A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F₁ hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F₁ hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.     

Genome Properties and Prospects of Genomic Prediction of Hybrid Performance in a Breeding Program of Maize

Maize (Zea mays L.) serves as model plant for heterosis research and is the crop where hybrid breeding was pioneered. We analyzed genomic and phenotypic data of 1254 hybrids of a typical maize hybrid breeding program based on the important Dent × Flint heterotic pattern. Our main objectives were to investigate genome properties of the parental lines (e.g., allele frequencies, linkage disequilibrium, and phases) and examine the prospects of genomic prediction of hybrid performance. We found high consistency of linkage phases and large differences in allele frequencies between the Dent and Flint heterotic groups in pericentromeric regions. These results can be explained by the Hill–Robertson effect and support the hypothesis of differential fixation of alleles due to pseudo-overdominance in these regions. In pericentromeric regions we also found indications for consistent marker–QTL linkage between heterotic groups. With prediction methods GBLUP and BayesB, the cross-validation prediction accuracy ranged from 0.75 to 0.92 for grain yield and from 0.59 to 0.95 for grain moisture. The prediction accuracy of untested hybrids was highest, if both parents were parents of other hybrids in the training set, and lowest, if none of them were involved in any training set hybrid. Optimizing the composition of the training set in terms of number of lines and hybrids per line could further increase prediction accuracy. We conclude that genomic prediction facilitates a paradigm shift in hybrid breeding by focusing on the performance of experimental hybrids rather than the performance of parental lines in testcrosses.     

Genome‐wide association analyses reveal the genetic basis of combining ability in rice

Combining ability is a measure for selecting elite parents and predicting hybrid performance in plant breeding. However, the genetic basis of combining ability remains unclear and a global view of combining ability from diverse mating designs is lacking. We developed a North Carolina II (NCII) population of 96 Oryza sativa and four male sterile lines to identify parents of greatest value for hybrid rice production. Statistical analyses indicated that general combining ability (GCA) and specific combining ability (SCA) contributed variously to different agronomic traits. In a genome‐wide association study (GWAS) of agronomic traits, GCA and SCA, we identified 34 significant associations (P < 2.39 × 10^?7). The superior alleles of GCA loci (Ghd8, GS3 and qSSR4) accumulated in parental lines with high GCA and explained 30.03% of GCA variance in grain yield, indicating that molecular breeding of high GCA parental lines is feasible. The distinct distributions of these QTLs contributed to the differentiation of parental GCA in subpopulations. GWAS of SCA identified 12 more loci that showed dominance on corresponding agronomic traits. We conclude that the accumulation of superior GCA and SCA alleles is an important contributor to heterosis and QTLs that greatly contributed to combining ability in our study would accelerate the identification of elite inbred lines and breeding of super hybrids.     

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture

Key Message

Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model.

Abstract

In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F₁-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

     

Transmission of important chromosomal regions under selection revealed in rice pedigree breeding programs

Genetic analysis across a whole plant genome based on pedigree information offers considerable potential for enhancing genetic gain from plant breeding programs through quantitative trait loci (QTL) mapping and marker-assisted selection. Here, we report its application for graphically genotyping varieties used in Chinese japonica rice (Oryza sativa L.) pedigree breeding programs. We identified 34 important chromosomal regions from the founder parent that are under selection in the breeding programs, and by comparing donor genomic regions that are under selection with QTL locations of agronomic traits, we found that QTL clustered in important genomic regions, in accordance with association analyses of natural populations and other previous studies. The convergence of genomic regions under selection with QTL locations suggests that donor genomic regions harboring key genes/QTL for important agronomic traits have been selected by plant breeders since the 1950s from the founder rice plants. The results provide better understanding of the effects of selection in breeding programs on the traits of rice cultivars. They also provide potentially valuable information for enhancing rice breeding programs through screening candidate parents for targeted molecular markers, improving crop yield potential and identifying suitable genetic material for use in future breeding programs.     

SSR heterogenic patterns of parents for marking and predicting heterosis in rice breeding

The most important concerns of hybrid rice breeders are selection of donors to improve parental lines and prediction of hybrid performance. In this study, SSR molecular marker technology and a half-diallel method were used to address these related hybrid production issues. The results show that genetic diversity among the parental lines is certainly related to heterosis. The heterozygosity of each parental pair is significantly associated with the general combining ability, not with the specific combining ability. However, neither genetic diversity nor heterozygosity is a good indicator for predicting heterosis. From these results, it is suggested that donors for improving parents of hybrids be selected from the improved inbred lines by conventional breeding programs. In this investigation, we also discovered that four favorable alleles and six favorable heterogenic patterns on the parental lines significantly contribute to the heterosis of their hybrids in grain yield, whereas six unfavorable alleles and six unfavorable heterogenic patterns significantly reduce heterosis. These noticeable findings could be, in practice, useful for hybrid rice breeding programs with SSR marker-assisted selection. It is suggested that the optimal combinations with the superior grain yield could be bred out by assembling those favorable alleles into their parental lines and by removing the unfavorable alleles from the parental lines. This study also indicates that there is still a great heterosis potential to be exploited in indica/indica hybrids by the same strategy. In indica/japonica hybrid breeding programs, it may also be important to remove unfavorable alleles rather than broaden genetic diversity or heterozygosity of the parents.     

Genomic divergence in cotton germplasm related to maturity and heterosis

Commercial varieties of upland cotton(Gossypium hirsutum) have undergone extensive breeding for agronomic traits, such as fiber quality, disease resistance,and yield. Cotton breeding programs have widely used Chinese upland cotton source germplasm(CUCSG) with excellent agronomic traits. A better understanding of the genetic diversity and genomic characteristics of these accessions could accelerate the identification of desirable alleles. Here, we analyzed 10,522 high-quality singlenucleotide polymorphisms(SNP) with the CottonSNP63 K microarray in 137 cotton accessions(including 12 hybrids of upland cotton). These data were used to investigate the genetic diversity, population structure,and genomic characteristics of each population and the contribution of these loci to heterosis. Three subgroups were identified, in agreement with their knownpedigrees, geographical distributions, and times since introduction. For each group, we identified lineagespecific genomic divergence regions, which potentially harbor key alleles that determine the characteristics of each group, such as early maturity-related loci. Investigation of the distribution of heterozygous loci, among 12 commercial cotton hybrids, revealed a potential role for these regions in heterosis. Our study provides insight into the population structure of upland cotton germplasm. Furthermore, the overlap between lineagespecific regions and heterozygous loci, in the high-yield hybrids, suggests a role for these regions in cotton heterosis.     

The hybrid protein interactome contributes to rice heterosis as epistatic effects

Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein?protein interactions specific to F₁ hybrids (F₁‐specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter‐subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four‐thousand, six‐hundred and twelve F₁‐specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F₁‐specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F₁‐specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F₁‐specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F₁‐specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F₁‐specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large‐scale protein interactome datasets are generated in hybrid rice in the future.     

Accuracy of predicting genomic breeding values for carcass merit traits in Angus and Charolais beef cattle

Accuracy of predicting genomic breeding values for carcass merit traits including hot carcass weight, longissimus muscle area (REA), carcass average backfat thickness (AFAT), lean meat yield (LMY) and carcass marbling score (CMAR) was evaluated based on 543 Angus and 400 Charolais steers genotyped on the Illumina BovineSNP50 Beadchip. For the genomic prediction within Angus, the average accuracy was 0.35 with a range from 0.32 (LMY) to 0.37 (CMAR) across different training/validation data‐splitting strategies and statistical methods. The within‐breed genomic prediction for Charolais yielded an average accuracy of 0.36 with a range from 0.24 (REA) to 0.46 (AFAT). The across‐breed prediction had the lowest accuracy, which was on average near zero. When the data from the two breeds were combined to predict the breeding values of either breed, the prediction accuracy averaged 0.35 for Angus with a range from 0.33 (REA) to 0.39 (CMAR) and averaged 0.33 for Charolais with a range from 0.18 (REA) to 0.46 (AFAT). The prediction accuracy was slightly higher on average when the data were split by animal's birth year than when the data were split by sire family. These results demonstrate that the genetic relationship or relatedness of selection candidates with the training population has a great impact on the accuracy of predicting genomic breeding values under the density of the marker panel used in this study.     

Molecularmarker diversity and hybrid sterility in indica-japonica rice crosses

 The partial sterility of hybrids between the indica and japonica rice subspecies of Asian cultivated rice is a serious constraint for utilizing inter-subspecific heterosis in hybrid rice breeding. In this study, we have investigated the relationship between molecular-marker polymorphism and indica-japonica hybrid fertility using a diallel set involving 20 rice accessions including 9 indica and 11 japonica varieties. Spikelet fertility of the resulting 190 F₁s and their parents was examined in a replicated field trial. Intra-subspecific hybrids showed much higher spikelet fertility than inter-subspecific hybrids except in crosses involving wide-compatibility varieties. The parents were surveyed for DNA polymorphism using 96 RFLP and ten SSR markers, which revealed extensive genetic differentiation between indica and japonica varieties. A large number of markers detected highly significant effects on hybrid fertility. The chromosomal locations for many of the positive markers coincided well with previously identified loci for hybrid sterility. The correlation between hybrid fertility and parental distance was low in both intra- and inter-subspecific crosses. The results suggest that the genetic basis of indica-japonica hybrid sterility is complex. It is the qualitative, rather than the quantitative, difference between the parents that determines the fertility of hybrids. Received: 3 January 1997/Accepted: 17 January 1997     

Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.)

Rice is one of the most important cereal crops, providing the daily dietary intake for approximately 50% of the global human population. Here, we re-sequenced 259 rice accessions, generating 1371.65 Gb of raw data. Furthermore, we performed genome-wide association studies (GWAS) on 13 agronomic traits using 2.8 million single nucleotide polymorphisms (SNPs) characterized in 259 rice accessions. Phenotypic data and best linear unbiased prediction (BLUP) values of each of the 13 traits over two years of each trait were used for the GWAS. The results showed that 816 SNP signals were significantly associated with the 13 agronomic traits. Then we detected candidate genes related to target traits within 200 kb upstream and downstream of the associated SNP loci, based on linkage disequilibrium (LD) blocks in the whole rice genome. These candidate genes were further identified through haplotype block constructions. This comprehensive study provides a timely and important genomic resource for breeding high yielding rice cultivars.     

Heterozygosis drives maize hybrids to select elite 2,4-diacethylphloroglucinol-producing Pseudomonas strains among resident soil populations

By comparing the distribution of two genomic markers among Pseudomonas strains recovered from the rhizosphere of two maize hybrids with those of strains recovered from the rhizosphere of their four respective parental lines, we showed that both hybrids supported more elite probiotic strains than the parents. Elite Pseudomonas strains showed genomic potential for both an appropriate in vitro 2,4-diacetylphloroglucinol (DAPG) productivity, and a superior root-colonization ability. The actual biocontrol and root-colonization abilities of these strains were confirmed by bioassays on five fungal strains and on axenic maize plants. Furthermore, results on the abundance and genetic diversity of resident DAPG+ Pseudomonas strains indicated that each hybrid was able to select its own specific DAPG+ population, whereas the four parental lines were not. The evidence that heterozygosis can drive maize plants to select elite probiotic rhizospheric DAPG+ Pseudomonas strains opens the way to a new strategy in the set up of plant breeding for low-input and organic agriculture.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Genomic selection for grain yield and quality traits in durum wheat

The prediction accuracies of genomic selection depend on several factors, including the genetic architecture of target traits, the number of traits considered at a given time, and the statistical models. Here, we assessed the potential of single-trait (ST) and multi-trait (MT) genomic prediction models for durum wheat on yield and quality traits using a breeding panel (BP) of 170 varieties and advanced breeding lines, and a doubled-haploid (DH) population of 154 lines. The two populations were genotyped with the Infinium iSelect 90K SNP assay and phenotyped for various traits. Six ST-GS models (RR-BLUP, G-BLUP, BayesA, BayesB, Bayesian LASSO, and RKHS) and three MT prediction approaches (MT-BayesA, MT-Matrix, and MT-SI approaches which use economic selection index as a trait value) were applied for predicting yield, protein content, gluten index, and alveograph measures. The ST prediction accuracies ranged from 0.5 to 0.8 for the various traits and models and revealed comparable prediction accuracies for most of the traits in both populations, except BayesA and BayesB, which better predicted gluten index, tenacity, and strength in the DH population. The MT-GS models were more accurate than the ST-GS models only for grain yield in the BP. Using BP as a training set to predict the DH population resulted in poor predictions. Overall, all the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but we recommend the simple computational models RR-BLUP or G-BLUP for predicating single trait and MT-SI for predicting yield and protein simultaneously.     

Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions

Genotyping and phenotyping large natural populations provide opportunities for population genomic analysis and genome-wide association studies (GWAS). Several rice populations have been re-sequenced in the past decade; however, many major Chinese rice cultivars were not included in these studies. Here, we report large-scale genomic and phenotypic datasets for a collection mainly comprised of 1,275 rice accessions of widely planted cultivars and parental hybrid rice lines from China. The population was divided into three indica/Xian and three japonica/Geng phylogenetic subgroups that correlate strongly with their geographic or breeding origins. We acquired a total of 146 phenotypic datasets for 29 agronomic traits under multi-environments for different subpopulations. With GWAS, we identified a total of 143 significant association loci, including three newly identified candidate genes or alleles that control heading date or amylose content. Our genotypic analysis of agronomically important genes in the population revealed that many favorable alleles are underused in elite accessions, suggesting they may be used to provide improvements in future breeding efforts. Our study provides useful resources for rice genetics research and breeding.

     

Genomic Prediction of Testcross Performance in Canola (Brassica napus)

Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.     

Molecular marker heterozygosity and hybrid performance in indica and japonica rice

An essential assumption underlying markerbased prediction of hybrid performance is a strong linear correlation between molecular marker heterozygosity and hybrid performance or heterosis. This study was intended to investigate the extent of the correlations between molecular marker heterozygosity and hybrid performance in crosses involving two sets of rice materials, 9 indica and 11 japonica varieties. These materials represent a broad spectrum of the cultivated rice gene pool including landraces, primitive cultivars, historically important cultivars, modern elite cultivars and parents of superior hybrids. Varieties within each set were intermated in all possible nonreciprocal pairs resulting in 36 crosses in the indica set and 55 in the japonica set. The F₁s and their parents, 111 entries in total, were examined for performance of seven traits in a replicated field trial. The parents were surveyed for polymorphisms using 96 RFLP and ten SSR markers selected at regular intervals from a published molecular marker linkage map. Molecular marker genotypes of the F₁ hybrids were deduced from the parental genotypes. The analysis showed that, with very few exceptions, correlations in the indica dataset were higher than in that of their japonica counterparts. Among the seven traits analyzed, plant height showed the highest correlation between heterozygosity and hybrid performance and heteorsis in both indica and japonica datasets. Correlations were low to intermediate between hybrid performance and heterozygosity (both general and specific) in yield and yield component traits in both indica and japonica sets, and also low to intermediate between specific heterozygosity and heterosis in the indica set, whereas very little correlation was detected between heterosis and heterozygosity (either general or specific) in the japonica set. In comparison to the results from our previous studies, we concluded that the relationship between molecular marker heterozygosity and heterosis is variable, depending on the genetic materials used in the study, the diversity of rice germplasms and the complexity of the genetic basis of heterosis.     

Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle

Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.     

Prediction of hybrid grain yield performances in Indica Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) with effect-increasing loci

One hundred and fifty-one rice hybrids produced in two sets of half-dialell crosses and their parents (13 cytoplasmic male sterile lines and 19 restorers) were used to predict the F₁ performances of seven yield traits through the parental genetic distances (GD) based on SSR markers. The positive loci (PL) and effect-increasing loci (IL), which were screened from SSR polymorphic loci by the F₁ traits of 32 parents, together with total loci (TL), were utilized to estimate parental GD and the models were found to predict the traits of hybrids derived from different parents, fixed parents, and different environments, respectively. The results were as follows: (1) 550 polymorphic loci were detected from 174 SSR markers: a dendrogram based on these loci could separate all the sterile and restorer lines used in the present study, which indicated that parental genetic diversity of F₁ was large; (2) the correlations between F₁ traits and parental GDs based on IL ranged from 0.61 to 0.87 with a mean of 0.76, and they were higher than those on TL or on PL; (3) predictions based on IL for F₁ traits (except grain weight per plant) derived from different environments were ideal, but worse for F₁ traits derived from different parents; and (4) IL was more effective than TL and PL in predicting traits of F₁ with fixed parents, and predictions for fixed restorer combinations were more effective than those for fixed sterile line combinations. These results should facilitate molecular prediction for hybrid yield and other traits by means of both elite sterile and restorer lines.