals (designated 4B.1 and 4B.2 QTL). For the 4B.1 QTL, the Rht-B1 gene, of which Rht-B1b allele reduces plant height (PH) by 21.18-29.34 cm (34.34-53.71%), was demonstrated to be the most likely candidate gene with pleiotropic effects on grain size and TGW. For the 4B.2 QTL, the NIL^JD6 consistently showed an increase in TGW of 3.51-7.68 g (8.84-22.77%) compared with NIL^ND3338 across different field trials, along with a significant increase in PH of 2.26-6.71 cm (3.92-12.01%). Moreover, both QTL intervals had a larger effect on grain width than on grain length. Additionally, the first significant difference in 100-grain fresh weight and 100-grain dry weight between the NIL pairs of the 4B.1 QTL interval (Rht-B1) was observed at 6 days after pollination (DAP), while the differences were first visible at 30 DAP for the 4B.2 QTL interval. Collectively, our work provides a new example of QTL dissection for grain weight in wheat and lays a foundation for further map-based cloning of the major QTL that have potential applications in wheat molecular breeding for high yield.

Meta-QTL analysis and identification of candidate genes for drought tolerance in bread wheat (Triticum aestivum L.)

Meta-QTL (MQTL) analysis for drought tolerance was undertaken in bread wheat to identify consensus and robust MQTLs using 340 known QTLs from 11 earlier studies; 13 MQTLs located on 6 chromosomes (1D, 3B, 5A, 6D, 7A and 7D) were identified, with maximum of 4 MQTLs on chromosome 5A. Mean confidence intervals for MQTLs were much narrower (mean, 6.01 cM; range 2.07-19.46 cM), relative to those in original QTLs (mean, 13.6 cM; range, 1.0-119.1 cM). Two MQTLs, namely MQTL4 and MQTL12, were major MQTLs with potential for use in marker-assisting breeding. As many as 228 candidate genes (CGs) were also identified using 6 of the 13 MQTLs. In-silico expression analysis of these 228 CGs allowed identification of 14 important CGs, with + 3 to - 8 fold change in expression under drought (relative to normal conditions) in a tolerant cv. named TAM107. These CGs encoded proteins belonging to the following families: NAD-dependent epimerase/dehydratase, protein kinase, NAD(P)-binding domain protein, heat shock protein 70 (Hsp70), glycosyltransferase 2-like, etc. Important MQTLs and CGs identified in the present study should prove useful for future molecular breeding and for the study of molecular basis of drought tolerance in cereals in general and wheat in particular.

Germplasms, genetics and genomics for better control of disastrous wheat Fusarium head blight

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

Grain protein content and thousand kernel weight QTLs identified in a durum × wild emmer wheat mapping population tested in five environments

Genetic dissection of GPC and TKW in tetraploid durum × WEW RIL population, based on high-density SNP genetic map, revealed 12 GPC QTLs and 11 TKW QTLs, with favorable alleles for 11 and 5 QTLs, respectively, derived from WEW. Wild emmer wheat (Triticum turgidum ssp. dicoccoides, WEW) was shown to exhibit high grain protein content (GPC) and therefore possess a great potential for improvement of cultivated wheat nutritional value. Genetic dissection of thousand kernel weight (TKW) and grain protein content (GPC) was performed using a high-density genetic map constructed based on a recombinant inbred line (RIL) population derived from a cross between T. durum var. Svevo and WEW acc. Y12-3. Genotyping of 208 F₆ RILs with a 15 K wheat single nucleotide polymorphism (SNP) array yielded 4166 polymorphic SNP markers, of which 1510 were designated as skeleton markers. A total map length of 2169 cM was obtained with an average distance of 1.5 cM between SNPs. A total of 12 GPC QTLs and 11 TKW QTLs were found under five different environments. No significant correlations were found between GPC and TKW across all environments. Four major GPC QTLs with favorable alleles from WEW were found on chromosomes 4BS, 5AS, 6BS and 7BL. The 6BS GPC QTL coincided with the physical position of the NAC transcription factor TtNAM-B1, underlying the cloned QTL, Gpc-B1. Comparisons of the physical intervals of the GPC QTLs described here with the results previously reported in other durum × WEW RIL population led to the discovery of seven novel GPC QTLs. Therefore, our research emphasizes the importance of GPC QTL dissection in diverse WEW accessions as a source of novel alleles for improvement of GPC in cultivated wheat.

Grain protein content and thousand kernel weight QTLs identified in a durum × wild emmer wheat mapping population tested in five environments

Genetic dissection of GPC and TKW in tetraploid durum × WEW RIL population, based on high-density SNP genetic map, revealed 12 GPC QTLs and 11 TKW QTLs, with favorable alleles for 11 and 5 QTLs, respectively, derived from WEW. Wild emmer wheat (Triticum turgidum ssp. dicoccoides, WEW) was shown to exhibit high grain protein content (GPC) and therefore possess a great potential for improvement of cultivated wheat nutritional value. Genetic dissection of thousand kernel weight (TKW) and grain protein content (GPC) was performed using a high-density genetic map constructed based on a recombinant inbred line (RIL) population derived from a cross between T. durum var. Svevo and WEW acc. Y12-3. Genotyping of 208 F₆ RILs with a 15 K wheat single nucleotide polymorphism (SNP) array yielded 4166 polymorphic SNP markers, of which 1510 were designated as skeleton markers. A total map length of 2169 cM was obtained with an average distance of 1.5 cM between SNPs. A total of 12 GPC QTLs and 11 TKW QTLs were found under five different environments. No significant correlations were found between GPC and TKW across all environments. Four major GPC QTLs with favorable alleles from WEW were found on chromosomes 4BS, 5AS, 6BS and 7BL. The 6BS GPC QTL coincided with the physical position of the NAC transcription factor TtNAM-B1, underlying the cloned QTL, Gpc-B1. Comparisons of the physical intervals of the GPC QTLs described here with the results previously reported in other durum × WEW RIL population led to the discovery of seven novel GPC QTLs. Therefore, our research emphasizes the importance of GPC QTL dissection in diverse WEW accessions as a source of novel alleles for improvement of GPC in cultivated wheat.

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat

Potassium use efficiency, a complex trait, directly impacts the yield potential of crop plants. Low potassium efficiency leads to a high use of fertilizers, which is not only farmer unfriendly but also deteriorates the environment. Genome-wide association studies (GWAS) are widely used to dissect complex traits. However, most studies use single-locus one-dimensional GWAS models which do not provide true information about complex traits that are controlled by multiple loci. Here, both single-locus GWAS (MLM) and multi-locus GWAS (pLARmEB, FASTmrMLM, mrMLM, FASTmrEMMA) models were used with genotyping from 90 K Infinium SNP array and phenotype derived from four normal and potassium-stress environments, which identified 534 significant marker-trait associations (MTA) for agronomic and potassium related traits: pLARmEB = 279, FASTmrMLM = 213, mrMLM = 35, MLM = 6, FASTmrEMMA = 1. Further screening of these MTA led to the detection of eleven stable loci: q1A, q1D, q2B-1, q2B-2, q2D, q4D, q5B-1, q5B-2, q5B-3, q6D, and q7A. Moreover, Meta-QTL (MQTL) analysis of four independent QTL studies for potassium deficiency in bread wheat located 16 MQTL on 13 chromosomes. One locus identified in this study (q5B-1) colocalized with an MQTL (MQTL_{_11} ), while the other ten loci were novel associations. Gene ontology of these loci identified 20 putative candidate genes encoding functional proteins involved in key pathways related to stress tolerance, sugar metabolism, and nutrient transport. These findings provide potential targets for breeding potassium stress resistant wheat cultivars and advocate the advantages of multi-locus GWAS models for studying complex traits.

Genome-Wide Analyses Reveal Footprints of Divergent Selection and Drought Adaptive Traits in Synthetic-Derived Wheats

Crop-wild introgressions have long been exploited without knowing the favorable recombination points. Synthetic hexaploid wheats are one of the most exploited genetic resources for bread wheat improvement. However, despite some QTL with major effects, much less is known about genome-wide patterns of introgressions and their effects on phenotypes. We used two genome-wide association approaches: SNP-GWAS and haplotype-GWAS to identify SNPs and haplotypes associated with productivity under water-limited conditions in a synthetic-derived wheat (SYN-DER) population. Haplotype-GWAS further enriched and identified 20 more genomic regions associated with drought adaptability that did not overlap with SNP-GWAS. Since GWAS is biased to the phenotypes in the study and may fail to detect important genetic diversity during breeding, we used five complementary analytical approaches (t-test, Tajima’s D, nucleotide diversity (π), Fst, and EigenGWAS) to identify divergent selections in SYN-DER compared to modern bread wheat. These approaches consistently pinpointed 89 ‘selective sweeps’, out of which 30 selection loci were identified on D-genome. These key selections co-localized with important functional genes of adaptive traits such as TaElf3-D1 (1D) for earliness per se (Eps), TaCKX-D1 (3D), TaGS1a (6D) and TaGS-D1 (7D) for grain size, weight and morphology, TaCwi-D1 (5D) influencing drought tolerance, and Vrn-D3 (7D) for vernalization. Furthermore, 55 SNPs and 23 haplotypes of agronomic and physiological importance such as grain yield, relative water content and thousand grain weight in SYN-DER, were among the top 5% of divergent selections contributed by synthetic hexaploid wheats. These divergent selections associated with improved agronomic performance carry new alleles that have been introduced to wheat. Our results demonstrated that GWAS and selection sweep analyses are powerful approaches for investigating favorable introgressions under strong selection pressure and the use of crop-wild hybridization to assist the improvement of wheat yield and productivity under moisture limiting environments.

Candidate genes and genome-wide association study of grain protein content and protein deviation in durum wheat

Stable QTL for grain protein content co-migrating with nitrogen-related genes have been identified by the candidate genes and genome-wide association mapping approaches useful for marker-assisted selection. Grain protein content (GPC) is one of the most important quality traits in wheat, defining the nutritional and end-use properties and rheological characteristics. Over the years, a number of breeding programs have been developed aimed to improving GPC, most of them having been prevented by the negative correlation with grain yield. To overcome this issue, a collection of durum wheat germplasm was evaluated for both GPC and grain protein deviation (GPD) in seven field trials. Fourteen candidate genes involved in several processes related to nitrogen metabolism were precisely located on two high-density consensus maps of common and durum wheat, and six of them were found to be highly associated with both traits. The wheat collection was genotyped using the 90 K iSelect array, and 11 stable quantitative trait loci (QTL) for GPC were detected in at least three environments and the mean across environments by the genome-wide association mapping. Interestingly, seven QTL were co-migrating with N-related candidate genes. Four QTL were found to be significantly associated to increases of GPD, indicating that selecting for GPC could not affect final grain yield per spike. The combined approaches of candidate genes and genome-wide association mapping led to a better understanding of the genetic relationships between grain storage proteins and grain yield per spike, and provided useful information for marker-assisted selection programs.

Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs

Numerous quantitative trait loci (QTL) have been mapped in tetraploid and hexaploid wheat and wheat relatives, mostly with simple sequence repeat (SSR) or single nucleotide polymorphism (SNP) markers. To conduct meta-analysis of QTL requires projecting them onto a common genomic framework, either a consensus genetic map or genomic sequence. The latter strategy is pursued here. Of 774 QTL mapped in wheat and wheat relatives found in the literature, 585 (75.6%) were successfully projected onto the Aegilops tauschii pseudomolecules. QTL mapped with SNP markers were more successfully projected (92.2%) than those mapped with SSR markers (66.2%). The QTL were not distributed homogeneously along chromosome arms. Their frequencies increased in the proximal-to-distal direction but declined in the most distal regions and were weakly correlated with recombination rates along the chromosome arms. Databases for projected SSR markers and QTL were constructed and incorporated into the Ae. tauschii JBrowse. To facilitate meta-QTL analysis, eight clusters of QTL were used to estimate standard deviations ([Formula: see text]) of independently mapped QTL projected onto the Ae. tauschii genome sequence. The standard deviations [Formula: see text] were modeled as an exponential decay function of recombination rates along the Ae. tauschii chromosomes. We implemented four hypothesis tests for determining the membership of query QTL. The hypothesis tests and estimation procedure for [Formula: see text] were implemented in a web portal for meta-analysis of projected QTL. Twenty-one QTL for Fusarium head blight resistance mapped on wheat chromosomes 3A, 3B, and 3D were analyzed to illustrate the use of the portal for meta-QTL analyses.

Meta-Analysis of the QTLome of Fusarium Head Blight Resistance in Bread Wheat: Refining the Current Puzzle

Background: Fusarium Head Blight (FHB) is a worldwide devastating disease of bread wheat (Triticum aestivum L.). Genetic resistance is the most effective way to control FHB and many QTL related to this trait have been mapped on the wheat genetic map. This information, however, must be refined to be more efficiently used in breeding programs and for the advance of the basic research. The objective of the present study was to in-depth analyze the QTLome of FHB resistance in bread wheat, further integrating genetic, genomic, and transcriptomic data, aiming to find candidate genes. Methods: An exhaustive bibliographic review on 76 scientific papers was carried out collecting information about QTL related to FHB resistance mapped on bread wheat. A dense genetic consensus map with 572,862 loci was generated for QTL projection. Meta-analysis could be performed on 323 QTL. Candidate gene mining was carried out within the most refined loci, containing genes that were cross-validated with publicly available transcriptional expression data of wheat under Fusarium infection. Most highlighted genes were investigated for protein evidence. Results: A total of 556 QTL were found in the literature, distributed on all sub-genomes and chromosomes of wheat. Meta-analysis generated 65 meta-QTL, and this refinement allows one to find markers more tightly linked to these regions. Candidate gene mining within the most refined meta-QTL, meta-QTL 1/chr. 3B, harvested 324 genes and transcriptional data cross-validated 10 of these genes, as responsive to FHB. One is of these genes encodes a Glycosiltransferase and the other encodes for a Cytochrome P450, and these such proteins have already been verified as being responsible for FHB resistance, but the remaining eight genes still have to be further studied, as promising loci for breeding. Conclusions: The QTLome of FHB resistance in wheat was successfully assembled and a refinement in terms of number and length of loci was obtained. The integration of the QTLome with genomic and transcriptomic data has allowed for the discovery of promising candidate genes for use in breeding programs.