QTL identification of grain protein concentration and its genetic correlation with starch concentration and grain weight using two populations in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Protein is one of the three main storage chemical components in maize grains, and is negatively correlated with starch concentration (SC). Our objective was to analyse the influence of genetic backgrounds on QTL detection for protein concentration (PC) and to reveal the molecular genetic associations between PC and both SC and grain weight (GWP). Two hundred and eighty-four (Pop1) and 265 (Pop2) F_2:3 families were developed from two crosses between one high-oil maize inbred GY220 and two normal maize inbreds 8984 and 8622 respectively, and were genotyped with 185 and 173 pairs of SSR markers. PC, SC and GWP were evaluated under two environments. Composite interval mapping (CIM) and multiple interval mapping (MIM) methods were used to detect single-trait QTL for PC, and multiple-trait QTL for PC with both SC and GWP. No common QTL were shared between the two populations for their four and one PC QTL. Common QTL with opposite signs of effects for PC and SC/GWP were detected on three marker intervals at bins 6.07–6.08, 8.03 and 8.03–8.04. Multiple-traits QTL mapping showed that tightly-linked QTL, pleiotropic QTL and QTL having effects with opposite directions for PC and SC/GWP were all observed in Pop1, while all QTL reflected opposite effects in Pop2.     

QTL mapping for maize resistance and yield under infestation with Sesamia nonagrioides

The Mediterranean corn borer (MCB) is the most important maize insect pest in the Mediterranean region. The main objective was to map quantitative trait loci (QTL) for yield performance under infestation with MCB, resistance and agronomic traits in a maize RIL population derived from an inbred cross European flint × Reid. Six QTL for resistance traits were located: one QTL for tunnel length (bin 9.03; p = 19.8 %), one QTL for stalk lodging (bin 3.07, p = 11.5 %), and four QTL for ear resistance (bins 1.07, 5.03/5.05, and 8.04; p = 25–63 %). Twelve QTL for agronomic traits were located: a QTL for yield under infestation (bin 5.03, p = 15 %); two QTL for grain moisture (bins 1.07 and 8.05); two QTL for days to anthesis (bin 1.07 and 8.05); two QTL for days to silking (bins 8.04 and 10.02); three QTL for plant height (bins 5.04, 8.05 and 9.03); and two QTL for ear height (bins 8.05 and 9.03). No genetic correlations between yield and other traits were observed. The cross validation (CV) approach showed that the estimation biases for QTL for resistance traits were higher than those for agronomic traits. This work stresses the importance of the region 9.03 for controlling corn borer resistance and suggests the presence of QTL with small effect on ear-resistance traits. At the same genomic region, there are also genes that control plant and ear height and future works could elucidate whether these genes are the same or are closely linked. The QTL for yield seem to play an important role in MCB tolerance in this genetic background. Large biases observed for QTL effects by CV were mainly due to the small sample size used and were higher for resistance traits due to their larger genetic complexity. We consider that it is more appropriate to select for grain yield under infestation instead of selecting for resistance traits because resistance to MCB could have unfavorable associations with agronomic traits.     

Detection and integration of quantitative trait loci for grain yield components and oil content in two connected recombinant inbred line populations of high-oil maize

Improvement in grain yield is an important objective in high-oil maize breeding. In this study, one high-oil maize inbred was crossed with two normal maize inbreds to produce two connected recombinant inbred line (RIL) populations with 282 and 263 F_7:8 families, respectively. The field experiments were conducted under four environments, and eight grain yield components and grain oil content were evaluated. Two genetic linkage maps were constructed using 216 and 208 polymorphic SSR markers. Quantitative trait loci (QTL) were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL in both populations. A total of 199 QTL were detected, 122 in population 1 and 87 in population 2. Seven, 11 and 19 QTL showed consistency across five environments, across two RIL populations and with respective F_2:3 generations, respectively. 183 QTL were integrated in 28 meta-QTL (mQTL). QTL with contributions over 15% were consistently detected in 3–4 cases and integrated in mQTL. Each mQTL included 3–19 QTL related to 1–4 traits, reflecting remarkable QTL co-location for grain yield components and oil content. Further research and marker-assisted selection (MAS) should be concentrated on 37 consistent QTL and four genetic regions of mQTL with more than 10 QTL at bins 3.04–3.05, 7.02, 8.04–8.05 and 9.04–9.05. Near-isogenic lines for 100-grain-weight QTL at bin 7.02–7.03, for ear-length QTL at bin 7.02–7.03 and for rows-per-ear QTL at bin 3.08 are now in construction using MAS. Co-located candidate genes could facilitate the identification of candidate genes for grain yield in maize.     

Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea mays L.)

Southern leaf blight (SLB) caused by the fungus Cochliobolus heterostrophus (Drechs.) Drechs. is a major foliar disease of maize worldwide. Our objectives were to identify quantitative trait loci (QTL) for resistance to SLB and flowering traits in recombinant inbred line (RIL) population derived from the cross of inbred lines LM5 (resistant) and CM140 (susceptible). A set of 207 RILs were phenotyped for resistance to SLB at three time intervals for two consecutive years. Four putative QTL for SLB resistance were detected on chromosomes 3, 8 and 9 that accounted for 54% of the total phenotypic variation. Days to silking and anthesis–silking interval (ASI) QTL were located on chromosomes 6, 7 and 9. A comparison of the obtained results with the published SLB resistance QTL studies suggested that the detected bins 9.03/02 and 8.03/8.02 are the hot spots for SLB resistance whereas novel QTL were identified in bins 3.08 and 8.01/8.04. The linked markers are being utilized for marker‐assisted mobilization of QTL conferring resistance to SLB in elite maize backgrounds. Fine mapping of identified QTL will facilitate identification of candidate genes underlying SLB resistance.     

Use of Mutant-Assisted Gene Identification and Characterization (MAGIC) to identify novel genetic loci that modify the maize hypersensitive response

The partially dominant, autoactive maize disease resistance gene Rp1-D21 causes hypersensitive response (HR) lesions to form spontaneously on leaves and stems in the absence of pathogen recognition. The maize nested association mapping (NAM) population consists of 25 200-line subpopulations each derived from a cross between the maize line B73 and one of 25 diverse inbred lines. By crossing a line carrying the Rp1-D21 gene with lines from three of these subpopulations and assessing the F(1) progeny, we were able to map several novel loci that modify the maize HR, using both single-population quantitative trait locus (QTL) and joint analysis of all three populations. Joint analysis detected QTL in greater number and with greater confidence and precision than did single population analysis. In particular, QTL were detected in bins 1.02, 4.04, 9.03, and 10.03. We have previously termed this technique, in which a mutant phenotype is used as a "reporter" for a trait of interest, Mutant-Assisted Gene Identification and Characterization (MAGIC).     

Identification of quantitative trait loci for leaf area and chlorophyll content in maize (Zea mays) under low nitrogen and low phosphorus supply

To investigate responses to nitrogen and phosphorus stress, 218 recombinant inbred maize (Zea mays L.) lines were grown under low nitrogen, low phosphorus, and control (i.e., nitrogen and phosphorus sufficient) conditions and evaluated at the silking stage for various traits, including leaf area, leaf chlorophyll content, flowering time, the interval between anthesis and silking, and grain yield. Among the 83 quantitative trait loci (QTL) detected, 29 were for controls, another 29 were for low nitrogen, and 25 were low phosphorus. These loci indicate that there were both common and specific genetic mechanisms underlying the investigated traits. Overlapping QTL for leaf size (area, length, and width) leaf chlorophyll level, flowering time, anthesis?Csilking interval, and grain yield were located at chromosome bin 2.03/2.04, bin 2.06/2.07/2.08, bin 4.01/4.02, bin 5.03/5.04, bin 6.07, bin 9.03, and bin 10.03/10.04. Many of these loci overlapped with previously reported loci controlling root growth as well as tolerance or response to nutrient deficiency. These QTL identify chromosome regions as targets for genetic improvement of low nitrogen and low phosphorus tolerance.     

Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize

High-oil maize is a useful genetic resource for genomic investigation in plants. To determine the genetic basis of oil concentration and composition in maize grain, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was phenotyped using gas chromatography, and genotyped with 228 molecular markers. A total of 42 individual QTL, associated with fatty acid compositions and oil concentration, were detected in 21 genomic regions. Five major QTL were identified for measured traits, one each of which explained 42.0% of phenotypic variance for palmitic acid, 15.0% for stearic acid, 27.7% for oleic acid, 48.3% for linoleic acid, and 15.7% for oil concentration in the RIL population. Thirty-six loci were involved in 24 molecular marker pairs of epistatic interactions across all traits, which explained phenotypic variances ranging from 0.4 to 6.1%. Seven of 18 mapping candidate genes related to lipid metabolism were localized within or were close to identified individual QTL, explaining 0.7–13.2% of the population variance. These results demonstrated that a few major QTL with large additive effects could play an important role in attending fatty acid compositions and increasing oil concentration in used germplasm. A larger number of minor QTL and a certain number of epistatic QTL, both with additive effects, also contributed to fatty acid compositions and oil concentration.     

Three main genetic regions for grain development revealed through QTL detection and meta-analysis in maize

Grain weight is one of the three direct yield components, being developed through a dynamic process of grain filling in maize. In this study, 258 recombinant inbred lines derived from a cross between a dent corn and a popcorn inbred were evaluated for grain fresh and dry weight at 10, 20, 30, and 40?days after pollination (DAP) and the activities of ADP-Glc pyrophosphorylase (AGPP), granule-bound starch synthase (GBSS), and soluble starch synthase (SSS) at 30 DAP. Grain-filling rate (GFR) and increasing rate of fresh weight (FWIR) were calculated during all periods. Quantitative trait locus (QTL) mapping was conducted for all traits. Meta-QTL (mQTL) was revealed by meta-analysis using BioMercator. Totally, 161 QTL were detected for six traits. QTL on chromosomes 1, 7, and 10 were detected in most cases, with 43, 54, and 28 QTL, respectively. For each trait, 1?C4 QTL were detected but no QTL for GBSS. Three mQTL at bins 7.02?C7.03, 1.03?C1.04, and 10.05?C10.06 included 47, 24, and 23 QTL detected in this study. Together with 28 QTL for grain weight detected in our previous research, they included 53, 28, and 25 QTL, respectively. Five identified expressed sequence tags (EST), five candidate genes with related functions, and QTL for grain weight in other research were co-located in these regions. It is worth concentrating further research on these regions to develop near-isogenic lines (NILs) of common QTL and their chromosome segment substitution lines (CSSL). Also, cloning and function validation for co-located EST and candidate genes could facilitate identification of genes for grain development and final weight.     

Epistatic interaction is an important genetic basis of grain yield and its components in maize

A population of 294 recombinant inbred lines (RIL) derived from Yuyu22, an elite maize hybrid extending broadly in China, has been constructed to investigate the genetic basis of grain yield, and associated yield components in maize. The main-effect quantitative trait loci (QTL), digenic epistatic interactions, and their interactions with the environment for grain yield and its three components were identified by using the mixed linear model approach. Thirty-two main-effect QTL and forty-four pairs of digenic epistatic interactions were detected for the four measured traits in four environments. Our results suggest that both additive effects and epistasis (additive × additive) effects are important genetic bases of grain yield and its components in the RIL population. Only 30.4% of main-effect QTL for ear length were involved in epistatic interactions. This implies that many loci in epistatic interactions may not have significant effects for traits alone but may affect trait expression by epistatic interaction with the other loci.     

Identification of QTL for Early Vigor and Stay-Green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea mays L.)

We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC₁F_2:3 backcross populations (LPSpop and DTPpop) under well-watered (4 environments; WW) and drought stressed (6; DS) conditions to facilitate breeding efforts towards drought tolerant maize. GY reached 5.6 and 5.8 t/ha under WW in the LPSpop and the DTPpop, respectively. Under DS, grain yield was reduced by 65% (LPSpop) to 59% (DTPpop) relative to WW. GY was strongly associated with the normalized vegetative index (NDVI; r ranging from 0.61 to 0.96) across environmental conditions and with an early flowering under drought stressed conditions (r ranging from -0.18 to -0.25) indicative of the importance of early vigor and drought escape for GY. Out of the 105 detected QTL, 53 were overdominant indicative of strong heterosis. For 14 out of 18 detected vigor QTL, as well as for eight flowering time QTL the trait increasing allele was derived from CML491. Collocations of early vigor QTL with QTL for stay green (bin 2.02, WW, LPSpop; 2.07, DS, DTPpop), the number of ears per plant (bins 2.02, 2.05, WW, LPSpop; 5.02, DS, LPSpop) and GY (bin 2.07, WW, DTPpop; 5.04, WW, LPSpop), reinforce the importance of the observed correlations. LOD scores for early vigor QTL in these bins ranged from 2.2 to 11.25 explaining 4.6 (additivity: +0.28) to 19.9% (additivity: +0.49) of the observed phenotypic variance. A strong flowering QTL was detected in bin 2.06 across populations and environmental conditions explaining 26–31.3% of the observed phenotypic variation (LOD: 13–17; additivity: 0.1–0.6d). Improving drought tolerance while at the same time maintaining yield potential could be achieved by combining alleles conferring early vigor from the recurrent parent with alleles advancing flowering from the donor. Additionally bin 8.06 (DTPpop) harbored a QTL for GY under WW (additivity: 0.27 t/ha) and DS (additivity: 0.58 t/ha). R² ranged from 0 (DTPpop, WW) to 26.54% (LPSpop, DS) for NDVI, 18.6 (LPSpop, WW) to 42.45% (LPSpop, DS) for anthesis and from 0 (DTPpop, DS) to 24.83% (LPSpop, WW) for GY. Lines out-yielding the best check by 32.5% (DTPpop, WW) to 60% (DTPpop, DS) for all population-by-irrigation treatment combination (except LPSpop, WW) identified are immediately available for the use by breeders.     

Mapping of QTL for resistance to the Mediterranean corn borer attack using the intermated B73 × Mo17 (IBM) population of maize

The Mediterranean corn borer or pink stem borer (MCB, Sesamia nonagrioides Lefebvre) causes important yield losses as a consequence of stalk tunneling and direct kernel damage. B73 and Mo17 are the source of the most commercial valuable maize inbred lines in temperate zones, while the intermated B73 × Mo17 (IBM) population is an invaluable source for QTL identification. However, no or few experiments have been carried out to detect QTL for corn borer resistance in the B73 × Mo17 population. The objective of this work was to locate QTL for resistance to stem tunneling and kernel damage by MCB in the IBM population. We detected a QTL for kernel damage at bin 8.05, although the effect was small and two QTL for stalk tunneling at bins 1.06 and 9.04 in which the additive effects were 4 cm, approximately. The two QTL detected for MCB resistance were close to other QTL consistently found for European corn borer (ECB, Ostrinia nubilalis Hübner) resistance, indicating mechanisms of resistance common to both pests or gene clusters controlling resistance to different plagues. The precise mapping achieved with the IBM population will facilitate the QTL pyramiding and the positional cloning of the detected QTL.     

QTL consistency and meta-analysis for grain yield components in three generations in maize

Grain yield is the most important and complex trait in maize. In this study, a total of 258 F₉ recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F_2:3 and BC₂F₂) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R ²) over 15% were consistently detected in 3–4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R ² (19.3–24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC₂F₂ and F_2:3 generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F_2:3 generations, and two between the RIL and BC₂F₂ generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7–12 QTL associated with 2–6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.     

Two consensus quantitative trait loci clusters controlling anthesis–silking interval, ear setting and grain yield might be related with drought tolerance in maize

Unravelling the molecular basis of drought tolerance will provide novel opportunities for improving crop yield under water-limited conditions. The present study was conducted to identify quantitative trait loci (QTLs) controlling anthesis–silking interval (ASI), ear setting percentage (ESP) and grain yield (GY). The mapping population included 234 F₂ plants derived from the cross X178 (drought tolerant) × B73 (drought susceptible). The corresponding F_2:3 progenies, along with their parents, were evaluated for the above-mentioned traits under both well-watered and water-stressed field conditions in three different trials carried out in central and southern China. Interval mapping and composite interval mapping identified 45 and 65 QTLs for the investigated traits, respectively. Two QTL clusters influencing ASI and ESP on chromosomes 1 (bin 1.03) and 9 (bins 9.03–9.05) were identified in more than two environments, showing sizeable additive effects and contribution to phenotypic variance; these two QTL clusters influenced GY only in one environment. No significant interaction was detected between the two genomic regions. A comparative analysis of these two QTL clusters with the QTLs controlling maize drought tolerance previously described in three mapping populations confirmed and extended their relevance for marker-assisted breeding to improve maize production under water-limited conditions.     

Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize

QTL mapping for plant-height traits has not been hitherto reported in high-oil maize. A high-oil maize inbred ‘GY220’ was crossed with two dent maize inbreds (‘8984’ and ‘8622’) to generate two connected F_2:3 populations. Four plant-height traits were evaluated in 284 and 265 F_2:3 families. Single-trait QTL mapping and multiple-trait joint QTL mapping was used to detect QTLs for the traits and the genetic relationship between plant height (PH) and two other plant-height traits. A total of 28 QTLs and 12 pairs of digenic interactions among detected QTLs for four traits were detected in the two F_2:3 families. Only one marker was shared between the two populations. Joint analysis of PH with ear height (EH) and PH with top height (TH) detected 32 additional QTLs. Our results showed that QTL detection for PH was dependent on the genetic background of dent corn inbreds. Multiple-trait joint QTL analysis could increase the number of detected QTLs.     

玉米籽粒淀粉含量遗传基础与调控机制*

玉米是世界上种植面积最大、总产量最高的粮食作物,其籽粒重量的70%来自于淀粉。淀粉不仅是人类及其他动物的主要能量来源,同时也是化工等行业的重要原料。利用拟南芥、水稻等模式植物,淀粉合成相关基因克隆与功能研究已取得较多进展。近年来,随着玉米淀粉含量相关遗传学研究的深入开展,通过数量性状位点(quantitative trait locus mapping,QTL)定位、全基因组关联分析(genome-wide association study, GWAS)及各种组学分析方法,发现了较多新的与淀粉含量相关的遗传位点及候选基因,但是尚缺乏归纳总结。综述了玉米籽粒淀粉合成与调控研究进展,对玉米籽粒淀粉含量相关的QTL和基因进行汇总和分析,通过构建一致性物理图谱,提炼玉米籽粒淀粉含量遗传定位热点区间,这为进一步解析玉米籽粒淀粉合成与代谢相关基因的功能提供参考,并为分子标记辅助育种提供遗传资源。     

Using three overlapped RILs to dissect genetically clustered QTL for fiber strength on Chro.D8 in Upland cotton

Fiber strength is an important trait among cotton fiber qualities due to ongoing changes in spinning technology. Major quantitative trait loci (QTL) for fiber quality enable molecular marker-assisted selection (MAS) to effectively improve fiber quality of cotton cultivars. We previously identified a major QTL for fiber strength derived from 7235 in Upland cotton. In the present study, in order to fine-map fiber strength QTL, we chose three recombinant inbred lines (RIL), 7TR-133, 7TR-132, and 7TR-214, developed from a cross between 7235 and TM-1 for backcrossing to TM-1 to develop three large mapping populations. Phenotypic data for fiber strength traits were collected in Nanjing (JES/NAU) and Xinjiang (BES/XJ) in 2006 and 2007. Three simple sequence repeat (SSR) genetic linkage maps on Chro.24(D8) were constructed using these three backcrossed populations. The SSR genetic maps were constructed using 907 individuals in (7TR-133 × TM-1)F₂ (Pop A), 670 in (7TR-132 × TM-1)F₂ (Pop B), and 940 in (7TR-214 × TM-1)F₂ (Pop C). The average distance between SSR loci was 0.62, 1.7, and 0.56 cM for the three maps. MapQTL 5 software detected five-clustered QTL (2.5 < LOD < 29.8) on Chro.D8 for fiber strength following analysis of three RIL backcrossed F₂/F_2:3 progenies at JES/NAU and BES/XJ over 2 years. Five QTL for fiber strength exhibited a total phenotypic variance (PV) of 28.8–59.6%.     

Fine Mapping and Candidate Gene Prediction of a Pleiotropic Quantitative Trait Locus for Yield-Related Trait in Zea mays

The yield of maize grain is a highly complex quantitative trait that is controlled by multiple quantitative trait loci (QTLs) with small effects, and is frequently influenced by multiple genetic and environmental factors. Thus, it is challenging to clone a QTL for grain yield in the maize genome. Previously, we identified a major QTL, qKNPR6, for kernel number per row (KNPR) across multiple environments, and developed two nearly isogenic lines, SL57-6 and Ye478, which differ only in the allelic constitution at the short segment harboring the QTL. Recently, qKNPR6 was re-evaluated in segregating populations derived from SL57-6×Ye478, and was narrowed down to a 2.8 cM interval, which explained 56.3% of the phenotypic variance of KNPR in 201 F_2∶3 families. The QTL simultaneously affected ear length, kernel weight and grain yield. Furthermore, a large F₂ population with more than 12,800 plants, 191 recombinant chromosomes and 10 overlapping recombinant lines placed qKNPR6 into a 0.91 cM interval corresponding to 198Kb of the B73 reference genome. In this region, six genes with expressed sequence tag (EST) evidence were annotated. The expression pattern and DNA diversity of the six genes were assayed in Ye478 and SL57-6. The possible candidate gene and the pathway involved in inflorescence development were discussed.     

Genetic dissection of sorghum grain quality traits using diverse and segregating populations

Key message

Coordinated association and linkage mapping identified 25 grain quality QTLs in multiple environments, and fine mapping of the Wx locus supports the use of high-density genetic markers in linkage mapping.

Abstract

There is a wide range of end-use products made from cereal grains, and these products often demand different grain characteristics. Fortunately, cereal crop species including sorghum [Sorghum bicolor (L.) Moench] contain high phenotypic variation for traits influencing grain quality. Identifying genetic variants underlying this phenotypic variation allows plant breeders to develop genotypes with grain attributes optimized for their intended usage. Multiple sorghum mapping populations were rigorously phenotyped across two environments (SC Coastal Plain and Central TX) in 2 years for five major grain quality traits: amylose, starch, crude protein, crude fat, and gross energy. Coordinated association and linkage mapping revealed several robust QTLs that make prime targets to improve grain quality for food, feed, and fuel products. Although the amylose QTL interval spanned many megabases, the marker with greatest significance was located just 12 kb from waxy (Wx), the primary gene regulating amylose production in cereal grains. This suggests higher resolution mapping in recombinant inbred line (RIL) populations can be obtained when genotyped at a high marker density. The major QTL for crude fat content, identified in both a RIL population and grain sorghum diversity panel, encompassed the DGAT1 locus, a critical gene involved in maize lipid biosynthesis. Another QTL on chromosome 1 was consistently mapped in both RIL populations for multiple grain quality traits including starch, crude protein, and gross energy. Collectively, these genetic regions offer excellent opportunities to manipulate grain composition and set up future studies for gene validation.

     

Identification of a Novel Allele of TaCKX6a02 Associated with Grain Size,Filling Rate and Weight of Common Wheat

Cytokinin oxidase (CKX) plays a crucial role in plant growth and development by reversibly inactivating cytokinin (CTK). Twenty-four primer pairs, designed from ESTs of the TaCKX genes family of common wheat, were used to identify their allelic variations associated with grain size, weight, and filling rate in 169 recombinant inbred lines (RIL) derived from Jing 411 × Hongmangchun 21. TaCKX6a02, a member of TaCKX gene family, amplified by primer pair T31–32, showed a close association with grain traits in this RIL population. Statistical analysis indicated that allelic variation of TaCKX6a02 had significant correlation with grain size, weight, and filling rate (GFR; P < 0.001) under varied environments. The TaCKX6a02-D1a allele from Jing411 significantly increased grain size, weight and grain filling rate, compared with TaCKX6a02-D1b from Hongmangchun 21. TaCKX6a02 was located on chromosome 3DS in the interval of Xbarc1119 and Xbarc1162, with a genetic distance of 1.4 cM. The location was further confirmed using Chinese Spring nulli–tetrasomic lines. A major QTL (quantitative trait locus) tightly linked to TaCKX6a02 was detected in the RIL population, explaining 17.1~38.2% of phenotype variations for grain size, weight, GFRmax and GFRmean in different environments. In addition, significant effects of variations of TaCKX6a02 on grain weight and GFR were further validated by association analysis among 102 wheat varieties in two cropping seasons. 12.8~35.1% of phenotypic variations were estimated for these genotypes. A novel 29-bp InDel behind the stop codon was detected by DNA sequence analysis between the two alleles of TaCKX6a02-D1. The gene-specific marker, TKX3D, was designed according to the novel variation, and can be used in marker-assisted selection (MAS) for grain size, weight, and GFR in common wheat.