控制玉米雄穗分枝数目和雄穗重的主效QTL的定位

利用2套具有共同亲本黄早四且分别含有230个及235个家系的F2：3群体,结合2年多点的表型鉴定,运用完备复合区间作图方法对不同生态环境下（2007-北京、2008-北京、2007-河南、2008-河南、2007-新疆以及2008-新疆）的玉米雄穗分枝数和雄穗重进行QTL定位。同时,利用基于混合线性模型的QTLNetwork-2.0软件进行基因×环境互作及上位性分析。6个环境下2个群体共检测到51个与雄穗分枝数和雄穗重相关的QTL（Q/H群体32个,Y/H群体19个）,其中包括7个主效QTL,并在Q/H群体中确定了2个重要的QTL,即位于7.01bin的Qqtpbn7-1和位于7.02bin的Qqtw7-2。对比2个群体的定位结果,共挖掘到3个在不同遗传背景下的＂一致性＂QTL,这些在不同环境及不同遗传背景下能够稳定存在的QTL可为玉米雄穗相关性状的生产应用以及精细定位提供有价值的参考。     

控制玉米雄穗分枝数目和雄穗重的主效QTL的定位

利用2套具有共同亲本黄早四且分别含有230个及235个家系的F_2:3群体, 结合2年多点的表型鉴定, 运用完备复合区间作图方法对不同生态环境下(2007-北京、2008-北京、2007-河南、2008-河南、2007-新疆以及2008-新疆)的玉米雄穗分枝数和雄穗重进行QTL定位。同时, 利用基于混合线性模型的QTLNetwork-2.0软件进行基因×环境互作及上位性分析。6个环境下2个群体共检测到51个与雄穗分枝数和雄穗重相关的QTL(Q/H群体32个, Y/H群体19个), 其中包括7个主效QTL, 并在Q/H群体中确定了2个重要的QTL, 即位于7.01bin的Qqtpbn7-1和位于7.02bin的Qqtw7-2。对比2个群体的定位结果, 共挖掘到3个在不同遗传背景下的“一致性”QTL, 这些在不同环境及不同遗传背景下能够稳定存在的QTL可为玉米雄穗相关性状的生产应用以及精细定位提供有价值的参考。     

Analysis of the genetic architecture of maize ear and grain morphological traits by combined linkage and association mapping

Key message

Using combined linkage and association mapping, 26 stable QTL and six stable SNPs were detected across multiple environments for eight ear and grain morphological traits in maize. One QTL, PKS2, might play an important role in maize yield improvement.

Abstract

In the present study, one bi-parental population and an association panel were used to identify quantitative trait loci (QTL) for eight ear and grain morphological traits. A total of 108 QTL related to these traits were detected across four environments using an ultra-high density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319, and 26 QTL were identified in more than two environments. Furthermore, 64 single nucleotide polymorphisms (SNPs) were found to be significantly associated with the eight ear and grain morphological traits (?log₁₀(P)?>?4) in an association panel of 240 maize inbred lines. Combining the two mapping populations, a total of 17 pleiotropic QTL/SNPs (pQTL/SNPs) were associated with various traits across multiple environments. PKS2, a stable locus influencing kernel shape identified on chromosome 2 in a genome-wide association study (GWAS), was within the QTL confidence interval defined by the RILs. The candidate region harbored a short 13-Kb LD block encompassing four SNPs (SYN11386, PHM14783.16, SYN11392, and SYN11378). In the association panel, 13 lines derived from the hybrid PI78599 possessed the same allele as Qi319 at the PHM14783.16 (GG) locus, with an average value of 0.21 for KS, significantly lower than that of the 34 lines derived from Ye478 that carried a different allele (0.25, P?<?0.05). Therefore, further fine mapping of PKS2 will provide valuable information for understanding the genetic components of grain yield and improving molecular marker-assisted selection (MAS) in maize.

     

干旱胁迫对不同玉米自交系苗期渗透调节的影响

以5个抗旱性不同的玉米骨干自交系黄早四、掖478、郑58、旱21、齐319为试验材料,对持续水分胁迫下玉米幼苗的叶片相对含水量、渗透调节能力(OA)、脯氨酸含量和可溶性糖含量的变化规律进行了研究。结果表明:随着干旱胁迫的加重,叶片相对含水量呈现下降的趋势,渗透调节能力和可溶性糖含量呈现先上升后又下降的趋势,脯氨酸含量呈现持续上升的趋势;在持续干旱条件下,不同自交系各个指标的变化幅度不同,说明不同种质资源对干旱胁迫的响应方式不同,渗透调节能力也有差异。在水分胁迫的前7d中,渗透调节能力逐渐增加,第7天时达到最大值,OA从大到小的排列顺序为齐319郑58掖478旱21黄早四;但此后,除旱21外,其余4个自交系的OA都随之下降。旱21和齐319以可溶性糖和脯氨酸为主要的渗透调节因子,黄早四则以脯氨酸为主要的渗透调节物质。     

<Emphasis Type="Italic">qNCLB7.02</Emphasis>, a novel QTL for resistance to northern corn leaf blight in maize

Northern corn leaf blight (NCLB), which is caused by the hemibiotrophic fungal pathogen Setosphaeria turcica, is a devastating foliar disease that results in considerable maize yield losses. In the present study, quantitative trait locus (QTL) analysis was conducted across two environments using an ultra-high-density bin map constructed using recombinant inbred lines (RILs) derived from a cross between Ye478 and Qi319. A total of 11 QTLs, located on chromosomes 1, 4, 5, 6, 7, 8, 9, and 10, were detected that confer resistance to physiological race 0 of NCLB. Each QTL could explain 3.53–15.29% of the total phenotypic variation in disease resistance after artificial inoculation in two environments. Among these QTL, qNCLB7.02, which is located on chromosome 7, had the largest effect, accounting for 10.11 and 15.29% of the phenotypic variation in resistance in two field trials and BLUP. The common confidence interval (CI) for qNCLB7.02 was 1.4 Mb, according to the B73 RefGen_v3 sequence. The resistance effect of qNCLB7.02 was validated in 2016 by using chromosome segment substitution lines (CSSLs) derived from Qi319 as the donor in the genetic background of Ye478. The type 6 CSSL, which harbors introgressed qNCLB7.02, was found to be significantly associated with resistance to NCLB by linked marker bnlg1808 and exhibited greater resistance than the other CSSLs that did not carry this QTL (P?=?0.0008). The combination of linkage mapping in RILs and validation in CSSLs is a powerful approach for the dissection of QTL for disease resistance in maize.     

QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance

Despite numerous published reports of quantitative trait loci (QTL) for drought-related traits, practical applications of such QTL in maize improvement are scarce. Identifying QTL of sizeable effects that express more or less uniformly in diverse genetic backgrounds across contrasting water regimes could significantly complement conventional breeding efforts to improve drought tolerance. We evaluated three tropical bi-parental populations under water-stress (WS) and well-watered (WW) regimes in Mexico, Kenya and Zimbabwe to identify genomic regions responsible for grain yield (GY) and anthesis-silking interval (ASI) across multiple environments and diverse genetic backgrounds. Across the three populations, on average, drought stress reduced GY by more than 50 % and increased ASI by 3.2 days. We identified a total of 83 and 62 QTL through individual environment analyses for GY and ASI, respectively. In each population, most QTL consistently showed up in each water regime. Across the three populations, the phenotypic variance explained by various individual QTL ranged from 2.6 to 17.8 % for GY and 1.7 to 17.8 % for ASI under WS environments and from 5 to 19.5 % for GY under WW environments. Meta-QTL (mQTL) analysis across the three populations and multiple environments identified seven genomic regions for GY and one for ASI, of which six mQTL on chr.1, 4, 5 and 10 for GY were constitutively expressed across WS and WW environments. One mQTL on chr.7 for GY and one on chr.3 for ASI were found to be ‘adaptive’ to WS conditions. High throughput assays were developed for SNPs that delimit the physical intervals of these mQTL. At most of the QTL, almost equal number of favorable alleles was donated by either of the parents within each cross, thereby demonstrating the potential of drought tolerant × drought tolerant crosses to identify QTL under contrasting water regimes.     

Mapping of quantitative trait loci for kernel row number in maize across seven environments

Genetic factors controlling quantitative inheritance of grain yield and its components have been intensively investigated during recent decades using diverse populations in maize (Zea mays L.). Notwithstanding this, quantitative trait loci (QTL) for kernel row number (KRN) with large and consistent effect have not been identified. In this study, a linkage map of 150 simple sequence repeat (SSR) loci was constructed by using a population of 500 F2 individuals derived from a cross between elite inbreds Ye478 and Dan340. The linkage map spanned a total of 1478 cM with an average interval of 10.0 cM. A total of 397 F2:3 lines were evaluated across seven diverse environments for mapping QTL for KRN. Some QTL for grain yield and its components had previously been confirmed with this population across environments. A total of 13 QTL for KRN were identified, with each QTL explaining from 3.0 to 17.9% of phenotypic variance. The gene action for KRN was mainly additive to partial dominance. A large-effect QTL (qkrn7) with partial dominance effect accounting for 17.9% of the phenotypic variation for KRN was identified on chromosome 7 near marker umc1865 with consistent gene effect across seven diverse environments. This study has laid a foundation for map-based cloning of this major QTL and for developing molecular markers for marker-assisted selection of high KRN.     

Validation of quantitative trait loci for Fusarium head blight and kernel discoloration in barley

Validation of quantitative trait loci (QTLs) is a prerequisite to marker assisted selection (MAS), however, only a fraction of QTLs identified for important plant traits have been independently tested for validation. Resistance to the diseases kernel discoloration (KD) and Fusarium head blight (FHB) in barley is complex and technically difficult to assess, and therefore QTLs for these traits are suitable targets for MAS. We selected two lines, from a QTL mapping population created using the resistant variety Chevron, and crossed them to susceptible parents to generate two validation populations. Genetic maps of both populations were developed for five chromosomes covering 15 selected regions containing QTLs for FHB severity, KD score and concentration of deoxynivalenol (DON), a mycotoxin produced by the FHB pathogen. QTL analyses using these validation populations confirmed that five of the possible 15 QTL regions were associated with at least one of the three traits. While some QTL were detected inconsistently across environments, QTL that could be subjected to validation in both populations were confirmed in both populations in seven out of eight instances. A QTL for KD score on chromosome 6(6H) was confirmed in both validation populations in eight of nine environments and was also associated with FHB in three of six environments. A QTL for FHB on chromosome 2(2H) was confirmed and was also associated with KD and heading date. Marker assisted selection at these two QTLs should enhance disease resistance, however, the QTL on chromosome 2(2H) will also delay heading date.     

QTL analysis of agronomic traits in barley based on the doubled haploid progeny of two elite North American varieties representing different germplasm groups

A better understanding of the genetics of complex traits, such as yield, may be achieved by using molecular tools. This study was conducted to estimate the number, genome location, effect and allele phase of QTLs determining agronomic traits in the two North American malting barley (Hordeum vulgare L.) quality variety standards. Using a doubled haploid population of 140 lines from the cross of two-rowed Harrington×six-rowed Morex, agronomic phenotypic data sets from nine environments, and a 107-marker linkage map, we performed QTL analyses using simple interval mapping and simplified composite interval mapping procedures. Thirty-five QTLs were associated, either across environments or in individual environments, with five grain and agronomic traits (yield, kernel plumpness, test weight, heading date, and plant height). Significant QTL×environment interaction was detected for all traits. These interactions resulted from both changes in the magnitude of response and changes in the sign of the allelic effect. QTLs for multiple traits were coincident. The vrs1 locus on chromosome 2 (2H), which determines inflorescence row type, was coincident with the largest-effect QTL determining four traits (yield, kernel plumpness, test weight, and plant height). QTL analyses were also conducted separately for each sub-population (six-rowed and two-rowed). Seven new QTLs were detected in the sub-populations. Positive transgressive segregants were found for all traits, but they were more prevalent in the six-rowed sub-population.QTL analysis should be useful for identifying candidate genes and introgressing favorable alleles between germplasm groups. Received: 18 August 2000 / Accepted: 15 December 2000     

Mapping QTL for Grain Yield and Plant Traits in a Tropical Maize Population

The vast majority of reported QTL mapping for maize (Zea mays L.) traits are from temperate germplasm and, also, QTL by environment interaction (QTL × E) has not been thoroughly evaluated and analyzed in most of these papers. The maize growing areas in tropical regions are more prone to environmental variability than in temperate areas, and, therefore, genotype by environment interaction is of great concern for maize breeders. The objectives of this study were to map QTL and to test their interaction with environments for several traits in a tropical maize population. Two-hundred and fifty-six F_2:3 families evaluated in five environments, a genetic map with 139 microsatellites markers, and the multiple-environment joint analysis (mCIM) were used to map QTL and to test QTL × E interaction. Sixteen, eight, six, six, nine, and two QTL were mapped for grain yield, ears per plant, plant lodging, plant height, ear height, and number of leaves, respectively. Most of these QTL interacted significantly with environments, most of them displayed overdominance for all traits, and genetic correlated traits had a low number of QTL mapped in the same genomic regions. Few of the QTL mapped had already been reported in both temperate and tropical germplasm. The low number of stable QTL across environments imposes additional challenges to design marker-assisted selection in tropical areas, unless the breeding programs could be directed towards specific target areas.     

Fine Mapping and Candidate Gene Prediction of a Pleiotropic Quantitative Trait Locus for Yield-Related Trait in Zea mays

The yield of maize grain is a highly complex quantitative trait that is controlled by multiple quantitative trait loci (QTLs) with small effects, and is frequently influenced by multiple genetic and environmental factors. Thus, it is challenging to clone a QTL for grain yield in the maize genome. Previously, we identified a major QTL, qKNPR6, for kernel number per row (KNPR) across multiple environments, and developed two nearly isogenic lines, SL57-6 and Ye478, which differ only in the allelic constitution at the short segment harboring the QTL. Recently, qKNPR6 was re-evaluated in segregating populations derived from SL57-6×Ye478, and was narrowed down to a 2.8 cM interval, which explained 56.3% of the phenotypic variance of KNPR in 201 F_2∶3 families. The QTL simultaneously affected ear length, kernel weight and grain yield. Furthermore, a large F₂ population with more than 12,800 plants, 191 recombinant chromosomes and 10 overlapping recombinant lines placed qKNPR6 into a 0.91 cM interval corresponding to 198Kb of the B73 reference genome. In this region, six genes with expressed sequence tag (EST) evidence were annotated. The expression pattern and DNA diversity of the six genes were assayed in Ye478 and SL57-6. The possible candidate gene and the pathway involved in inflorescence development were discussed.     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

Multiple-line cross quantitative trait locus mapping in sugar beet (Beta vulgaris L.)

Linkage mapping based on multiple-line crosses is a promising strategy for mapping quantitative trait loci (QTL) underlying important agronomic traits. The main goal of this survey was to study the advantages of QTL mapping across versus within biparental populations using experimental data from three connected sugar beet (Beta vulgaris L.) populations evaluated for beet yield and potassium and sodium content. For the combined analysis across populations, we used two approaches for cofactor selection. In Model A, we assumed identical cofactors for every segregating population. In contrast, in Model B we selected cofactors specific for every segregating population. Model A performed better than Model B with respect to the number of QTL detected and the total proportion of phenotypic variance explained. The QTL analyses across populations revealed a substantially higher number of QTL compared to the analyses of single biparental populations. This clearly emphasizes the potential to increase QTL detection power with a joint analysis across biparental populations.     

QTL detection for stover yield and quality traits using two connected populations in high-oil maize

Both yield and quality traits for stover portion were important for forage and biofuel production utility in maize. A high-oil maize inbred GY220 was crossed with two normal-oil dent maize inbred lines 8984 and 8622 to generate two connected F_2:3 populations with 284 and 265 F_2:3 families. Seven yield and quality traits were evaluated under two environments. The variance components of genotype (σ_g²), environment (σ_e²) and genotype × environment interactions (σ_ge²) were all significant for most traits in both populations. Different levels of correlations were observed for all traits. QTL mapping was conducted using composite interval mapping (CIM) for data under each environment and in combined analysis in both populations. Totally, 45 and 42 QTL were detected in the two populations. Only five common QTL across the two populations, and one and three common QTL across the two environments in the two populations were detected, reflecting substantial influence of genetic backgrounds and environments on the results of QTL detection for stover traits. Combined analysis across two environments failed to detect most QTL mapped using individual environmental data in both populations. Few of the detected QTL displayed digenic epistatic interactions. Common QTL among all traits were consistent with their correlations. Some QTL herein have been detected in previous researches, and linked with candidate genes for enzymes postulated to have direct and indirect roles in cell wall components biosynthesis.     

Selection on QTL and complex traits in complex environments

Understanding genetic variation for complex traits in heterogeneous environments is a fundamental problem in biology. In this issue of Molecular Ecology, Fournier‐Level et al. ( 2013 ) analyse quantitative trait loci (QTL) influencing ecologically important phenotypes in mapping populations of Arabidopsis thaliana grown in four habitats across its native European range. They used causal modelling to quantify the selective consequences of life history and morphological traits and QTL on components of fitness. They found phenology QTL colocalizing with known flowering time genes as well as novel loci. Most QTL influenced fitness via life history and size traits, rather than QTL having direct effects on fitness. Comparison of phenotypes among environments found no evidence for genetic trade‐offs for phenology or growth traits, but genetic trade‐offs for fitness resulted because flowering time had opposite fitness effects in different environments. These changes in QTL effects and selective consequences may maintain genetic variation among populations.     

Association mapping for pre-harvest sprouting resistance in white winter wheat

Association mapping identified quantitative trait loci (QTLs) and the markers linked to pre-harvest sprouting (PHS) resistance in an elite association mapping panel of white winter wheat comprising 198 genotypes. A total of 1,166 marker loci including DArT and SSR markers representing all 21 chromosomes of wheat were used in the analysis. General and mixed linear models were used to analyze PHS data collected over 4 years. Association analysis identified eight QTLs linked with 13 markers mapped on seven chromosomes. A QTL was detected on each arm of chromosome 2B and one each on chromosome arms 1BS, 2DS, 4AL, 6DL, 7BS and 7DS. All except the QTL on 7BS are located in a location similar to previous reports and, if verified, the QTL on 7BS is likely to be novel. Principal components and the kinship matrix were used to account for the presence of population structure but had only a minor effect on the results. Although, none of the QTLs was highly significant across all environments, a QTL on the long arm of chromosome 4A was detected in three different environments and also using the best linear unbiased predictions over years. Although previous reports have identified this as a major QTL, its effects were minor in our biparental mapping populations. The results of this study highlight the benefits of association mapping and the value of using elite material in association mapping for plant breeding programs.     

Genome-wide linkage mapping of yield-related traits in three Chinese bread wheat populations using high-density SNP markers

Key message

We identified 21 new and stable QTL, and 11 QTL clusters for yield-related traits in three bread wheat populations using the wheat 90 K SNP assay.

Abstract

Identification of quantitative trait loci (QTL) for yield-related traits and closely linked molecular markers is important in order to identify gene/QTL for marker-assisted selection (MAS) in wheat breeding. The objectives of the present study were to identify QTL for yield-related traits and dissect the relationships among different traits in three wheat recombinant inbred line (RIL) populations derived from crosses Doumai?×?Shi 4185 (D?×?S), Gaocheng 8901?×?Zhoumai 16 (G?×?Z) and Linmai 2?×?Zhong 892 (L?×?Z). Using the available high-density linkage maps previously constructed with the wheat 90 K iSelect single nucleotide polymorphism (SNP) array, 65, 46 and 53 QTL for 12 traits were identified in the three RIL populations, respectively. Among them, 34, 23 and 27 were likely to be new QTL. Eighteen common QTL were detected across two or three populations. Eleven QTL clusters harboring multiple QTL were detected in different populations, and the interval 15.5–32.3 cM around the Rht-B1 locus on chromosome 4BS harboring 20 QTL is an important region determining grain yield (GY). Thousand-kernel weight (TKW) is significantly affected by kernel width and plant height (PH), whereas flag leaf width can be used to select lines with large kernel number per spike. Eleven candidate genes were identified, including eight cloned genes for kernel, heading date (HD) and PH-related traits as well as predicted genes for TKW, spike length and HD. The closest SNP markers of stable QTL or QTL clusters can be used for MAS in wheat breeding using kompetitive allele-specific PCR or semi-thermal asymmetric reverse PCR assays for improvement of GY.

     

Quantitative trait loci mapping in five new large recombinant inbred line populations of Arabidopsis thaliana genotyped with consensus single-nucleotide polymorphism markers

Quantitative approaches conducted in a single mapping population are limited by the extent of genetic variation distinguishing the parental genotypes. To overcome this limitation and allow a more complete dissection of the genetic architecture of complex traits, we built an integrated set of 15 new large Arabidopsis thaliana recombinant inbred line (RIL) populations optimized for quantitative trait loci (QTL) mapping, having Columbia as a common parent crossed to distant accessions. Here we present 5 of these populations that were validated by investigating three traits: flowering time, rosette size, and seed production as an estimate of fitness. The large number of RILs in each population (between 319 and 377 lines) and the high density of evenly spaced genetic markers scored ensure high power and precision in QTL mapping even under a minimal phenotyping framework. Moreover, the use of common markers across the different maps allows a direct comparison of the QTL detected within the different RIL sets. In addition, we show that following a selective phenotyping strategy by performing QTL analyses on genotypically chosen subsets of 164 RILs (core populations) does not impair the power of detection of QTL with phenotypic contributions >7%.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population II: conventional QTL analysis

Modifying plant root systems is considered a means of crop improvement targeted to low-resource environments, particularly low nutrient and drought-prone agriculture. The identification of quantitative trait loci (QTLs) for root traits has stimulated marker-assisted breeding to this end, but different QTLs have been detected in different populations of the same species, and importantly, in the same population when grown in different experimental environments. The presence of QTL × environment interaction is implicated, and this must be characterised if the utility of the target QTLs is to be realised. Previous attempts to do this suffer from a lack of control over replicate environments and inadequate statistical rigour. The Bala × Azucena mapping population was grown in two replicate experiments of four treatment environments, a control, a low light, a low soil nitrogen and a low soil water treatment. After a 4 weeks growth, maximum root length, maximum root thickness, root mass below 50 cm, total plant dry mass, % root mass and shoot length were measured. A summary of the overall results is presented in an accompanying paper. Here, QTL analysis by composite interval mapping is presented. A total of 145 QTLs were detected, mapping to 37 discrete loci on all chromosomes. Superficial evidence of QTL × E (great difference in LOD score) was tested by single-marker analysis which confirmed QTL × E for five loci representing only five individual trait-loci interactions. Some loci appeared to be stable across environments. Some QTLs were clearly more or less active under low light, low nitrogen or drought. A few notable loci on chromosomes 1, 2, 3, 5, 7 and 9 are briefly discussed. Also discussed are some remaining statistical shortcomings that will be addressed in another companion paper.