Association mapping for yield and grain quality traits in rice (Oryza sativa L.)

Association analysis was applied to a panel of accessions of Embrapa Rice Core Collection (ERiCC) with 86 SSR and field data from two experiments. A clear subdivision between lowland and upland accessions was apparent, thereby indicating the presence of population structure. Thirty-two accessions with admixed ancestry were identified through structure analysis, these being discarded from association analysis, thus leaving 210 accessions subdivided into two panels. The association of yield and grain-quality traits with SSR was undertaken with a mixed linear model, with markers and subpopulation as fixed factors, and kinship matrix as a random factor. Eight markers from the two appraised panels showed significant association with four different traits, although only one (RM190) maintained the marker-trait association across years and cultivation. The significant association detected between amylose content and RM190 was in agreement with previous QTL analyses in the literature. Herein, the feasibility of undertaking association analysis in conjunction with germplasm characterization was demonstrated, even when considering low marker density. The high linkage disequilibrium expected in rice lines and cultivars facilitates the detection of marker-trait associations for implementing marker assisted selection, and the mining of alleles related to important traits in germplasm.     

Population structure and association mapping of yield contributing agronomic traits in foxtail millet

Key message

Association analyses accounting for population structure and relative kinship identified eight SSR markers ( p < 0.01) showing significant association ( R ²  = 18 %) with nine agronomic traits in foxtail millet.

Abstract

Association mapping is an efficient tool for identifying genes regulating complex traits. Although association mapping using genomic simple sequence repeat (SSR) markers has been successfully demonstrated in many agronomically important crops, very few reports are available on marker-trait association analysis in foxtail millet. In the present study, 184 foxtail millet accessions from diverse geographical locations were genotyped using 50 SSR markers representing the nine chromosomes of foxtail millet. The genetic diversity within these accessions was examined using a genetic distance-based and a general model-based clustering method. The model-based analysis using 50 SSR markers identified an underlying population structure comprising five sub-populations which corresponded well with distance-based groupings. The phenotyping of plants was carried out in the field for three consecutive years for 20 yield contributing agronomic traits. The linkage disequilibrium analysis considering population structure and relative kinship identified eight SSR markers (p < 0.01) on different chromosomes showing significant association (R ² = 18 %) with nine agronomic traits. Four of these markers were associated with multiple traits. The integration of genetic and physical map information of eight SSR markers with their functional annotation revealed strong association of two markers encoding for phospholipid acyltransferase and ubiquitin carboxyl-terminal hydrolase located on the same chromosome (5) with flag leaf width and grain yield, respectively. Our findings on association mapping is the first report on Indian foxtail millet germplasm and this could be effectively applied in foxtail millet breeding to further uncover marker-trait associations with a large number of markers.     

Population structure in a wheat core collection and genomic loci associated with yield under contrasting environments

A set of 96 winter wheat accessions sampled from a variety of geographic origins, including cultivars and breeding lines, were characterized with 46 genome-wide SSR loci for genetic diversity and population structure. The genetic diversity within these accessions was examined using a genetic distance-based and a model-based clustering method. The model-based analysis identified an underlying population structure comprising of four distinct sub-populations which corresponded well with distance-based groupings. Information on the population structure is taken into account in an association mapping study of grain yield from a 3-years field trial incorporating fully irrigated, rainfed and drought stress treatments. A total of 21 marker-grain yield associations (P?<?0.01) were identified with nine SSR markers. Most associations were detected only in one to three environments (treatment/year combination), with an average R ² value around 13?%. However, marker gwm484 (on chromosome 2D) was associated with yield in six environments, including irrigated, rainfed and drought stress treatments, suggesting it could be used to improve grain yield across a range of environments. Variation in grain yield at this locus was associated with earliness, early vigour, kernels per spikelet and harvest index. Microsatellite locus psp3200 (on chromosome 6D) was associated with yield in dry and hot environments, which was related to earliness, early vigour, productive tillering and total biomass per plant. Partial least squares regression, with nine environmental factors, showed that precipitation from tillering to maturity was the main environmental factor causing marker?×?environment associations for grain yield.     

Association mapping of grain color,phenolic content,flavonoid content and antioxidant capacity in dehulled rice

Phytochemicals such as phenolics and flavonoids in rice grain are antioxidants that are associated with reduced risk of developing chronic diseases including cardiovascular disease, type-2 diabetes and some cancers. Understanding the genetic basis of these traits is necessary for the improvement of nutritional quality by breeding. Association mapping based on linkage disequilibrium has emerged as a powerful strategy for identifying genes or quantitative trait loci (QTL) underlying complex traits in plants. In this study, genome-wide association mapping using models controlling both population structure (Q) and relative kinship (K) were performed to identify the marker loci/QTLs underlying the naturally occurring variations of grain color and nutritional quality traits in 416 rice germplasm accessions including red and black rice. A total of 41 marker loci were identified for all the traits, and it was confirmed that Ra (i.e., Prp-b for purple pericarp) and Rc (brown pericarp and seed coat) genes were main-effect loci for rice grain color and nutritional quality traits. RM228, RM339, fgr (fragrance gene) and RM316 were important markers associated with most of the traits. Association mapping for the traits of the 361 white or non-pigmented rice accessions (i.e., excluding the red and black rice) revealed a total of 11 markers for four color parameters, and one marker (RM346) for phenolic content. Among them, Wx gene locus was identified for the color parameters of lightness (L*), redness (a*) and hue angle (H ^o). Our study suggested that the markers identified in this study can feasibly be used to improve nutritional quality or health benefit properties of rice by marker-assisted selection if the co-segregations of the marker–trait associations are validated in segregating populations.     

Validation of gene based marker-QTL association for grain dimension traits in rice

Validation of marker-QTL association for genes grain size 3 (GS3), grain weight 2 (GW2), seed width 5 (qSW5) and a QTL qgrl7.1 for grain length was undertaken in a set of 242 diverse rice germplasm. Further, the study was extended to an F₂ mapping population derived from cross of Sonasal, a short grain aromatic rice landrace with Pusa Basmati 1121, a variety with extra long slender grains. Seven gene specific markers, namely, SF28, SR17, RGS1and RGS2 based on GS3, W004 for GW2, MS40671 for qSW5 and RM505 for qgrl7.1, were used for validation. Single marker analysis revealed significant association of these markers to grain size and shape. The marker SF28 explained highest phenotypic variance (37 %) while the marker W004 explained lowest variance (2.6 %) for grain length in the germplasm set at the significance level P?<?0.05. Three markers namely, SF28, MS40671 and RM505 were polymorphic between the parents Sonasal and Pusa Basmati 1121. In the F₂ population, the marker SF28 linked to gene GS3 explained highest phenotypic variance (32.5 %), while RM505 linked to qgrl7.1 explained 5.4 % of phenotypic variance for grain length. The marker SF28 was found to be most robust in the validation studies both in germplasm and F₂ population. The validated gene specific markers can be utilised in marker assisted selection for improving grain size and shape as these traits have significant contribution towards grain quality and grain yield. This is the first study on validation of gene based markers for grain dimension traits in Indian rice germplasm.     

Microsatellite based linkage disequilibrium analyses reveal <Emphasis Type="Italic">Saltol</Emphasis> haplotype fragmentation and identify novel QTLs for seedling stage salinity tolerance in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A set of 84 diverse rice genotypes were assessed for seedling stage salt tolerance and their genetic diversity using 41 polymorphic SSR markers comprising of 19 Saltol QTL linked and 22 random markers. Phenotypic screening under hydroponics identified three indica landraces (Badami, Shah Pasand and Pechi Badam), two Oryza rufipogon accessions (NKSWR2 and NKSWR17) and one each of Basmati rice (Seond Basmati) and japonica cultivars (Tompha Khau) as salt tolerant, having similar tolerance as of Pokkali and FL478. Among the salt tolerant genotypes, biomass showed positive correlation with shoot fresh weight and negative association with root and shoot Na⁺ content. The results indicated repression of Na⁺ loading within the tolerant plants. Linkage disequilibrium (LD) of the Saltol linked markers was weak, suggestive of high fragmentation of Pokkali haplotype, a result of evolutionary active recombination events. Poor haplotype structure of the Saltol region, may reduce its usefulness in marker assisted breeding programmes, if the target foreground markers chosen are wide apart. LD mapping identified eight robust marker-trait associations (QTLs), of which RM10927 was found linked to root and shoot Na⁺ content and RM10871 with shoot Na⁺/K⁺ ratio. RM271 on chromosome 10, an extra Saltol marker, was found associated to root Na⁺/K⁺ ratio. This marker showed a distinct allele among O. rufipogon accessions. There were also other novel loci detected on chromosomes 2, 5 and 10 influencing salt tolerance in the tested germplasm. Although Saltol remained as the key locus, the role of other genomic regions cannot be neglected in tailoring seedling stage salt tolerance in rice.     

Marker–trait association analysis of kernel hardness and related agronomic traits in a core collection of wheat lines

Identification of marker–trait associations is the first step towards marker-assisted selection in plant breeding. Here we assess genetic diversity and population structure of 94 diverse wheat elite lines and use genome-wide association mapping to identify marker–trait associations for five important traits: kernel hardness (KHA), thousand-kernel weight, grain protein content, test weight (TWT), and plant height (PHT). The 94 accessions employed in this study were grouped into three subpopulations based on the first three principal components, which accounted for 51.5 % of the variations. A mixed linear model was used to detect marker–trait associations incorporating covariance of population structure and relative kinship. A total of six marker loci was significantly associated with KHA, TWT, and PHT after the correction of false discovery rate (α _c  = 0.05). The gene pinB was found to be highly associated with KHA, and is reported to be a major determinant of KHA together with the gene pinA at the Ha locus on chromosome 5D. Marker XwPt-7187 on chromosome 2A was also significantly associated with KHA, two Diversity Arrays Technology markers XwPt-1250 and XwPt-4628 with TWT, and marker Xgwm512 with PHT, making the first report of marker–trait associations in these genomic regions.     

Mapping two major effect grain dormancy QTL in rice

The intrachromosomal positions of the two grain dormancy quantitative trait loci (QTL) qSdn-1 (chromosome 1) and qSdn-5 (chromosome 5) were obtained from the segregation analysis of the advanced backcross populations derived from the cross between rice (Oryza sativa L.) cultivars N22 (indica) and Nanjing35 (japonica). Marker-assisted selection (MAS) was applied to select derivatives carrying one or both of qSdn-1 and qSdn-5 in a genetic background which was nearly isogenic to Nanjing35. An analysis of dormancy in the BC4F2 population allowed qSdn-1 to be located between the simple sequence repeat (SSR) markers RM11669 and RM1216; the QTL explained 24.58% of the overall phenotypic variation and the most closely linked marker was RM11694. qSdn-5 was mapped between RM480 and RM3664, and explained 17.58% of the overall phenotypic variation. The SSR locus RM19080 mapped within 0.4 cM of qSdn-5. No epistasis was observed between qSdn-1 and qSdn-5. The mean germination rates of lines containing qSdn-1, qSdn-5 and both qSdn-1 and qSdn-5 were 7.9, 11.1 and 6.1%, respectively, whereas that of the check line lacking both QTL was 86.3%. The SSR loci linked most tightly to qSdn-1 and qSdn-5 are suitable for MAS for reduced pre-harvest sprouting in rice. The dormancy of both qSdn-1 and qSdn-5 could be readily broken by a 7-day post-harvest treatment at 50°C.     

Molecular mapping of four blast resistance genes using recombinant inbred lines of 93-11 and nipponbare

Molecular mapping of new blast resistance genes is important for developing resistant rice cultivars using marker-assisted selection. In this study, 259 recombinant inbred lines (RILs) were developed from a cross between Nipponbare and 93-11, and were used to construct a 1165.8-cM linkage map with 131 polymorphic simple sequence repeat (SSR) markers. Four major quantitative trait loci (QTLs) for resistance to six isolates of Magnaporthe oryzae were identified: qPi93-1, qPi93-2, qPi93-3, and qPiN-1. For the three genes identified in 93-11, qPi93-1 is linked with SSR marker RM116 on the short arm of chromosome 11 and explains 33% of the phenotypic variation in resistance to isolate CHE86. qPi93-2 is linked with SSR marker RM224 on the long arm of chromosome 11 and accounts for 31% and 25% of the phenotypic variation in resistance to isolates 162-8B and ARB50, respectively. qPi93-3 is linked with SSR marker RM7102 on chromosome 12 and explains 16%, 53%, and 28% of the phenotypic variation in resistance to isolates CHE86, ARB52, and ARB94, respectively. QTL qPiN-1 from Nipponbare is associated with SSR marker RM302 on chromosome 1 and accounts for 34% of the phenotypic variation in resistance to isolate PO6-6. These new genes can be used to develop new varieties with blast resistance via marker-aided selection and to explore the molecular mechanism of rice blast resistance.     

Association mapping validates previously identified quantitative trait loci for salt tolerance in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Salinity is the main abiotic stress that limits rice (Oryza sativa L.) production worldwide. An association mapping project was designed to validate quantitative trait loci (QTLs) in rice associated with Na⁺, K⁺ and Ca⁺⁺ accumulation traits identified in our previous study of linkage mapping. Twenty four varieties/lines of rice were phenotyped for biochemical and yield traits. Among these varieties/lines, two mapping parents, Pokkali and IR-36, of our previous linkage mapping study were also included. For marker-trait assessments, both general linear model (GLM) and mixed linear model (MLM) analyses were performed. Thirteen significant marker-trait associations at P ≤ 0.001 were identified. Associated markers for these marker-trait associations were RM503, RM225, RM152, and RM254 located on chromosomes 3, 6, 8, and 11, respectively. Previously identified QTLs in linkage mapping study for Na⁺ uptake, Ca⁺⁺ uptake, total cations uptake, Ca⁺⁺ uptake ratio, K⁺ uptake ratio, and Na⁺/K⁺ uptake were validated in this study. Heritability values for these traits ranged from 1.00e-05 to 1. Linked markers for these validated QTLs were RM140, RM243, RM203, RM480, RM137, and RM254 located on chromosomes 1, 1, 3, 5, 8, and 11, respectively. These markers will be a valuable resource for marker-assisted breeding (MAB) approach to develop elite salt tolerant rice cultivars. This study demonstrates the potential of association mapping approach to validate previously identified QTLs.     

Fine mapping of a grain weight quantitative trait locus on rice chromosome 8 using near-isogenic lines derived from a cross between Oryza sativa and Oryza rufipogon

A quantitative trait locus (QTL) for grain weight (GW) was detected near SSR marker RM210 on chromosome 8 in backcross populations derived from a cross between the Korean japonica cultivar Hwaseongbyeo and Oryza rufipogon (IRGC 105491). The O. rufipogon allele increased GW in the Hwaseongbyeo background despite the fact that O. rufipogon was the small-seeded parent. Using sister BC₃F₃ near-isogenic lines (NILs), gw8.1 was validated and mapped to a 6.1 cM region in the interval between RM42 and RM210 (P≤0.0001). Substitution mapping with eight BC₃F₄ sub-NILs further narrowed the interval containing gw8.1 to about 306.4 kb between markers RM23201.CNR151 and RM30000.CNR99. A yield trial using homozygous BC₃F₄ sister sub-NILs and the Hwaseongbyeo recurrent parent indicated that the NIL carrying an O. rufipogon chromosome segment across the entire gw8.1 target region out-yielded its sister NIL (containing Hwaseongbyeo chromosome in the RM42–RM210 interval) by 9% (P=0.029). The higher-yielding NIL produced 19.3% more grain than the Hwaseongbyeo recurrent parent (P=0.018). Analysis of a BC₃F₄ NIL indicated that the variation for GW is associated with variation in grain shape, specifically grain length. The locus, gw8.1 is of particular interest because of its independence from undesirable height and grain quality traits. SSR markers tightly linked to the GW QTL will facilitate cloning of the gene underlying this QTL as well as marker-assisted selection for variation in GW in an applied breeding program.     

Genetic Variation and Association Analysis of the SSR Markers Linked to the Major Drought-Yield QTLs of Rice

Drought is one of the major abiotic stresses, which hampers the production of rice worldwide. Informative molecular markers are valuable tools for improving the drought tolerance in various varieties of rice. The present study was conducted to evaluate the informative simple sequence repeat (SSR) markers in a diverse set of rice genotypes. The genetic diversity analyses of the 83 studied rice genotypes were performed using 34 SSR markers closely linked to the major quantitative trait loci (QTLs) of grain yield under drought stress (qDTYs). In general, our results indicated high levels of polymorphism. In addition, we screened these rice genotypes at the reproductive stage under both drought stress and nonstressful conditions. The results of the regression analysis demonstrated a significant relationship between 11 SSR marker alleles and the plant paddy weight under stressful conditions. Under the nonstressful conditions, 16 SSR marker alleles showed a significant correlation with the plant paddy weight. Finally, four markers (RM279, RM231, RM166, and RM231) demonstrated a significant association with the plant paddy weight under both stressful and nonstressful conditions. These informative-associated alleles may be useful for improving the crop yield under both drought stress and nonstressful conditions in breeding programs.     

Genetic analysis and molecular mapping of low amylose gene du12(t) in rice (Oryza sativa L.)

Key message

We obtained interesting results for genetic analysis and molecular mapping of the du12(t) gene.

Abstract

Control of the amylose content in rice is the major strategy for breeding rice with improved quality. In this study, we conducted genetic analysis and molecular mapping to identify the dull gene in the dull rice, Milyang262. A single recessive gene, tentatively designated as du12(t), was identified as the dull gene that leads to the low amylose character of Milyang262. To investigate the inheritance of du12(t), genetic analysis on an F₂ population derived from a cross between the gene carrier, Milyang262, and a moderate amylose content variety, Junam, was conducted. A segregation ratio of 3:1 (χ ² = 1.71, p = 0.19) was observed, suggesting that du12(t) is a single recessive factor that controls the dull character in Milyang262. Allelism tests confirmed that du12(t) is not allelic to other low amylose controlling genes, wx or du1. Recessive class analysis was performed to localize the du12(t) locus. Mapping of du12(t) was conducted on F₂ and F₃ populations of Baegokchal/Milyang262 cross. Linkage analysis of 120 F₂ plants revealed that RM6926 and RM3509 flank du12(t) at a 2.38-Mb region. To refine the du12(t) locus position, 986 F₂ and 289 F₃ additional normal plants were screened by the flanking markers. Twenty-six recombinant plants were identified and later genotyped with four additional adjacent markers located between RM6926 and RM3509. Finally, du12(t) was mapped to an 840-kb region on the distal region of the long arm of chromosome 6, delimited by SSR markers RM20662 and RM412, and co-segregated by RM3765 and RM176.     

Mapping QTLs for improving grain yield using the USDA rice mini-core collection

Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker–trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their co-associations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.     

Genetic Analysis and Molecular Mapping of a Novel Gene Conferring Resistance to Rice Stripe Virus

Rice stripe virus (RSV) is one of the most damaging diseases affecting rice in East Asia. Rice variety 502 is highly resistant to RSV, while variety 5112 is extremely susceptible. Field statistical data revealed that all “502 × 5112” F₁ individuals were resistant to RSV and the ratio of resistant to susceptible plants was 3:1 in the F₂ population and 1:1 in the BC₁F₁ population. These results indicated that a dominant gene, designated RSV1, controlled the resistance. Simple sequence repeat (SSR) analysis was subsequently carried out in an F₂ population. Sixty SSR markers evenly distributed on the 12 rice chromosomes were screened and tested. Two markers, RM229 and RM206, showed linkage with RSV1. Based on this result, six SSR markers flanking RM229 and RM206 were further selected and tested. Results indicated that SSR markers RM457 and RM473E were linked to RSV1 with a genetic distance of 4.5 and 5.0 cM, respectively. All of the four SSR markers (RM229, RM473E, RM457 and RM206) linked to RSV1 were all located on chromosome 11, therefore RSV1 should be located on chromosome 11 also. In order to find some new markers more closely linked to the RSV1 gene, sequence-related amplified polymorphism (SRAP) analysis was performed. A total of 30 SRAP primer-pairs were analyzed, and one marker SR1 showed linkage with RSV1 at a genetic distance of 2.9 cM. Finally, RSV1 gene was mapped on chromosome 11 between SSR markers RM457 and SRAP marker SR1 with a genetic distance of 4.5 cM and 2.9 cM, respectively.     

Identification of SSR Markers for Salt-tolerance in Rice Variety CSR10 by Selective Genotyping

A population of 171 F3 genotypes derived from a cross between CSR10 (salt tolerant, indica) and Taraori Basmati (HBC19) was evaluated for various salt-tolerance attributes at vegetative stage using a hydroponic culture system. Substantial variation was observed in F₃ population for relative growth rate (range 0.065–0.187), Na-K ratio (0.023–0.376) and visual injury symptoms (score 1–9). The mean individual score of CSR10 × HBC19 F₃ plants ranged from 1.7 to 9.0 with mean value of 5.07. Seven of the F₃ plants showed transgressive segregation for salt tolerance. F₃ individuals at both extremes of the response distribution were selected and genotyped using 30 SSR markers displaying polymorphism between the two parental genotypes. As many as 18/30 SSR markers showed distorted segregation ratios among the 30 selected salt-tolerant and salt-sensitive CSR10 × HBC19 F3 plants. Linear regression analysis showed significant association of three markers (RM162 mapped on chromosome 6, and RM209 and RM287 on chromosome 11) with relative growth rate and two markers (RM212 on chromosome 1 and RM206 on chromosome 11) with Na-K ratio explaining 31.3% and 25.6% of phenotypic variation, respectively.     

Fine mapping of grain weight QTL, tgw11 using near isogenic lines from a cross between Oryza sativa and O. grandiglumis

In our previous study, we reported the grain weight (GW) QTL, tgw11 in isogenic lines derived from a cross between Oryza sativa ssp. Japonica cv. Hwaseong and O. grandiglumis. The O. grandiglumis allele at tgw11 decreased GW in the Hwaseong background. To fine-map tgw11, one F₅ plant homozygous for the O. grandiglumis DNA in the target region on chromosome 11 was selected from F₄ line, CR1242 segregating for tgw11 and crossed with Hwaseong to produce secondary F₂ and F₃ populations. QTL analysis using 760 F₂ plants confirmed the existence of tgw11 with an R² value of 15.0%. This QTL explained 32.2% of the phenotypic variance for GW in 91 F₃ lines. Substitution mapping with 65 F₃ lines with informative recombination breakpoints in the target region was carried out to narrow down the position of the tgw11. The result indicated that tgw11 was located in the 900-kb interval between two SSR markers, RM224 and RM27358. QTLs for grain width and grain thickness were also located in the same interval suggesting that a single gene is involved in controlling these three traits. Analysis of F₃ lines indicated that the variation in TGW is associated with variation in grain shape, specifically grain thickness and grain width. Genetic analysis indicated that the O. grandiglumis allele for small seed was dominant over the Hwaseong allele. SSR markers tightly linked to the GW QTL would be useful in marker-assisted selection for variation in GW in breeding program.     

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.

     

Discovery of loci determining pre-harvest sprouting and dormancy in wheat and barley applying segregation and association mapping

Three wheat and two barley populations were studied in order to find loci responsible for dormancy and pre-harvest sprouting. A classical quantitative trait loci analysis was combined with an association mapping approach. Many quantitative trait loci and marker trait associations could be detected on all seven chromosome groups of wheat and on the chromosomes 2H, 3H, 5H, 6H, and 7H of barley. Especially, the known regions on chromosomes 3A and 4A for wheat and 5H for barley were confirmed. Putative functions could be found via a candidate homologues search and via expressed sequence tag annotation. On chromosome 3A, the viviparous1 gene is located which is associated to preharvest sprouting and dormancy. On chromosome 4A, a protein is detected which belongs to the aquaporin family. In barley, an association with the aleurain gene on chromosome 5H was found. The expression of aleurain is regulated by abscisic acid and gibberelic acid. An influence of both hormones on dormancy and pre-harvest sprouting is known. It can be concluded that dormancy and pre-harvest sprouting are very complex traits regulated by multigenes and/or quantitative trait loci.