Molecular mapping of quantitative trait loci for three kernel-related traits in maize using a double haploid population

Kernel size and kernel weight are important factors possibly involved in the determination of grain yield in maize, so identifying the genetic basis of kernel-related traits provides insights into the breeding of high-yield maize varieties. Kernel length (KL), kernel width (KW) and hundred kernel weight (HKW) were evaluated in three various planting conditions for the 240 field-grown double haploid (DH) lines derived from the single-cross hybrid Xianyu335. Variations in KL, KW and HKW were observed among DH lines, and all three traits showed a broad sense heritability of 76%. A total of 964 single nucleotide polymorphisms (SNPs) from the MaizeSNP3072 chip was utilised to create a high-density genetic map of 1546.4 cM and to identify quantitative trait loci (QTLs). Using composite interval mapping, a total of five, seven and five QTLs have been mapped for KL, KW and HKW, respectively. qkl1-2 and qkl4-1 explained 17.8% and 14.2% of the phenotypic variation in KL, respectively, and the other three QTLs contributed 3.2–4.0%. The phenotypic variation explained (PVE) of seven QTLs responsible for KW ranged from 3.3 to 9.5%. Three QTLs for HKW, qhkw1, qhkw5 and qhkw10 each explained more than 10% of the phenotypic variation, and qhkw4 and qhkw9 accounted for 3.0% and 6.0%, respectively. Due to their detection in multiple planting environments, the loci mapped here appear to be potential targets for the improvement of maize grain yield.     

Single nucleotide polymorphism tightly linked to a major QTL on chromosome 7A for both kernel length and kernel weight in wheat

Thousand-kernel weight (TKW) is one of the major components of grain yield in wheat (Triticum aestivum). Identifying major quantitative trait loci (QTLs) for TKW and developing effective markers are prerequisite for success in marker-assisted selection (MAS) to improve wheat yield through breeding. This study mapped a major QTL, designated as TaTKW-7AL, for increasing TKW on the long arm of chromosome 7A of ‘Clark’ to a 1.3-cM interval between single nucleotide polymorphism (SNP) markers IWB13913 and IWA5913. This QTL explained 19.7 % of the phenotypic variation for TKW. A QTL for increasing kernel length (KL), one of the major components of TKW, was mapped in the same interval as TaTKW-7AL, suggesting that increased TKW by the QTL in ‘Clark’ is most likely due to the increased KL. Association analysis on a diversity panel of 200 US winter wheat accessions also identified a haplotype of three SNP markers (IWB13913, IWB6693 and IWA5913) that were tightly associated with the both KL and TKW. The analysis of allele frequencies of the haplotype in the diversity panel suggested that the favorable allele of TaTKW-7AL has not been strongly selected for in practice and has potential to be used to improve grain yield in US hard winter wheat breeding. Two user-friendly flanking KASPar markers, IWB13913 and IWA5913, were developed for MAS of TaTKW-7AL.     

Genetic Dissection of Wheat Kernel Hardness Using Conditional QTL Mapping of Kernel Size and Protein-Related Traits

Kernel hardness (KH) is one of the primary quality parameters for common wheat (Triticum aestivum L.) and has a major impact on milling, flour quality, and end-product properties. In addition to Puroindoline (Pin) mutations and differences in Pin expression, other factors, such as kernel size and protein-related traits, play noticeable roles in determining hardness, but at the quantitative trait locus (QTL) level, the influence of these factors remains unclear. In this study, genetic relationships between KH and kernel size traits and between KH and protein-related traits were demonstrated by unconditional and conditional mapping using a wheat 90K genotyping assay with a segregating population of 173 recombinant inbred lines in four environments. Eight additive QTL for KH were detected using unconditional QTL mapping analysis; these QTL were primarily distributed on chromosomes 4B, 5A, 5B, and 6D, with phenotypic variation that ranged from 0.2 to 17.7%. In addition, one pair of epistatic QTL (QKH3B.4-65/QKH4B.6-2) was identified by unconditional mapping, and this pair accounted for 1.6% of the phenotypic variation. Through conditional mapping, after excluding the influences of kernel size and protein-related traits, 14 QTL were discovered and accounted for 0.6–18.5% of the phenotypic variation. Of them, the stable QTL QKH4B.4-17 made the largest contribution, which was partially contributed by the kernel length (KL), kernel thickness (KT), and dry gluten content (DGC). Furthermore, QKH4B.4-17 was crucially contributed by the kernel width (KW), kernel diameter (KD), kernel protein content (KPC), and wet gluten content (WGC) and was independent of the sedimentation volume (SV) and gluten index (GI). Another major QTL, QKH5B.10-63, was independent of the KW and KT; partly due to the variations in KL, KD, DGC, and WGC; and conclusively contributed by the KPC, SV, and GI. Seven additional QTL were only detected in the conditional analysis and were crucially contributed by kernel size or protein-related traits. These results demonstrated that kernel size and protein-related traits play significant roles in determining KH. The present study increases the understanding of the relationships between KH and kernel size and between KH and protein-related traits at the QTL level.     

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2-236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.     

Quantitative trait loci associated with agronomic traits and stripe rust in winter wheat mapping population using single nucleotide polymorphic markers

Heavy rain during the wheat seedling stage, drought in the flowering stage, and high temperatures with high humidity prior to harvest all contribute to substantial reductions in overall wheat yields in the Chinese province of Sichuan. In this study, we explored the effects of Rht-B1 and Yr18 in Chuannong16 (CN16) and a population derived from breeding line 30481. The population of 188 recombinant inbred lines was genotyped using an iSelect 90,000 single nucleotide polymorphism array and two functional markers for Rht-B1 and Yr18, and was phenotyped over 2 years in replicated trials. Grain yield was highly correlated with leaf color, plant height, and thousand kernel weights, and was negatively correlated with sedimentation. Plant height was positively correlated with grain yield and leaf color and negatively correlated with the number of tillers, thousand kernel weight, and sedimentation volume. In addition, sedimentation was negatively correlated with all five of the other traits (plant height, leaf color, tillers per square meter, grain yield, and thousand kernel weight) using both genetic and phenotypic correlation. The semi-dwarf allele Rht-B1b reduced plant height, grain yield, and thousand kernel weight. Yr18 did not affect stripe rust or other agronomic traits in the population examined. A total of 15 quantitative trait locii (QTLs) were identified for seven traits over 2 years, except for grain yield. One pleiotropic QTL on chromosome 4B was significantly associated with leaf color, thousand kernel weight, and plant height, but it was in different scaffolds with Rht-B1 on the physical map. We found a co-segregation SNP marker with Yr18 in our population; they were not in the same region on the physical map. This may be due to the relatively small population size and limited recombinant events in the population.     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.     

Contributions of <Emphasis Type="Italic">TaSUTs</Emphasis> to grain weight in wheat under drought

Key message

The homologous genes to OsSUT1-5 in wheat were identified and detailed analysed. TaSUT1 was the predominant sucrose transporter group and it illustrated the genotypic variations towards drought during grain filling.

Abstract

Sucrose transporters (SUT) play crucial roles in wheat stem water soluble carbohydrate (WSC) remobilization to grain. To determine the major functional SUT gene groups in shoot parts of wheat during grain development, drought tolerant varieties, Westonia and Kauz, were investigated in field drought experiments. Fourteen homologous genes to OsSUT1-5 were identified on five homeologous groups, namely TaSUT1_4A, TaSUT1_4B, TaSUT1_4D; TaSUT2_5A, TaSUT2_5B, TaSUT2_5D; TaSUT3_1A, TaSUT3_1D; TaSUT4_6A, TaSUT4_6B, TaSUT4_6D; TaSUT5_2A, TaSUT5_2B, and TaSUT5_2D, and their gene structures were analysed. Wheat plants above the ground were harvested from pre-anthesis to grain maturity and the stem, leaf sheath, rachis, lemma and developing grain were used for analysing TaSUT gene expression. Grain weight, thousand grain weight, kernel number per spike, biomass and stem WSC were characterized. The study showed that among the five TaSUT groups, TaSUT1 was the predominant sucrose transporting group in all organs sampled, and the expression was particularly high in the developing grain. In contrast to TaSUT1, the gene expression levels of TaSUT2, TaSUT3 and TaSUT4 were lower, except for TaSUT3 which showed preferential expression in the lemma before anthesis. The TaSUT5 gene group was very weakly expressed in all tissues. The upregulated gene expression of TaSUT1 Westonia type in stem and grain reveal a crucial role in stem WSC remobilization to grain under drought. The high TaSUT1 gene expression and the significant correlations with thousand grain weight (TGW) and kernel number per spike demonstrated the contribution in Kauz’s high grain yield in an irrigated environment and high TGW in Westonia under drought stress. Further molecular level identification is required for gene marker development.

     

Characterization of grain protein content gene (<Emphasis Type="Italic">GPC</Emphasis>-<Emphasis Type="Italic">B1</Emphasis>) introgression lines and its potential use in breeding for enhanced grain zinc and iron concentration in spring wheat

At least two billion people around the world suffer from micronutrient deficiency, or hidden hunger, which is characterized by iron-deficiency anemia, vitamin A and zinc deficiency. As a key staple food crop, wheat provides 20% of the world’s dietary energy and protein, therefore wheat is an ideal vehicle for biofortification. Developing biofortified wheat varieties with genetically enhanced levels of grain zinc (Zn) and iron (Fe) concentrations, and protein content provides a cost-effective and sustainable solution to the resource-poor wheat consumers. Large genetic variation for Fe and Zn were found in the primitive and wild relatives of wheat, the potential high Zn and Fe containing genetic resources were used as progenitors to breed high-yielding biofortified wheat varieties with 30–40% higher Zn content. Grain protein content (GPC) determines processing and end-use quality of wheat for making diverse food products. The GPC-B1 allele from Triticum turgidum L. var. dicoccoides have been well characterized for the increase in GPC and the associated pleiotropic effect on grain Zn and Fe concentrations in wheat. In this study effect of GPC-B1 allele on grain Zn and Fe concentrations in wheat were measured in different genetic backgrounds and two different agronomic management practices (with- and without foliar Zn fertilization). Six pairs of near-isogenic lines differing for GPC-B1 gene evaluated at CIMMYT, Mexico showed that GPC-B1 influenced marginal increase for grain Zn, Fe concentrations, grain protein content and slight reduction in kernel weight and grain yield. However, the magnitude of GPC and grain Zn and Fe reductions varied depending on the genetic background. Introgression of GPC-B1 functional allele in combination with normal or delayed maturity alleles in the CIMMYT elite wheat germplasm has the potential to improve GPC and grain Zn and Fe concentrations without the negative effect on grain yield due to early senescence and accelerated maturity.     

Kernel softness in wheat is determined by starch granule bound Puroindoline proteins

Wheat has a vital position in agriculture because it is a staple food for masses and variation in grain hardness governs its applications. Soft wheats have softer endosperm texture that mills easily, so needs less energy to mill, produces smaller particles, and small amount of starch is damaged after milling as compared to hard wheat. Soft texture results from higher level of friabilin whereas hard texture results from low level of friabilin on starch granule surface. Friabilin, a marker of kernel texture is primarily composed of Puroindolines (PINs) and its genes (Pins) are located on the Hardness (Ha) locus. The Pins are the molecular-genetic basis of kernel softness in wheat. When both Pins are in their ‘wild state’ (Pina-D1a and Pinb-D1a), wheat kernel is soft. Absence or mutation in one of the Pins results in hard grain texture with different effects on end use and milling qualities. Pina-D1b genotypes gave harder grain texture, higher protein content, water absorption of flour, damaged starch granules and greater flour yield than hard wheat. Recently, other Pins like genes, Pin b variant genes located on the long arm of chromosome 7A were reported in bread wheat with more than 70% similarity to Pinb (Pinb-D1a) at the DNA level. Other genes located on chromosomes 1A, 2A, 5A, 7A, 5B, 2D and 6D also affect kernel texture. However the main determinants are the variants in the allelic diversity of Puroindoline family genes. Contemporary studies show that Pins are multifunctional family of genes having a range of functions from grain hardness to natural defense against insects and pathogens such as viruses, bacteria and fungi.     

A novel allele of <Emphasis Type="Italic">TaGW2</Emphasis>-<Emphasis Type="Italic">A1</Emphasis> is located in a finely mapped QTL that increases grain weight but decreases grain number in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Key message

A novel TaGW2-A1 allele was identified from a stable, robust QTL region, which is pleiotropic for thousand grain weight, grain number per spike, and grain morphometric parameters in wheat.

Abstract

Thousand grain weight (TGW) and grain number per spike (GNS) are two crucial determinants of wheat spike yield, and genetic dissection of their relationships can help to fine-tune these two components and maximize grain yield. By evaluating 191 recombinant inbred lines in 11 field trials, we identified five genomic regions on chromosomes 1B, 3A, 3B, 5B, or 7A that solely influenced either TGW or GNS, and a further region on chromosome 6A that concurrently affected TGW and GNS. The QTL of interest on chromosome 6A, which was flanked by wsnp_BE490604A_Ta_2_1 and wsnp_RFL_Contig1340_448996 and designated as QTgw/Gns.cau-6A, was finely mapped to a genetic interval shorter than 0.538 cM using near isogenic lines (NILs). The elite NILs of QTgw/Gns.cau-6A increased TGW by 8.33%, but decreased GNS by 3.05% in six field trials. Grain Weight 2 (TaGW2-A1), a well-characterized gene that negatively regulates TGW and grain width in wheat, was located within the finely mapped interval of QTgw/Gns.cau-6A. A novel and rare TaGW2-A1 allele with a 114-bp deletion in the 5′ flanking region was identified in the parent with higher TGW, and it reduced TaGW2-A1 promoter activity and expression. In conclusion, these results expand our knowledge of the genetic and molecular basis of TGW-GNS trade-offs in wheat. The QTLs and the novel TaGW2-A1 allele are likely useful for the development of cultivars with higher TGW and/or higher GNS.

     

Interactions between <Emphasis Type="Italic">Glu-1</Emphasis> and <Emphasis Type="Italic">Glu-3</Emphasis> loci and associations of selected molecular markers with quality traits in winter wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) DH lines

The quality of wheat depends on a large complex of genes and environmental factors. The objective of this study was to identify quantitative trait loci controlling technological quality traits and their stability across environments, and to assess the impact of interaction between alleles at loci Glu-1 and Glu-3 on grain quality. DH lines were evaluated in field experiments over a period of 4 years, and genotyped using simple sequence repeat markers. Lines were analysed for grain yield (GY), thousand grain weight (TGW), protein content (PC), starch content (SC), wet gluten content (WG), Zeleny sedimentation value (ZS), alveograph parameter W (APW), hectolitre weight (HW), and grain hardness (GH). A number of QTLs for these traits were identified in all chromosome groups. The Glu-D1 locus influenced TGW, PC, SC, WG, ZS, APW, GH, while locus Glu-B1 affected only PC, ZS, and WG. Most important marker-trait associations were found on chromosomes 1D and 5D. Significant effects of interaction between Glu-1 and Glu-3 loci on technological properties were recorded, and in all types of this interaction positive effects of Glu-D1 locus on grain quality were observed, whereas effects of Glu-B1 locus depended on alleles at Glu-3 loci. Effects of Glu-A3 and Glu-D3 loci per se were not significant, while their interaction with alleles present at other loci encoding HMW and LMW were important. These results indicate that selection of wheat genotypes with predicted good bread-making properties should be based on the allelic composition both in Glu-1 and Glu-3 loci, and confirm the predominant effect of Glu-D1d allele on technological properties of wheat grains.     

Genetic dissection of large grain shape in rice cultivar ‘Nanyangzhan’ and validation of a grain thickness QTL (<Emphasis Type="Italic">qGT3.1</Emphasis>) and a grain length QTL (<Emphasis Type="Italic">qGL3.4</Emphasis>)

Grain shape is an important agronomic trait in rice, which influences the yield and quality. In order to dissect the genetic basis of the large grain shape in ‘Nanyangzhan’, a recombinant inbred line (RIL) population derived from Nanyangzhan (NYZ) and Zhenshan 97B (ZS97) was used to map quantitative trait loci (QTLs) for grain length (GL), width (GW), thickness (GT), length-to-width ratio (LWR) and kilo-grain weight (KGW). A total of 53 QTLs were detected and distributed on 11 chromosomes in 2 years. Among those, QTLs for GW and GL showed a concentrated distribution on chromosome 2 and chromosome 3, respectively. NYZ, the parent with large grain shape, carried 44 alleles showing positive effects on the studied traits. In addition, the near-isogenic lines (NILs) of two novel QTLs, qGT3.1 and qGL3.4, were constructed with the background of ZS97. Results showed that NIL-qGT3.1 ^NYZ , the NIL carrying homozygous qGT3.1 regions from NYZ, showed an increased value of 0.12 mm in grain thickness on average as compared to NIL-qGT3.1 ^ZS . Similarly, NIL-qGL3.4 ^NYZ increased the length of each grain by 0.47 mm on average as compared to NIL-qGL3.4 ^ZS . Taken together, these results would be of great use in breeding rice cultivars with desirable grain shape.     

Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China’s wheat breeding

Understanding the genetics underlying yield formation of wheat is important for increasing wheat yield potential in breeding programs. Nanda2419 was a widely used cultivar for wheat production and breeding in China. In this study, we evaluated yield components and a few yield-related traits of a recombinant inbred line (RIL) population created by crossing Nanda2419 with the indigenous cultivar Wangshuibai in three to four trials at different geographical locations. Negative and positive correlations were found among some of these evaluated traits. Five traits had over 50 % trial-wide broad sense heritability. Using a framework marker map of the genome constructed with this population, quantitative trait loci (QTL) were identified for all traits, and epistatic loci were identified for seven of them. Our results confirmed some of the previously reported QTLs in wheat and identified several new ones, including QSn.nau-6D for effective tillers, QGn.nau-4B.2 for kernel number, QGw.nau-4D for kernel weight, QPh.nau-4B.2 and QPh.nau-4A for plant height, and QFlw.nau-5A.1 for flag leaf width. In the investigated population, Nanda2419 contributed all QTLs associated with higher kernel weight, higher leaf chlorophyll content, and a major QTL associated with wider flag leaf. Seven chromosome regions were related to more than one trait. Four QTL clusters contributed positively to breeding goal-based trait improvement through the Nanda2419 alleles and were detected in trials set in different ecological regions. The findings of this study are relevant to the molecular improvement of wheat yield and to the goal of screening cultivars for better breeding parents.     

A genetic analysis of the quality of northern-style Chinese steamed bread

Dissecting the genetic basis for the traits of northern-style Chinese steamed bread (NCSB) is of great significance for wheat quality breeding. Quantitative trait loci (QTLs) for the processing quality of NCSB were studied using a recombinant inbred line (RIL) consisting of 173 lines derived from a “Shannong01–35 × Gaocheng9411” cross. Twenty-four putative additive QTLs were detected on chromosomes 1A, 1B, 1D, 3A, 3B, 4A, 4B, 5B, 6B, and 7B. Of these QTLs, QTex1A.1-27, QHei5B.5-488, and QGum4B.4-17 had the highest contribution and accounted for 9.33, 10.9, and 12.0% of the phenotypic variations, respectively. Several co-located QTLs with additive effects were detected on chromosomes 1A, 1D, 4B, and 5B. Two clusters (RFL_CONTIG2160_524-WSNP_CAP12_C2438_1180601 and EX_C101685_705-RAC875_C27536_611) for height, total score, and texture and for chewiness, gumminess, and hardness were detected on chromosomes 1A and 4B, respectively. Two QTLs for chewiness and hardness (QCh1D-4, QHa1D-4) with additive effects were detected; these alleles could be good targets for improving the processing quality of steamed bread from common wheat (Triticum aestivum L.). In addition, QTLs for wheat flour quality and the associated correlations with NCSB were simultaneously analyzed. Negative correlations were detected between chewiness and the wet/dry gluten content (WGC/DGC) or protein content. Two QTLs (QCh4B.4-17 and QPr4B.4-17) and three QTLs (QCh4B.4-13, QWG4B.4-13, and QDG4B.4-13) clustered in the same chromosomal region. The detected QTL clusters should be further investigated during wheat breeding and could be used by breeders to improve wheat quality and especially the processing quality of NCSB.     

Temperature-dependent QTLs in <Emphasis Type="Italic">indica</Emphasis> alleles for improving grain quality in rice: increased prominence of QTLs responsible for reduced chalkiness under high-temperature conditions

Quantitative trait loci (QTLs) for the apparent quality of brown rice under high temperatures during ripening were analyzed using chromosomal segment substitution lines. Segments from the indica cultivar Habataki were substituted into a japonica cultivar with a Sasanishiki background. We found the following two QTLs for increasing grain quality in the Habataki allele on chromosome 3: (1) qTW3-2, located near the marker RM14702, decreased the percentage of total white immature (TWI) grains, and (2) qRG3-2, located near RM3766, increased the percentage of regular grains. The effects of these two QTLs were more obvious under high-temperature ripening conditions; hence, these loci are considered QTLs not only for reducing TWI grains but also for increasing high-temperature tolerance. Additionally, we found two QTLs, i.e., qTW3-1 and qRG3-1, responsible for reduced grain quality near RM14314 on chromosome 3. Although the QTL for narrow grains in the Habataki allele qNG3 was genetically linked to qTW3-2, the effect was only slightly significant, and the length/width ratio of qNG3-carrying grains was within the range observed in widely grown japonica cultivars. Incorporating the Habataki region, including qRG3-2 and qTW3-2 but not qTW3-1 and qRG3-1, in addition to previously reported grain quality QTLs in breeding japonica cultivars will improve high-temperature tolerance and grain quality.     

Premature progression of anther early developmental programs accompanied by comprehensive alterations in transcription during high-temperature injury in barley plants

Kernel number per spike is one of the most important yield components of wheat. To map QTLs related to kernel number including spike length (SPL), spikelet number per spike (SPN), fertile spikelet number (FSPN), sterile spikelet number (SSPN) and compactness, and to characterize the inheritance modes of the QTLs and two-locus interactions, 136 recombinant inbred lines (RILs) derived from ‘Nanda2419’ x ‘Wangshuibai’ and an immortalized F₂ population (IF₂) generated by randomly permutated intermating of these RILs were investigated. QTL mapping made use of the previously constructed over 3300 cM linkage map of the RIL population. Three, five, two, two and six chromosome regions were identified, respectively, for their association with SPL, SPN, FSPN, SSPN, and compactness in at least two of the three environments examined. All compactness QTLs but one shared the respective intervals of QSpn.nau-5A and the SPL QTLs. Xcfd46–Xwmc702 interval on chromosome 7D was related to all traits but SSPN and had consistently the largest effects. The fact that not all the compactness QTL intervals were related to both SPL and SPN indicates that compactness is regulated by different mechanisms. Interval coincidence between QTLs of SPL and SPN and between QTLs of FSPN and SSPN was minimal. For all the traits, favorable alleles exist in both parents. Inheritance modes from additiveness to overdominance of the QTLs were revealed and two-locus interactions were detected, implying that the traits studied are under complex genetic control. The results could contribute to wheat yield improvement and better use of Wangshuibai and Nanda2419 the two special germplasms in wheat breeding program.     

Characterization of an IAA-glucose hydrolase gene <Emphasis Type="Italic">TaTGW6</Emphasis> associated with grain weight in common wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In rice, the TGW6 gene determines grain weight and encodes a protein with indole-3-acetic acid (IAA)-glucose hydrolase activity. Its homolog in wheat, TaTGW6, is considered as a candidate gene related to grain development. To amplify this gene, we designed primers based on a homologous conserved domain of the rice TGW6 gene. Sequence analysis indicated that TaTGW6 comprises only one exon, with 1656 bp in total and an open reading frame of 1035 bp. Three alleles at TaTGW6 locus detected by the primer pair TG23 were designated as TaTGW6-a, TaTGW6-b and TaTGW6-c, respectively. Compared with TaTGW6-a, TaTGW6-b had a 6-bp InDel at the position 170 downstream of initiation codon, and TaTGW6-c was a null mutant. Both TaTGW6-b and TaTGW6-c could significantly increase grain size and weight other than TaTGW6-a; however, the former two alleles showed a low frequency distribution in modern varieties. TaTGW6 was located on chromosome 4AL using a recombinant inbred line population and a set of Chinese Spring nullisomic-tetrasomic lines. It was linked to the SSR locus Xbarc1047 with a genetic distance of 6.62 cM and explained 15.8–21.0 % of phenotypic variation of grain weight in four environments. Association analysis using a natural population and Chinese wheat mini-core collections additionally validated the relationship of TaTGW6 with grain weight; the gene could explain 7.7–12.4 % of phenotypic variation in three environments. Quantitative real-time PCR revealed that TaTGW6-b showed relatively lower expression than TaTGW6-a in immature grain at 20 and 30 days post-anthesis and in mature grain. The low expression of TaTGW6 generally associated with low IAA content, but with high grain weight. The novel functional marker, designated as TG23, can be used for marker-assisted selection to improve grain weight in wheat and also provides insights into the regulatory mechanism underlying grain weight.     

Chromosomal intervals responsible for tissue culture response of wheat immature embryos

To study the genetic mechanism underlying the tissue culture response (TCR) of immature embryos, callus induction and regeneration were performed in two separate trials using the recombinant inbred line (RIL) derived from a cross of Nanda2419 with Wangshuibai. In the first trial, immature embryos were collected from plants grown in the greenhouse in the winter of 2005; while in the second trial, immature embryos were collected from donor plants grown in the field during the growing season. Through whole genome screening, seven chromosome regions conditioning percent embryos forming embryogenic callus (PEFEC) and one conditioning percent callus pieces regenerating plantlets (PCRP) were detected. These QTLs were distributed on chromosomes of homoeologous groups 2, 3, 5 and 7. Among all, QPefec.nau-3B.2, QPefec.nau-7D, and QPcrp.nau-3A were consistently identified. The relationship of these identified wheat TCR QTLs with those of other cereal crops has been evaluated. PCR markers linked to TCR QTLs would facilitate germplasm identification, marker-assisted evaluation and utilization of these QTLs.     

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015–2016 and 2016–2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents (‘SeriM82’ and ‘Babax’). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864 cm length and 4.4 cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL?×?environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL?×?environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.     

Genetic mapping and confirmation of quantitative trait loci for grain chalkiness in rice

Grain chalkiness is a highly undesirable trait affecting rice grain quality and milled rice yield. In order to clarify the genetic basis of chalkiness, a recombinant inbred line population (RIL) derived from a cross between Beilu130 (a japonica cultivar with high chalkiness) and Jin23B (an indica cultivar with low chalkiness) was developed for quantitative trait locus (QTL) mapping. A total of 10 QTLs for white belly rate (WBR) and white core rate (WCR) were detected on eight different chromosomes over 2 years. Two QTLs for WBR (qWBR2 and qWBR5) showed similar chromosomal locations with GW2 and qSW5/GW5, which have been cloned previously to control the grain width and should be the right candidate genes. Three novel minor QTLs controlling WCR, qWCR1, qWCR3, and qWCR4 were further validated in near isogenic F₂ populations (NIL-F₂) and explained 26.1, 18.3, and 21.1% of the phenotypic variation, respectively. These QTLs could be targets for map-based cloning of the candidate genes to elucidate the molecular mechanism of chalkiness and for marker-assisted selection (MAS) in rice grain quality improvement.