Genetic Analysis of Heterosis for Yield Influencing Traits in Brassica juncea Using a Doubled Haploid Population and Its Backcross Progenies

The exploitation of heterosis through hybrid breeding is one of the major breeding objectives for productivity increase in crop plants. This research analyzes the genetic basis of heterosis in Brassica juncea by using a doubled haploid (DH) mapping population derived from F1 between two heterotic inbred parents, one belonging to the Indian and the other belonging to the east European gene pool, and their two corresponding sets of backcross hybrids. An Illumina Infinium Brassica 90K SNP array-based genetic map was used to identify yield influencing quantitative trait loci (QTL) related to plant architecture, flowering, and silique- and seed-related traits using five different data sets from multiple trials, allowing the estimation of additive and dominance effects, as well as digenic epistatic interactions. In total, 695 additive QTL were detected for the 14 traits in the three trials using five data sets, with overdominance observed to be the predominant type of effect in determining the expression of heterotic QTL. The results indicated that the design in the present study was efficient for identifying common QTL across multiple trials and populations, which constitute a valuable resource for marker-assisted selection and further research. In addition, a total of 637 epistatic loci were identified, and it was concluded that epistasis among loci without detectable main effects plays an important role in controlling heterosis in yield of B. juncea.

QTL for Main Stem Node Number and Its Response to Plant Densities in 144 Soybean FW-RILs

Although the main stem node number of soybean [Glycine max (L.) Merr. ] is an important yield-related trait, there have been limited studies on the effect of plant density on the identification of quantitative trait loci (QTL) for main stem node number (MSNN). To address this issue, here, 144 four-way recombinant inbred lines (FW-RILs) derived from Kenfeng 14, Kenfeng 15, Heinong 48, and Kenfeng 19 were used to identify QTL for MSNN with densities of 2.2 × 105 (D1) and 3 × 105 (D2) plants/ha in five environments by linkage and association studies. As a result, the linkage and association studies identified 40 and 28 QTL in D1 and D2, respectively, indicating the difference in QTL in various densities. Among these QTL, five were common in the two densities; 36 were singly identified for response to density; 12 were repeatedly identified by both response to density and phenotype of two densities. Thirty-one were repeatedly detected across various methods, densities, and environments in the linkage and association studies. Among the 24 common QTL in the linkage and association studies, 15 explained a phenotypic variation of more than 10%. Finally, Glyma.06G094400, Glyma.06G147600, Glyma.19G160800.1, and Glyma.19G161100 were predicted to be associated with MSNN. These findings will help to elucidate the genetic basis of MSNN and improve molecular assistant selection in high-yield soybean breeding.

Stable QTL for malate levels in ripe fruit and their transferability across Vitis species

Malate is a major contributor to the sourness of grape berries (Vitis spp.) and their products, such as wine. Excessive malate at maturity, commonly observed in wild Vitis grapes, is detrimental to grape and wine quality and complicates the introgression of valuable disease resistance and cold hardy genes through breeding. This study investigated an interspecific Vitis family that exhibited strong and stable variation in malate at ripeness for five years and tested the separate contribution of accumulation, degradation, and dilution to malate concentration in ripe fruit in the last year of study. Genotyping was performed using transferable rhAmpSeq haplotype markers, based on the Vitis collinear core genome. Three significant QTL for ripe fruit malate on chromosomes 1, 7, and 17, accounted for over two-fold and 6.9 g/L differences in ripe fruit malate, and explained 40.6% of the phenotypic variation. QTL on chromosomes 7 and 17 were stable in all and in three out of five years, respectively. Variation in pre-veraison malate was the major contributor to variation in ripe fruit malate (39%) and their associated QTL overlapped on chromosome 7, indicating a common genetic basis. However, use of transferable markers on a closely related Vitis family did not yield a common QTL across families. This suggests that diverse physiological mechanisms regulate the levels of this key metabolite in the Vitis genus, a conclusion supported by a review of over a dozen publications from the past decade, showing malate-associated genetic loci on all 19 chromosomes.

Genetic Dissection of Grain Yield and Agronomic Traits in Maize under Optimum and Low-Nitrogen Stressed Environments

Understanding the genetic basis of maize grain yield and other traits under low-nitrogen (N) stressed environments could improve selection efficiency. In this study, five doubled haploid (DH) populations were evaluated under optimum and N-stressed conditions, during the main rainy season and off-season in Kenya and Rwanda, from 2014 to 2015. Identifying the genomic regions associated with grain yield (GY), anthesis date (AD), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear position (EPO), and leaf senescence (SEN) under optimum and N-stressed environments could facilitate the use of marker-assisted selection to develop N-use-efficient maize varieties. DH lines were genotyped with genotyping by sequencing. A total of 13, 43, 13, 25, 30, 21, and 10 QTL were identified for GY, AD ASI, PH, EH, EPO, and SEN, respectively. For GY, PH, EH, and SEN, the highest number of QTL was found under low-N environments. No common QTL between optimum and low-N stressed conditions were identified for GY and ASI. For secondary traits, there were some common QTL for optimum and low-N conditions. Most QTL conferring tolerance to N stress was on a different chromosome position under optimum conditions.

Major seed size QTL on chromosome A05 of peanut (Arachis hypogaea) is conserved in the US mini core germplasm collection

Pod and seed size are important characteristics for the peanut industry and have been under strong selection pressure since peanut domestication. In order to dissect the genetic control of peanut pod and seed size, a recombinant inbred mapping population from a cross of Florida-07 by GP-NC WS 16 was used to determine the genomic regions associated with traits including 100 pod weight, 100 seed weight, pod weight of double-seeded pods, seed weight of double-seeded pods, and area of double-seeded pods. Nine QTL on linkage groups (LGs) A05, A06, A09, B10, B04, A03, B05, and B08 were associated with pod and seed size. A majority of the QTL have small effects except the locus on LG A05 (93 to 102 Mbp) which explained up to 66% phenotypic variation for all measured pod and seed traits. A comparison of QTL previously reported for yield component traits showed a common QTL on LG A05 was detected in two genetic populations whose parentage is distinct from those used in this study. The markers tightly linked to this major QTL were informative in distinguishing large versus small-seeded germplasm lines in the mini core collection originating from thirty-one countries, suggesting selection for this seed size QTL in large-seeded ecotypes. However, the large seed size allele appeared to co-segregate with a late leaf spot disease susceptibility allele inherited from the Florida-07 parent. Therefore, peanut breeders need to weigh the pros and cons before integrating the large seed size QTL from Florida-07 in their breeding program.

Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models

Environmental association analyses (EAA) seek to identify genetic variants associated with local adaptation by regressing local environmental conditions at collection sites on genome-wide polymorphisms. The rationale is that environmental conditions impose selective pressure on trait(s), and these traits are regulated in part by variation at a genomic level. Here, we present an alternative multivariate genomic approach that can be utilized when both phenotypic and environmental data are available for the population. This framework utilizes Bayesian networks (BN) to elucidate interdependancies between local environmental conditions and empirical phenotypes, and jointly estimates the direct and indirect genetic covariances between empirical phenotypes and environmental conditions using a mixed-effects structural equation model (SEM). Direct genomic covariance between empirical phenotypes and environmental conditions may provide insight into whether QTL that affect adaptation to an environmental gradient also affects the observed phenotype. To demonstrate the utility of this approach, we leveraged two existing datasets consisting of 55 climate variables for 1,130 Arabidopsis accessions and empirical phenotypes for fitness and phenology collected on 515 accessions in two common garden locations in Europe. BN showed that plasticity for fitness and phenology was highly dependant on local environmental conditions. Moreover, genomic SEM revealed relatively high positive genomic correlation between plasticity in fitness and environmental variables that describe the favorability of the local environment for plant growth, indicating the presence of common QTL or independent QTL that are tightly linked. We believe the frameworks presented in this manuscript can provide new insights into the genetic basis of local adaptation.

Identification of QTL for leaf angle at canopy-wide levels in maize

Leaf angle (LA) is one of the most important canopy architecture related traits of maize (Zea mays L.). Currently, there is an urgent need to elucidate the genetic mechanism of LA at canopy-wide levels for optimizing dense-planting canopy architecture. In present study, one RIL population derived from two parent lines which show distinct plant architecture was used to perform QTL mapping for LA at eight leaves below the tassel under three environments. Dozens of QTL for LA at eight leaves were identified, which were mapped on all maize chromosomes except for the tenth chromosome. Among them, there were nine common QTL as they were identified for LA more than 1 leaves or in two or three environments. And individual QTL could explain 1.29% - 20.14% of the phenotypic variation and affect LA of 1-8 leaves, including qLA5.1 affected LA of all eight leaves, qLA3.1 affected LA of the upper leaves (1stLA to 4thLA), and qLA9.1 could affect LA of the lower leaves (5thLA to 8thLA). Furthermore, the results indicated that the genetic architecture of LA at eight leaves was different. Specifically, 8thLA was mainly affected by major and minor QTL; 1stLA, 4thLA and 5thLA were affected by epistatic interactions beside major and minor QTL; while the other four LAs were simultaneously affected by major QTL, minor QTL, epistatic interactions and environments. These results provide a comprehensive understanding of genetic basis of LA at canopy-wide levels, which will be beneficial to design ideal plant architecture under dense planting in maize.Author contribution statementJ. L. and D. T. designed and supervised the study, D. T., Z.C., J.N., Q.J., P.L., L.W., J.Z., C.L. performed the phenotypic data collection. D. T. analyzed the data and drafted the manuscript, D. T. and Z.C. revised and finalized the manuscript. All the authors read and approved the manuscript.Key messageDozens of QTL for leaf angle of eight consecutive leaves were identified in the RIL population across three environments, providing the information that optimization of canopy architecture at various canopy levels.

Quantitative trait loci mapping for plant architecture traits across two upland cotton populations using SSR markers

SUMMARYCotton plant architecture is an important agronomic trait affecting yield and quality. In the present study, two F2:3 upland cotton (Gossypium hirsutum L.) populations were developed from Baimian2/TM-1 and Baimian2/CIR12 to map quantitative trait loci (QTL) for cotton plant architecture traits using simple sequence repeat (SSR) markers. A total of 73 QTL (37 significant and 36 suggestive) affecting plant architecture traits were detected in both populations. Four common QTL, qTFN-17 for total fruit nodes, qFBN-17 for fruit branch nodes, qFBL-17 for fruit branch length and qTFB-17a/qTFB-17b (qTFB-17) for total fruit branches, were found across the two populations. These common QTL should have high reliability and could be used for marker-assisted selection (MAS) to facilitate cotton plant architecture. The two common QTL, qTFN-17 and qFBL-17, were especially significant in both populations, and moreover, they explained >0·100 of the phenotypic variation in at least one population. These two QTL should be considered preferentially for MAS. The synergistic alleles and the negative alleles could be utilized in cotton plant architecture breeding programmes according to specific breeding objectives.