Identification of QTL for flag leaf length in common wheat and their pleiotropic effects

Leaf size is an important factor contributing to the photosynthetic capability of wheat plants. It also significantly affects various agronomic traits. In particular, the flag leaves contribute significantly to grain yield in wheat. A recombinant inbred line (RIL) population developed between varieties with significant differences in flag leaf traits was used to map quantitative trait loci (QTL) of flag leaf length (FLL) and to evaluate its pleiotropic effects on five yield-related traits, including spike length (SL), spikelet number per spike (SPN), kernel number per spike (KN), kernel length (KL), and thousand-kernel weight (TKW). Two additional RIL populations were used to validate the detected QTL and reveal the relationships in different genetic backgrounds. Using the diversity arrays technology (DArT) genetic linkage map, three major QTL for FLL were detected, with single QTL in different environments explaining 8.6–23.3% of the phenotypic variation. All the QTL were detected in at least four environments, and validated in two related populations based on the designed primers. These QTL and the newly developed primers are expected to be valuable for fine mapping and marker-assisted selection in wheat 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.

     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

Identification of germplasm with stacked QTL underlying seed traits in an inbred soybean population from cultivars Essex and Forrest

Soybean (Glycine max (L.) Merr.) seed provides valuable oil (~200 g/kg) and protein (~400 g/kg) co-products. Seed composition variations result from several quantitative trait loci (QTL) that act through development. The objectives here were to identify loci underlying seed traits in the Essex × Forrest (EF94)-derived recombinant inbred line (RIL) population which has low frequencies of marker polymorphisms. Seed weight, protein, and oil were measured over 3 years: 2001, 2003, and 2005. Essex’s seeds were larger (141 mg/seed), higher in protein (406 g/kg), and lower in oil (190 g/kg) than Forrest’s (115 mg/seed, 395 g protein/kg, and 203 g oil/kg). Marker analysis included 413 markers for trait associations used for ANOVA, interval mapping, and composite interval mapping. Eleven QTL in nine genomic regions were associated (LOD >2; P < 0.0077) with seed traits. Two QTL, for mean protein and seed size, were clustered on linkage group (LG) E (chromosome Gm16). QTL for protein content alone were found on LG C2 (Gm6), LG D1b (Gm2), LG H (Gm12), and LG I (Gm20). The alleles from Essex, the high-protein parent, underlay higher protein (4–10 g/kg) at four of five loci. A QTL for mean oil was found on LG A2 (Gm18) and on LG I (Gm 20). The alleles from Forrest underlay higher oil (3–4 g/kg). Five separate QTL for mean seed weight were found on LG A1 (Gm05), LG N (Gm15), two on LG B1 (Gm11) and one on LG N (Gm3). The alleles from Essex underlay greater seed weight (0.4–0.66 g/100 seeds). The QTL positions were consistent with reported loci. Germplasm that contained all five beneficial alleles at the QTL underlying protein was significantly higher in protein and yield than Essex (409.7–412.3 g/kg) and included RILs 49 and 62. The germplasm identified can be useful for further breeding of the many traits and QTL measured in each line.     

Genetic architecture of quantitative trait loci associated with morphological and agronomic trait differences in a wild by cultivated barley cross.

Hordeum vulgare subsp. spontaneum is the progenitor of cultivated barley (Hordeum vulgare L.). Domestication combined with plant breeding has led to the morphological and agronomic characteristics of modern barley cultivars. The objective of this study was to map the genetic factors that morphologically and agronomically differentiate wild barley from modern barley cultivars. To address this objective, we identified quantitative trait loci (QTLs) associated with plant height, flag leaf width, spike length, spike width, glume length in relation to seed length, awn length, fragility of ear rachis, endosperm width and groove depth, heading date, flag leaf length, number of tillers per plant, and kernel color in a Harrington/OUH602 advanced backcross (BC2F8) population. This population was genotyped with 113 simple sequence repeat markers. Thirty QTLs were identified, of which 16 were newly identified in this study. One to 4 QTLs were identified for each of the traits except glume length, for which no QTL was detected. The portion of phenotypic variation accounted for by individual QTLs ranged from about 9% to 54%. For traits with more than one QTL, the phenotypic variation explained ranged from 25% to 71%. Taken together, our results reveal the genetic architecture of morphological and agronomic traits that differentiate wild from cultivated barley.     

Potential for Marker-Assisted Simultaneous Improvement of Grain and Biomass Yield in Triticale

Triticale is a promising crop for agricultural biomass production but breeding has until now mainly focused on grain yield. Here, we evaluated the potential of marker-assisted simultaneous improvement of grain yield and biomass yield. To this end, we employed a large triticale doubled haploid population with 647 individuals derived from four families that were phenotyped for grain yield and biomass yield, as well as thousand-kernel weight, tiller density, and plant height in multi-environment field trials. Employing an association mapping approach, we identified quantitative trait loci (QTL) for all the five traits. The phenotypic correlation between grain yield and biomass yield was low, and we detected only one overlapping QTL suggesting different genetic architectures underlying both traits. Our results indicate that a marker-based selection for either grain yield or biomass yield does not adversely affect the other traits. Furthermore, an improvement of the multiplicative yield traits can to some extent also be achieved by selection for QTL identified for the component traits. Taken together, our results suggest that marker-assisted breeding can assist the establishment of dual-purpose triticale cultivars with high grain and biomass yield.     

Wheat kernel dimensions: how do they contribute to kernel weight at an individual QTL level?

Kernel dimensions (KD) contribute greatly to thousand-kernel weight (TKW) in wheat. In the present study, quantitative trait loci (QTL) for TKW, kernel length (KL), kernel width (KW) and kernel diameter ratio (KDR) were detected by both conditional and unconditional QTL mapping methods. Two related F(8:9) recombinant inbred line (RIL) populations, comprising 485 and 229 lines, respectively, were used in this study, and the trait phenotypes were evaluated in four environments. Unconditional QTL mapping analysis detected 77 additive QTL for four traits in two populations. Of these, 24 QTL were verified in at least three trials, and five of them were major QTL, thus being of great value for marker assisted selection in breeding programmes. Conditional QTL mapping analysis, compared with unconditional QTL mapping analysis, resulted in reduction in the number of QTL for TKW due to the elimination of TKW variations caused by its conditional traits; based on which we first dissected genetic control system involved in the synthetic process between TKW and KD at an individual QTL level. Results indicated that, at the QTL level, KW had the strongest influence on TKW, followed by KL, and KDR had the lowest level contribution to TKW. In addition, the present study proved that it is not all-inclusive to determine genetic relationships of a pairwise QTL for two related/causal traits based on whether they were co-located. Thus, conditional QTL mapping method should be used to evaluate possible genetic relationships of two related/causal traits.     

QTL mapping and phenotypic variation for root architectural traits in maize (Zea mays L.)

Key message

QTL were identified for root architectural traits in maize.

Abstract

Root architectural traits, including the number, length, orientation, and branching of the principal root classes, influence plant function by determining the spatial and temporal domains of soil exploration. To characterize phenotypic patterns and their genetic control, three recombinant inbred populations of maize were grown for 28 days in solid media in a greenhouse and evaluated for 21 root architectural traits, including length, number, diameter, and branching of seminal, primary and nodal roots, dry weight of embryonic and nodal systems, and diameter of the nodal root system. Significant phenotypic variation was observed for all traits. Strong correlations were observed among traits in the same root class, particularly for the length of the main root axis and the length of lateral roots. In a principal component analysis, relationships among traits differed slightly for the three families, though vectors grouped together for traits within a given root class, indicating opportunities for more efficient phenotyping. Allometric analysis showed that trajectories of growth for specific traits differ in the three populations. In total, 15 quantitative trait loci (QTL) were identified. QTL are reported for length in multiple root classes, diameter and number of seminal roots, and dry weight of the embryonic and nodal root systems. Phenotypic variation explained by individual QTL ranged from 0.44 % (number of seminal roots, NyH population) to 13.5 % (shoot dry weight, OhW population). Identification of QTL for root architectural traits may be useful for developing genotypes that are better suited to specific soil environments.     

Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments

Key message

We identified 27 stable loci associated with agronomic traits in spring wheat using genome-wide association analysis, some of which confirmed previously reported studies. GWAS peaks identified in regions where no QTL for grain yield per se has been mapped to date, provide new opportunities for gene discovery and creation of new cultivars with desirable alleles for improving yield and yield stability in wheat.

Abstract

We undertook large-scale genetic analysis to determine marker-trait associations (MTAs) underlying agronomic and physiological performance in spring wheat using genome-wide association studies (GWAS). Field trials were conducted at seven sites in three countries (Sudan, Egypt, and Syria) over 2–3 years in each country. Twenty-five agronomic and physiological traits were measured on 188 wheat genotypes. After correcting for population structure and relatedness, a total of 245 MTAs distributed over 66 loci were associated with agronomic traits in individual and mean performance across environments respectively; some of which confirmed previously reported loci. Of these, 27 loci were significantly associated with days to heading, thousand kernel weight, grain yield, spike length, and leaf rolling for mean performance across environments. Despite strong QTL by environment interactions, eight of the loci on chromosomes 1A, 1D, 5A, 5D, 6B, 7A, and 7B had pleiotropic effects on days to heading and yield components (TKW, SM^?2, and SNS). The winter-type alleles at the homoeologous VRN1 loci significantly increased days to heading and grain yield in optimal environments, but decreased grain yield in heat prone environments. Top 20 high-yielding genotypes, ranked by additive main effects and multiplicative interaction (AMMI), had low kinship relationship and possessed 4–5 favorable alleles for GY MTAs except two genotypes, Shadi-4 and Qafzah-11/Bashiq-1–2. This indicated different yield stability mechanisms due to potentially favorable rare alleles that are uncharacterized. Our results will enable wheat breeders to effectively introgress several desirable alleles into locally adapted germplasm in developing wheat varieties with high yield stability and enhanced heat tolerance.

     

QTL mapping for yield and photosynthetic related traits under different water regimes in wheat

The improvement for drought tolerance requires understanding of the genetic control of wheat (Triticum aestivum L.) reaction to drought. In this study, a set of 131 recombinant inbred lines of wheat were investigated under well-watered (WW) and drought stress (DS) environments across 2 years to map quantitative trait loci (QTLs) for yield and physiological traits. A total of 225 QTLs were detected, including 32 non-environment-specific loci that were significant in both DS and WW, one drought-specific locus and two watering-specific loci. Three consistently-expressed QTLs (QTkw-3A.2, QTss-1A, and QScn-7A.1) were identified in at least three environments and the QTkw-1D.1 was significant in DS across the 2 years. By unconditional and conditional QTL analysis, spike number per plant and kernel number per spike were more important than thousand-kernel weight for grain yield (GY) at the given genetic background. Meta-analysis identified 67 meta-QTLs that contained QTLs for at least two traits. High frequency co-location of QTLs was found among either the spike-related traits or the six physiological traits. Four photosynthesis traits (CHL, LWUE, P _N, and C _i) were co-located with GY and/or yield components on various MQTLs. The results provided QTLs that warrant further study for drought tolerance breeding and are helpful for understanding the genetic basis of drought tolerance and the genetic contribution of yield components to GY at individual QTL level in wheat.     

大白菜部分形态性状的QTL定位与分析

应用352个标记位点的大白菜AFLP和RAPD图谱和一套栽培品种间杂交获得的重组自交系群体,采用复合区间作图的方法对大白菜9个形态性状进行QTL定位及遗传效应研究。在14个连锁群上检测到50个QTL:其中控制株型的QTL有5个;控制株高的QTL有6个;控制开展度的QTL有5个;控制最大叶长的QTL有7个;控制最大叶宽的QTL有4个;控制叶形指数的QTL有6个;控制中肋长的QTL有7个;控制中肋宽的QTL有4个;控制抽苔的QTL有6个。另外,估算了单个QTL的遗传贡献率和加性效应。这将为大白菜品种改良中形态性状的分子标记辅助选择提供理论依据。     

Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China’s wheat breeding

Understanding the genetics underlying yield formation of wheat is important for increasing wheat yield potential in breeding programs. Nanda2419 was a widely used cultivar for wheat production and breeding in China. In this study, we evaluated yield components and a few yield-related traits of a recombinant inbred line (RIL) population created by crossing Nanda2419 with the indigenous cultivar Wangshuibai in three to four trials at different geographical locations. Negative and positive correlations were found among some of these evaluated traits. Five traits had over 50 % trial-wide broad sense heritability. Using a framework marker map of the genome constructed with this population, quantitative trait loci (QTL) were identified for all traits, and epistatic loci were identified for seven of them. Our results confirmed some of the previously reported QTLs in wheat and identified several new ones, including QSn.nau-6D for effective tillers, QGn.nau-4B.2 for kernel number, QGw.nau-4D for kernel weight, QPh.nau-4B.2 and QPh.nau-4A for plant height, and QFlw.nau-5A.1 for flag leaf width. In the investigated population, Nanda2419 contributed all QTLs associated with higher kernel weight, higher leaf chlorophyll content, and a major QTL associated with wider flag leaf. Seven chromosome regions were related to more than one trait. Four QTL clusters contributed positively to breeding goal-based trait improvement through the Nanda2419 alleles and were detected in trials set in different ecological regions. The findings of this study are relevant to the molecular improvement of wheat yield and to the goal of screening cultivars for better breeding parents.     

Construction of an integrative linkage map and QTL mapping of grain yield-related traits using three related wheat RIL populations

Key message

A novel high-density consensus wheat genetic map was obtained based on three related RIL populations, and the important chromosomal regions affecting yield and related traits were specified.

Abstract

A prerequisite for mapping quantitative trait locus (QTL) is to build a genetic linkage map. In this study, three recombinant inbred line populations (represented by WL, WY, and WJ) sharing one common parental line were used for map construction and subsequently for QTL detection of yield-related traits. PCR-based and diversity arrays technology markers were screened in the three populations. The integrated genetic map contains 1,127 marker loci, which span 2,976.75 cM for the whole genome, 985.93 cM for the A genome, 922.16 cM for the B genome, and 1,068.65 cM for the D genome. Phenotypic values were evaluated in four environments for populations WY and WJ, but three environments for population WL. Individual and combined phenotypic values across environments were used for QTL detection. A total of 165 putative additive QTL were identified, 22 of which showed significant additive-by-environment interaction effects. A total of 65 QTL (51.5 %) were stable across environments, and 23 of these (35.4 %) were common stable QTL that were identified in at least two populations. Notably, QTkw-5B.1, QTkw-6A.2, and QTkw-7B.1 were common major stable QTL in at least two populations, exhibiting 11.28–16.06, 5.64–18.69, and 6.76–21.16 % of the phenotypic variance, respectively. Genetic relationships between kernel dimensions and kernel weight and between yield components and yield were evaluated. Moreover, QTL or regions that commonly interact across genetic backgrounds were discussed by comparing the results of the present study with those of previous similar studies. The present study provides useful information for marker-assisted selection in breeding wheat varieties with high yield.     

Identification of QTL in soybean underlying resistance to herbivory by Japanese beetles (Popillia japonica, Newman)

Soybean [Glycine max (L.) Merr.] was one of the most important legume crops in the world in 2010. Japanese beetles (JB; Popillia japonica, Newman) in the US were an introduced and potentially damaging insect pest for soybean. JBs are likely to spread across the US if global warming occurs. Resistance to JB in soybean was previously reported only in plant introductions. The aims here were to identify loci underlying resistance to JB herbivory in recombinant inbred lines (RILs) derived from the cross of Essex × Forrest cultivars (EF94) and to correlate those with loci with factors that confer insect resistance in soybean cultivars. The RIL population was used to map 413 markers, 238 satellite markers and 177 other DNA markers. Field data were from two environments over 2 years. Pest severity (PS) measured defoliation on a 0–9 scale. Pest incidence (PI) was the percentage of plants within each RIL with beetles on them. Antibiosis and antixenosis data were from feeding assays with detached leaves in petri plates. Five QTL were detected for the mean PS field trait (16% < R ² < 27%). The loci were within the intervals Satt632–A2D8 on linkage group (LG) A2 (chromosome 8); Satt583–Satt415 on LG B1 (11); Satt009–Satt530 on LG N (3); and close to two markers OB02_140 (LG E; 20 cM from Satt572) and OZ15_150 LG (19 cM from Satt291 C2). Two QTL were detected for the mean PI field trait (16% < R ² < 18%) close to Satt385 on LG A1 and Satt440 on LG I. The no choice feeding studies detected three QTL that were significant; two for antixenosis (22% < R ² < 24%) between Satt632–A2D8 on LG A2 (8) and Sat_039–Satt160 on LG F (13); and a major locus effect (R ² = 54%) for antibiosis on LG D2 (17) between Satt464–Satt488. Therefore, loci underlying resistance to JB herbivory were a mixture of major and minor gene effects. Some loci were within regions underlying resistance to soybean cyst nematode (LGs A2 and I) and root knot nematode (LG F) but not other major loci underlying resistance to nematode or insect pests (LGs G, H and M).     

QTL mapping of flag leaf-related traits in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Key message

QTL controlling flag leaf length, flag leaf width, flag leaf area and flag leaf angle were mapped in wheat.

Abstract

This study aimed to advance our understanding of the genetic mechanisms underlying morphological traits of the flag leaves of wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population derived from ND3331 and the Tibetan semi-wild wheat Zang1817 was used to identify quantitative trait loci (QTLs) controlling flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf angle (FLANG). Using an available simple sequence repeat genetic linkage map, 23 putative QTLs for FLL, FLW, FLA, and FLANG were detected on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D. Individual QTL explained 4.3–68.52% of the phenotypic variance in different environments. Four QTLs for FLL, two for FLW, four for FLA, and five for FLANG were detected in at least two environments. Positive alleles of 17 QTLs for flag leaf-related traits originated from ND3331 and 6 originated from Zang1817. QTLs with pleiotropic effects or multiple linked QTL were also identified on chromosomes 1B, 4B, and 5A; these are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

     

Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat

A high-resolution chromosome arm-specific mapping population was used in an attempt to locate/detect gene(s)/QTL for different root traits on the short arm of rye chromosome 1 (1RS) in bread wheat. This population consisted of induced homoeologous recombinants of 1RS with 1BS, each originating from a different crossover event and distinct from all other recombinants in the proportions of rye and wheat chromatin present. It provides a simple and powerful approach to detect even small QTL effects using fewer progeny. A promising empirical Bayes method was applied to estimate additive and epistatic effects for all possible marker pairs simultaneously in a single model. This method has an advantage for QTL analysis in minimizing the error variance and detecting interaction effects between loci with no main effect. A total of 15 QTL effects, 6 additive and 9 epistatic, were detected for different traits of root length and root weight in 1RS wheat. Epistatic interactions were further partitioned into inter-genomic (wheat and rye alleles) and intra-genomic (rye–rye or wheat–wheat alleles) interactions affecting various root traits. Four common regions were identified involving all the QTL for root traits. Two regions carried QTL for almost all the root traits and were responsible for all the epistatic interactions. Evidence for inter-genomic interactions is provided. Comparison of mean values supported the QTL detection.     

QTL mapping for maize resistance and yield under infestation with Sesamia nonagrioides

The Mediterranean corn borer (MCB) is the most important maize insect pest in the Mediterranean region. The main objective was to map quantitative trait loci (QTL) for yield performance under infestation with MCB, resistance and agronomic traits in a maize RIL population derived from an inbred cross European flint × Reid. Six QTL for resistance traits were located: one QTL for tunnel length (bin 9.03; p = 19.8 %), one QTL for stalk lodging (bin 3.07, p = 11.5 %), and four QTL for ear resistance (bins 1.07, 5.03/5.05, and 8.04; p = 25–63 %). Twelve QTL for agronomic traits were located: a QTL for yield under infestation (bin 5.03, p = 15 %); two QTL for grain moisture (bins 1.07 and 8.05); two QTL for days to anthesis (bin 1.07 and 8.05); two QTL for days to silking (bins 8.04 and 10.02); three QTL for plant height (bins 5.04, 8.05 and 9.03); and two QTL for ear height (bins 8.05 and 9.03). No genetic correlations between yield and other traits were observed. The cross validation (CV) approach showed that the estimation biases for QTL for resistance traits were higher than those for agronomic traits. This work stresses the importance of the region 9.03 for controlling corn borer resistance and suggests the presence of QTL with small effect on ear-resistance traits. At the same genomic region, there are also genes that control plant and ear height and future works could elucidate whether these genes are the same or are closely linked. The QTL for yield seem to play an important role in MCB tolerance in this genetic background. Large biases observed for QTL effects by CV were mainly due to the small sample size used and were higher for resistance traits due to their larger genetic complexity. We consider that it is more appropriate to select for grain yield under infestation instead of selecting for resistance traits because resistance to MCB could have unfavorable associations with agronomic traits.     

Genetic analysis of natural variations in the architecture of Arabidopsis thaliana vegetative leaves

To ascertain whether intraspecific variability might be a source of information as regards the genetic controls underlying plant leaf morphogenesis, we analyzed variations in the architecture of vegetative leaves in a large sample of Arabidopsis thaliana natural races. A total of 188 accessions from the Arabidopsis Information Service collection were grown and qualitatively classified into 14 phenotypic classes, which were defined according to petiole length, marginal configuration, and overall lamina shape. Accessions displaying extreme and opposite variations in the above-mentioned leaf architectural traits were crossed and their F(2) progeny was found to be not classifiable into discrete phenotypic classes. Furthermore, the leaf trait-based classification was not correlated with estimates on the genetic distances between the accessions being crossed, calculated after determining variations in repeat number at 22 microsatellite loci. Since these results suggested that intraspecific variability in A. thaliana leaf morphology arises from an accumulation of mutations at quantitative trait loci (QTL), we studied a mapping population of recombinant inbred lines (RILs) derived from a Landsberg erecta-0 x Columbia-4 cross. A total of 100 RILs were grown and the third and seventh leaves of 15 individuals from each RIL were collected and morphometrically analyzed. We identified a total of 16 and 13 QTL harboring naturally occurring alleles that contribute to natural variations in the architecture of juvenile and adult leaves, respectively. Our QTL mapping results confirmed the multifactorial nature of the observed natural variations in leaf architecture.     

High-density linkage mapping of yield components and epistatic interactions in maize with doubled haploid lines from four crosses

Grain yield (GY) is a genetically complex and physiologically multiplicative trait which can be decomposed into the components kernel number (KN) and 100-kernel weight (HKW). Genetic analysis of these less complex yield component traits may give insights into the genetic architecture and predictive ability of complex traits. Here, we investigated how the incorporation of component traits and epistasis in quantitative trait locus (QTL) mapping approaches influences the accuracy of GY prediction. High-density genetic maps with 7,000–10,000 polymorphic single nucleotide polymorphisms were constructed for four biparental populations. The populations comprised between 99 and 227 doubled haploid maize lines which were phenotyped in field trials in two environments. Heritability was highest for HKW (88–89 %), intermediate for KN (72–80 %), and lowest for GY (64–83 %). Mapped QTL explained in total 21–55 %, 22–67 %, and 24–75 % of the genotypic variance for GY, KN, and HKW, respectively. Support intervals of QTL were short, indicating that QTL were located with high precision. Co-located QTLs with same parental origin of favorable alleles were detected within populations for different traits and between populations for the same traits. Using GY predictions based on the detected QTL, prediction accuracies (r) determined by cross validation ranged from 0.18 to 0.52. Epistatic models did not outperform the corresponding additive models. In conclusion, models based on QTL positions of component traits support the identification of favorable alleles for multiplicative traits and provide a basis to select superior inbred lines by marker-assisted breeding.