Identification of quantitative trait loci underlying seed protein and oil contents of soybean across multi‐genetic backgrounds and environments

Seed protein and oil contents are important quantitative traits in soybean. Previously, quantitative trait loci (QTL) associated with seed protein and oil were mostly identified in single genetic background. The objective of this work was to identify QTL and their epistatic effects underlying seed protein and oil contents in three recombinant inbred line populations (two of them used one common female parent) across eight environments by composite interval mapping. Forty QTL underlying protein content and 35 QTL underlying oil content were identified. Among them, nine were universal QTL underlying protein content and four were universal QTL underlying oil content. Epistatic interactions between QTL underlying seed protein/oil and different genetic backgrounds were detected. Different pairs of epistatic interactions were observed in diverse genetic backgrounds across multi‐environments. Common marker intervals were observed to simultaneously underlie seed protein and oil contents with different epistatic interactions. The results in this study suggested that a specific genotype with high oil content and low protein content might significantly affect the selection of soybean lines for high seed protein.     

Epistatic and QTL × environment interaction effects on leaf area‐associated traits in maize

Leaves play important roles, including in photosynthesis and transpiration, during plant development. Therefore, studying the genetic mechanisms affecting leaf size may contribute to improving plant architecture through molecular design. However, the genetic mechanisms that underlie these traits remain poorly understood. In this study, quantitative trait loci (QTL) for traits related to leaf area were identified using a set of recombinant inbred lines evaluated in three environments by 1226 single nucleotide polymorphic markers. In total, 16 QTL were detected with four QTL showing effects of greater than 10%. Five of the QTL explained 46.02%, seven of the QTL explained 46.77%, and four of the QTL explained 30.03% of the phenotypic variance of leaf length, width and area, respectively. Additional epistatic effects were identified for all of the maize chromosomes, except for chromosomes 7, 8 and 9. All of the epistatic effects involved pairs of loci on different chromosomes. Thus, a complex network controlling these traits was found in maize. These results provide useful information for understanding the molecular mechanisms controlling maize leaf size.     

Molecular mapping of quantitative trait loci for plant growth, yield and yield related traits across three diverse locations in a doubled haploid rice population

A doubled haploid (DH) population of 125lines derived from IR64 × Azucena, an indica – japonica cross were grown in three different locations in India during the wet season of 1995. The parents of mapping population had diverse phenotypic values for the eleven traits observed. The DH lines exhibited considerable amount of variation for all the traits. Transgressive segregants were observed. Interval analysis with threshold LOD > 3.00 detected a total of thirty four quantitative trait loci (QTL) for eleven traits across three locations. The maximum number of twenty QTL were detected at Punjab location of North India. A total of seven QTL were identified for panicle length followed by six QTL for plant height. Eight QTL were identified on three chromosomes which were common across locations. A maximum of seven QTL were identified for panicle length with the peak LOD score of 6.01 and variance of 26.80%. The major QTL for plant height was located on Chromosome 1 with peak LOD score of 16.06 flanked by RZ730-RZ801 markers. Plant height had the maximum number of common QTL across environment at the same marker interval. One QTL was identified for grain yield per plant and four QTL for thousand grain weight. Clustering of QTL for different traits at the same marker intervals was observed for plant height, panicle exsertion, panicle number, panicle length and biomass production. This suggests that pleiotropism and or tight linkage of different traits could be the plausible reason for the congruence of several QTL. Common QTL identified across locations and environment provide an excellent opportunity for selecting stable chromosomal regions contributing to yield and yield components to develop QTL introgressed lines that can be deployed in rice breeding program. This revised version was published online in July 2006 with corrections to the Cover Date.     

Molecular mapping of quantitative trait loci for grain moisture at harvest in maize

In maize, high grain moisture (GM) at harvest causes problems in harvesting, threshing, artificial drying, storage, transportation and processing. Understanding the genetic basis of GM will be useful for breeding low‐GM varieties. A quantitative genetics approach was used to identify quantitative trait loci (QTL) related to GM at harvest in field‐grown maize. The GM of a double haploid population consisting of 240 lines derived from Xianyu335 was evaluated in three planting seasons and a high‐density genetic linkage map covering 1546.4 cM was constructed. The broad‐sense heritability of GM at harvest was 71.0%. Using composite interval mapping, six QTL for GM at harvest were identified on five chromosomes (Chr). Two QTL located on Chr1, qgm1‐1 and qgm1‐2, explained 5.0% and 10.8% of the phenotypic variation in GM at harvest, respectively. The QTL qgm2, qgm3, qgm4 and qgm5 accounted for 3.3%, 8.3%, 5.4% and 11.0% of the mean phenotypic variation, respectively. Because of their consistent detection over multiple planting seasons, the detected QTL appear to be robust and reliable for the breeding of low‐GM varieties.     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

Quantitative trait loci mapping of maize (Zea mays) ear traits under low-phosphorus stress

We constructed an F_2:3 population of 180 individuals from a cross between the maize genotypes 082 (high phosphorus use efficiency) and Ye107 (low phosphorus use efficiency). We used the population to perform quantitative trait loci (QTL) mapping for eight ear traits (ear diameter [ED], ear length [EL], ear weight [EW], ear kernel weight [EKW], row number per ear [ERN], bald tip length [BTL], kernel number per row [RKN] and number of seeds per plant [NSP]) in three low-phosphorus environments. A total of 36 QTL for ear traits were detected in at least one of three single environments and were located on all maize chromosomes except for chromosome 10; the explained phenotypic variation ranged from 5.91% to 12.80%. The QTL JAqEKW5-1, JAqEL3-1, JAqEW1-1, JAqBTL1-1 and JAqNSP5-2 were identified and stably expressed in single-environment and joint-environment analyses. Some novel QTL that have not been reported previously were also detected. Most of the identified QTL had stable effects in all low-phosphorus environments, suggesting that they may be useful for molecular breeding to develop low-phosphorus tolerant maize varieties.     

Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize

Maize is the most important staple crop worldwide. Many of its agronomic traits present with a high level of heterosis. Combining ability was proposed to exploit the rule of heterosis, and general combining ability (GCA) is a crucial measure of parental performance. In this study, a recombinant inbred line population was used to construct testcross populations by crossing with four testers based on North Carolina design II. Six yield-relevant traits were investigated as phenotypic data. GCA effects were estimated for three scenarios based on the heterotic group and the number of tester lines. These estimates were then used to identify quantitative trait loci (QTL) and dissect genetic basis of GCA. A higher heritability of GCA was obtained for each trait. Thus, testing in early generation of breeding may effectively select candidate lines with relatively superior GCA performance. The GCA QTL detected in each scenario was slightly different according to the linkage mapping. Most of the GCA-relevant loci were simultaneously detected in all three datasets. Therefore, the genetic basis of GCA was nearly constant although discrepant inbred lines were appointed as testers. In addition, favorable alleles corresponding to GCA could be pyramided via marker-assisted selection and made available for maize hybrid breeding.     

Identification of quantitative trait loci with main and epistatic effects for plant architecture traits in Upland cotton (Gossypium hirsutum L.)

Plant architecture is important for cotton cultivation and breeding. In this study, two mapping generations/populations F₂ and F_2:3 in Upland cotton (Gossypium hirsutum L.), derived from ‘Baimian1’ and TM‐1, were used to identify quantitative trait loci (QTLs) for 10 plant architecture traits. A total of 55 main‐effect QTLs (M‐QTLs) were detected. Four common M‐QTLs, qTFB‐10(F₂/F_2:3) for total fruit branches, qFBL‐26b(F₂)/qFBL‐26(F_2:3) for fruit branch length, qFBA‐5(F₂/F_2:3) for fruit branch angle and qFBN‐26b(F₂)/qFBN‐26(F_2:3) for fruit branch nodes, were found. The synergistic alleles and the negative alleles can be utilized in cotton plant architecture breeding programmes according to specific breeding objectives. Altogether 54 pairs of epistatic QTLs (E‐QTLs) exhibiting the interactions of additive‐by‐additive (AA), additive‐by‐dominant (AD), dominant‐by‐additive (DA) and dominant‐by‐dominant (DD) were detected. The epistasis appeared to be an important contributor to genetic variation in cotton plant architecture traits. Therefore, the identified markers associated with E‐QTLs as well as M‐QTLs will be of importance in future breeding programmes to develop cotton cultivars exhibiting desirable plant architecture.     

Dissection of the genetic architecture for grain quality‐related traits in three RIL populations of maize (Zea mays L.)

To manipulate the composition of the maize kernel to meet future needs, an understanding of the molecular regulation of kernel quality‐related traits is required. In this study, the quantitative trait loci (QTL) for the concentrations of grain protein, starch and oil were identified using three sets of RIL populations in three environments. The genetic maps and the initial QTL were integrated using meta‐analyses. A total of 38 QTL were identified, including 15 in population 1, 12 in population 2 and 11 in population 3. The individual effects ranged from 2.87% to 13.11% of the phenotypic variation, with seven QTL each contributing over 10%. One common QTL was found for the concentrations of grain protein and starch in bin 3.09 in the three environments and the three RIL populations. Of the 38 initial QTL, 22 were integrated into eight mQTL by meta‐analysis. mQTL3 and mQTL8 of the key mQTL in which the initial QTL displayed R² > 10% included six and three initial QTL for grain protein and starch concentrations from two or three populations, respectively. These results will provide useful information for marker‐assisted selection to improve the quality of the maize kernel.     

QTL analysis of deep-sowing tolerance during seed germination in the maize IBM Syn4 RIL population

In arid or semi-arid regions, deep-sowing is an effective treasure to ensure seeds absorbing water from deep soil layer at present. However, the existing maize varieties have poor tolerance to deep-sowing, which is attributed to that few genes are explored and utilised. In this study, 243 IBM Syn4 recombinant inbred lines (RIL) constructed with B73 and Mo17 as parents and 1,339 DNA markers evenly distributed in 10 chromosomes, were used for QTL analysis of deep-sowing tolerance during seed germination. There were significant differences in germination-related traits between the parental lines at 12.5 cm sowing depth. Among them, 7, 7, 5, 10 and 2 QTLs for emergence rate, seedling length, plumule length, mesocotyl length and coleoptile length were detected, respectively. These QTLs explained 2.75% to 10.49% of the phenotypic variance with LOD scores ranging from 2.50 to 8.27. In addition, 12 overlapping QTLs formed five QTL clusters on chromosomes 3, 5, 7 and 9. This study provides a basis for molecular marker-assisted breeding and functional study in deep-sowing germination of maize.     

Mapping and validation of the quantitative trait loci for leaf stay‐green‐associated parameters in maize

Functional stay‐green is generally regarded as a desirable trait of varieties in major crops including maize. In this study, we used an F_3:4 recombinant inbred line population with 165 lines from a cross between a stay‐green inbred line (Zheng58) and a model inbred line (B73) using 211 polymorphic simple sequence repeat markers to map quantitative trait loci for three stay‐green‐associated parameters, chlorophyll content, photosystem II photochemical efficiency and stay‐green area, at maturity stage, detected a total of 23 quantitative trait loci (QTL) on nine chromosomes. Single QTL explained 3.7–13.5% of the phenotypic variance. Additionally, we validated some important stay‐green QTL using a heterogeneous inbred family approach and found that the stay‐green‐associated parameters were significantly correlated with the plant yield. This study may contribute to a better insight into the regulatory mechanism behind leaf stay‐green in maize and a novel development of elite maize varieties with delayed leaf senescence through molecular marker‐assisted selection.     

Identification of quantitative trait loci for seed traits and floral morphology in a field-grown Lolium perenne × Lolium multiflorum mapping population

Lolium perenne L. (perennial ryegrass), and Lolium multiflorum Lam. (annual or Italian ryegrass), differ in several traits related to seed yield. Generally, L. multiflorum spikes are larger than L. perenne spikes, and have more spikelets, more florets per spikelet, larger seeds and awns. The greater number of spikelets and florets and larger seeds are associated with higher seed yield in L. multiflorum . Ryegrass ( Lolium sp.) cultivars are produced by seed multiplication and understanding the genetics of seed production traits would aid in plant improvement. A total of 30 QTL for seed production related traits were identified in this study. The QTLs were primarily located on linkage groups 2 and 4 which appear to be the most important for distinguishing L.   multiflorum and L. perenne . These QTL will be used to develop molecular markers for marker-assisted breeding and screening of L. perenne seed lots to detect seed contamination with L. multiflorum .     

Quantitative trait loci mapping of forage agronomic traits in six mapping populations derived from European elite maize germplasm

Four agronomic traits were analysed including dry matter concentration (DMC) and dry matter yield (DMY) for stover, plant height (PHT) and days from planting to silking (DPS). We mapped quantitative trait loci (QTL) in three populations with doubled haploid lines (DHL), one RIL population and two testcross (TC) populations derived from crosses between two of the four populations mentioned above to elite tester lines, based on field phenotyping at multiple locations and years for each; 146–168 SSRs were used for genotyping of the four mapping populations. Significant high phenotypic and genotypic correlations were found for all traits at two locations, while DMC was negatively correlated with the other traits. A total of 42, 41, 54, and 45 QTL were identified for DMC, DMY, PHT, and DPS, respectively, with 9, 7, 12, and 7 major QTL for each trait. Most detected QTL displayed significant interactions with environment. Major QTL detected in more than two populations will contribute to marker‐assisted breeding and also to fine mapping candidate genes associated with maize agronomic traits.     

Identification of quantitative trait loci (QTLs) for seed protein concentration in soybean and analysis for additive effects and epistatic effects of QTLs under multiple environments

Soybean protein concentration is a key trait driver of successful soybean quality. A recombination inbred lines derived from a cross between ‘Charleston’ and ‘Dongnong594’, were planted in three environments across four years in China. Then, the genetic effects were partitioned into additive main effects, epistatic main effects and their environment interaction effects by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Forty‐three quantitative trait loci QTLs were identified on 17 of 20 soybean chromosomes excluding Ch 7, Ch 8 and Ch 17. Two QTLs showed a good stability across multiple environments, qPRO20‐1 was detected under four environments, which explained 4.4–9.95% phenotypic variances and the allele was from ‘Charleston’ among four environments. qPRO7‐5 was detected under three environments, which explained 7.2–14.5% phenotypic variances and the allele was from ‘Dongnong 594’, three pathway genes of protein biosynthesis were detected in the interval of qPRO7‐5. The additive main‐effect QTLs contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible by marker‐assisted selection to improve soybean protein concentration.     

Identification and application of major quantitative trait loci for panicle length in rice (Oryza sativa) through single‐segment substitution lines

Panicle length (PL), an important yield‐related trait, strongly affects yield components, such as grain number, grain density and rice quality. More than 200 panicle length quantitative trait loci (PL QTLs) are identified, but only a small number are applied in rice breeding. In this study, we performed QTL analysis for PL using 42 single‐segment substitution lines (SSSLs) derived from nine donors in the genetic background of HJX74. Fourteen QTLs and five heterosis QTLs (HQTLs) for PL were recognised. Three QTLs and four HQTLs acted positively, and the other eleven QTLs and one HQTL acted negatively. By scanning the single heterozygous background region of the F₂ population with large‐genetic‐effect SSSLs, we mapped PL loci qPL6‐2 and qPL7‐1 to different locations on chromosomes 6 and 7, respectively, in three consecutive years of independent trials. The genetic effects of these QTLs were further assessed. qPL6‐2 demonstrated the most positive additive effect (QTL), whereas qPL7‐1 achieved the most positive dominant effect (HQTL) for PL. These results indicated that the pyramiding of PL QTLs might increase grain yield and facilitate the application of the beneficial allele in hybrid rice breeding.     

Mapping of quantitative trait loci for seminal root morphology and gravitropic response in rice

In order to gain a better understanding of the complex root traits observed in previous studies using a mapping population derived from a Bala × Azucena cross, an experiment was conducted growing plants in agar-filled Perspex chambers with the aim of identifying quantitative trait loci (QTLs) for both seminal root morphology (SRM) and gravitropic response. A total of four main effect QTLs were detected for SRM (a measurement of the degree of a wavy/curly seminal root phenotype); two were located on chromosome 2, one at the top of chromosome 3 and one on chromosome 11. Two main effect QTLs were detected for the gravitropic response (the degree of bending of the growing seminal root when subjected to a 90° rotation); one on chromosome 6 and 1 on chromosome 11. As well as main effect QTLs, an epistatic interaction was observed for each of the traits. For SRM an interaction was detected between the top and the bottom of chromosome 4. For the gravitropic response an interaction was observed between a location on chromosome 6 and 11. Both these interactions were confirmed by analysis of variance using marker classes and the epistatic gravitropic response was also confirmed using a pair of near isogenic lines. All the SRM QTLs detected in this experiment co-localise with root growth QTLs (root penetration or morphology) detected previously in the mapping population. This information could prove valuable in attempts to identify candidate genes for these potentially valuable QTLs because we could postulate that the underlying genes should be involved in the pathway of gravity detection, signal transduction or the growth response to gravity.     

Molecular mapping of quantitative trait loci for field resistance to Fusarium head blight in a European winter wheat population

Fusarium head blight (FHB), one of the most destructive diseases of wheat in many parts of the world, can reduce the grain quality due to mycotoxin contamination up to rejection for usage as food or feed. Objective of this study was to map quantitative trait loci (QTL) associated with FHB resistance in the winter wheat population ‘G16‐92’ (resistant)/‘Hussar’. In all, 136 recombinant inbred lines were evaluated in field trials in 2001 and 2002 after spray inoculation with a Fusarium culmorum suspension. The area under disease progress curve was calculated based on the visually scored FHB symptoms. For means across all environments two FHB resistance QTL located on chromosomes 1A, and 2BL were identified. The individual QTL explained 9.7% and 14.1% of the phenotypic variance and together 26.7% of the genetic variance. The resistance QTL on 1A coincided with a QTL for plant height in contrast to the resistance QTL on 2BL that appeared to be independently inherited from morphological characteristics like plant height and ear compactness. Therefore, especially the QTL on 2BL could be of great interest for breeding towards FHB resistance.     

Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population

Molecular breeding is becoming more practical as better technology emerges. The use of molecular markers in plant breeding for indirect selection of important traits can favorably impact breeding efficiency. The purpose of this research is to identify quantitative trait loci (QTL) on molecular linkage groups (MLG) which are associated with seed protein concentration, seed oil concentration, seed size, plant height, lodging, and maturity, in a population from a cross between the soybean cultivars ‘Essex’ and ‘Williams.’ DNA was extracted from F₂ generation soybean leaves and amplified via polymerase chain reaction (PCR) using simple sequence repeat (SSR) markers. Markers that were polymorphic between the parents were analyzed against phenotypic trait data from the F₂ and F_4:6 generation. For the F₂ population, significant additive QTL were Satt540 (MLG M, maturity, r² = 0.11; height, r² = 0.04, seed size, r²= 0.06], Satt373 (MLG L, seed size, r² = 0.04; height, r² = 0.14), Satt50 (MLG A1, maturity r² = 0.07), Satt14 (MLG D2, oil, r² = 0.05), and Satt251 (protein r² = 0.03, oil, r² =0.04). Significant dominant QTL for the F₂ population were Satt540 (MLG M,height, r² = 0.04; seed size, r² = 0.06) and Satt14 (MLG D2, oil, r² = 0.05). In the F_4:6 generation significant additive QTL were Satt239 (MLGI, height, r² = 0.02 at Knoxville, TN and r² = 0.03 at Springfield, TN), Satt14 (MLG D2, seed size, r² = 0.14 at Knoxville, TN), Satt373 (MLG L, protein, r² = 0.04 at Knoxville, TN) and Satt251 (MLG B1, lodging r² = 0.04 at Springfield, TN). Averaged over both environments in the F_4:6 generation, significant additive QTL were identified as Satt251 (MLG B1, protein, r² = 0.03), and Satt239 (MLG I, height, r² = 0.03). The results found in this study indicate that selections based solely on these QTL would produce limited gains (based on low r² values). Few QTL were detected to be stable across environments. Further research to identify stable QTL over environments is needed to make marker-assisted approaches more widely adopted by soybean breeders. This revised version was published online in July 2006 with corrections to the Cover Date.