Genetic analysis of plant height using two immortalized populations of “CRI12 × J8891” in Gossypium hirsutum L.

Plant height is an important plant architecture trait that determines the canopy structure, photosynthetic capacity and lodging resistance of upland cotton populations. To understand the genetic basis of plant height for marker-assisted breeding, quantitative trait loci (QTL) analysis was conducted based on the genetic map of recombinant inbred lines (RILs) derived from the cross “CRI12 × J8891” (Gossypium hirsutum L.). Three methods, including composite interval mapping, multiple interval mapping and multi-marker joint analysis, were used to detect QTL across multiple environments in the RILs and in the immortalized F₂ population developed through intermating between RILs. A total of 19 QTL with genetic main effects and/or genetic × environment interaction effects were identified on 15 chromosomes or linkage groups, each explaining 5.8–14.3 % of the phenotypic variation. Five digenic epistatic QTL pairs, mainly involving additive × additive and/or dominance × dominance, were detected in different environments. Seven out of eight interacting loci were main-effect QTL, suggesting that these loci act as major genes as well as modifying genes in the expression of plant height. The results demonstrate that additive effects, dominance and epistasis are all important for the genetic constitution of plant height, with additive effects playing a more important role in reducing plant height. QTL showing stability across environments that were repeatedly detected by different methods can be used in marker-assisted breeding.     

QTL analysis for wheat falling number in a recombinant inbred line population segregated with 1BL/1RS translocation in a rainfed agricultural area of China

Falling number (FN) is an inner quality trait in wheat (Triticum aestivum L.) ultimately determining the end use of wheat kernels. In this 3-year study, 171 recombinant inbred lines derived from Chuannong17 (a 1BL/1RS tranlocation parent) × Mianyang11 were planted in the Sichuan Basin, a rainfed agricultural area in southwestern China. In this climate, we found that FN had significant differences between 1BL/1RS translocation lines and non-1BL/1RS translocation lines in two of the 3 years and the heavy fluctuation of rainfall and temperature resulted in decreasing FN in grain filling period. We used 191 simple sequence repeats markers to construct a genetic linkage map and then detected 11 additive effect FN quantitative trait loci (QTL) on chromosomes 2B, 3D, 4A, 4D, 6B and 7D, explaining 5.48–31.91% of the phenotypic variance. The FN QTL on chromosomes 4A, 4D and 6B were major or stable and detected at least in 2 years, whereas the Qfn.sicau-3D.1 in 2015 year explained the maximum phenotypic variation (about 31.91%). Furthermore, FN QTLs additive and epistatic effects as well as their interactions with environment were estimated by a mixed linear model approach. We found that the additive effect QTLs had no significant additive × environment interaction, while the paired QTLs had significant additive × additive epistatic effects however none had a significant additive × additive epistasis × environment interaction effect, excluding the paired QTLs Qfn.sicau-3B/Qfn.sicau-5B.     

Constitution of resistance to common cutworm in terms of antibiosis and antixenosis in soybean RIL populations

Common cutworm (CCW; Spodoptera litura Fabricius) is a major leaf-feeding pest in Asia. The focus of this study was to explore the genetic mechanism for resistance to CCW in terms of antibiosis and antixenosis through mapping QTL (Quantitative trait locus/loci) in soybean using two recombinant inbred line populations. Larva weight (LW) and pupa weight (PW) were evaluated as indicators for antibiosis and damaged leaf percentage as the indicator for antixenosis to CCW. The obvious transgressive segregation indicated a complementary genetic status between the parents. The genetic structure for antibiosis and antixenosis was similar, about 51.1–75.7 % of the phenotypic variation (PV) accounted for by genetic variation, where 42.2–60.3 %, or the majority, was explained by the collective unmapped minor QTL. And, 0–6 additive QTL each explained 0.0–11.8 % in a total of 0.0–27.4 % of PV, and 0–3 epistatic QTL pairs each explained 0.0–7.6 % in a total of 0.0–14.0 % of PV. However, the detected QTL compositions for antibiosis and antixenosis were quite different with only one QTL qCCW10_1 shared by both antibiosis and antixenosis with 8.9–11.8 and 4.7 % contribution to PV, respectively. Within antibiosis between LW and PW, the detected QTL overlapped (r = 0.53–0.78). Among the detected QTL, qCCW6_1, qCCW10_1 and qCCW12_2 were the major contributors to antibiosis, and qCCW10_1, qCCW10_2 and qCCW12_1 the major contributors to antixenosis. Since only some major QTL could be used for marker-assisted breeding, the main concern is how to use the large amount of undetected minor QTL.     

Identification of QTLs for seedling vigor in winter wheat

Seedling vigor is an important trait characterized by rapid development of vegetative cover above the soil surface, particularly in rainfed environments. To elucidate the genetic basis of seedling vigor, 207 F_2:4 lines derived from Jingdong 8/Aikang 58 were investigated in the field at Beijing and Gaoyi, Hebei province, during the 2011–2012 and 2012–2013 cropping seasons. A total of 148 SSR markers polymorphic between the parents, and the RhtD1b-specific marker were used to map QTL for seedling vigor by inclusive composite interval mapping (ICIM). Normalized difference vegetation index (NDVI) and ground cover (GC) before winter and in late spring showed a continuous distribution. Significant differences in NDVI and GC among lines were detected. Broad-sense heritability of NDVI and GC ranged from 0.69 to 0.74 based on the mean values averaged across four environments. GC was highly correlated with NDVI (r = 0.74–0.85, P < 0.001). Four seedling traits were positively correlated with thousand kennel weight and grain yield. Thirty-seven QTLs distributed on 14 chromosomes for the seedling traits were identified; 18 were on genome A chromosomes, 8 on genome B chromosomes and 11 on genome D chromosomes. QTLs QGCs.caas-3B.1, QGCw-caas-1D and QGCw-caas-5D explained more than 20 % of the phenotypic variation in seedling traits, with over-dominance effects. QGCw.caas-1A.1, QNDVIw-caas-6D, QGCs-caas-6A, QGCs-caas-2A.2, QNDVIs-caas-3A, and QGCw-caas-5B explained more than 10 % of the phenotypic variation in seedling traits. The major QTLs QGCs-caas-6A and QGCw.caas-5B showing strong additive effects and linked with agronomic traits, could be used for molecular marker-assisted selection to improve seedling vigor and yield-related traits.     

QTL mapping and analysis of epistatic interactions for grain yield and yield-related traits in <Emphasis Type="Italic">Triticum turgidum</Emphasis> L. var. <Emphasis Type="Italic">durum</Emphasis>

A quantitative trait loci (QTL) analysis of grain yield and yield-related traits was performed on 93 durum wheat recombinant inbred lines derived from the cross UC1113 × Kofa. The mapping population and parental lines were analyzed considering 19 traits assessed in different Argentine environments, namely grain yield, heading date, flowering time, plant height, biomass per plant, and spikelet number per ear, among others. A total of 224 QTL with logarithm of odds ratio (LOD) ≥ 3 and 47 additional QTL with LOD > 2.0 were detected. These QTL were clustered in 35 regions with overlapping QTL, and 12 genomic regions were associated with only one phenotypic trait. The regions with the highest number of multi-trait and stable QTL were 3BS.1, 3BS.2, 2BS.1, 1BL.1, 3AL.1, 1AS, and 4AL.3. The effects of epistatic QTL and QTL × environment interactions were also analyzed. QTL putatively located at major gene loci (Rht, Vrn, Eps, and Ppd) as well as additional major/minor QTL involved in the complex genetic basis of yield-related traits expressed in Argentine environments were identified. Interestingly, the 3AL.1 region was found to increase yield without altering grain quality or crop phenology.     

利用永久F2群体定位小麦株高的QTL

为研究小麦株高的遗传机制,利用DH群体构建了一套包含168个杂交组合的小麦永久F₂群体, 并于2007年种植于山东泰安和山东聊城。构建了一套覆盖小麦21条染色体的遗传连锁图谱并利用该图谱的324个SSR标记对小麦株高进行QTL定位研究,使用基于混合线性模型的QTLNetwork 2.0软件进行QTL分析。在永久F₂群体中定位了7个株高QTL,包括4个加性QTL,一个显性QTL,一对上位性QTL,共解释株高变异的20%,其中位于4D染色体的qPh4D,具有最大的遗传效应,贡献率为7.5%;位于2D 染色体显性效应位点qPh2D,可解释1.6%的表型变异;位于5B~6D染色体上位效应位点,可解释1.7%的表型变异。还发现加性效应、显性效应和上位效应对小麦株高的遗传起重要作用,并且基因与环境具有互作效应,结果表明利用永久F₂群体进行QTL定位研究的方法有助于分子标记辅助育种。     

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.     

Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield

A durum wheat recombinant inbred line population developed from PDW 233 × Bhalegaon 4 cross was analyzed in five environments to understand the genetic network responsible for test weight (TW), thousand kernel weight (TKW), grain yield (YLD), spike length (SL), spikelets per spike (SPS), kernels per spike (KER) and kernel weight per spike (KWS). Genotype, environment and their interactions were main sources of variance for all the traits. TW and TKW were influenced by 11 main effect QTL and 6 digenic epistatic interactions detected on chromosomes 2A, 2B, 4B and 7A. Grain yield was influenced by three epistatic interactions and five main effect QTL, of which two on chromosome 2A were most consistent. A major QTL for spike length was observed on chromosome 3B. QTL for spike characters were distributed over 9 chromosomes. All the traits showed significant influence of digenic epistasis (QQ) and, to a certain extent, QTL × environment interactions (QQE). Therefore, while breeding for complex traits like kernel characters and grain yield components, these interactions should also be considered important. The consistent QTL on chromosome 2A between the marker interval Xgwm71.2–Xubc835.4 with pleiotropic effect on TW and TKW, may be utilized in early generation selection to improve TW and TKW and thereby the milling potential of the durum wheat.     

QTL mapping for developmental behavior of plant height in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

A recombinant inbred line (RIL) population with 305 lines derived from a cross of Hanxuan 10 × Lumai 14 was used to identify the dynamic quantitative trait loci (QTL) for plant height (PH) in wheat (Triticum aestivum L.). Plant heights of RILs were measured at five stages in three environments. Total of seven genomic regions covering PH QTL clusters on different chromosomes identified from a DH population derived from the same cross as the RIL were used as the candidate QTLs and extensively analyzed. Five additive QTLs and eight pairs of epistatic QTLs significantly affecting plant height development were detected by unconditional QTL mapping method. Six additive QTLs and four pairs of epistatic QTLs were identified using conditional mapping approach. Among them, three additive QTLs (QPh.cgb-1B.3, QPh.cgb-4D.1, QPh.cgb-5B.2) and three pairs of epistatic QTLs (QPh.cgb-1B.1–QPh.cgb-1B.3, QPh.cgb-2A.1–QPh.cgb-2D.1, QPh.cgb-2D.1–QPh.cgb-5B.2) were common QTLs detected by both methods. Three QTLs (QPh.cgb-4D.1, QPh.cgb-5B.3, QPh.cgb-5B.4) were expressed under both drought and well-water conditions. The present data are useful for wheat genetic manipulations through molecular marker-assisted selection (MAS), and provides new insights into understanding the genetic mechanism and regulation network underlying the development of plant height in crops. Our result in this study indicated that combining unconditional and conditional mapping methods could make it possible to reveal not only the stable/conserved QTLs for the developmental traits such as plant height but also the dynamic expression feature of the QTLs.     

Tagging quantitative trait loci for heading date and plant height in important breeding parents of rice (Oryza sativa)

Heading date and plant height are important determinants for plant growth rate. In this study, simple sequence repeat markers were used to tag quantitative trait loci (QTL) using a recombinant inbred line mapping population derived from two important breeding parents, genetic stock Kaybonnetlpa1-1 and indica cultivar Zhe733, using data collected under field and greenhouse conditions. Interval mapping, composite interval mapping, and multiple interval mapping were performed to map QTL for heading date and plant height, and to identify epistatic interactions between the QTL. qHD3.1 on chromosome 3 from KBNTlpa1-1 had the largest effect on heading date contributing an average of 28.4 % of the total phenotypic variation. qHD7.1, 7.2, and 8.1 also had a significant contribution to heading date from Zhe733 averaging 8.1, 12.8, and 12.8 % of the phenotypic variance, respectively, and there was a positive additive-by-additive epistatic interaction between qHD7.1 and qHD8.1. QTL, qPHT1.1 and qPHT3.1, for plant height were detected on chromosomes 1 and 3, respectively. qPHT1.1 contributed the largest effect representing 38.2 % of the total phenotypic variation. Comparison of the QTL identified in our study with previous results revealed that the chromosomal locations for QTL coincided closely with positions reported previously in other rice populations worldwide, suggesting that these QTL have coevolved and become domesticated. The tightly linked SSR markers that flank these QTL should be desirable for tagging heading date and plant height genes and facilitating their incorporation into advanced breeding lines using marker assisted selection.     

Pattern of genetic inheritance of morphological and agronomic traits of sorghum associated with resistance to sorghum shoot fly, <Emphasis Type="Italic">Atherigona soccata</Emphasis>

Sorghum shoot fly, Atherigona soccata is an important pest of sorghum during the seedling stage, which influences both fodder and grain yield. To understand the nature of inheritance of shoot fly resistance in sorghum, we performed generation mean analysis using two crosses IS 18551 × Swarna and M 35-1 × ICSV 700 during the 2013–2014 cropping seasons. The F₁, F₂, BC₁ and BC₂ progenies, along with the parental lines were evaluated for agronomic and morphological traits associated with resistance/susceptibility to sorghum shoot fly, A. soccata. The cross IS 18551 × Swarna exhibited significant differences between the parents for shoot fly deadhearts (%) in the postrainy season. The progenies of this cross exhibited lower shoot fly damage, suggesting that at least one of the parents should have genes for resistance to develop shoot fly-resistant hybrids. Leaf glossiness, leafsheath pigmentation and plant vigor score during the seedling stage exhibited non-allelic gene interactions with dominant gene action, whereas 100 seed weight showed both additive and dominant gene interactions. Presence of awns showed recessive nature of the awned gene. Generation mean analysis suggested that both additive and dominance gene effects were important for most of the traits evaluated in this study, but dominance had a more pronounced effect.     

Dynamic QTL analysis and validation for plant height using maternal and paternal backcrossing populations in Upland cotton

Plant height determines plant biomass yield, harvest index and economic yield. We analyzed quantitative trait loci (QTL) and gene action controlling plant height. We generated the maternal and paternal testcrossing (TC/M and TC/P) populations based on a recombinant inbred line population. Data for plant height at t1, t2, t3, t4 or t5 stages were collected over 2 years from 3 TC/M field trials and 2 TC/P field trials. At single-locus level, 32 QTLs at five stages and 24 conditional QTLs at four intervals were detected, and 14 QTLs shared in different years or populations or stages. Plant height displayed dynamic characteristics through expression of QTLs. A total of 21 novel QTLs were detected and 11 QTLs validated the previous results. And 19 QTLs explained over 10% of phenotypic variation, such as qPH-Chr9-2, qPH-Chr19-4 and qPH-Chr22-4. The region of NAU5330-NAU1269 on chromosome 19 may be a desired target for genetic improvement of plant height in Upland cotton. In addition, five and eight heterotic loci were identified in TC/M and TC/P populations, respectively. Additive, partial dominance and overdominance effects were observed in both TC populations. We also identified 43 epistatic QTLs and QTLs by environment interactions by inclusive composite interval mapping method. Taken together, additive, partial dominance and overdominance effects together with epistasis explained the genetic basis of plant height in Upland cotton.     

Genetic dissection of rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) tiller,plant height,and grain yield based on QTL mapping and metaanalysis

Tiller number per plant (TN) and plant height (PH) are important agronomic traits related to grain yield (GY) in rice (Oryza sativa L.). A total of 30 additive quantitative trait loci (A-QTL) and 9 significant additive × environment interaction QTLs (AE-QTL) were detected, while the phenotypic and QTL correlations confirmed the intrinsic relationship of the three traits. These QTLs were integrated with 986 QTLs from previous studies by metaanalysis. Consensus maps contained 7156 markers for a total map length of 1112.71 cM, onto which 863 QTLs were projected; 78 meta-QTLs (MQTLs) covering 11 of the 30 QTLs were detected from the cross between Dongnong422 and Kongyu131 in this study. A total of 705 predicted genes were distributed over the 21 MQTL intervals with physical length <0.3 Mb; 13 of the 21 MQTLs, and 34 candidate genes related to grain yield and plant development, were screened. Five major QTLs, viz. qGY6-2, qPH7-2, qPH6-3, qTN6-1, and qTN7-1, were not detected in the MQTL intervals and could be used as newly discovered QTLs. Candidate genes within these QTL intervals will play a meaningful role in molecular marker-assisted selection and map-based cloning of rice TN, PH, and GY.     

Further QTL mapping for yield component traits using introgression lines in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) under drought field environments

To better understand the underlying mechanisms of agronomic traits related to drought resistance and discover candidate genes or chromosome segments for drought-tolerant rice breeding, a fundamental introgression population, BC₃, derived from the backcross of local upland rice cv. Haogelao (donor parent) and super yield lowland rice cv. Shennong265 (recurrent parent) had been constructed before 2006. Previous quantitative trait locus (QTL) mapping results using 180 and 94 BC₃F_6,7 rice introgression lines (ILs) with 187 and 130 simple sequence repeat (SSR) markers for agronomy and physiology traits under drought in the field have been reported in 2009 and 2012, respectively. In this report, we conducted further QTL mapping for grain yield component traits under water-stressed (WS) and well-watered (WW) field conditions during 3 years (2012, 2013 and 2014). We used 62 SSR markers, 41 of which were newly screened, and 492 BC₄F_2,4 core lines derived from the fourth backcross between D123, an elite drought-tolerant IL (BC₃F₇), and Shennong265. Under WS conditions, a total of 19 QTLs were detected, all of which were associated with the new SSRs. Each QTL was only identified in 1 year and one site except for qPL-12-1 and qPL-5, which additively increased panicle length under drought stress. qPL-12-1 was detected in 2013 between new marker RM1337 and old marker RM3455 (34.39 cM) and was a major QTL with high reliability and 15.36% phenotypic variance. qPL-5 was a minor QTL detected in 2013 and 2014 between new marker RM5693 and old marker RM3476. Two QTLs for plant height (qPHL-3-1 and qPHP-12) were detected under both WS and WW conditions in 1 year and one site. qPHL-3-1, a major QTL from Shennong265 for decreasing plant height of leaf located on chromosome 3 between two new markers, explained 22.57% of phenotypic variation with high reliability under WS conditions. On the contrary, qPHP-12 was a minor QTL for increasing plant height of panicle from Haogelao on chromosome 12. Except for these two QTLs, all other 17 QTLs mapped under WS conditions were not mapped under WW conditions; thus, they were all related to drought tolerance. Thirteen QTLs mapped from Haogelao under WS conditions showed improved drought tolerance. However, a major QTL for delayed heading date from Shennong265, qDHD-12, enhanced drought tolerance, was located on chromosome 12 between new marker RM1337 and old marker RM3455 (11.11 cM), explained 21.84% of phenotypic variance and showed a negative additive effect (shortening delay days under WS compared with WW). Importantly, chromosome 12 was enriched with seven QTLs, five of which, including major qDHD-12, congregated near new marker RM1337. In addition, four of the seven QTLs improved drought resistance and were located between RM1337 and RM3455, including three minor QTLs from Haogelao for thousand kernel weight, tiller number and panicle length, respectively, and the major QTL qDHD-12 from Shennong265. These results strongly suggested that the newly screened RM1337 marker may be used for marker-assisted selection (MAS) in drought-tolerant rice breeding and that there is a pleiotropic gene or cluster of genes linked to drought tolerance. Another major QTL (qTKW-1-2) for increasing thousand kernel weight from Haogelao was also identified under WW conditions. These results are helpful for MAS in rice breeding and drought-resistant gene cloning.     

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Wheat (Triticum aestivum L.) yield is directly proportional to physio-morphological traits. A high-density genetic map consisting of 2575 markers was used for mapping QTL controlling stay-green and agronomic traits in wheat grown under four diverse water regimes. A total of 108 additive QTL were identified in target traits. Among them, 28 QTL for chlorophyll content (CC) were detected on 11 chromosomes, 43 for normalized difference vegetation index (NDVI) on all chromosomes except 5B, 5D, and 7D, five for spikes per plant (NSP) on different chromosomes, nine for plant height (PH) on four chromosomes, and 23 for thousand-kernel weight (TKW) on 11 chromosomes. Considering all traits, the phenotypic variation explained (PVE) ranged from 3.61 to 41.62%. A major QTL, QNDVI.cgb-5A.7, for NDVI with a maximum PVE of 20.21%, was located on chromosome 5A. A stable and major PH QTL was observed on chromosome 4D with a PVE close to 40%. Most distances between QTL and corresponding flanking markers were less than 1 cM, and approximately one-third of the QTL coincided with markers. Each of 16 QTL clusters on 10 chromosomes controlled more than one trait and therefore could be regarded as pleiotropic regions in response to different water regimes. Forty-one epistatic QTL were identified for all traits having PVE of 6.00 to 25.07%. Validated QTL closely linked to flanking markers will be beneficial for marker-assisted selection in improving drought-tolerance in wheat.     

Quantitative trait analysis of flowering time in spring rapeseed (B. napus L.)

The inheritance of flowering time trait in spring-type rapeseed (Brassica napus L.) is poorly understood, and the investigations on mapping of quantitative trait loci (QTL) for the trait are only few. We identified QTL underlying variation for flowering time in a doubled haploid (DH) mapping population of nonvernalization-responsive canola (B. napus L.) cultivar 465 and line 86 containing introgressions from Houyou11, a Chinese early-flowering cultivar in Brassica rapa L. Significant genetic variation in flowering time and response to photoperiod were observed among the DH lines from 465/86. A molecular linkage map was generated comprising three types of markers loci. QTL analysis indicated that flowering time is a complex trait and is controlled by at least 4 major loci, localized on four different linkage groups A6, A7, C8 and C9. These loci each accounted for between 9.2 and 12.56 % of the total genotypic variation for first flowering. The published high-density maps for flowering time mapping used different marker systems, and the parents of our crosses have different genetic origins, with either spring-type B. napus or B. rapa. So we cannot determine whether the QTL on the same linkage groups were in the same region or not. There was evidence of additive × additive epistatic effects for flowering time in the DH population. Epistasis existed not only between main-effect QTLs, but also between QTLs with minor effects. Four pair of epistasis effects between minor QTLs explained about 20 % of the genetic variance observed in the DH population. The results indicated that minor QTLs for flowering time should not be ignored. Significant genotypes × environment interactions were also found for the quantitative traits, and with significant change in the ranking of the DH lines in different environments. The results implied that FQ3 was a non-environment-specific QTL and may control flowering time by autonomous pathway. FQ4 were winter-environment-specific QTL and may control flowering time by photoperiod-pathway. Identification of the chromosomal location and effect of the genes influencing flowering time may hasten the development of canola varieties having an optimal time for flowering in target environments such as for high altitude areas, via marker-assisted selection.     

Molecular tagging of QTLs for fiber quality and yield in the upland cotton cultivar Acala-Prema

Cotton is a high-value per acre crop that is produced as a raw material for the textile industry. With the development of new technologies in the textile industry, much attention has been paid to fiber quality in conjunction with yield. The introgression cultivar “Acala Prema” is extensively planted in the Carolina/USA for its good fiber qualities, high yields and tolerance to Verticillium wilt. To conduct QTL mapping for fiber quality and yield in Acala-Prema, we developed a population of 180 recombinant inbred lines (RILs) from a single seed derived from a cross between this line and Chinese cultivar 86-1. We examined the yield performance of the RILs in five Chinese environments and fiber qualities in seven Chinese environments. A genetic linkage map comprising 279 loci was constructed using this RIL population, chiefly with SSR markers, and QTLs were repeatedly identified across diverse environments using the composite interval mapping method. A total of 86 nonredundant QTLs for yield and its components and fiber qualities were independently detected in five or seven environments; Prema alleles were responsible for the increase in trait values for 46 QTLs, while 86-1 was responsible for 40 QTLs. Notably, we detected the stable fiber strength QTL qFS-D3-1, which explained 4.51–17.55 % of PV, with LOD scores ranging from 2.83 to 7.09, and the fiber length qFL-D11-1, which explained 10.02–25.34 % of the PV. Eighteen environment epistatic QTLs were also detected. The QTLs detected in this study provide new information for improving fiber quality and may be especially valuable for marker-assisted selection.     

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.     

QTL mapping for anthocyanin and proanthocyanidin content in red rice

Anthocyanins and proanthocyanidins are the primary pigments of red rice and are also important functional nutrients for human health. To identify novel quantitative trait loci (QTLs) underlying anthocyanins and proanthocyanidins (ANC and PAC) in rice, a recombinant inbred line (RIL) derived from a cross of red rice ‘Hong Xiang 1’ (‘HX1’) and white rice ‘Song 98-131’ (‘S98-131’) was cultivated in six environments. A genetic map containing 126 markers covering 1833.4 cM with an average of 14.55 cM between markers was constructed. A total of 21 additive QTLs (A-QTLs) for ANC and PAC were identified from six environments using the IciMapping v3.3 software. Two new QTLs, qANC3 and qPAC12-4, were detected in several environments, and explained significant phenotypic variance. Nine QTLs of ANC and PAC were detected with additive × environmental interaction effects (AE effects) by QTLNetwork 2.1 software, but no epistatic and epistatic × environmental interaction effects (AA and AAE effects) were detected. The information obtained in this study could be useful for fine mapping and molecular marker-assisted selection of ANC and PAC in rice.     

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.