Genetic mapping for resistance to gray leaf spot in maize

The molecular marker technology has been used on mapping of quantitative trait loci (QTLs) associated with plant resistance. The objectives of this research were to estimate genetic parameters and to map genomic regions involved in the resistance to gray leaf spot in maize. Ninety F₃ families from the BS03 (susceptible) and BS04 (resistant) cross were used. Field trials were performed using a 10 × 10 square lattice design with three replications. Data from 62 SSR markers were used for linkage analysis. The locations of the QTLs on the linkage groups were determined by composite interval mapping method and the phenotypic variance explained by each marker was determined by regression analysis. Several QTLs associated to disease resistance were identified in the population BS03 × BS04. Some QTLs showed significant effects over the different environments studied. The existence of significant QTLs in common among different environments indicates these genomic regions as possible new tools for marker-assisted selection in maize breeding programs.     

Mapping quantitative trait loci (QTLs) underlying seed vitamin E content in soybean with main,epistatic and QTL × environment effects

Soybean (Glycine max (L.) Merr.) seed contains small amounts of tocopherol, a non‐enzymatic antioxidant known as lipid‐soluble vitamin E (VE). Dietary VE contributes to a decreased risk of chronic diseases in humans and has several beneficial effects on resistance to stress in plants, and increasing VE content is an important breeding goal for increasing the nutritional value of soybean. In this study, quantitative trait loci (QTLs) underlying VE content with main, epistatic and QTL × environment effects were identified in a population of F_5 : 6 recombinant inbred lines from a cross between ‘Hefeng 25’ (a low‐VE cultivar) and ‘OAC Bayfield’ (a high‐VE cultivar). A total of 18 QTLs were detected that showed additive main effects (a) and/or additive × environment interaction effects (ae) in different environments. Moreover, 19 epistatic pairs of QTLs were found to be associated with α‐tocopherol (α‐Toc), γ‐tocopherol (γ‐Toc), δ‐tocopherol (δ‐Toc) and total VE (TE) contents. The QTLs identified in multienvironments could provide more information about QTL by environment interactions and could be useful for the marker‐assistant selection of soybean cultivars with high seed VE contents.     

Identification of QTL for stalk sugar-related traits in a population of recombinant inbred lines of maize

Increasing sugar content in silage maize stalk improves its forage quality and palatability. The genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement, yet little information on QTL for stalk sugar content in maize has been reported. To this end, we investigated QTLs associated with stalk sugar traits including Brix, plant height (PHT), three ear leaves area (TELA), and days to silking (DTS) in two environments using a population of 202 recombinant inbred lines from a cross between YXD053, which has a high stalk sugar content, and Y6-1, which has a low stalk sugar content. A genetic map with 180 SSR and 10 AFLP markers was constructed, which spanned 1,648.6 cM of the maize genome with an average marker distance of 8.68 cM, and QTLs were detected using composite interval mapping. Seven QTLs controlling Brix were mapped on chromosomes 1, 2, 6 and 9 in the combined environments. These QTLs could explain 2.69–13.08 % of the phenotypic variance. One major QTL for Brix on chromosome 2 located between the markers bnlg1909 and umc1635 explained 13.08 % of the phenotypic variance. Y6-1 also contributed QTL allele for increased Brix on chromosome 6. One major QTLs controlling PHT on chromosome 1 and TELA on chromosome 4 were also identified and accounted for 13.68 and 12.49 % of the phenotypic variance, respectively. QTL alleles for increased DTS were located on chromosomes 1 and 5 of YXD053. Significant epistatic effects were identified in four traits, but no significant QTL × environment interactions were observed. The information presented here may be valuable for stalk sugar content improvement via marker-assisted selection in silage maize breeding programs.     

Genetic analysis of genotype × iron nutrition interaction on coleoptile elongation rate in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

High iron levels in rice soils represent a major problem for seedling establishment and crop growth, and rapid coleoptile elongation is the mechanism for the rice to cope with the induced stress. Quantitative trait loci (QTLs) analysis for coleoptile elongation rate (CER) in rice (Oryza sativa L.) was performed to study the inheritance of CER and its response to Fe nutrition. A recombinant inbred line (RIL) population of 244 lines derived from the cross zhenshan97B/miyang46 was germinated in 2004 under four Fe concentrations (0, 1.79, 7.16, and 14.32 mM). Seven QTLs with additive effects of stimulating CER were detected under the four Fe concentrations and they were localized on chromosome 1, 4, 5 and 7 with LOD ranging from 2.88 to 15.94 and their contribution to total phenotypic variance ranging from 4.17% to 15.87%, respectively. In addition, 21 QTLs with additive × additive epistasis were detected on all chromosomes but 4 and 9. The detected QTLs with additive effect mainly came from the male parent ZS97B. The detected number of QTLs with additive and epistatic effects for CER varied with Fe concentration. An additive QTL with G × Fe effect was detected between RZ460 and RZ730 markers of chromosome 1 using multi-environmental model of QTL Mapper 1.6 and considering Fe concentration as an environmental factor. The pattern of CER in the different Fe concentrations was well characterized by the genetic model of quantitative traits. It was found that some RILs had higher CER than both parents in each Fe concentration.     

Genetic dissection of seed vigour under artificial ageing conditions using two joined maize recombinant inbred line populations

Seed vigour plays an important role in agricultural production, and seeds with high sowing quality are necessary for improving agriculture production. In our study, two connected maize recombinant inbred line (RIL) populations derived from Yu82 × Shen137 and Yu537A × Shen137 crosses were evaluated for the mean germination time (MGT) and other related traits under three artificial ageing treatments. We used meta‐analysis to integrate genetic maps and identify quantitative trait loci (QTLs) across the two populations. In total, 74 QTLs and 20 meta‐QTLs (mQTLs) were identified. Four key mQTLs, mQTL2‐2, mQTL5‐4, mQTL6 and mQTL8, which contained initial QTLs with R² values >10% and included 5–9 initial QTLs, may be hot spots of important QTLs for the associated traits. Twenty‐two key candidate genes associated with four seed vigour‐related traits mapped to 14 mQTLs. In particular, the GRMZM2G163749, GRMZM2G122172/GRMZM2G554885/GRMZM2G122871 and GRMZM2G150367 genes mapped within the important mQTL5–4, mQTL6 and mQTL8 regions, respectively. Fine mapping for the genetic regions of these three mQTLs merits further study and could be utilized for marker‐assisted breeding.     

Genomic regions associated with resistance to peanut bud necrosis disease (PBND) in a recombinant inbred line (RIL) population

Parents and 318 F₈ recombinant inbred lines (RILs) derived from the cross, TAG 24 × ICGV 86031 were evaluated for peanut bud necrosis disease (PBND) resistance and agronomic traits under natural infestation of thrips at a disease hotspot location for 2 years. Significant genotype, environment and genotype × environment interaction effects suggested role of environment in development and spread of the disease. Quantitative trait loci (QTL) analysis using QTL Cartographer identified a total of 14 QTL for six traits of which five QTL were for disease incidence. One quantitative trait locus q60DI located on LG_AhII was identified using both QTL Cartographer and QTL Network. Another QTL q90DI was detected with a high PVE of 12.57 using QTL Cartographer. A total of nine significant additive × additive (AA) interactions were detected for PBND disease incidence and yield traits with two and seven interactions displaying effects in favour of the parental and recombinant genotype combinations, respectively. This is the first attempt on QTL discovery associated with PBND resistance in peanut. Superior RILs identified in the study can be recycled or released as variety following further evaluations.     

Molecular genetic analysis of flour color using a doubled haploid population in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Flour color is an important trait in the assessment of flour quality for the production of many end products. In this study, quantitative trait loci (QTLs) with additive effects, epistatic effects, and QTL × environment (QE) interactions for flour color in bread wheat (Triticum aestivum L.) were studied, using a set of 168 doubled haploid (DH) lines derived from a Huapei 3 × Yumai 57 cross. A genetic map was constructed using 283 simple sequence repeats (SSR) and 22 expressed sequence tags (EST)-SSR markers. The DH and parents were evaluated for flour color in three environments. QTL analyses were performed using QTLNetwork 2.0 software based on a mixed linear model approach. A total of 18 additive QTLs and 24 pairs of epistatic QTLs were detected for flour color, which were distributed on 19 of the 21 chromosomes. One major QTL, qa1B, closely linked to barc372 0.1 cM, could account for 25.64% of the phenotypic variation of a* without any influence from the environments. So qa1B could be used in the molecular marker-assisted selection (MAS) in wheat breeding programs. The results showed that both additive and epistatic effects were important genetic basis for flour color, and were also sometimes subject to environmental modifications. The information obtained in this study should be useful for manipulating the QTLs for flour color by MAS in wheat breeding programs. Kun-Pu Zhang and Guang-Feng Chen contributed equally to this study.     

QTL mapping for heading date,leaf area and chlorophyll content under cold and drought stress in two related recombinant inbred line populations (Japonica rice) and meta‐analysis

Rice is highly susceptible to drought and cold. The goal of this study was to identify the QTLs affecting the rice heading date (HD), leaf area (LA) and chlorophyll content (CC) under cold and drought stress. A total of twenty‐nine and thirty‐eight additive QTLs were detected from two crosses of ‘Dongnong422’/‘Kongyu131’ and ‘Xiaobaijingzi’/‘Kongyu131’, respectively. qHD1‐7‐1, qHD1‐7‐2, qHD1‐2‐1, qLA1‐7‐1, qLA1‐6‐3 and qCC1‐7‐1 show adaptive effects under cold treatment, while qHD2‐2‐3, qHD2‐2‐2, qLA2‐7‐3 and qCC2‐5‐1 were important for explaining the genetic mechanism during drought tolerance. Additionally, nine and five additive × environment interaction QTLs were detected for two RILs, respectively. RIL26 and RIL73 were two lines that are associated with cold and drought for HD. 339 QTLs related to HD, CC and LA of rice from database and our research were projected onto the genetic map. Nine cloned genes and nineteen homologous candidate genes related to HD, CC, cold tolerance and drought tolerance were mapped by meta‐analysis. These results lay the foundation for the fine mapping of QTLs related to HD, LA and CC for marker‐assisted selection.     

大豆叶片性状和叶绿素含量QTL间的上位性和环境互作效应

以丰产性好、抗旱力强的栽培大豆晋豆23为母本,山西农家品种半野生大豆灰布支黑豆为父本杂交衍生的447个RIL作为供试群体。将亲本及447个家系分别于2011、2012和2013年采用随机试验种植,按照标准测量叶长、叶宽和叶柄长3个性状,并于2012年8月1日和8月8日和2013年8月2日和8月9日各测量1次叶绿素含量。采用QTLNETwork 2.0混合线性模型分析方法和主基因+多基因混合遗传分离分析法,对大豆叶片性状和叶绿素含量进行遗传分析和QTL间的上位性和环境互作效应研究。结果表明,叶长受2对加性-加性×加性上位性混合主基因控制,叶宽受3对等效主基因控制,叶柄长受4对加性-加性×加性上位性主基因控制,叶绿素含量受4对加性主基因控制;检测到10个与叶长、叶宽、叶柄长和叶绿素含量相关的QTL,分别位于A1、A2、C2、H_1、L和O染色体。其中2个叶长QTL分别位于C2和L染色体,是2对加性×加性上位互作效应及环境互作效应QTL;3个叶宽加性与环境互作QTL分别位于A2、C2和O染色体;2个叶柄长QTL分别位于L和O染色体;3个叶绿素含量QTL分别位于A1、C2和H_1染色体。叶片性状和叶绿素含量的遗传机制较复杂,加性效应、加性×加性上位互作效应及环境互作效应是大豆叶片性状和叶绿素含量的重要遗传基础。建议大豆分子标记辅助育种中,一方面要考虑起主要作用的QTL,另一方面要注重上位性QTL的影响,这对于性状的遗传和稳定表达具有积极的意义。     

Quantitative trait loci (QTL) mapping of silique length and petal colour in Brassica rapa

Recombinant inbred lines (RILs) derived from a cross between Brassica rapa L. cv. ‘Sampad’, and an inbred line 3‐0026.027 was used to map the loci controlling silique length and petal colour. The RILs were evaluated under four environments. Variation for silique length in the RILs ranged from normal, such as ‘Sampad’, to short silique, such as 3‐0026.027. Three QTL, SLA3, SLA5 and SLA7, were detected on the linkage groups A3, A5 and A7, respectively. These QTL explained 36.0 to 42.3% total phenotypic variance in the individual environments and collectively 32.5% phenotypic variance. No additive × additive epistatic interaction was detected between the three QTL. Moreover, no QTL × environment interaction was detected in any of the four environments. The number of loci for silique length detected based on QTL mapping agrees well with the results from segregation analysis of the RILs. In case of petal colour, a single locus governing this trait was detected on the linkage group A2.     

Identification of quantitative trait loci for leaf‐related traits in an IBM Syn10 DH maize population across three environments

Leaf‐related traits (leaf length, leaf width, leaf area and leaf angle) are very important for the yield of maize (Zea mays L) due to their influence on plant type. Therefore, it is necessary to identify quantitative trait loci (QTLs) for leaf‐related traits. In this report, 221 doubled haploid lines (DHLs) of an IBM Syn10 DH population were provided for QTL mapping. In total, 54 QTLs were detected for leaf‐related traits in single environments using a high‐density genetic linkage map. Among them, only eight common QTLs were identified across two or three environments, and the common QTLs for the four traits explained 4.38%–19.99% of the phenotypic variation. qLL‐2‐1 (bin 2.09), qLW‐2‐2 (bin 2.09), qLW‐6‐3 (bin 6.07) and qLA‐5‐2 (bin 2.09) were detected in previous studies, and qLL‐1‐1, qLAr‐1‐1, qLAr‐2‐1 and qLA‐7‐1 may be new QTLs. Notably, qLW‐6‐3 and qLA‐5‐2 were found to be major QTLs explaining 19.99% and 10.96% of the phenotypic variation, respectively. Interestingly, we found three pairs of QTLs (qLW‐2‐2 and qLAr‐2‐1, qLW‐8‐1 and qLL‐8‐2, qLL‐3‐3 and qLAr‐3‐3) that control different traits and that were located on the same chromosome or in a nearby location. Moreover, nine pairs of loci with epistatic effects were identified for the four traits. These results may provide the foundation for QTL fine mapping and for an understanding of the genetic basis of variation in leaf‐related traits.     

Mapping QTLs for salt tolerance with additive,epistatic and QTL × treatment interaction effects at seedling stage in wheat

Quantitative trait loci (QTLs) for salt tolerance with additive, epistatic and QTL × treatment interaction effects at seedling stage in wheat were identified. A set of 131 recombinant inbred lines derived from cross Chuan 35050 × Shannong 483 were evaluated under salt stress and normal conditions. Wide variation was found for all studied traits. A total of 18 additive and 16 epistatic QTLs were detected, among which five and 11 were with significant QTL × treatment effects. Ten QTL clusters were identified, and each may represent a single gene or closely linked genes. The locus controlling shoot K⁺/Na⁺ concentration ratio and shoot Na⁺ concentration on chromosome 5A may be identical to Nax2. The interval Xgwm6‐Xgwm538 on chromosome 4B for total dry weight was also identified in a previous study, both near the marker Xgwm6. The marker Xgwm6 may be useful for marker‐assisted selection. Six pairs of homoeologous QTLs were detected, showing synteny among the A, B and D genomes. These results facilitate understanding the mechanisms and the genetic basis of salt tolerance in wheat.     

A fine‐scale genetic linkage map reveals genomic regions associated with economic traits in walnut (Juglans regia)

A genetic linkage map of walnut containing 2,220 single nucleotide polymorphisms (SNPs) in 16 linkage groups (LGs) was constructed using an F₁ mapping population from a cross between “Chandler” and “Idaho,” two contrasting heterozygous parents. Five quantitative yield traits, lateral fruitfulness, harvest date and three nut traits (shell thickness, nut weight and kernel fill) were then mapped on to linkage groups. A significant quantitative trait locus (QTL) in LG 11 with negative additive effects suggested heterozygote superiority in the expression of lateral bearing. A set of three QTLs explaining ~10% of the variation in harvest date was located in LG 1. Shell thickness, nut weight and kernel fill were under the control of two to three linked pleiotropic QTLs in LG 1 segregating from “Idaho.” The marginal positive additive effects of QTLs for harvest date, shell thickness and nut weight and small negative additive effects for kernel fill suggested that the QTLs had a marginal effect on the expression of these traits.     

Quantitative trait loci mapping for yield-related traits under low and high planting densities in maize (Zea mays)

Average maize yield per hectare has increased significantly because of the improvement in high-density tolerance, but little attention has been paid to the genetic mechanism of grain yield response to high planting density. Here, we used a population of 301 recombinant inbred lines (RILs) derived from the cross YE478 × 08–641 to detect quantitative trait loci (QTLs) for 16 yield-related traits under two planting densities (57,000 and 114,000 plants per ha) across four environments. These yield-related traits responded differently to high-density stress. A total of 110 QTLs were observed for these traits: 33 QTLs only under low planting density, 50 QTLs under high planting density and 27 QTLs across both densities. Only two major QTLs, qCD6 and qWKEL2-2, were identified across low- and high-density treatments. Seven environmentally stable QTLs were also observed containing qED6, qWKEL3, qRN3-3, qRN7-2, qRN9-2 and qRN10 across both densities, as well as qRN9-1 under low density. In addition, 16 and eight pairs of loci with epistasis interaction (EPI) were detected under low and high planting densities, respectively. Additionally, nine and 17 loci showed QTL × environment interaction (QEI) under low- and high-density conditions, respectively. These interactions are of lesser importance than the main QTL effects. We also observed 26 pleiotropic QTL clusters, and the hotspot region 3.08 concentrated nine QTLs, suggesting its great importance for maize yield. These findings suggested that multiple minor QTLs, loci with EPI and QEI, pleiotropy and the complex network of “crosstalk” among them for yield-related traits were greatly influenced by plant density, which increases our understanding of the genetic mechanism of yield-related traits for high-density tolerance.     

烤烟6个农艺性状的QTL定位

由于烟草的分子标记开发和遗传图谱构建十分困难,迄今烟草中有关数量性状基因座(QTL)的定位研究仍非常有限。本研究利用一个由207个株系组成的烤烟DH群体及基于该群体所构建的含有24个连锁群、611个SSR标记,总长为1 882.1 cM的遗传图谱,采用复合区间作图方法,对株高(PH)、茎围(SG)、节距(IL)、叶片数(LN)、最大腰叶长(LWL)和最大腰叶宽(WWL) 6个与叶片产量有关的农艺性状进行QTL定位分析。共检测到69个QTL,大部分QTL的效应值较小,仅有4个具有较大的效应值,可解释大约15%~20%的表型变异。6个性状之间大多彼此相关。与此相符,在基因组中发现存在许多小区域,每个区域包含两个或两个以上紧密连锁的不同性状的QTL。     

Impact of epistasis and QTL × environmental interaction on the oil filling rate of soybean seed at different developmental stages

The oil accumulation in the developing soybean seed has been shown to be a dynamic process with different rates and activities at different phases affected by both genotype and environment. The objective of the present study was to investigate additive, epistatic and quantitative trait loci (QTL) × environment interaction (QE) effects of the QTL controlling oil filling rate in soybean seed. A total of 143 recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used in this study to obtain 2 years of field data (2004 and 2005). A total of 26 QTL with significantly unconditional and conditional additive (a) effect and/or additive × environment interaction (ae) effect at different filling stages were identified on 14 linkage groups. Among the QTL with significant a effects, 18 QTL showed positive effects and 6 QTL had negative effects on seed filling rate of oil content during seed development. A total of 29 epistatic pairwise QTL underlying seed filling rate were identified at different filling stages. About 28 pairs of the QTL showed additive × additive epistatic (aa) effects and 14 pairs of the QTL showed aa × environment interaction (aae) effects at different filling stages. QTL with aa and aae (additive × additive × environment) effects appeared to vary at different filling stages. Our results demonstrated that oil filling rate in soybean seed were under genetic, developmental and environmental control.     

Genetic structure composed of additive QTL,epistatic QTL pairs and collective unmapped minor QTL conferring oil content and fatty acid components of soybeans

The relative importance of various types of quantitative trait locus (QTL) conferring oil content and its fatty acid components in soybean seeds was assessed through testing a recombinant inbred line (RIL) population (derived from KF1 × NN1138-2) in randomized blocks experiments in 2004–2006. The contents of oil and oleic, linoleic, linolenic, palmitic and stearic acids were determined with automatic Soxhlet extraction system and gas chromatography, respectively. Based on the established genetic linkage map with 834 markers, QTLNetwork2.0 was used to detect QTL under the genetic model composed of additive, additive × additive (epistasis), additive × year and epistasis × year effects. The contributions to the phenotypic variances of additive QTL and epistatic QTL pairs were 15.7% (3 QTL) and 10.8% (2 pairs) for oil content, 10.4% (3 QTL) and 10.3% (3 pairs) for oleic acid, 11.6% (3 QTL) and 8.5% (2 pairs) for linoleic acid, 28.5% (7 QTL) and 7.6% (3 pairs) for linolenic acid, 27.0% (6 QTL) and 16.6% (7 pairs) for palmitic acid and 29.7% (5 QTL) and 4.3% (1 pair) for stearic acid, respectively. Those of additive QTL by year interaction were small and no epistatic QTL pair by year interaction was found. Among the 27 additive QTL and 36 epistatic QTL (18 pairs), three are duplicated between the two QTL types. A large difference was found between the genotypic variance among RILs and the total variance of mapped QTL, which accounted for 52.9–74.8% of the genotypic variation, much larger than those of additive QTL and epistatic QTL pairs. This part of variance was recognized as that due to a collection of unmapped minor QTL, like polygenes in biometrical genetics, and was designated as collective unmapped minor QTL. The results challenge the breeders for how to pyramid different types of QTL. In addition, the present study supports the mapping strategy of a full model scanning followed by verification with other procedures corresponding to the first results.     

Genetic analysis of prolificacy in “Sikkim Primitive”—A prolific maize (Zea mays) landrace of North‐Eastern Himalaya

Prolificacy assumes significance for development of high‐yielding baby corn hybrids. “Sikkim Primitive” is a native landrace of North‐Eastern Himalaya, and is the highest prolific maize germplasm. So far, the genetics of prolificacy in “Sikkim Primitive” has not been deciphered. Here, a prolific inbred (MGU‐SP‐101) developed from “Sikkim Primitive” was crossed with four non‐prolific inbreds viz., LM13, BML7, HKI161 and HKI1128. Six generations (P₁, P₂, F₁, F₂, BC₁P₁ and BC₁P₂) of the crosses were evaluated at two locations during rainy season 2018. MGU‐SP‐101 possessed 2.50–3.78 ears per plant compared to 1.06–1.86 among non‐prolific inbreds. The variation for ears was the highest in F₂s (1–8), followed by BC₁P₁ (1–7) and BC₁P₂ (1–5). The quantitative inheritance pattern of prolificacy with prevalence of non‐allelic interactions of duplicate epistasis type has been observed. Dominance × dominance effect was predominant over additive × additive and additive × dominance effects. Total number of major gene blocks ranged from 0.41 to 2.86, thereby suggesting the involvement of at least one major gene/QTL governing the prolificacy. This is the first report of genetic dissection of prolificacy in “Sikkim Primitive”.     

QTL mapping of bio-energy related traits in Sorghum

Plant height (PHT), stem and leaf fresh weight (SLFW), juice weight (JW) and sugar content of stem (Brix) are important traits for biofuel production in sweet Sorghum. QTL analysis of PHT, SLFW, JW and Brix was conducted with composite interval mapping using F₂ and F_2:3 populations derived from the cross between grain Sorghum (Shihong137) × sweet Sorghum (L-Tian). Three QTLs controlling PHT were mapped on SBI-01, SBI-07 and SBI-09 under four different environments. These QTLs could explain 10.16 to 45.29% of the phenotypic variance. Two major effect QTLs on SBI-07 and SBI-09 were consistently detected under four environments. Eight QTLs controlling SLFW were mapped across three environments and accounted for 5.49–25.36% of the phenotypic variance. One major QTL on SBI-09 located between marker Sb5-206 and SbAGE03 was observed under three environments. Four QTLs controlling Brix were identified under two environments and accounted for 11.03–17.65% of the phenotypic variance. Six QTLs controlling JW were detected under two environments, and explained 6.63–23.56% of the phenotypic variance. QTLs for JW on SBI-07 and SBI-09 were consistent in two environments showing higher environmental stability. In addition, two chromosome regions on SBI-07 and SBI-09 were identified in our study having major effect on PHT, SFLW and JW. The results would be useful for the genetic improvement of sweet Sorghum to be used for biofuel production.