Mapping and validation of major quantitative trait loci for kernel length in wild barley (Hordeum vulgare ssp. spontaneum)

Genome-wide association studies reveal novel loci for grain size in two-rowed barley (Hordeum vulgare L.)

KEY MESSAGE

SNP-based and InDel-based GWAS on multi-environment data identified genomic regions associated with barley grain size. Barley yield and quality are greatly influenced by grain size. Improving barley grain size in breeding programs requires knowledge of genetic loci and alleles in germplasm resources. In this study, a collection of 334 worldwide two-rowed barley accessions with extensive genetic diversity was evaluated for grain size including grain length (GL), grain width (GW), and thousand-grain weight (TGW) across six independent field trials. Significant differences were observed in genotype and environments for all measured traits. SNP- and InDel-based GWAS were applied to dissect the genetic architecture of grain size with an SLAF-seq strategy. Two approaches using the FarmCPU model revealed 38 significant marker-trait associations (MTAs) with PVE ranging from 0.01% to 20.68%. Among these MTAs, five were on genomic regions where no previously reported QTL for grain size. Superior alleles of TGW-associated SNP233060 and GL-associated InDel11006 exhibited significantly higher levels of phenotype. The significant MTAs could be used in marker-assisted selection breeding.

Genome-wide profiling of genetic variations reveals the molecular basis of aluminum stress adaptation in Tibetan wild barley

Aluminum (Al) toxicity in acidic soil is a major factor affecting crop productivity. The extensive genetic diversity found in Tibetan wild barley germplasms offers a valuable reservoir of alleles associated with aluminum tolerance. Here, resequencing of two Al-tolerant barley genotypes (Tibetan wild barley accession XZ16 and cultivated barley Dayton) identified a total of 19,826,182 and 16,287,277 single nucleotide polymorphisms (SNPs), 1628,052 and 1386,973 insertions/deletions (InDels), 61,532 and 57,937 structural variations (SVs), 248,768 and 240,723 copy number variations (CNVs) in XZ16 and Dayton, respectively, and uncovered approximately 600 genes highly related to Al tolerance in barley. Comparative genomic analyses unveiled 71 key genes that contain unique genetic variants in XZ16 and are predominantly associated with organic acid exudation, Al sequestration, auxin response, and transcriptional regulation. Manipulation of these key genes at the genetic and transcriptional level is a promising strategy for developing optimal haplotype combinations and new barley cultivars with improved Al tolerance. This study represents the first comprehensive examination of genetic variation in Al-tolerant Tibetan wild barley through genome-wide profiling. The obtained results make the deep insight into the mechanisms underlying barley adaptation to Al toxicity, and identified the candidate genes useful for improvement of Al tolerance in barley.

Genetic Localization and Homologous Genes Mining for Barley Grain Size

Grain size is an important agronomic trait determining barley yield and quality. An increasing number of QTLs (quantitative trait loci) for grain size have been reported due to the improvement in genome sequencing and mapping. Elucidating the molecular mechanisms underpinning barley grain size is vital for producing elite cultivars and accelerating breeding processes. In this review, we summarize the achievements in the molecular mapping of barley grain size over the past two decades, highlighting the results of QTL linkage analysis and genome-wide association studies. We discuss the QTL hotspots and predict candidate genes in detail. Moreover, reported homologs that determine the seed size clustered into several signaling pathways in model plants are also listed, providing the theoretical basis for mining genetic resources and regulatory networks of barley grain size.

The double round-robin population unravels the genetic architecture of grain size in barley

Grain number, size and weight primarily determine the yield of barley. Although the genes regulating grain number are well studied in barley, the genetic loci and the causal gene for sink capacity are poorly understood. Therefore, the primary objective of our work was to dissect the genetic architecture of grain size and weight in barley. We used a multi-parent population developed from a genetic cross between 23 diverse barley inbreds in a double round-robin design. Seed size-related parameters such as grain length, grain width, grain area and thousand-grain weight were evaluated in the HvDRR population comprising 45 recombinant inbred line sub-populations. We found significant genotypic variation for all seed size characteristics, and observed 84% or higher heritability across four environments. The quantitative trait locus (QTL) detection results indicate that the genetic architecture of grain size is more complex than previously reported. In addition, both cultivars and landraces contributed positive alleles at grain size QTLs. Candidate genes identified using genome-wide variant calling data for all parental inbred lines indicated overlapping and potential novel regulators of grain size in cereals. Furthermore, our results indicated that sink capacity was the primary determinant of grain weight in barley.

Discovery of DNA polymorphisms via genome-resequencing and development of molecular markers between two barley cultivars

Genome resequencing uncovers genome-wide DNA polymorphisms that are useful for the development of high-density InDel markers between two barley cultivars. Discovering genomic variations and developing genetic markers are crucial for genetics studies and molecular breeding in cereal crops. Although InDels (insertions and deletions) have become popular because of their abundance and ease of detection, discovery of genome-wide DNA polymorphisms and development of InDel markers in barley have lagged behind other cereal crops such as rice, maize and wheat. In this study, we re-sequenced two barley cultivars, Golden Promise (GP, a classic British spring barley variety) and Hua30 (a Chinese spring barley variety), and mapped clean reads to the reference Morex genome, and identified in total 13,933,145 single nucleotide polymorphisms (SNPs) and 1,240,456 InDels for GP with Morex, 11,297,100 SNPs and 781,687 InDels for Hua30 with Morex, and 13,742,399 SNPs and 1,191,597 InDels for GP with Hua30. We further characterized distinct types, chromosomal distribution patterns, genome location, functional effect, and other features of these DNA polymorphisms. Additionally, we revealed the functional relevance of these identified SNPs/InDels regarding different flowering times between Hua30 and GP within 17 flowering time genes. Furthermore, we developed a series of InDel markers and validated them experimentally in 43 barley core accessions, respectively. Finally, we rebuilt population structure and phylogenetic tree of these 43 barley core accessions. Collectively, all of these genetic resources will facilitate not only the basic research but also applied research in barley.

Mega Meta-QTLs: A Strategy for the Production of Golden Barley (Hordeum vulgare L.) Tolerant to Abiotic Stresses

Abiotic stresses cause a significant decrease in productivity and growth in agricultural products, especially barley. Breeding has been considered to create resistance against abiotic stresses. Pyramiding genes for tolerance to abiotic stresses through selection based on molecular markers connected to Mega MQTLs of abiotic tolerance can be one of the ways to reach Golden Barley. In this study, 1162 original QTLs controlling 116 traits tolerant to abiotic stresses were gathered from previous research and mapped from various populations. A consensus genetic map was made, including AFLP, SSR, RFLP, RAPD, SAP, DArT, EST, CAPS, STS, RGA, IFLP, and SNP markers based on two genetic linkage maps and 26 individual linkage maps. Individual genetic maps were created by integrating individual QTL studies into the pre-consensus map. The consensus map covered a total length of 2124.43 cM with an average distance of 0.25 cM between markers. In this study, 585 QTLs and 191 effective genes related to tolerance to abiotic stresses were identified in MQTLs. The most overlapping QTLs related to tolerance to abiotic stresses were observed in MQTL6.3. Furthermore, three MegaMQTL were identified, which explained more than 30% of the phenotypic variation. MQTLs, candidate genes, and linked molecular markers identified are essential in barley breeding and breeding programs to develop produce cultivars resistant to abiotic stresses.

Combined RNA-seq and Phenotype Analysis Reveals a Potential Molecular Mechanism of the Difference in Grain Size of Naked Barley From the Qinghai-Tibetan Plateau

To understand the molecular mechanism controlling the size of barley grains, a number of traits were analyzed and RNA-seq was conducted on grains of two barley materials with a significant difference in thousand-grain weight (TGW) after flowering. The trait dataset delineates the dynamic changes in grain size after flowering, and it provides an understanding of the source of the difference in TGW. By comparing the transcripts of barley grains at several stages after flowering, we identified the gene expression characteristics and significantly enriched pathways in each stage. At the early stage of grain development, genes involved in fatty acid metabolism, plant hormone signal transduction, and pathways involved in cytoskeleton formation were significantly upregulated. At the later stage of grain development, genes involved in starch synthesis, glucose metabolism, and other pathways were significantly upregulated. Further, we used weighted gene coexpression network analysis (WGCNA) and correlation analysis of trait datasets to identify the coexpressed gene modules significantly associated with traits, such as grain length (GL), grain width (GW), and dry weight (DW). After comparing the modules with the differentially expressed gene (DEG) set, 12 candidate genes were selected, and among these, four genes were homologous to genes that regulate grain size in rice and other plants. The combined analysis identified many potential key regulatory factors that may control barley grain size and yield potential, thus providing new insights into the molecular mechanism of barley grain size.

Identification of QTL for barley grain size

Background

Barley grain size is one of the key factors determining storage capacity during grain filling. Large, well-filled grains also have a high malt extract potential. Grain size is a complex quantitative trait and can be easily affected by environmental factors thus the identification of genes controlling the trait and the use of molecular markers linked to the genes in breeding program is the most effective way of improving grain size.

Methods

Grain sizes of 188 doubled-haploid (DH) lines derived from the cross of a Japanese malting barley variety (Naso Nijo) and a Chinese feed barley variety (TX9425) were obtained from three different sites in two consecutive years. The average data were used for identifying QTL for grain size.

Results

A total of four significant QTL were identified for grain length (GL) and three for grain width (GW). The two major GL QTL are located at similar positions to the QTL for malt extract on 2H and uzu gene on 3H, respectively. However, the GL QTL on 2H is more likely a different one from the malt extract QTL as most of the candidate genes are located outside the fine mapped QTL region for malt extract. The GL QTL on 3H is closely linked with uzu gene but not due to a pleiotropic effect of uzu. The three QTL for grain width on 1H, 2H and 5H, respectively, were located at same position to those for GL.

Identification and validation of stable quantitative trait loci for grain filling rate in common wheat (Triticum aestivum L.)

We identified and validated two stable grain filling rate (GFR) quantitative trait loci (QTL) in wheat that positively influenced several yield-related traits. Among them, QGfr.sicau-7D.1 was a novel GFR QTL. The grain filling rate (GFR) plays a crucial role in determining grain yield. To advance the current understanding of the genetic characteristics underlying the GFR in common wheat, three recombinant inbred line populations were used to map and validate GFR quantitative trait loci (QTL). Using a high-density genetic linkage map, 10 GFR QTL were detected. They were located on chromosomes 2D, 4A, 4B, 5B, 6D, 7A and 7D, explained 4.99-12.62% of the phenotypic variation. Two of them, QGfr.sicau-6D and QGfr.sicau-7D.1, were detected in all four environments tested and their genetic effect was validated by closely linked kompetitive allele specific PCR (KASP) markers in different genetic backgrounds. The effects of these two GFR QTL on other yield-related traits were also estimated. QGfr.sicau-6D had a significant positive influence (p < 0.01) on thousand kernel weight, kernel width, kernel volume, and kernel surface area. QGfr.sicau-7D.1 had a significant positive influence (p < 0.01) on thousand kernel weight and kernel length. Furthermore, QGfr.sicau-7D.1 was a completely novel QTL for GFR; several genes associated with grain growth and development were predicted in its physical interval. These results will facilitate molecular marker-assisted selection of wheat with high-confidence QTL for GFR and fine mapping of genes associated with GFR, thereby contributing to yield improvement.

Fine mapping qGL2H, a major locus controlling grain length in barley (Hordeum vulgare L.)

A major grain length QTL on chromosome 2H was fine mapped to a 140.9 Kb region containing three genes. Increasing yield is an important target for barley breeding programs. One approach to increase yield is by enhancing individual grain weights through the regulation of grain size. Fine mapping major grain size-related quantitative trait loci is necessary for future marker-assisted selection strategies, yet studies of this nature are limited in barley. In the present study, we utilised a doubled haploid population derived from two Australian malt barley varieties, Vlamingh and Buloke, coupled with extensive genotypic and phenotypic data from three independent environments. A major grain length locus identified on chromosome 2H designated qGL2H was fine mapped to a 140.9 Kb interval. qGL2H was able to account for 25.4% of the phenotypic variation for grain length and 10.2% for grain yield. Underlying qGL2H were three high-confidence predicted genes. One of these genes encodes a MYB transcription factor and represents a promising candidate for further genetic research.

Fine mapping qGL2H, a major locus controlling grain length in barley (Hordeum vulgare L.)

A major grain length QTL on chromosome 2H was fine mapped to a 140.9 Kb region containing three genes. Increasing yield is an important target for barley breeding programs. One approach to increase yield is by enhancing individual grain weights through the regulation of grain size. Fine mapping major grain size-related quantitative trait loci is necessary for future marker-assisted selection strategies, yet studies of this nature are limited in barley. In the present study, we utilised a doubled haploid population derived from two Australian malt barley varieties, Vlamingh and Buloke, coupled with extensive genotypic and phenotypic data from three independent environments. A major grain length locus identified on chromosome 2H designated qGL2H was fine mapped to a 140.9 Kb interval. qGL2H was able to account for 25.4% of the phenotypic variation for grain length and 10.2% for grain yield. Underlying qGL2H were three high-confidence predicted genes. One of these genes encodes a MYB transcription factor and represents a promising candidate for further genetic research.

Natural Variation Uncovers Candidate Genes for Barley Spikelet Number and Grain Yield under Drought Stress

Drought stress can occur at any growth stage and can affect crop productivity, which can result in large yield losses all over the world. In this respect, understanding the genetic architecture of agronomic traits under drought stress is essential for increasing crop yield potential and harvest. Barley is considered the most abiotic stress-tolerant cereal, particularly with respect to drought. In the present study, worldwide spring barley accessions were exposed to drought stress beginning from the early reproductive stage with 35% field capacity under field conditions. Drought stress had significantly reduced the agronomic and yield-related traits such as spike length, awn length, spikelet per spike, grains per spike and thousand kernel weight. To unravel the genetic factors underlying drought tolerance at the early reproductive stage, genome-wide association scan (GWAS) was performed using 121 spring barley accessions and a 9K single nucleotide polymorphisms (SNPs) chip. A total number of 101 significant SNPs, distributed over all seven barley chromosomes, were found to be highly associated with the studied traits, of which five genomic regions were associated with candidate genes at chromosomes 2 and 3. On chromosome 2H, the region between 6469300693-647258342 bp includes two candidate drought-specific genes (HORVU2Hr1G091030 and HORVU2Hr1G091170), which are highly associated with spikelet and final grain number per spike under drought stress conditions. Interestingly, the gene expression profile shows that the candidate genes were highly expressed in spikelet, grain, spike and leaf organs, demonstrating their pivotal role in drought tolerance. To the best of our knowledge, we reported the first detailed study that used GWAS with bioinformatic analyses to define the causative alleles and putative candidate genes underlying grain yield-related traits under field drought conditions in diverse barley germplasm. The identified alleles and candidate genes represent valuable resources for future functional characterization towards the enhancement of barley cultivars for drought tolerance.

A High-Density Genetic Linkage Map of SLAFs and QTL Analysis of Grain Size and Weight in Barley (Hordeum vulgare L.)

Grain size is an important agronomic trait determines yield in barley, and a high-density genetic map is helpful to accurately detect quantitative trait loci (QTLs) related to grain traits. Using specific-locus amplified fragment sequencing (SLAF-seq) technology, a high-density genetic map was constructed with a population of 134 recombinant inbred lines (RILs) deriving from a cross between Golden Promise (GP) and H602, which contained 12,635 SLAFs with 26,693 SNPs, and spanned 896.74 cM with an average interval of 0.07 cM on seven chromosomes. Based on the map, a total of 16 QTLs for grain length (GL), grain width and thousand-grain weight were detected on 1H, 2H, 4H, 5H, and 6H. Among them, a major QTL locus qGL1, accounting for the max phenotypic variance of 16.7% was located on 1H, which is a new unreported QTL affecting GL. In addition, the other two QTLs, qGL5 and qTGW5, accounting for the max phenotypic variances of 20.7 and 21.1%, respectively, were identified in the same region, and sequencing results showed they are identical to HvDep1 gene. These results indicate that it is a feasible approach to construct a high-quality genetic map for QTL mapping by using SLAF markers, and the detected major QTLs qGL1, qGL5, and qTGW5 are useful for marker-assisted selection (MAS) of grain size in barley breeding.

Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations

Maize lethal necrosis (MLN) occurs when maize chlorotic mottle virus (MCMV) and sugarcane mosaic virus (SCMV) co-infect maize plant. Yield loss of up to 100% can be experienced under severe infections. Identification and validation of genomic regions and their flanking markers can facilitate marker assisted breeding for resistance to MLN. To understand the status of previously identified quantitative trait loci (QTL)in diverse genetic background, F₃ progenies derived from seven bi-parental populations were genotyped using 500 selected kompetitive allele specific PCR (KASP) SNPs. The F₃ progenies were evaluated under artificial MLN inoculation for three seasons. Phenotypic analyses revealed significant variability (P ≤ 0.01) among genotypes for responses to MLN infections, with high heritability estimates (0.62 to 0.82) for MLN disease severity and AUDPC values. Linkage mapping and joint linkage association mapping revealed at least seven major QTL (qMLN3_130 and qMLN3_142, qMLN5_190 and qMLN5_202, qMLN6_85 and qMLN6_157 qMLN8_10 and qMLN9_142) spread across the 7-biparetal populations, for resistance to MLN infections and were consistent with those reported previously. The seven QTL appeared to be stable across genetic backgrounds and across environments. Therefore, these QTL could be useful for marker assisted breeding for resistance to MLN.

Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Grain filling is an important growth process in formation of yield and quality for barley final yield determination. To explore the grain development behavior during grain filling period in barley, a high-density genetic map with 1962 markers deriving from a doubled haploid (DH) population of 122 lines was used to identify dynamic quantitative trait locus (QTL) for grain filling rate (GFR) and five grain size traits: grain area (GA), grain perimeter (GP), grain length (GL), grain width (GW) and grain diameter (GD). Unconditional QTL mapping is to detect the cumulative effect of genetic factors on a phenotype from development to a certain stage. Conditional QTL mapping is to detect a net effect of genetic factors on the phenotype at adjacent time intervals. Using unconditional, conditional and covariate QTL mapping methods, we successfully detected 34 major consensus QTLs. Moreover, certain candidate genes related to grain size, plant height, yield, and starch synthesis were identified in six QTL clusters, and individual gene was specifically expressed in different grain filling stages. These findings provide useful information for understanding the genetic basis of the grain filling dynamic process and will be useful for molecular marker-assisted selection in barley breeding.

A new source of root-knot nematode resistance from Arachis stenosperma incorporated into allotetraploid peanut (Arachis hypogaea)

Root-knot nematode is a very destructive pathogen, to which most peanut cultivars are highly susceptible. Strong resistance is present in the wild diploid peanut relatives. Previously, QTLs controlling nematode resistance were identified on chromosomes A02, A04 and A09 of Arachis stenosperma. Here, to study the inheritance of these resistance alleles within the genetic background of tetraploid peanut, an F₂ population was developed from a cross between peanut and an induced allotetraploid that incorporated A. stenosperma, [Arachis batizocoi x A. stenosperma]^4×. This population was genotyped using a SNP array and phenotyped for nematode resistance. QTL analysis allowed us to verify the major-effect QTL on chromosome A02 and a secondary QTL on A09, each contributing to a percentage reduction in nematode multiplication up to 98.2%. These were validated in selected F_2:3 lines. The genome location of the large-effect QTL on A02 is rich in genes encoding TIR-NBS-LRR protein domains that are involved in plant defenses. We conclude that the strong resistance to RKN, derived from the diploid A. stenosperma, is transferrable and expressed in tetraploid peanut. Currently it is being used in breeding programs for introgressing a new source of nematode resistance and to widen the genetic basis of agronomically adapted peanut lines.

Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize

Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F_2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F₂ population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F₂ population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.

Dissecting the Genetic Basis of Grain Size and Weight in Barley (Hordeum vulgare L.) by QTL and Comparative Genetic Analyses

Grain size and weight are crucial components of barley yield and quality and are the target characteristics of domestication and modern breeding. Despite this, little is known about the genetic and molecular mechanisms of grain size and weight in barley. Here, we evaluated nine traits determining grain size and weight, including thousand grain weight (Tgw), grain length (Gl), grain width (Gw), grain length-width ratio (Lwr), grain area (Ga), grain perimeter (Gp), grain diameter (Gd), grain roundness (Gr), and factor form density (Ffd), in a double haploid (DH) population for three consecutive years. Using five mapping methods, we successfully identified 60 reliable QTLs and 27 hotspot regions that distributed on all chromosomes except 6H which controls the nine traits of grain size and weight. Moreover, we also identified 164 barley orthologs of 112 grain size/weight genes from rice, maize, wheat and 38 barley genes that affect grain yield. A total of 45 barley genes or orthologs were identified as potential candidate genes for barley grain size and weight, including 12, 20, 9, and 4 genes or orthologs for barley, rice, maize, and wheat, respectively. Importantly, 20 of them were located in the 14 QTL hotspot regions on chromosome 1H, 2H, 3H, 5H, and 7H, which controls barley grain size and weight. These results indicated that grain size/weight genes of other cereal species might have the same or similar functions in barley. Our findings provide new insights into the understanding of the genetic basis of grain size and weight in barley, and new information to facilitate high-yield breeding in barley. The function of these potential candidate genes identified in this study are worth exploring and studying in detail.

QTL identification and KASP marker development for productive tiller and fertile spikelet numbers in two high-yielding hard white spring wheat cultivars

Selecting high-yielding wheat cultivars with more productive tillers per unit area (PTN) combined with more fertile spikelets per spike (fSNS) is difficult. QTL mapping of these traits may aid understanding of this bottleneck and accelerate precision breeding for high yield via marker-assisted selection. PTN and fSNS were assessed in four to five trials from 2015 to 2017 in a doubled haploid population derived from two high-yielding cultivars "UI Platinum" and "SY Capstone." Two QTL for PTN (QPTN.uia-4A and QPTN.uia-6A) and four QTL for fSNS (QfSNS.uia-4A, QfSNS.uia-5A, QfSNS.uia-6A, and QfSNS.uia-7A) were identified. The effects of the QTL were primarily additive and, therefore, pyramiding of multiple QTL may increase PTN and fSNS. However, the two QTL for PTN were positioned in the flanking regions for the two QTL for fSNS on chromosomes 4A and 6A, respectively, suggesting either possible pleiotropic effect of the same QTL or tightly linked QTL and explaining the difficulty of selecting both high PTN and fSNS in phenotypic selection. Kompetitive allele-specific PCR (KASP) markers for all identified QTL were developed and validated in a recombinant inbred line (RIL) population derived from the same two cultivars. In addition, KASP markers for three of the QTL (QPTN.uia-6A, QfSNS.uia-6A, and QfSNS.uia-7A) were further validated in a diverse spring wheat panel, indicating their usefulness under different genetic backgrounds. These KASP markers could be used by wheat breeders to select high PTN and fSNS.

Identification of QTLs controlling grain protein concentration using a high-density SNP and SSR linkage map in barley (Hordeum vulgare L.)

BACKGROUND

Grain protein concentration (GPC) is a major determinant of quality in barley (Hordeum vulgare L.). Breeding barley cultivars with high GPC has practical value for feed and food properties. The aim of the present study was to identify quantitative trait loci (QTLs) for GPC that could be detected under multiple environments.

RESULTS

A population of 190 recombinant inbred lines (RILs) deriving from a cross between Chinese landrace ZGMLEL with high GPC (> 20%) and Australian cultivar Schooner was used for linkage and QTL analyses. The genetic linkage map spanned 2353.48 cM in length with an average locus interval of 2.33 cM. GPC was evaluated under six environments for the RIL population and the two parental lines. In total, six environmentally stable QTLs for GPC were detected on chromosomes 2H (1), 4H (1), 6H (1), and 7H (3) and the increasing alleles were derived from ZGMLEL. Notably, the three QTLs on chromosome 7H (QGpc.ZiSc-7H.1, QGpc.ZiSc-7H.2, and QGpc.ZiSc-7H.3) that linked in coupling phase were firstly identified. Moreover, the genetic effects of stable QTLs on chromosomes 2H, 6H and 7H were validated using near isogenic lines (NILs).

CONCLUSIONS

Collectively, the identified QTLs expanded our knowledge about the genetic basis of GPC in barley and could be selected to develop cultivars with high grain protein concentration.

Validation of QTL mapping and transcriptome profiling for identification of candidate genes associated with nitrogen stress tolerance in sorghum

BACKGROUND

Quantitative trait loci (QTLs) detected in one mapping population may not be detected in other mapping populations at all the time. Therefore, before being used for marker assisted breeding, QTLs need to be validated in different environments and/or genetic backgrounds to rule out statistical anomalies. In this regard, we mapped the QTLs controlling various agronomic traits in a recombinant inbred line (RIL) population in response to Nitrogen (N) stress and validated these with the reported QTLs in our earlier study to find the stable and consistent QTLs across populations. Also, with Illumina RNA-sequencing we checked the differential expression of gene (DEG) transcripts between parents and pools of RILs with high and low nitrogen use efficiency (NUE) and overlaid these DEGs on to the common validated QTLs to find candidate genes associated with N-stress tolerance in sorghum.

RESULTS

An F₇ RIL population derived from a cross between CK60 (N-stress sensitive) and San Chi San (N-stress tolerant) inbred sorghum lines was used to map QTLs for 11 agronomic traits tested under different N-levels. Composite interval mapping analysis detected a total of 32 QTLs for 11 agronomic traits. Validation of these QTLs revealed that of the detected, nine QTLs from this population were consistent with the reported QTLs in earlier study using CK60/China17 RIL population. The validated QTLs were located on chromosomes 1, 6, 7, 8, and 9. In addition, root transcriptomic profiling detected 55 and 20 differentially expressed gene (DEG) transcripts between parents and pools of RILs with high and low NUE respectively. Also, overlay of these DEG transcripts on to the validated QTLs found candidate genes transcripts for NUE and also showed the expected differential expression. For example, DEG transcripts encoding Lysine histidine transporter 1 (LHT1) had abundant expression in San Chi San and the tolerant RIL pool, whereas DEG transcripts encoding seed storage albumin, transcription factor IIIC (TFIIIC) and dwarfing gene (DW2) encoding multidrug resistance-associated protein-9 homolog showed abundant expression in CK60 parent, similar to earlier study.

CONCLUSIONS

The validated QTLs among different mapping populations would be the most reliable and stable QTLs across germplasm. The DEG transcripts found in the validated QTL regions will serve as future candidate genes for enhancing NUE in sorghum using molecular approaches.