Quantitative Trait Loci For Agronomic Traits Research Articles

Approaches in Characterizing Genetic Structure and Mapping in a Rice Multiparental Population.

Multi-parent Advanced Generation Intercross (MAGIC) populations are fast becoming mainstream tools for research and breeding, along with the technology and tools for analysis. This paper demonstrates the analysis of a rice MAGIC population from data filtering to imputation and processing of genetic data to characterizing genomic structure, and finally quantitative trait loci (QTL) mapping. In this study, 1316 S6:8 indica MAGIC (MI) lines and the eight founders were sequenced using Genotyping by Sequencing (GBS). As the GBS approach often includes missing data, the first step was to impute the missing SNPs. The observable number of recombinations in the population was then explored. Based on this case study, a general outline of procedures for a MAGIC analysis workflow is provided, as well as for QTL mapping of agronomic traits and biotic and abiotic stress, using the results from both association and interval mapping approaches. QTL for agronomic traits (yield, flowering time, and plant height), physical (grain length and grain width) and cooking properties (amylose content) of the rice grain, abiotic stress (submergence tolerance), and biotic stress (brown spot disease) were mapped. Through presenting this extensive analysis in the MI population in rice, we highlight important considerations when choosing analytical approaches. The methods and results reported in this paper will provide a guide to future genetic analysis methods applied to multi-parent populations.

Mapping the A Genome for QTL Conditioning Resistance to Fusarium Head Blight in a Wheat Population with Triticum timopheevii Background.

Development and use of resistant wheat cultivars is the most practical and economical approach for the control of Fusarium head blight (FHB). In the present study, a population of recombinant inbred lines derived from the cross between 'AC Brio' (a Canadian bread wheat cultivar moderately susceptible to FHB) and 'TC 67' (an FHB-resistant cultivar derived from Triticum timopheevii) was used to map quantitative trait loci (QTL) for FHB resistance using microsatellite molecular markers. Multiple interval mapping detected several QTL for FHB resistance on the chromosomes 5AL and 6A. The QTL detected in the marker interval of cfd6.1-barc48 on chromosome 5AL explained 10.9, 5.2, and 7.8% of phenotypic variation for disease incidence (type I resistance), disease severity (a combination of type I and type II resistance), and Fusarium-damaged kernels (FDK) (type IV resistance) under field conditions, respectively. The second QTL mapped to 5AL, in the marker interval of cfd39-cfa2185, explained 19.4 and 20.6% of phenotypic variation for FDK under field conditions and disease severity in the greenhouse (type II resistance), respectively. The QTL located on chromosome 6A conferred resistance to disease incidence and severity under field conditions and to disease severity in the greenhouse, explaining 6.8 to 11.8% of phenotypic variation for these traits. Several QTL for agronomic traits were also mapped in this study, including one and two QTL to the chromosomes 2A and 5AL, respectively, all for plant height, and two QTL to chromosome 6A for plant height and flowering date, respectively. The 5AL QTL for FHB resistance mapped in the marker interval of cfd39-cfa2185 in the present study is a novel QTL that originated from T. timopheevii and is reported here for the first time. Further validation of this QTL is required for wheat breeding programs to enhance resistance levels to FHB.

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.

Comparison and analysis of main effects, epistatic effects, and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice

Two genetic linkage maps based on doubled haploid (DH) and recombinant inbred lines (RILs) populations, derived from the same indica-japonica cross ‘Samgang × Nagdong’, were constructed to analyze the quantitative trait loci (QTLs) affecting agronomic traits in rice. The segregations of agronomic traits in RILs population showed larger variations than those in DH population. A total of 10 and 12 QTLs were identified on six chromosomes using DH population and seven chromosomes using RILs population, respectively. Three stable QTLs including pl9.1, ph1.1, and gwp11.1 were detected through different years. The percentages of phenotypic variation explained by individual QTLs ranged from 8 to 18% in the DH population and 9 to 33% in the RILs population. Twenty-three epistatic QTLs were identified in the DH population, while 21 epistatic QTLs were detected in the RILs population. Epistatic interactions played an important role in controlling the agronomic traits genetically. Four significant main-effect QTLs were involved in the digenic interactions. Significant interactions between QTLs and environments (QE) were identified in two populations. The QTLs affecting grain weight per panicle (GWP) were more sensitive to the environmental changes. The comparison and QTLs analysis between two populations across different years should help rice breeders to comprehend the genetic mechanisms of quantitative traits and improve breeding programs in marker-assisted selection (MAS).

QTL mapping for resistance against the European corn borer (Ostrinia nubilalis H.) in early maturing European dent germplasm

The European corn borer (ECB, Ostrinia nubilalis Hubner) is a major pest of maize in Central Europe. We mapped and characterized quantitative trait loci (QTLs) involved in resistance of maize against ECB damage, compared them with QTLs for agronomic traits, and evaluated the usefulness of marker-assisted selection (MAS) for improving ECB resistance in early maturing European maize germplasm. A total 226 F3 families from the cross D06 (resistant) × D408 (susceptible), together with 93 RFLP and two SSR markers were used for the QTL analyses. For each F3 family we measured the length of tunnels produced by larval stalk mining (TL), stalk damage ratings (SDR), and relative grain yield (RGY) in field experiments, with two replications in two environments in 1 year. The agronomic traits comprised grain yield under insecticide protection (GYP) and manual ECB larval infestation (GYI), the date of anthesis (ANT), and the in vitro digestibility of organic matter (IVDOM) of stover. Estimates of genotypic variance (σ2 g) were highly significant for all traits. Six QTLs for TL and five QTLs for SDR were detected, explaining about 50.0% of σ2 g. Most QTLs showed additive gene action for TL and dominance for SDR. No QTL was found for RGY. The number of QTLs detected for the agronomic traits ranged from two for GYI to 12 for ANT, explaining 12.5 to 57.3% of σ2 g, respectively. Only a single QTL was in common between the two resistance traits, as expected from the moderate trait correlation and the moderate proportions of σ2 g explained. Based on these results, MAS for improving ECB resistance can be competitive when cost-effective PCR-based marker systems are applied. However, it remains to be established whether the putative QTL regions for ECB resistance detected in the population D06 × D408 are consistent across other early maturing European maize germplasms.