Analysis of an intraspecific RIL population uncovers genomic segments harbouring multiple QTL for seed relevant traits in lentil (<Emphasis Type="Italic">Lens culinaris</Emphasis> L.)

Improving seed related traits remains key objective in lentil breeding. In recent years, genomic resources have shown great promise to accelerate crop improvement. However, limited genomic resources in lentil greatly restrict the use of genomics assisted breeding. The present investigation aims to build an intraspecific genetic linkage map and identify the QTL associated with important seed relevant traits using 94 recombinant inbreds (WA 8649090 × Precoz). A total of 288 polymorphic DNA markers including simple sequence repeat (SSR), inter simple sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD) were assayed on mapping population. The resultant genetic linkage map comprised 220 loci spanning 604.2 cM of the lentil genome, with average inter-marker distance of 2.74 cM. QTL mapping in this RIL population uncovered a total of 18 QTL encompassing nine major and nine minor QTL. All major QTL were detected for seed related traits viz., seed diameter (SD), seed thickness (ST), seed weight (SW) and seed plumpness (SP) across two locations. A considerable proportion of the phenotypic variation (PV) was accounted to these QTL. For instance, one major QTL on LG5 controlling SW (QTL 15) explained 50% PV in one location, while the same QTL accounted for 34.18% PV in other location. Importantly, the genomic region containing multiple QTL for different seed traits was mapped to a 17-cM region on LG5. The genomic region harbouring QTL for multiple traits opens up exciting opportunities for genomics assisted improvement of lentil.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F₉ recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5–12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.     

Unifying Genetic Canalization,Genetic Constraint,and Genotype-by-Environment Interaction: QTL by Genomic Background by Environment Interaction of Flowering Time in Boechera stricta

Natural populations exhibit substantial variation in quantitative traits. A quantitative trait is typically defined by its mean and variance, and to date most genetic mapping studies focus on loci altering trait means but not (co)variances. For single traits, the control of trait variance across genetic backgrounds is referred to as genetic canalization. With multiple traits, the genetic covariance among different traits in the same environment indicates the magnitude of potential genetic constraint, while genotype-by-environment interaction (GxE) concerns the same trait across different environments. While some have suggested that these three attributes of quantitative traits are different views of similar concepts, it is not yet clear, however, whether they have the same underlying genetic mechanism. Here, we detect quantitative trait loci (QTL) influencing the (co)variance of phenological traits in six distinct environments in Boechera stricta, a close relative of Arabidopsis. We identified nFT as the QTL altering the magnitude of phenological trait canalization, genetic constraint, and GxE. Both the magnitude and direction of nFT''s canalization effects depend on the environment, and to our knowledge, this reversibility of canalization across environments has not been reported previously. nFT''s effects on trait covariance structure (genetic constraint and GxE) likely result from the variable and reversible canalization effects across different traits and environments, which can be explained by the interaction among nFT, genomic backgrounds, and environmental stimuli. This view is supported by experiments demonstrating significant nFT by genomic background epistatic interactions affecting phenological traits and expression of the candidate gene, FT. In contrast to the well-known canalization gene Hsp90, the case of nFT may exemplify an alternative mechanism: Our results suggest that (at least in traits with major signal integrators such as flowering time) genetic canalization, genetic constraint, and GxE may have related genetic mechanisms resulting from interactions among major QTL, genomic backgrounds, and environments.     

Linkage analysis and identification of quantitative trait loci associated with freeze tolerance and turf quality traits in St. Augustinegrass

St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] is a warm-season turfgrass commonly grown in the southern USA. In this study, the first linkage map for all nine haploid chromosomes of the species was constructed for cultivar ‘Raleigh’ and cultivar ‘Seville’ using a pseudo-F₂ mapping strategy. A total of 160 simple sequence repeat markers were mapped to nine linkage groups (LGs) covering a total distance of 1176.24 cM. To demonstrate the usefulness of the map, quantitative trait loci (QTL) were mapped controlling field winter survival, laboratory-based freeze tolerance, and turf quality traits. Multiple genomic regions associated with these traits were identified. Moreover, overlapping QTL were found for winterkill and spring green up on LG 3 (99.21 cM); turf quality, turf density, and leaf texture on LG 3 (68.57–69.50 cM); and surviving green tissue and regrowth on LGs 1 (38.31 cM), 3 (77.70 cM), 6 (49.51 cM), and 9 (34.20 cM). Additional regions, where QTL identified in both field and laboratory-based/controlled environment freeze testing co-located, provided strong support that these regions are good candidates for true gene locations. These results present the first complete linkage map produced for St. Augustinegrass, providing a template for further genetic mapping. Additionally, markers linked to the QTL identified may be useful to breeders for transferring these traits into new breeding lines and cultivars.     

Genetic structure of a QTL hotspot on chromosome 2 in sweet cherry indicates positive selection for favorable haplotypes

A genomic region of particular interest for sweet cherry (Prunus avium L.) breeding is a quantitative trait locus (QTL) “hotspot” on chromosome 2. QTLs for fruit size, firmness, sweetness, and flowering time are reported to map to this region. An understanding of genetic diversity, allele sources, linkage relationships, and historical recombinations is critical to enable the combining of favorable alleles at multiple loci. The objectives of this study were to characterize, visualize, and interpret the genetic structure of this previously identified QTL hotspot within North American sweet cherry breeding germplasm, using a pedigree-based haploblocking approach. Across the 29.4 cM (6.3 Mbp) region defined by single nucleotide polymorphism (SNP) information from the RosBREED cherry 6K SNP array v1, a total of 12 recombination events falling into six inter-marker regions were traced within the pedigree of elite and wild germplasm (n = 55). These recombinations defined five haploblocks containing 5–15 markers and exhibiting 7–11 haplotypes each. Over the entire QTL hotspot, 30 extended haplotypes were identified for which parental gametes could be determined. When the haploblocks and their haplotypes were used to explore genetic diversity, ancestry, and recombination patterns, and then integrated with previous QTL results for fruit size, the results indicated that favorable alleles at this QTL hotspot are under positive selection in breeding. The genetic framework provided by a haploblock approach and knowledge of haplotype-level diversity sets the stage for assigning breeding utility to these haplotypes.     

Identification of QTL for agronomic traits of importance for olive breeding

Development of methodologies for early selection is one of the most important goals of olive breeding programs at present. In this context, the identification of molecular markers associated with beneficial alleles could allow the development of marker-assisted selection (MAS) strategies in olive breeding programs. Fruit-related and plant vigor traits, which are of key importance for olive selection and breeding, were analyzed during two seasons in a progeny derived from the cross ‘Picual’ × ‘Arbequina.’ Quantitative trait loci (QTL) analyses were performed using MapQTL 4.0. A total of 22 putative QTLs were identified in the map of ‘Arbequina.’ QTLs clustered in linkage groups (LG) 1, 10 and 17. QTLs for oil-related traits located in LG 1 and 10 explained around 20–30 % of the phenotypic variability depending on the season and the trait. QTL for moisture-related traits were detected in LG 1, 10 and 17, and QTLs for the ratio pulp to stone were identified in LG 10 and 17 explaining around 15–20 %. Interaction between QTLs for the same trait was investigated. The significance of these results was discussed considering the co-localization of QTLs and Pearson correlations among traits. Five additional QTLs were detected in the map of ‘Picual.’ Four of them clustered in LG 17 indicating the presence of a QTL for fruit weight explaining around 12.7–15.2 % of the variability. Additionally, a QTL for trunk diameter was detected in LG 14 explaining 16 % of the variation. These results represent an important step toward the application of MAS in olive breeding programs.     

QTL-seq for rapid identification of candidate genes for flowering time in broccoli × cabbage

Key message

A major QTL controlling early flowering in broccoli × cabbage was identified by marker analysis and next-generation sequencing, corresponding to GRF6 gene conditioning flowering time in Arabidopsis.

Abstract

Flowering is an important agronomic trait for hybrid production in broccoli and cabbage, but the genetic mechanism underlying this process is unknown. In this study, segregation analysis with BC₁P1, BC₁P2, F₂, and F_2:3 populations derived from a cross between two inbred lines “195” (late-flowering) and “93219” (early flowering) suggested that flowering time is a quantitative trait. Next, employing a next-generation sequencing-based whole-genome QTL-seq strategy, we identified a major genomic region harboring a robust flowering time QTL using an F₂ mapping population, designated Ef2.1 on cabbage chromosome 2 for early flowering. Ef2.1 was further validated by indel (insertion or deletion) marker-based classical QTL mapping, explaining 51.5% (LOD = 37.67) and 54.0% (LOD = 40.5) of the phenotypic variation in F₂ and F_2:3 populations, respectively. Combined QTL-seq and classical QTL analysis narrowed down Ef1.1 to a 228-kb genomic region containing 29 genes. A cabbage gene, Bol024659, was identified in this region, which is a homolog of GRF6, a major gene regulating flowering in Arabidopsis, and was designated BolGRF6. qRT-PCR study of the expression level of BolGRF6 revealed significantly higher expression in the early flowering genotypes. Taken together, our results provide support for BolGRF6 as a possible candidate gene for early flowering in the broccoli line 93219. The identified candidate genomic regions and genes may be useful for molecular breeding to improve broccoli and cabbage flowering times.

     

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714–718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.     

Quantitative trait locus analysis of multiple agronomic traits in the model legume Lotus japonicus.

The first quantitative trait locus (QTL) analysis of multiple agronomic traits in the model legume Lotus japonicus was performed with a population of recombinant inbred lines derived from Miyakojima MG-20 x Gifu B-129. Thirteen agronomic traits were evaluated in 2004 and 2005: traits of vegetative parts (plant height, stem thickness, leaf length, leaf width, plant regrowth, plant shape, and stem color), flowering traits (flowering time and degree), and pod and seed traits (pod length, pod width, seeds per pod, and seed mass). A total of 40 QTLs were detected that explained 5%-69% of total variation. The QTL that explained the most variation was that for stem color, which was detected in the same region of chromosome 2 in both years. Some QTLs were colocated, especially those for pod and seed traits. Seed mass QTLs were located at 5 locations that mapped to the corresponding genomic positions of equivalent QTLs in soybean, pea, chickpea, and mung bean. This study provides fundamental information for breeding of agronomically important legume crops.     

Simultaneous improvement and genetic dissection of grain yield and its related traits in a backbone parent of hybrid rice (Oryza sativa L.) using selective introgression

Three populations with a total of 125 BC2F3:4 introgression lines (ILs) selected for high yields from three BC2F2 populations were used for genetic dissection of rice yield and its related traits. The progeny testing in replicated phenotyping across two environments and genotyping with 140 polymorphic simple sequence repeat markers allowed the identification of 21 promising ILs that had significantly higher yields than the recurrent parent Shuhui527 (SH527). A total of 94 quantitative trait loci (QTL) were identified using the selective introgression method based on Chi-squared (χ ²) and multi-locus probability tests and the RSTEP-LRT method based on stepwise regression. These QTL were mostly mapped to 12 clusters on seven rice chromosomes. Several important properties of the QTL affecting grain yield (GY) and its related traits were revealed. The first one was the presence of strong and frequent non-random associations between or among QTL that affect low-heritability traits (GY and spikelet number per panicle, SN) in the ILs with high trait values. Second, beneficial alleles at 88.9 % GY and 75 % SN QTL for increased productivity were from the donors, suggesting that direct phenotypic selection for high yield in our introgression breeding program was a powerful way to transfer beneficial alleles at many loci from the donors into SH527. Third, most QTL were in clusters with large effects on multiple traits, which should be the focal points in further investigations and marker-assisted selection in rice. The majority of the QTL identified were expressed only in one of the environments, suggesting that differential expression of QTL in different environments is the primary genetic basis of genotype × environment interaction. Finally, a large variation in both the direction and magnitude of QTL effects was detected for different donor alleles at seven QTL in the same genetic background and environments. This finding suggests the possible presence of functional diversity among the donor alleles at these loci. The promising ILs and QTL identified provide valuable materials and genetic information for further improving the yield potential of SH527, which is a backbone restorer of hybrid rice in China.     

Quantitative trait locus analysis of a recombinant inbred line population derived from a Lycopersicon esculentum x Lycopersicon cheesmanii cross

Quantitative trait loci influencing fruit traits were identified by restriction fragment length polymorphism (RFLP) analysis in a population of recombinant inbred lines (RIL) derived from a cross of the cultivated tomato, Lycopersicon esculentum with a related wild species Lycopersicon cheesmanii. One hundred thirty-two polymorphic RFLP loci spaced throughout the tomato genome were scored for 97 F₈ RIL families. Fruit weight and soluble solids were measured in replicated trials during 1991 and 1992. Seed weight was measured in 1992. Significant (P<0.01 level) quantitative trait locus (QTL) associations of marker loci were identified for each trait. A total of 73 significant marker locus-trait associations were detected for the three traits measured. Fifty-three of these associations were for fruit weight and soluble solids, many of which involved marker loci signficantly associated with both traits. QTL with large effects on all three traits were detected on chromosome 6. Greater homozygosity at many loci in the RIL population as compared to F₂ populations and greater genomic coverage resulted in increased precision in the estimation of QTL effects, and large proportions of the total phenotypic variance were explained by marker class variation at significant marker loci for many traits. The RIL population was effective in detecting and discriminating among QTL for these traits previously identified in other investigations despite skewed segregation ratios at many marker loci. Large additive effects were measured at significant marker loci. Lower fruit weight, higher soluble solids, and lower seed weight were generally associated with RFLP alleles from theL. cheesmanii parent.     

Mapping of days to flower and seed yield in spring oilseed <Emphasis Type="Italic">Brassica napus</Emphasis> carrying genome content introgressed from <Emphasis Type="Italic">Brassica oleracea</Emphasis>

Earliness of flowering and maturity and high seed yield are important objectives of breeding spring Brassica napus canola. Previously, we have introgressed earliness of flowering from Brassica oleracea into spring B. napus canola through interspecific crossing between these two species. In this paper, we report quantitative trait locus (QTL) mapping of days to flower and seed yield by use of publicly available markers and markers designed based on flowering time genes and a doubled haploid population, derived from crossing of the spring canola parent and an early flowering line developed from a B. napus × B. oleracea cross, tested in nine field trials for over 5 years. Five genomic regions associated with days to flower were identified on C1, C2, C3, and C6 of which the single QTL of C1 was detected in all trials; in all cases, the allele introgressed from B. oleracea reduced the number of days to flower. BLASTn search in the Brassica genomes located the physical position of the QTL markers and identified putative flowering time genes in these regions. In the case of seed yield, ten QTL from eight linkage groups were detected; however, none could be consistently detected in all trials. The QTL region of C1 associated with days to flower did not show significant association with seed yield in more than 80% of the field trials; however, in a single trial, the allele introgressed from B. oleracea exerted a negative effect on seed yield. Thus, the genomic regions and molecular markers identified in this research could potentially be used in breeding for the development of early flowering B. napus canola cultivars without affecting seed yield in a majority of the environments.     

Identification and validation of genomic regions that affect shoot fly resistance in sorghum [<Emphasis Type="Italic">Sorghum bicolor</Emphasis> (L.) Moench]

Shoot fly is one of the most important pests affecting the sorghum production. The identification of quantitative trait loci (QTL) affecting shoot fly resistance enables to understand the underlying genetic mechanisms and genetic basis of complex interactions among the component traits. The aim of the present study was to detect QTL for shoot fly resistance and the associated traits using a population of 210 RILs of the cross 27B (susceptible) × IS2122 (resistant). RIL population was phenotyped in eight environments for shoot fly resistance (deadheart percentage), and in three environments for the component traits, such as glossiness, seedling vigor and trichome density. Linkage map was constructed with 149 marker loci comprising 127 genomic-microsatellite, 21 genic-microsatellite and one morphological marker. QTL analysis was performed by using MQM approach. 25 QTL (five each for leaf glossiness and seedling vigor, 10 for deadhearts, two for adaxial trichome density and three for abaxial trichome density) were detected in individual and across environments. The LOD and R ² (%) values of QTL ranged from 2.44 to 24.1 and 4.3 to 44.1%, respectively. For most of the QTLs, the resistant parent, IS2122 contributed alleles for resistance; while at two QTL regions, the susceptible parent 27B also contributed for resistance traits. Three genomic regions affected multiple traits, suggesting the phenomenon of pleiotrophy or tight linkage. Stable QTL were identified for the traits across different environments, and genetic backgrounds by comparing the QTL in the study with previously reported QTL in sorghum. For majority of the QTLs, possible candidate genes were identified. The QTLs identified will enable marker assisted breeding for shoot fly resistance in sorghum.     

QTL analysis and QTL-based prediction of flowering phenology in recombinant inbred lines of barley

Combining ecophysiological modelling and genetic mapping has increasingly received attention from researchers who wish to predict complex plant or crop traits under diverse environmental conditions. The potential for using this combined approach to predict flowering time of individual genotypes in a recombinant inbred line (RIL) population of spring barley (Hordeum vulgare L.) was examined. An ecophysiological phenology model predicts preflowering duration as affected by temperature and photoperiod, based on the following four input traits: f(o) (the minimum number of days to flowering at the optimum temperature and photoperiod), theta1 and theta2 (the development stages for the start and the end of the photoperiod-sensitive phase, respectively), and delta (the photoperiod sensitivity). The model-input trait values were obtained from a photoperiod-controlled greenhouse experiment. Assuming additivity of QTL effects, a multiple QTL model was fitted for the model-input traits using composite interval mapping. Four to seven QTL were identified for each trait. Each trait had at least one QTL specific to that trait alone. Other QTL were shared by two or all traits. Values of the model-input traits predicted for the RILs from the QTL model were fed back into the ecophysiological model. This QTL-based ecophysiological model was subsequently used to predict preflowering duration (d) for eight field trial environments. The model accounted for 72% of the observed variation among 94 RILs and 94% of the variation among the two parents across the eight environments, when observations in different environments were pooled. However, due to the low percentage (34-41%) of phenotypic variation accounted for by the identified QTL for three model-input traits (theta1, theta2 and delta), the QTL-based model accounted for somewhat less variation among the RILs than the model using original phenotypic input trait values. Nevertheless, days to flowering as predicted from the QTL-based ecophysiological model were highly correlated with days to flowering as predicted from QTL-models per environment for days to flowering per se. The ecophysiological phenology model was thus capable of extrapolating (QTL) information from one environment to another.     

Molecular Mapping and Characterization of Genes Governing Time to Flowering, Seed Weight, and Plant Height in an Intraspecific Genetic Linkage Map of Chickpea (Cicer arietinum)

Drought is the major constraint to chickpea productivity worldwide. Utilizing early flowering genotypes and larger seed size have been suggested as strategies for breeding in drought zones. Therefore, this study aimed to identify potential markers linked to days-to-flowering, 100-seed weight, and plant height in a chickpea intraspecific F_2:3 population derived from the cross ILC3279 × ICCV2. A closely linked marker (TA117) on linkage group LG3 was identified for the days-to-flowering trait, explaining 33% of the variation. In relation to plant height, a quantitative trait loci (QTL) was located in LG3, close to the Ts5 marker, that explained 29% of phenotypic variation. A QTL for 100-seed weight located in LG4, close to TA176, explained 51% of variation. The identification of a locus linked both to high 100-seed weight and days-to-flowering may account for the correlation observed between these traits in this and other breeding attempts.     

Do large effect QTL fractionate? A case study at the maize domestication QTL teosinte branched1

Quantitative trait loci (QTL) mapping is a valuable tool for studying the genetic architecture of trait variation. Despite the large number of QTL studies reported in the literature, the identified QTL are rarely mapped to the underlying genes and it is usually unclear whether a QTL corresponds to one or multiple linked genes. Similarly, when QTL for several traits colocalize, it is usually unclear whether this is due to the pleiotropic action of a single gene or multiple linked genes, each affecting one trait. The domestication gene teosinte branched1 (tb1) was previously identified as a major domestication QTL with large effects on the differences in plant and ear architecture between maize and teosinte. Here we present the results of two experiments that were performed to determine whether the single gene tb1 explains all trait variation for its genomic region or whether the domestication QTL at tb1 fractionates into multiple linked QTL. For traits measuring plant architecture, we detected only one QTL per trait and these QTL all mapped to tb1. These results indicate that tb1 is the sole gene for plant architecture traits that segregates in our QTL mapping populations. For most traits related to ear morphology, we detected multiple QTL per trait in the tb1 genomic region, including a large effect QTL at tb1 itself plus one or two additional linked QTL. tb1 is epistatic to two of these additional QTL for ear traits. Overall, these results provide examples for both a major QTL that maps to a single gene, as well as a case in which a QTL fractionates into multiple linked QTL.     

Multi-environment QTL analyses for drought-related traits in a recombinant inbred population of chickpea (Cicer arientinum L.)

A recombinant inbred line (RIL) population, comprising 181 lines derived from ILC588 × ILC3279, was evaluated in 10 environments across three locations with different moisture gradients. A drought resistance score (DRS) and three phenology traits—plant height (PLHT), days to flowering (DFLR), and days to maturity (MAT)—were recorded along with seven yield-related traits—grain yield (GY), biological yield (BY), harvest index (HI), the number of pods/3 plants (Pod), percentage of empty pods (%Epod), 100 seed weight (100 sw), and seed number/3 plants (SN). Two RILs (152, 162) showed the best GYs and DRSs under stressed and non-stressed environments. The quantitative trait loci (QTLs) analyses detected 93 significant QTLs (LOD ≥ 2.0) across the genome × environment interactions. The highest phenotypic variation (>24 %) was explained by the QTL_DFLR in Terbol-11. Four common possible pleiotropic QTLs on LG3 and LG4 were identified as associated with DFLR, DRS, GY, MAT, HI, SN, and Pod. No significant epistatic interactions were found between these QTLs and the other markers. However, the QTL for DRS was detected as a conserved QTL in three late planting environments. The markers H6C-07 (on LG3) and H5G01 (on LG4) were associated with QTLs for many traits in all environments studied except two. The allele ‘A’ of marker H6C07 (from the tolerant parent ILC588) explained 80 % of the yield increase under late planting and 29.8 % of that under dry environments. Concentrating on LG3 and LG4 in molecular breeding programs for drought could speed up improvement for these traits.     

Quantitative trait loci (QTL) mapping of blush skin and flowering time in a European pear (<Emphasis Type="Italic">Pyrus communis</Emphasis>) progeny of ‘Flamingo’ × ‘Abate Fetel’

Blush skin and flowering time are agronomic traits of interest to the Agricultural Research Council (ARC) Infruitec-Nietvoorbij pear breeding programme. The genetic control of these traits was investigated in the pear progeny derived from ‘Flamingo’ (blush cultivar) × ‘Abate Fetel’ (slightly blush) made up of 121 seedlings. Blush skin was scored phenotypically over three seasons and flowering time was scored over two seasons. A total of 160 loci from 137 simple sequence repeat (SSR) markers were scored in the progeny and used to construct parental genetic linkage maps. Quantitative trait loci (QTL) analysis revealed two QTLs for blush skin, a major QTL on linkage group (LG) 5 in ‘Flamingo’, and a major QTL on LG9 in ‘Abate Fetel’. Two SSR markers, NB101a and SAmsCO865954, were closely linked with the major QTL on LG5 in ‘Flamingo’, with alleles 139 bp and 462 bp in coupling, respectively. These markers were present in approximately 90% of the seedlings scored as good blush (class 4) based on the average data set. These two markers were used to genotype other pear accessions to validate the QTL on LG5 with the view of marker-assisted selection. Two candidate genes, MYB86 and UDP-glucosyl transferase, were associated with the QTL on LG5 and MYB21 and MYB39 were associated with the QTL on LG9. QTL analysis for flowering time revealed a major QTL located on LG9 in both parents. Marker GD142 with allele 161 bp from ‘Flamingo’ was present in approximately 88% of the seedlings that flowered earlier than either parent, based on the average data set. The QTLs and linked markers will facilitate marker-assisted selection for the improvement of these complex traits.     

Identification of Quantitative Trait Loci (QTLs) for Fruit Quality Traits in Apple

A segregating mapping population of “Co-op 17” × “Co-op 16” was used to identify quantitative trait loci (QTLs) associated with various fruit quality traits in apple. Phenotypic data were collected over 2 years for fruit circumference (in centimeter), diameter at midpoint (in centimeter), length (in centimeter), weight (in gram), total soluble solids (in degree Brix), and total titratable acids (in percent) for the segregating population. The phenotypic data along with a previously constructed genetic map, based on simple sequence repeat markers derived from expressed sequence tag and bacterial artificial chromosome end sequence databases, were used in marker–trait association analysis. Interval mapping identified two QTLs linked to fruit size components on linkage groups 03 and 05 with limit of detection scores of 3.27–4.06 and 3.29–4.02 along with phenotypic variation accounting for 15.4–46.4 and 18.3–21.9 %, respectively.