Testcross Hybrids Research Articles

Combining ability of new maize lines according to grain productivity and its components

The most important indicator of the initial material’s value in breeding for heterosis is its combining ability. The purpose of the current work was to study the combining ability of new self-pollinated maize lines according to grain productivity and its main components. The study was conducted at the FSBSI “ARC “Donskoy”, located in the southern part of the Rostov region with unstable moisture, in 2021–2023. The objects of the studies were 9 new constant self-pollinated maize lines and the line ‘RD 329’, used as a standard. The objects of the studies were also 3 testers, simple maize hybrids ‘Kros 1’, ‘Kros 2’, ‘Kros 3’ and 30 new test-cross hybrids of maize. There have been identified new highly heterotic maize hybrids of practical interest ‘Kros1×Sl 303’ (5.05 t/ha), ‘Kros1×Sl 308’ (4.75 t/ha), ‘Kros1×Sl 309’ (4.84 t/ha). There have been identified new self-pollinated lines ‘Sl 303’ (gi = 0.56), ‘Sl 308’ (gi = 0.43) and ‘Sl 309’ (gi = 0.47) with high combining ability according to the trait ‘grain productivity’ and are recommended for use in programs of hybridization. Self-pollinated lines with high GCA according to grain productivity, as a rule, had higher GCA for such productivity traits as ‘a number of grains per ear’, ‘one ear weight’ and ‘a number of ears per plant’. This contributed to the formation of a large number of grains per ear in test-cross maize hybrids (623–655 pcs.), the formation of ears with a large weight (131–147 g), and the absence of sterile plants in dry conditions. The combining ability according to the trait ‘1000-grain weight’ had no effect on productivity of test-cross hybrids in dry conditions. The use of self-pollinated lines with high values of productivity traits and transmitting high values to hybrids made it possible to develop new productive hybrid combinations.

Agronomic Performance and Resistance to Maize Lethal Necrosis in Maize Hybrids Derived from Doubled Haploid Lines

Maize (Zea mays L.) is one of the most widely cultivated grain crops globally. In sub-Saharan Africa (SSA), it plays an important role in ensuring both food and income security for smallholder farmers. This study was conducted to (i) assess the performances of testcross hybrids constituted from maize lethal necrosis (MLN) tolerant doubled haploid (DH) lines under various management conditions; (ii) estimate the combining ability effects and determine the nature of gene action in the DH lines; and (iii) identify DH lines and testcross hybrids for resistance to MLN, high grain yield, and other important traits. Eleven DH lines were crossed with 11 single-cross testers using the line-by-tester mating design, and 115 successful testcross hybrids were generated. These hybrids, along with five commercial check hybrids, were evaluated across four optimum management conditions, two MLN artificial inoculations, and one managed drought environment in Kenya. Under each management condition, the effects of genotypes, environments, and genotype-by-environment interactions were significant for grain yield (GY) and most other traits. Hybrids T1/L3, T10/L3, and T11/L3 exhibited higher grain yields under at least two management conditions. A combining ability analysis revealed that additive gene effects were more important than non-additive effects for GY and most other traits, except for leaf senescence (SEN) and MLN disease severity score. DH line L3 exhibited a desirable general combining ability (GCA) effect for GY, while L5 was the best general combiner for anthesis date (AD) and plant height (PH) across all management conditions. DH lines L2, L6, and L7 showed negative GCA effects for MLN disease severity. Single-cross testers T11 and T10 were good general combiners for GY under all management conditions. Hybrids T2/L11, T9/L10, and T2/L10 demonstrated high specific combining ability (SCA) effects for GY under all conditions. This study identified DH lines and testers with favorable GCA effects for grain yield, MLN resistance, and other agronomic traits that can be used in breeding programs to develop high-yielding and MLN-resistant maize varieties. Better-performing testcross hybrids identified in the current study could be verified through on-farm testing and released for commercial production to replace MLN-susceptible, low-yield hybrids grown in the target ecologies.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality. Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait. Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection. We performed a genome-wide association study (GWAS) and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpeño and non-Tuxpeño heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms (SNPs). Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses. More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines. Incorporating genotype-by-environment (G × E) interaction increased genomic prediction accuracy.

Transpiration efficiency variations in the pearl millet reference collection PMiGAP.

Transpiration efficiency (TE), the biomass produced per unit of water transpired, is a key trait for crop performance under limited water. As water becomes scarce, increasing TE would contribute to increase crop drought tolerance. This study is a first step to explore pearl millet genotypic variability for TE on a large and representative diversity panel. We analyzed TE on 537 pearl millet genotypes, including inbred lines, test-cross hybrids, and hybrids bred for different agroecological zones. Three lysimeter trials were conducted in 2012, 2013 and 2015, to assess TE both under well-watered and terminal-water stress conditions. We recorded grain yield to assess its relationship with TE. Up to two-fold variation for TE was observed over the accessions used. Mean TE varied between inbred and testcross hybrids, across years and was slightly higher under water stress. TE also differed among hybrids developed for three agroecological zones, being higher in hybrids bred for the wetter zone, underlining the importance of selecting germplasm according to the target area. Environmental conditions triggered large Genotype x Environment (GxE) interactions, although TE showed some high heritability. Transpiration efficiency was the second contributor to grain yield after harvest index, highlighting the importance of integrating it into pearl millet breeding programs. Future research on TE in pearl millet should focus (i) on investigating the causes of its plasticity i.e. the GxE interaction (ii) on studying its genetic basis and its association with other important physiological traits.

Selection of maize lines for estimating combining ability in topcrossings

Hybrid maize breeding is carried out based on interline hybridization using heterosis in the first generation. Hybridization efficiency can be increased using parental forms with high combining ability in crosses. The purpose of the study was to identify new self-pollinated lines of maize with high general and specific combining ability, to identify the impact on the accuracy of the estimation of related lines included in the study. The current study was carried out at the FSBSI Agricultural Research Center «Donskoy» in 2021 and 2022. The objects of the study were two testers PD 329, KV 399 and 10 self-pollinated lines, including two lines (KV 410 and DS 22/325) related to tester PD 329, twenty topcross maize hybrids. The method for estimating combining ability was complete topcrosses. Estimation of new self-pollinated lines by the topcross method made it possible to identify the lines ‘KV 498’ and ‘DS 498/203-3’ with a consistently high overall combining ability according to the trait ‘grain productivity’. The specific combining ability was characterized by variability depending on the year of study. Only the line ‘KV 401’ had a high SCR in all years of study. The inclusion of lines related to the testers in the set had no effect or only slightly affected the estimates of the TCS of the remaining unrelated lines. The effect on the SCS was not significant, however, the estimation of the SCS would be more accurate if related lines were excluded from the set. There have been identified such new testcross hybrids with large grain productivity as ‘PD 329 × KV 498’ (3.92 t/ha), ‘KV 399 × KV 498’ (4.71 t/ha),‘KV 399 × KV 401’ (4.80 t/ha), ‘KV 399 × DS 498/203-3’ (4.60 t/ha). They were obtained with the participation of new lines characterized by high general or specific combinative ability, which can be recommended to be included in crossbreeding programs to develop highly heterotic maize hybrids.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influences of epistasis and gene pleiotropy, ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits. The original traits and general combining ability of the parents, as well as the original traits and mid-parent heterosis of TC, were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected. Among these loci, 322 candidate genes located within or nearby the SALs were screened, 204 of which were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracies of the four models were generally not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracies of 5 (26%), 3 (16%), and 11 (58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines. They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Use of genomic prediction to screen sorghum B-lines in hybrid testcrosses.

Use of trifluoromethanesulfonamide (TFMSA), a male gametocide, increases the opportunities to identify promising B-lines because large quantities of F1 seed can be generated prior to the laborious task of B-line sterilization. Combining TFMSA technology with genomic selection could efficiently evaluate sorghum B-lines in hybrid combination to maximize the rates of genetic gain of the crop. This study used two recombinant inbred B-line populations, consisting of 217 lines, which were testcrossed to two R-lines to produce 434 hybrids. Each population of testcross hybrids were evaluated across five environments. Population-based genomic prediction models were assessed across environments using three different cross-validation (CV) schemes, each with 70% training and 30% validation sets. The validation schemes were as follows: CV1-hybrids chosen randomly for validation; CV2-B-lines were randomly chosen, and each chosen B-line had one of the two corresponding testcross hybrids randomly chosen for the validation; and CV3-B-lines were randomly chosen, and each chosen B-line had both corresponding testcross hybrids chosen for the validation. CV1 and CV2 presented the highest prediction accuracies; nonetheless, the prediction accuracies of the CV schemes were not statistically different in many environments. We determined that combining the B-line populations could improve prediction accuracies, and the genomic prediction models were able to effectively rank the poorest 70% of hybrids even when genomic prediction accuracies themselves were low. Results indicate that combining genomic prediction models and TFMSA technology can effectively aid breeders in predicting B-line hybrid performance in early generations prior to the laborious task of generating A/B-line pairs.

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers.

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

Training set designs for prediction of yield and moisture of maize test cross hybrids with unreplicated trials.

Unreplicated field trials and genomic prediction are both used to enhance the efficiency in early selection stages of a hybrid maize breeding program. No results are available on the optimal experimental design when combining both approaches. Our objectives were to investigate the effect of the training set design on the accuracy of genomic prediction in unreplicated maize test crosses. We carried out a cross validation study on basis of an experimental data set consisting of 1436 hybrids evaluated for yield and moisture for which genotyping information of 461 SNP markers were available. Training set designs of different size, implementing within environment prediction, within year prediction, across year prediction, and combinations of data sources across years and environments were compared with respect to their prediction accuracy. Across year prediction did not reach prediction accuracies that are useful for genomic selection. Within year prediction across environments provided useful correlations between observed and predicted breeding values. The prediction accuracies did not improve when adding to the training set data from previous years. We conclude that using all data available from unreplicated tests of the current breeding cycle provides a good accuracy of predicting test crosses, whereas adding data from previous breeding cycles, in which the genotypes are less related to the tested material, has only limited value for increasing the prediction accuracy.

Genome-wide association and genomic prediction for resistance to southern corn rust in DH and testcross populations.

Southern corn rust (SCR), caused by Puccinia polysora Underw, is a destructive disease that can severely reduce grain yield in maize (Zea mays L.). Owing to P. polysora being multi-racial, it is very important to explore more resistance genes and develop more efficient selection approaches in maize breeding programs. Here, four Doubled Haploid (DH) populations with 384 accessions originated from selected parents and their 903 testcross hybrids were used to perform genome-wide association (GWAS). Three GWAS processes included the additive model in the DH panel, additive and dominant models in the hybrid panel. As a result, five loci were detected on chromosomes 1, 7, 8, 8, and 10, with P-values ranging from 4.83×10-7 to 2.46×10-41. In all association analyses, a highly significant locus on chromosome 10 was detected, which was tight chained with the known SCR resistance gene RPPC and RPPK. Genomic prediction (GP), has been proven to be effective in plant breeding. In our study, several models were performed to explore predictive ability in hybrid populations for SCR resistance, including extended GBLUP with different genetic matrices, maker based prediction models, and mixed models with QTL as fixed factors. For GBLUP models, the prediction accuracies ranged from 0.56-0.60. Compared with traditional prediction only with additive effect, prediction ability was significantly improved by adding additive-by-additive effect (P-value< 0.05). For maker based models, the accuracy of BayesA and BayesB was 0.65, 8% higher than other models (i.e., RRBLUP, BRR, BL, BayesC). Finally, by adding QTL into the mixed linear prediction model, the accuracy can be further improved to 0.67, especially for the G_A model, the prediction performance can be increased by 11.67%. The prediction accuracy of the BayesB model can be further improved significantly by adding QTL information (P-value< 0.05). This study will provide important valuable information for understanding the genetic architecture and the application of GP for SCR in maize breeding.

Evaluating Introgression Sorghum Germplasm Selected at the Population Level While Exploring Genomic Resources as a Screening Method.

To exploit the novel genetic diversity residing in tropical sorghum germplasm, an expansive backcross nested-association mapping (BC-NAM) resource was developed in which novel genetic diversity was introgressed into elite inbreds. A major limitation of exploiting this type of genetic resource in hybrid improvement programs is the required evaluation in hybrid combination of the vast number of BC-NAM populations and lines. To address this, the utility of genomic information was evaluated to predict the hybrid performance of BC-NAM populations. Two agronomically elite BC-NAM populations were chosen for evaluation in which elite inbred RTx436 was the recurrent parent. Each BC1F3 line was evaluated in hybrid combination with an elite tester in two locations with phenotypes of grain yield, plant height, and days to anthesis collected on all test cross hybrids. Lines from both populations were found to outperform their recurrent parent. Efforts to utilize genetic distance based on genotyping-by-sequence (GBS) as a predictive tool for hybrid performance was ineffective. However, utilizing genomic prediction models using additive and dominance GBLUP kernels to screen germplasm appeared to be an effective method to eliminate inferior-performing lines that will not be useful in a hybrid breeding program.

Allelic Complementation in Hybrid Superiority of Popcorn to Multiple Foliar Diseases

Popcorn cultivation has been growing in and has positively affected the Brazilian economy. However, these crops are grown with genotypes susceptible to diseases, generating high losses. Thus, studies aimed at obtaining resistant genotypes are particularly interesting, as popcorn is susceptible to several pathogens. The most efficient, environmentally correct, and economical method of disease control is using resistant cultivars. The present study aimed to evaluate the performance of inbred lines of popcorn and their respective testcross hybrids in terms of diseases caused by fungi. For this purpose, 15 S7 inbred lines were crossed with five testers, four with a narrow genetic basis (inbred lines L270, L651, P1, and L70) and one with a broad genetic basis (open pollination variety PARA 172). The arrangement of treatments in incomplete blocks (lattice 10 × 10) with three replications was used. The testcross was efficient in the discrimination per se of the progenies and the testers. The inbred lines L685, L691, L696, and L684 and the PARA 172 tester showed potential resistance to P. polysora, B. maydis, and E. turcicum. The GT biplot method proved reliable in identifying efficient, responsive, and resistant inbred lines and revealing the hybrid 56 as the ideal genotype.

QTL and genomic prediction accuracy for grain yield and secondary traits in a maize population under heat and heat-drought stresses

ABSTRACT Heat and drought stresses negatively affect maize (Zea mays L.) productivity. We aimed to identify the genetic basis of tolerance to heat stress (HS) and combined heat and drought stress (HS+DS) and compare how QTL and whole genome selection (GS) could be leveraged to improve tolerance to both stresses. A set of 97 testcross hybrids derived from a maize bi-parental doubled-haploid population was evaluated during the summer seasons of 2014, 2015, and 2016 in Ciudad Obregon, Sonora, Mexico, under HS and HS+DS. Grain yield (GY) reached 5.7 t ha−1 under HS and 3.0 t ha−1 under HS+DS. Twenty-six QTL were detected across six environments, with LOD scores ranging from 2.03 to 3.86; the QTL explained 8.6% to 18.6% of the observed phenotypic variation. Hyperspectral biomass and structural index (HBSI) had higher genetic correlation with GY for HS (r = 0.97) and HS+DS (r = 0.74), relative to the correlation with crop water mass or greenness indices. Genetic correlations between GY and canopy temperature for HS (r = −0.89) and HS+DS (r = −0.75) or vegetation indices, along with clusters of QTL in bins 1.02, 1.05, and 2.05, underline the importance of these genomic areas for secondary traits associated with general vigor and greenness. Prediction accuracy of the model used for GS had values below those found in previous studies. We found a high-yielding hybrid that was tolerant to HS and HS+DS.

Combining Ability and Testcross Performance for Carotenoid Content of S2 Super Sweet Corn Lines Derived from Temperate Germplasm

Understanding the impact of gene action and combining ability on targeted traits in a hybrid breeding program is imperative. The objective of this study was to estimate the genetic effect and combining ability of S2 super sweet corn lines on carotenoid content. Twenty S2 lines were crossed with two hybrid testers by using the line × tester mating design. Regarding parents, forty hybrids and two commercial checks were evaluated for carotenoid content across two different growing seasons between 2019 and 2020. The result indicated that the non-additive gene action governed the inheritance of carotenoid content. Several promising S2 lines for individual carotenoids were identified; only L20 possessed different and positive GCA values for all observed carotenoids. Moreover, genotype T2 was a promising tester to identify superior lines for creating biofortified sweet corn hybrids. Testcross hybrids with satisfactory performance, desirable SCA estimates, and involving at least one of the pairwise parents with positive and high GCA were successfully defined. Hybrid T2 × L20 had high lutein, zeaxanthin, β-cryptoxanthin, and total carotenoids contents (ranging from 12.58 to 74.01 µg/g of dry weight), whereas hybrid T2 × L9 showed the highest content of β-carotene (4.19 µg/g of dry weight). We propose that high GCA and line at least one of the pairwise parents be included in indirect selections for the hybrid breeding of high-carotenoid sweet corn.

QTL Analysis and Heterosis Loci of Effective Tiller Using Three Genetic Populations Derived from Indica-Japonica Crosses in Rice

Effective panicle numbers (PNs) and Tiller numbers (TNs) are important traits affecting rice (Oryza sativa L.) architecture and grain yield. However, the molecular mechanisms underlying PN and TN heterosis remain unknown in rice. In addition, new PN- or TN-related genes need to be detected and discovered. In this study, in order to detect rice quantitative trait loci (QTLs) and the heterosis-related loci of PN or TN in rice, we developed a high generation recombinant inbred line (RIL) population from a cross of two elite cultivars, Luohui9 (Xian/Indica) and RPY geng (Geng/Japonica), and two testcross hybrid populations derived from the crosses of RILs and two cytoplasmic male sterile lines, YTA (Xian/Indica) and Z7A (Geng/Japonica). Finally, nine QTLs of PN across four seasons were identified, and two QTLs of TN in 191HB were mapped. Besides this, six heterosis-related QTLs of PN and five heterosis-related QTLs of TN were located. We found that heterosis-related QTLs of PN or TN covered multiple known genes, such as MOC1, TAC1 and OsETR2. Furthermore, homologous gene analysis identified one candidate gene of PN (LOC_10g25720). Together, these findings uncover multiple heterosis-related loci, and provide a new insight into the heterosis mechanism of PN and TN in rice.

De novo assembly of two chromosome-level rice genomes and bin-based QTL mapping reveal genetic diversity of grain weight trait in rice.

Following the “green revolution,” indica and japonica hybrid breeding has been recognized as a new breakthrough in further improving rice yields. However, heterosis-related grain weight QTLs and the basis of yield advantage among subspecies has not been well elucidated. We herein de novo assembled the chromosome level genomes of an indica/xian rice (Luohui 9) and a japonica/geng rice (RPY geng) and found that gene number differences and structural variations between these two genomes contribute to the differences in agronomic traits and also provide two different favorable allele pools to produce better derived recombinant inbred lines (RILs). In addition, we generated a high-generation (> F15) population of 272 RILs from the cross between Luohui 9 and RPY geng and two testcross hybrid populations derived from the crosses of RILs and two cytoplasmic male sterile lines (YTA, indica and Z7A, japonica). Based on three derived populations, we totally identified eight 1,000-grain weight (KGW) QTLs and eight KGW heterosis loci. Of QTLs, qKGW-6.1 and qKGW-8.1 were accepted as novel KGW QTLs that have not been reported previously. Interestingly, allele genotyping results revealed that heading date related gene (Ghd8) in qKGW-8.1 and qLH-KGW-8.1, can affect grain weight in RILs and rice core accessions and may also play an important role in grain weight heterosis. Our results provided two high-quality genomes and novel gene editing targets for grain weight for future rice yield improvement project.

High-resolution bin-based linkage mapping uncovers the genetic architecture and heterosis-related loci of plant height in indica-japonica derived populations.

Plant height (PH) is an important trait affecting the plant architecture, seed yield, and harvest index. However, the molecular mechanisms underlying PH heterosis remain unclear. In addition, useful PH-related genes must be urgently identified to facilitate ideal plant architecture breeding in rice (Oryza sativa L.). In the present study, to explore rice quantitative trait loci (QTLs) and heterosis-related loci of PH in rice, we developed a high-generation (>F15 ) population of 272 recombinant inbred lines (RIL) from a cross of two elite varieties, Luohui 9 (indica/xian) × RPY geng (japonica/geng), and two testcross hybrid populations derived from the crosses of RILs and two cytoplasmic male sterile lines (YTA [indica] and Z7A [japonica]). Using deep resequencing data, a high-density genetic map containing 4758 bin markers was constructed, with a total map distance of 2356.41 cM. Finally, 31 PH-related QTLs for different PH component lengths or tiller numbers across five seasons were identified. Two major environment-specific PH QTLs were stably detected in Hainan (qPH-3.1) or Hubei (qPH-5.1), which have undergone significant functional alterations in rice with changes in geographical environment. Based on comparative genomics, gene function annotation, homolog identification, and existing literature (pioneering studies), candidate genes for multiple QTLs were fine-mapped, and the candidate genes qPH-3.1 and qPH-5.1 for PH were further validated using CRISPR-Cas9 gene editing. Specifically, qPH-3.1 was characterized as a pleiotropic gene, and the qPH-3.1 knockout line showed reduced PH, delayed heading, a decreased seed setting rate, and increased tiller numbers. Importantly, 10 PH heterosis-related QTLs were identified in the testcross populations, and a better-parent heterosis locus (qBPH-5.2) completely covered qPH-5.1. Furthermore, the cross results of fixed-genotype RILs verified the dominant effects of qPH-3.1 and qPH-5.1. Together, these findings further our understanding of the genetic mechanisms of PH and offer multiple highly reliable gene targets for breeding rice varieties with ideal architecture and high yield potential in the immediate future.

Bin mapping-based QTL analyses using three genetic populations derived from indica-japonica crosses uncover multiple grain shape heterosis-related loci in rice.

Exploitation of heterosis between indica and japonica has important significance in scientific research and agriculture application. However, the molecular mechanism of grain shape heterosis in indica-japonica hybrid remains unknown in rice (Oryza sativa L.). To reveal the genetic mechanism of grain shape in indica--japonica hybrid, we constructed a high-generation recombinant inbred line (RIL) population and two testcross hybrid populations derived from the cross of RILs and two cytoplasmic male sterile material (YTA and Z7A) and then performed a bin mapping-based quantitative trait locus (QTL) mapping of multiple grain shape traits, such as grain length (GL), grain width (GW), and grain length-to-width ratio (GLWR). A total of sixteen QTLs and 30 heterosis-related QTLs of grain shape traits were detected. We found that GS3, GS5, and OsPPKL2 were also correlated with grain shape both in RILs and two testcross hybrid populations. Homologous gene analysis emphasized two candidate grain shape-associated genes (LOC_Os06g14260 and LOC_Os04g51950). Our findings uncover multiple grain shape heterosis-related loci and provides a new insight into heterosis mechanism of grain shape in rice.

Evaluation candidate of test cross hybrid QPM under ecosystem of Polewali Mandar West Sulawesi

Abtract. Quality Protein Maize (QPM) was specialty maize which is high content of lysine and tryptophan. These are two essential amino acids better nutritional for human body to anticipated of disease (kwashiorkor) on severe among children, and could be corrective use in balancing diets. The experiment on 2018 has been conducted to evaluated of nine candidate test cross hybrid of QPM vs. check Bima 13Q with CRD three replications under lowland in distrit of Polman West Sulawesi. Genetic material were planted in four rows 5.0 m lenght, spacing 75x20 cm, and applied fertilizer Urea, Ponska (300-200) kg/ha. The result shown that two test cross F1 QPM were (MSQ(S1)C0-26-1-1 x MR14Q) and (MSQ(S1)C0-43-1-1-1 x MR13Q) which the best with potential yield 10.66-10.95 t/ha. The LSD shown that there are significant different with check Bima 13Q on yield (wc.15%) and highly 30.0% and 31.6%. The two candidates were founded shelling percentage 77.9% and 79.0%, asi less than five days and could be promising as new variety and continuing in evaluation variety trial (evt) in west Sulawesi.

Water stress resistance of the new self-pollinated maize lines and hybrids

The directions of the economic use of maize are different, namely fodder, food, technical. Considering that maize belongs to mesophytes, the most harmful stress factor for it is drought. The breeding maize for drought tolerance is considered the most important direction for this crop. The purpose of the current paper was to present study results of the drought resistance of the initial material, namely the self-pollinated maize lines and hybrids. The study was carried out at the Agricultural Research Center “Donskoy”, located in the zone of insufficient moisture in 2018–2020. Drought tolerance was determined by the method of residual water deficit (RWD). As an initial material, there were used 24 self-pollinated maize lines and 50 test-cross hybrids. There have been identified the new drought-resistant middle-early and middle-ripening self-pollinated lines ‘KS 317 A’,’ KV 240’, ‘LSh 16’, ‘S 86’, ‘LSh 17’ and ‘LSh 2’, ‘SP 246 / 276-2’, ‘DS 498 / 203-4’, ‘DS 298 / 203-3’, ‘DS 257 / 85-0’, ‘SP 280-3’, ‘KB 373’, which had a low residual water deficit in the flowering phase (7.7–10.4%) and did not significantly increase it during the period from vegetation to milky-wax ripeness (up to 10,3–12.6%). The drought-resistant hybrids include ‘Stepnyak MV’, ‘GK 26 AM × DS 257 / 85-0’, ‘P 101 × Zp 498 A’, ‘KB 399 × S 232’, ‘GK 26 AM × KB 373’, ‘GK 26 AM × SP 246 / 276-2’, ‘C 204 × KS 318’. They were characterized by a low water deficit during the flowering period (7.4–10.4%) and its low increase (1.4–3.7%) during the growing season. Drought-resistant hybrids, as a rule, included drought-resistant lines. According to the results of the State Variety Testing, the new middle-ripening three-line maize hybrid ‘Stepnyak MV’ ((KB 262 M × KB 326 ZM) × KB 498 MV)), developed on the basis of the drought-resistant lines ‘KB 262 M’ and ‘KB 498 MV’, has been included into the State Register since 2019. The hybrid possessed a high grain yield (4.55 t/ha) in the dry years of 2018–2020. it was characterized by high values of the main economically valuable traits, such as high resistance to lodging (0.5% of lodged plants), high starch content in grain (72.0%), optimum grain moisture (14.0%) by the harvesting time.