Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions

Grain yield and grain weight of wheat are often decreased by water-limitation in the north-eastern cropping belt of Australia. Based on knowledge that CIMMYT lines are well-adapted in this region, a recombinant inbred line (RIL) population between two elite CIMMYT bread wheats (Seri M82 and Babax) was evaluated under water-limited environments. Fourteen productivity traits were evaluated in 192 progeny in up to eight trials. For three aggregations of the environments (all, high yield or low yield), multiple quantitative trait loci (QTL) were detected, each explaining <15% of variation. Co-location of multiple trait QTL was greatest on linkage groups 1B-a, 1D-b, 4A-a, 4D-a, 6A-a, 6B-a, 7A-a and an unassigned linkage group. Two putative QTL (LOD > 3) from Seri (6D-b and UA-d) increased grain yield and co-located with a suggestive (2 < LOD < 3) and a putative QTL for increased stem carbohydrate content (WSC), respectively; the latter QTL also co-located with a putative anthesis QTL for earlier flowering. Both QTL were detected only in high yield (>4t ha⁻¹) environments. A third increased grain yield QTL (7A-a) from Babax co-located with QTL for increased grain number. Six putative QTL increased grain weight and co-located with QTL for harvest index, grains per spike and spike number. Three putative QTL for increased grains per spike co-located with strong QTL for earlier flowering, increased grain weight and fewer spikes. A group of progeny that exceeded the mean grain yield and grain weight of commercial checks had an increased frequency of QTL for high WSC, large grain size, increased harvest index and greater height, but fewer stems, when compared to low yielding (20% less), low grain weight progeny. These findings were consistent with agronomic analyses of the germplasm and demonstrate that there should be opportunities to independently manipulate grain number and grain size which is typically difficult due to strong negative correlations.     

Mapping of main and epistatic effect QTLs associated to grain protein and gluten strength using a RIL population of durum wheat

Quality, specifically protein content and gluten strength are among the main objectives of a durum wheat breeding program. The aim of this work was to validate quantitative trait loci (QTLs) associated with grain protein content (GPC) and gluten strength measured by SDS sedimentation volume (SV) and to find additional QTLs expressed in Argentinean environments. Also, epistatic QTL and QTL x environmental interactions were analyzed. A mapping population of 93 RILs derived from the cross UC1113 x Kofa showing extreme values in gluten quality was used. Phenotypic data were collected along six environments (three locations, two years). Main effect QTLs associated with GPC were found in equivalent positions in two environments on chromosomes 3BS (R² = 21.0-21.6%) and 7BL (R² = 12.1-13%), and in one environment on chromosomes 1BS, 2AL, 2BS, 3BL, 4AL, 5AS, 5BL and 7AS. The most important and stable QTL affecting SV was located on chromosome 1BL (Glu-B1) consistently detected over the six environments (R² = 20.9- 54.2%). Additional QTLs were found in three environments on chromosomes 6AL (R² = 6.4-12.5%), and in two environments on chromosomes 6BL (R² = 11.5-12.1%), 7AS (R² = 8.2-10.2%) and 4BS (R² = 11–16.4%). In addition, pleiotropic effects were found affecting grain yield, test weight, thousand-kernel- weight and days to heading in some of these QTLs. Epistatic QTLs and QTL x environment interactions were found for both quality traits, mostly for GPC. The flanking markers of the QTLs detected in this work could be efficient tools to select superior genotypes for the mentioned traits.     

Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft × ‘extra-soft’ wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) cross

Kernel hardness or texture, used to classify wheat (Triticum aestivum L.) into soft and hard classes, is a major determinant of milling and baking quality. Wheat genotypes in the soft class that are termed ‘extra-soft’ (with kernel hardness in the lower end of the spectrum) have been associated with superior end-use quality. In order to better understand the relationship between kernel hardness, milling yield, and various agronomic traits, we performed quantitative trait mapping using a recombinant inbred line population derived from a cross between a common soft wheat line and a genotype classified as an ‘extra-soft’ line. A total of 47 significant quantitative trait loci (QTL) (LOD ≥ 3.0) were identified for nine traits with the number of QTL affecting each trait ranging from three to nine. The percentage of phenotypic variance explained by these QTL ranged from 3.7 to 50.3%. Six QTL associated with kernel hardness and break flour yield were detected on chromosomes 1BS, 4BS, 5BS, 2DS, 4DS, and 5DL. The two most important QTL were mapped onto orthologous regions on chromosomes 4DS (Xbarc1118–Rht-D1) and 4BS (Xwmc617–Rht-B1). These results indicated that the ‘extra-soft’ characteristic was not controlled by the Hardness (Ha) locus on chromosome 5DS. QTL for eight agronomic traits occupied two genomic regions near semi-dwarf genes Rht-D1 on chromosome 4DS and Rht-B1 on chromosome 4BS. The clustering of these QTL is either due to the pleiotropic effects of single genes or tight linkage of genes controlling these various traits.     

A multiparental cross population for mapping QTL for agronomic traits in durum wheat (Triticum turgidum ssp. durum)

Multiparental cross designs for mapping quantitative trait loci (QTL) provide an efficient alternative to biparental populations because of their broader genetic basis and potentially higher mapping resolution. We describe the development and deployment of a recombinant inbred line (RIL) population in durum wheat (Triticum turgidum ssp. durum) obtained by crossing four elite cultivars. A linkage map spanning 2664 cM and including 7594 single nucleotide polymorphisms (SNPs) was produced by genotyping 338 RILs. QTL analysis was carried out by both interval mapping on founder haplotype probabilities and SNP bi‐allelic tests for heading date and maturity date, plant height and grain yield from four field experiments. Sixteen QTL were identified across environments and detection methods, including two yield QTL on chromosomes 2BL and 7AS, with the former mapped independently from the photoperiod response gene Ppd‐B1, while the latter overlapped with the vernalization locus VRN‐A3. Additionally, 21 QTL with environment‐specific effects were found. Our results indicated a prevalence of environment‐specific QTL with relatively small effect on the control of grain yield. For all traits, functionally different QTL alleles in terms of direction and size of genetic effect were distributed among parents. We showed that QTL results based on founder haplotypes closely matched functional alleles at known heading date loci. Despite the four founders, only 2.1 different functional haplotypes were estimated per QTL, on average. This durum wheat population provides a mapping resource for detailed genetic dissection of agronomic traits in an elite background typical of breeding programmes.     

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

A intervarietal genetic map and QTL analysis for yield traits in wheat

A new genetic linkage map was constructed based on recombinant inbred lines (RILs) derived from the cross between the Chinese winter wheat (Triticum aestivum L.) varieties, Chuang 35050 and Shannong 483 (ChSh). The map included 381 loci on all the wheat chromosomes, which were composed of 167 SSR, 94 EST-SSR, 76 ISSR, 26 SRAP, 15 TRAP, and 3 Glu loci. This map covered 3636.7 cM with 1327.7 cM (36.5%), 1485.5 cM (40.9%), and 823.5 cM (22.6%) for A, B, and D genome, respectively, and contained 13 linkage gaps. Using the RILs and the map, we detected 46 putative QTLs on 12 chromosomes for grain yield (GY) per m², thousand-kernel weight (TKW), spike number (SN) per m², kernel number per spike (KNS), sterile spikelet number per spike (SSS), fertile spikelet number per spike (FSS), and total spikelet number per spike (TSS) in four environments. Each QTL explained 4.42–70.25% phenotypic variation. Four QTL cluster regions were detected on chromosomes 1D, 2A, 6B, and 7D. The most important QTL cluster was located on chromosome 7D near the markers of Xwmc31, Xgdm67, and Xgwm428, in which 8 QTLs for TKW, SN, SSS and FSS were observed with very high contributions (27.53–67.63%).     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.)

Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 × Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.     

Mapping quantitative trait loci for quality factors in an inter-class cross of US and Chinese wheat

Wheat quality factors are critical in determining the suitability of wheat (Triticum aestivum L.) for end-use product and economic value, and they are prime targets for marker-assisted selection. Objectives of this study were to identify quantitative trait loci (QTLs) that ultimately influence wheat market class and milling quality. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese hard wheat line Ning7840 and the soft wheat cultivar Clark and grown at three Oklahoma locations from 2001 to 2003. Milling factors such as test weight (volumetric grain weight, TW), kernel weight (KW), and kernel diameter (KD) and market class factors such as wheat grain protein content (GPC) and kernel hardness index (HI) were characterized on the basis of a genetic map constructed from 367 SSR and 241 AFLP markers covering all 21 chromosomes. Composite interval mapping identified eight QTLs for TW, seven for KW, six for KD, two each for GPC and HI measured by near-infrared reflectance (NIR) spectroscopy, and four for HI measured by single kernel characterization system. Positive phenotypic correlations were found among milling factors. Consistent co-localized QTLs were identified for TW, KW, and KD on the short arms of chromosomes 5A and 6A. A common QTL was identified for TW and KD on the long arm of chromosome 5A. A consistent major QTL for HI peaked at the Pinb-D1 locus on the short arm of chromosome 5D and explained up to 85% of the phenotypic variation for hardness. We identified QTLs for GPC on 4B and the short arm of 3A chromosomes. The consistency of quality factor QTLs across environments reveals their potential for marker-assisted selection.     

Genome-wide linkage and peak-wide association study of obesity-related quantitative traits in Caribbean Hispanics

Although obesity is more prevalent in Hispanics than non-Hispanic whites in the United States, little is known about the genetic etiology of the related traits in this population. To identify genetic loci influencing obesity in non-Mexican Hispanics, we performed a genome-wide linkage scan in 1,390 subjects from 100 Caribbean Hispanic families on six obesity-related quantitative traits: body mass index (BMI), body weight, waist circumference, waist-to-hip ratio, abdominal and average triceps skinfold thickness after adjusting for significant demographic and lifestyle factors. We then carried out an association analysis of the linkage peaks and the FTO gene in an independent community-based Hispanic subcohort (N = 652, 64% Caribbean Hispanics) from the Northern Manhattan Study. Evidence of linkage was strongest on 1q43 with multipoint LOD score of 2.45 (p = 0.0004) for body weight. Suggestive linkage evidence of LOD > 2.0 was also identified on 1q43 for BMI (LOD = 2.03), 14q32 for abdominal skinfold thickness (LOD = 2.17), 16p12 for BMI (LOD = 2.27) and weight (LOD = 2.26), and 16q23–24 for average triceps skinfold thickness (LOD = 2.32). In the association analysis of 6,440 single nucleotide polymorphisms (SNPs) under 1-LOD unit down regions of our linkage peaks on chromosome 1q43 and 16p12 as well as in the FTO gene, we found that two SNPs (rs6665519 and rs669231) on 1q43 and one FTO SNP (rs12447427) were significantly associated with BMI or body weight after adjustment for multiple testing. Our results suggest that in addition to FTO, multiple genetic loci, particularly those on 1q43 region, may contribute to the variations in obesity-related quantitative traits in Caribbean Hispanics.     

Systems genetic analysis of binge-like eating in a C57BL/6J x DBA/2J-F2 cross

Binge eating is a heritable trait associated with eating disorders and refers to the rapid consumption of a large quantity of energy-dense food that is, associated with loss of control and negative affect. Binge eating disorder is the most common eating disorder in the United States; however, the genetic basis is unknown. We previously identified robust mouse inbred strain differences between C57BL/6J and DBA/2J in binge-like eating of sweetened palatable food in an intermittent access, conditioned place preference paradigm. To map the genetic basis of changes in body weight and binge-like eating (BLE) and to identify candidate genes, we conducted quantitative trait locus (QTL) analysis in 128 C57BL/6J x DBA/2J-F2 mice combined with PheQTL and trait covariance analysis in GeneNetwork2 using legacy BXD-RI trait datasets. We identified a QTL on Chromosome 18 influencing changes in body weight across days in females (log of the odds [LOD] = 6.3; 1.5-LOD: 3–12 cM) that contains the candidate gene Zeb1. We also identified a sex-combined QTL influencing initial palatable food intake on Chromosome 5 (LOD = 5.8; 1.5-LOD: 21–28 cM) that contains the candidate gene Lcorl and a second QTL influencing escalated palatable food intake on Chromosome 6 in males (LOD = 5.4; 1.5-LOD: 50–59 cM) that contains the candidate genes Adipor2 and Plxnd1. Finally, we identified a suggestive QTL in females for slope of BLE on distal Chromosome 18 (LOD = 4.1; p = 0.055; 1.5-LOD: 23–35 cM). Future studies will use BXD-RI strains to fine map loci and support candidate gene nomination for gene editing.     

Relationships between grain protein content and grain yield components through quantitative trait locus analyses in a recombinant inbred line population derived from two elite durum wheat cultivars

Grain protein content (GPC) in durum wheat (Triticum turgidum var. durum) is negatively correlated with grain yield. To evaluate possible genetic interrelationships between GPC and grain yield per spike, thousand-kernel weight and kernel number per spike, quantitative trait loci (QTL) for GPC were mapped using GPC-adjusted data in a covariance analysis on yield components. Phenotypic data were evaluated in a segregating population of 120 recombinant inbred lines derived from crossing the elite cultivars Svevo and Ciccio. The material was tested at five environments in southern Italy. QTL were determined by composite interval mapping based on the Svevo?×?Ciccio linkage map described in Gadaleta et al. (2009) and integrated with DArT markers. The close relationship between GPC and yield components was reflected in the negative correlation between the traits and in the reduction of variance when GPC values were adjusted to yield components. Ten independent genomic regions involved in the expression of GPC were detected, six of which were associated with QTL for one or more grain yield components. QTL alleles with increased GPC effects were associated with QTL alleles with decreased effects on one or more yield component traits, or vice versa (i.e. the allelic effects were in opposite direction). Four QTL for GPC showed always significant effects, and these QTL should represent genes that influence GPC independently from variation in the yield components. Such genes are of special interest in wheat breeding since they would allow an increase in GPC without a concomitant decrease in grain yield.     

Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis

An F_4:5 population of 490 recombinant inbred lines (RILs) from the cross Apo/^2*Swarna was used to detect quantitative trait loci (QTL) with large effects on grain yield under drought stress using bulk-segregant analysis (BSA). Swarna is an important rainfed lowland rice variety grown on millions of hectares in Asia, but is highly susceptible to drought and aerobic soil conditions. Apo is an aerobic-adapted variety with moderate tolerance to drought. Two rice microsatellite (RM) markers, RM324, and RM416, located on chromosomes 2 and 3, respectively, were shown via BSA to be strongly associated with yield under lowland drought stress. The effects of these QTL were tested in a total of eight hydrological environments over a period of 3 years. The QTL linked to RM416 (DTY _3.1 ) had a large effect on grain yield under severe lowland drought stress, explaining about 31% of genetic variance for the trait (P < 0.0001). It also explained considerable variance for yield under mild stress in lowland conditions and aerobic environments. To our knowledge this is the first reported QTL that has a large effect on yield in both lowland drought and aerobic environments. The QTL linked to RM324 (DTY _2.1 ) had a highly significant effect on grain yield in lowland drought stress (R ² = 13–16%) and in two aerobic trials. The effect of these QTL on grain yield was verified to be not mainly due to phenology differences. Effects of DTY _3.1 on yield under stress have been observed in several other rice mapping populations studied at IRRI. Results of this study indicate that BSA is an effective method of identifying QTL alleles with large effects on rice yield under severe drought stress. The Apo alleles for these large-effect QTL for grain yield under drought and aerobic conditions may be immediately exploited in marker-assisted-breeding to improve the drought tolerance of Swarna.     

A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments

A population of 96 doubled haploid lines (DHLs) was prepared from F₁ plants of the hexaploid wheat cross Chinese Spring × SQ1 (a high abscisic acid-expressing breeding line) and was mapped with 567 RFLP, AFLP, SSR, morphological and biochemical markers covering all 21 chromosomes, with a total map length of 3,522 cM. Although the map lengths for each genome were very similar, the D genome had only half the markers of the other two genomes. The map was used to identify quantitative trait loci (QTLs) for yield and yield components from a combination of 24 site × treatment × year combinations, including nutrient stress, drought stress and salt stress treatments. Although yield QTLs were widely distributed around the genome, 17 clusters of yield QTLs from five or more trials were identified: two on group 1 chromosomes, one each on group 2 and group 3, five on group 4, four on group 5, one on group 6 and three on group 7. The strongest yield QTL effects were on chromosomes 7AL and 7BL, due mainly to variation in grain numbers per ear. Three of the yield QTL clusters were largely site-specific, while four clusters were largely associated with one or other of the stress treatments. Three of the yield QTL clusters were coincident with the dwarfing gene Rht-B1 on 4BS and with the vernalisation genes Vrn-A1 on 5AL and Vrn-D1 on 5DL. Yields of each DHL were calculated for trial mean yields of 6 g plant^–1 and 2 g plant^–1 (equivalent to about 8 t ha^–1 and 2.5 t ha^–1, respectively), representing optimum and moderately stressed conditions. Analyses of these yield estimates using interval mapping confirmed the group-7 effects on yield and, at 2 g plant^–1, identified two additional major yield QTLs on chromosomes 1D and 5A. Many of the yield QTL clusters corresponded with QTLs already reported in wheat and, on the basis of comparative genetics, also in rice. The implications of these results for improving wheat yield stability are discussed.     

Large‐scale GWAS in sorghum reveals common genetic control of grain size among cereals

Grain size is a key yield component of cereal crops and a major quality attribute. It is determined by a genotype’s genetic potential and its capacity to fill the grains. This study aims to dissect the genetic architecture of grain size in sorghum. An integrated genome‐wide association study (GWAS) was conducted using a diversity panel (n = 837) and a BC‐NAM population (n = 1421). To isolate genetic effects associated with genetic potential of grain size, rather than the genotype’s capacity to fill the grains, a treatment of removing half of the panicle was imposed during flowering. Extensive and highly heritable variation in grain size was observed in both populations in 5 field trials, and 81 grain size QTL were identified in subsequent GWAS. These QTL were enriched for orthologues of known grain size genes in rice and maize, and had significant overlap with SNPs associated with grain size in rice and maize, supporting common genetic control of this trait among cereals. Grain size genes with opposite effect on grain number were less likely to overlap with the grain size QTL from this study, indicating the treatment facilitated identification of genetic regions related to the genetic potential of grain size. These results enhance understanding of the genetic architecture of grain size in cereal, and pave the way for exploration of underlying molecular mechanisms and manipulation of this trait in breeding practices.     

Manipulating assimilate availability provides insight into the genes controlling grain size in sorghum

Variation in grain size, a major determinant of grain yield and quality in cereal crops, is determined by both the plant’s genetic potential and the available assimilate to fill the grain in the absence of stress. This study investigated grain size variation in response to variation in assimilate supply in sorghum using a diversity panel (n = 837) and a backcross-nested association mapping population (n = 1421) across four experiments. To explore the effects of genetic potential and assimilate availability on grain size, the top half of selected panicles was removed at anthesis. Results showed substantial variation in five grain size parameters with high heritability. Artificial reduction in grain number resulted in a general increase in grain weight, with the extent of the increase varying across genotypes. Genome-wide association studies identified 44 grain size quantitative trait locus (QTL) that were likely to act on assimilate availability and 50 QTL that were likely to act on genetic potential. This finding was further supported by functional enrichment analysis and co-location analysis with known grain number QTL and candidate genes. RNA interference and overexpression experiments were conducted to validate the function of one of the identified gene, SbDEP1, showing that SbDEP1 positively regulates grain number and negatively regulates grain size by controlling primary branching in sorghum. Haplotype analysis of SbDEP1 suggested a possible role in racial differentiation. The enhanced understanding of grain size variation in relation to assimilate availability presented in this study will benefit sorghum improvement and have implications for other cereal crops.     

Characterization of a cell wall invertase gene TaCwi-A1 on common wheat chromosome 2A and development of functional markers

Cell wall invertase (CWI) is a critical enzyme for sink tissue development and carbon partition, and has a high association with kernel weight. Characterization of Cwi genes and development of functional markers are of importance for marker-assisted selection in wheat breeding. In the present study, the full-length genomic DNA sequence of a Cwi gene located on wheat chromosome 2A, designated TaCwi-A1, was characterized by in silico cloning and experimental validation. TaCwi-A1 comprises seven exons and six introns, with 3,676 bp in total, and an open reading frame (ORF) of 1,767 bp. A pair of complementary dominant markers, CWI21 and CWI22, was developed based on allelic variations at the TaCwi-A1 locus. A 404-bp PCR fragment was amplified by CWI21 in varieties with lower kernel weights, whereas a 402-bp fragment was generated by CWI22 in the varieties with higher kernel weights. The markers CWI21 and CWI22 were located on chromosome 2AL using a F_2:3 population from a cross Doumai/Shi 4185, and a set of Chinese Spring nullisomic–tetrasomic lines. They were linked to the SSR locus Xbarc15-2AL with a genetic distance of 10.9 cM. QTL analysis indicated that TaCwi-A1 could explain 4.8% of phenotypic variance for kernel weight over 2 years. Two sets of Chinese landraces and two sets of commercial wheat varieties were used to validate the association of CWI21 and CWI22 with kernel weight. The results indicated that the functional markers CWI21 and CWI22 were closely related to kernel weight and could be used in wheat breeding for improving grain yield.     

Chromosomes 3B and 4D are associated with several milling and baking quality traits in a soft white spring wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.) population

Wheat is marketed based on end-use quality characteristics and better knowledge of the underlying genetics of specific quality parameters is essential to enhance the breeding process. A set of 188 recombinant inbred lines from a ‘Louise’ by ‘Penawawa’ mapping population was grown in two crop years at two locations in the Pacific Northwest region of the United States and data were collected on 17 end-use quality traits using established quality analysis protocols. Using an established genetic linkage map, composite interval mapping was used to identify QTL associated with 16 of the 17 quality traits. QTL were found on 13 of the 21 wheat chromosomes. A large number of QTL were located on chromosomes 3B and 4D and coincided with traits for milling quality and starch functionality. Chromosome 3B contained 10 QTL, which were localized to a 26.2 cM region. Chromosome 4D contained 7 QTL, all of which were located on an 18.8 cM region of this chromosome. The majority of the alleles for superior end-use quality were associated with the cultivar Louise. The identified QTL detected remained highly significant independent of grain yield and protein quantity. The identification of these QTL for end-use quality gives key insight into the relationship and complexity of end-use quality traits. It also improves our understanding of these relationships, thereby allowing plant breeders to make valuable gains from selection for these important traits.     

Development of selective markers linked to a major QTL for parthenocarpy in eggplant (Solanum melongena L.)

Parthenocarpy, the ability to set fruits without pollination, is a useful trait for setting fruit under unfavorable conditions. To identify the loci controlling parthenocarpy in eggplant (Solanum melongena L.), we constructed linkage maps by using co-dominant simple sequence repeat and single nucleotide polymorphism markers in F₂ populations derived from intraspecific crosses between two non-parthenocarpic lines (LS1934 and Nakate-Shinkuro) and a parthenocarpic line (AE-P03). Total map distances were 1,414.6 cM (ALF2: LS1934 × AE-P03) and 1,153.8 cM (NAF2: Nakate-Shinkuro × AE-P03), respectively. Quantitative trait locus (QTL) analyses revealed two QTLs on chromosomes 3 and 8, which we denoted as Controlling parthenocarpy3.1 (Cop3.1) and Cop8.1, respectively. The percentage of phenotypic variance explained (PVE) of Cop3.1 was 6.3% in ALF2 (LOD = 4.2) and 10.6% in NAF2 (LOD = 3.0). The PVE of Cop8.1 was 45.7% in ALF2 (LOD = 23.8) and 29.7% in NAF2 (LOD = 7.9). Using a population of backcross inbred lines, we confirmed the effect of Cop8.1, but there was no evidence to support the contribution of Cop3.1. We need to verify the effect of Cop3.1 under various temperature conditions. In addition, we clarified the effectiveness of selective SSR markers, emf21H22 and emh11J10, mapped on each side of Cop8.1 in other F₂ populations derived from various parental combinations. This is the first report concerning QTL analysis of parthenocarpy in eggplant using molecular markers. It will be useful in marker-assisted selection and in revealing the genomic mechanism underlying parthenocarpy in eggplant.     

Effects of introgression of two QTL for fusarium head blight resistance from Asian spring wheat by marker-assisted backcrossing into European winter wheat on fusarium head blight resistance,yield and quality traits

Breeding for fusarium head blight (FHB) resistance of wheat is a continuous challenge for plant breeders. Resistance to FHB is a quantitative trait, governed by several to many genes and modulated by environmental conditions. The presented study was undertaken to assess the effect on improving FHB resistance and on possible unwanted side effects (‘linkage drag’) of two resistance QTL, namely Fhb1 and Qfhs.ifa-5A, from the spring wheat line CM-82036 when transferred by marker-assisted backcrossing into several European winter wheat lines. To achieve these goals, we developed and evaluated fifteen backcross-two–derived families based on nine European winter wheat varieties as recipients and the FHB resistant variety CM-82036 as resistance donor. The QTL Qfhs.ifa-5A had a relatively small impact on increasing FHB resistance. On average lines with Fhb1 plus Qfhs.ifa-5A combined were only slightly more resistant compared to lines with Fhb1 alone. The obtained results suggest that the effect of the spring wheat–derived QTL on improving FHB resistance increases in the order Qfhs.ifa-5A < Fhb1 ≤ Qfhs.ifa-5A plus Fhb1 combined. The genetic background of the recipient line had a large impact on the resistance level of the obtained lines. No systematic negative effect of the spring wheat–derived QTL on grain yield, thousand grain weight, hectoliter weight and protein content was found. The use of spring wheat–derived FHB resistance QTL for breeding high yielding cultivars with improved FHB resistance appears therefore highly promising.