Genomic prediction of trait segregation in a progeny population: a case study of Japanese pear (Pyrus pyrifolia)

Phenotypic simulation for fruit-related traits in F1 progenies of chili peppers (Capsicum annuum) using genomic prediction based solely on parental information

Chili pepper (Capsicum spp.) fruits are used as vegetables, spices, and ornamental plants, necessitating various fruit characteristics. However, their genetic improvement is challenging through conventional crossbreeding due to the quantitative traits, which makes it difficult to predict phenotypes in the progeny. As a breakthrough, we focused on phenotypic simulation via genomic prediction (GP) and aimed to clarify its utility for fruit-related traits in chili peppers. The present study used 291 C. annuum accessions, including two populations: inbred lines and F1 accessions derived from 20 inbred parents. We collected data of fruit length, width, shape index (length/width), weight, and pericarp thickness, and obtained single nucleotide polymorphism data via multiplexed inter-simple sequence repeat genotyping by sequencing. We simulated the fruit-related traits in the F1 accessions by inputting their estimated genotypes (based on their parents) into the GP model using the GBLUP-GAUSS model, which was shown to be the most accurate regardless of population or trait differences in the present study. As a result, we observed strong positive correlations (r = 0.833-0.908) between the simulated and observed phenotypic values across all traits, suggesting that accurate ranking of F1 progenies based on fruit-related traits can be achieved using parental information. This is the first report demonstrating the utility of phenotypic simulation via GP in chili pepper breeding, offering valuable insights for its application in this field.

Chromosome-scale genome assembly of acerola (Malpighia emarginata DC.)

Acerola (Malpighia emarginata DC.) is a tropical evergreen shrub that produces vitamin C-rich fruits. Increasing fruit nutrition is one of the main targets of acerola breeding programs. Genomic tools have been shown to accelerate plant breeding even in fruiting tree species, which generally have a long-life cycle; however, the availability of genomic resources in acerola, so far, has been limited. In this study, as a first step toward developing an efficient breeding technology for acerola, we established a chromosome-scale genome assembly of acerola using high-fidelity long-read sequencing and genetic mapping. The resultant assembly comprises 10 chromosome-scale sequences that span a physical distance of 1,032.5 Mb and contain 35,892 predicted genes. Phylogenetic analysis of genome-wide SNPs in 60 acerola breeding materials revealed 3 distinct genetic groups. Overall, the genomic resource of acerola developed in this study, including its genome and gene sequences, genetic map, and phylogenetic relationship among breeding materials, will not only be useful for acerola breeding but will also facilitate genomic and genetic studies on acerola and related species.

Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01497-2.

Influence of the mating design on the additive genetic variance in plant breeding populations

KEY MESSAGE

Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. The additive genetic variance [Formula: see text] inherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of [Formula: see text]. Thus, estimates of [Formula: see text] depend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of [Formula: see text] from genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents (P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of [Formula: see text] and of the parameter b reflecting the covariance component in [Formula: see text] standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated [Formula: see text] estimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of [Formula: see text] and its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.

A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple

INTRODUCTION

Genomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants.

OBJECTIVES

We attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes.

METHODS

A novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted.

RESULTS

By using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively.

CONCLUSION

The BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

Genomic Selection for F1 Hybrid Breeding in Strawberry (Fragaria × ananassa)

Cultivated strawberry is the most widely consumed fruit crop in the world, and therefore, many breeding programs are underway to improve its agronomic traits such as fruit quality. Strawberry cultivars were vegetatively propagated through runners and carried a high risk of infection with viruses and insects. To solve this problem, the development of F₁ hybrid seeds has been proposed as an alternative breeding strategy in strawberry. In this study, we conducted a potential assessment of genomic selection (GS) in strawberry F₁ hybrid breeding. A total of 105 inbred lines were developed as candidate parents of strawberry F₁ hybrids. In addition, 275 parental combinations were randomly selected from the 105 inbred lines and crossed to develop test F₁ hybrids for GS model training. These populations were phenotyped for petiole length, leaf area, Brix, fruit hardness, and pericarp color. Whole-genome shotgun sequencing of the 105 inbred lines detected 20,811 single nucleotide polymorphism sites that were provided for subsequent GS analyses. In a GS model construction, inclusion of dominant effects showed a slight advantage in GS accuracy. In the across population prediction analysis, GS models using the inbred lines showed predictability for the test F₁ hybrids and vice versa, except for Brix. Finally, the GS models were used for phenotype prediction of 5,460 possible F₁ hybrids from 105 inbred lines to select F₁ hybrids with high fruit hardness or high pericarp color. These F₁ hybrids were developed and phenotyped to evaluate the efficacy of the GS. As expected, F₁ hybrids that were predicted to have high fruit hardness or high pericarp color expressed higher observed phenotypic values than the F₁ hybrids that were selected for other objectives. Through the analyses in this study, we demonstrated that GS can be applied for strawberry F₁ hybrid breeding.

Predicting Rice Heading Date Using an Integrated Approach Combining a Machine Learning Method and a Crop Growth Model

Accurate prediction of heading date under various environmental conditions is expected to facilitate the decision-making process in cultivation management and the breeding process of new cultivars adaptable to the environment. Days to heading (DTH) is a complex trait known to be controlled by multiple genes and genotype-by-environment interactions. Crop growth models (CGMs) have been widely used to predict the phenological development of a plant in an environment; however, they usually require substantial experimental data to calibrate the parameters of the model. The parameters are mostly genotype-specific and are thus usually estimated separately for each cultivar. We propose an integrated approach that links genotype marker data with the developmental genotype-specific parameters of CGMs with a machine learning model, and allows heading date prediction of a new genotype in a new environment. To estimate the parameters, we implemented a Bayesian approach with the advanced Markov chain Monte-Carlo algorithm called the differential evolution adaptive metropolis and conducted the estimation using a large amount of data on heading date and environmental variables. The data comprised sowing and heading dates of 112 cultivars/lines tested at 7 locations for 14 years and the corresponding environmental variables (day length and daily temperature). We compared the predictive accuracy of DTH between the proposed approach, a CGM, and a single machine learning model. The results showed that the extreme learning machine (one of the implemented machine learning models) was superior to the CGM for the prediction of a tested genotype in a tested location. The proposed approach outperformed the machine learning method in the prediction of an untested genotype in an untested location. We also evaluated the potential of the proposed approach in the prediction of the distribution of DTH in 103 F₂ segregation populations derived from crosses between a common parent, Koshihikari, and 103 cultivars/lines. The results showed a high correlation coefficient (ca. 0.8) of the 10, 50, and 90th percentiles of the observed and predicted distribution of DTH. In this study, the integration of a machine learning model and a CGM was better able to predict the heading date of a new rice cultivar in an untested potential environment.

Genomic Prediction and Selection for Fruit Traits in Winter Squash

Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.

Usefulness Criterion and Post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression

Predicting the usefulness of crosses in terms of expected genetic gain and genetic diversity is of interest to secure performance in the progeny and to maintain long-term genetic gain in plant breeding. A wide range of crossing schemes are possible including large biparental crosses, backcrosses, four-way crosses, and synthetic populations. In silico progeny simulations together with genome-based prediction of quantitative traits can be used to guide mating decisions. However, the large number of multi-parental combinations can hinder the use of simulations in practice. Analytical solutions have been proposed recently to predict the distribution of a quantitative trait in the progeny of biparental crosses using information of recombination frequency and linkage disequilibrium between loci. Here, we extend this approach to obtain the progeny distribution of more complex crosses including two to four parents. Considering agronomic traits and parental genome contribution as jointly multivariate normally distributed traits, the usefulness criterion parental contribution (UCPC) enables to (i) evaluate the expected genetic gain for agronomic traits, and at the same time (ii) evaluate parental genome contributions to the selected fraction of progeny. We validate and illustrate UCPC in the context of multiple allele introgression from a donor into one or several elite recipients in maize (Zea mays L.). Recommendations regarding the interest of two-way, three-way, and backcrosses were derived depending on the donor performance. We believe that the computationally efficient UCPC approach can be useful for mate selection and allocation in many plant and animal breeding contexts.

Predicting segregation of multiple fruit-quality traits by using accumulated phenotypic records in citrus breeding

In the breeding of citrus (Citrus spp.), suitable fruit quality is essential for consumer acceptance of new cultivars. To identify parental combinations producing F₁ progeny with fruit-quality traits exceeding certain selection criteria, we developed a simple and practical method for predicting multiple-trait segregation in an F₁ progeny population. This method uses breeding values of parental genotypes and an additive genetic (co)variance matrix calculated by the best linear unbiased prediction method to construct a model for trait segregation in F₁ progeny. To confirm the validity of our proposed method, we calculated the breeding values and additive genetic (co)variances based on phenotypic records on nine fruit-quality traits in 2122 genotypes, and constructed a trait segregation model. Subsequently, we applied the trait segregation model to all pairs of the 2122 genotypes (i.e., 2,252,503 combinations), and predicted the most promising combinations and evaluated their probabilities of producing superior genotypes exceeding the nine fruit-quality traits of satsuma mandarin (Citrus unshiu Marcow.) or ‘Shiranuhi’ (‘Kiyomi’ × ‘Nakano No. 3’ ponkan), two popular citrus cultivars in Japan. We consider these results to be useful not only for selecting good parental combinations for fruit quality or other important traits but also for determining the scale of breeding programs required to achieve specific breeding goals.

Genome-wide association study and genomic prediction using parental and breeding populations of Japanese pear (Pyrus pyrifolia Nakai)

Breeding of fruit trees is hindered by their large size and long juvenile period. Genome-wide association study (GWAS) and genomic selection (GS) are promising methods for circumventing this hindrance, but preparing new large datasets for these methods may not always be practical. Here, we evaluated the potential of breeding populations evaluated routinely in breeding programs for GWAS and GS. We used a pear parental population of 86 varieties and breeding populations of 765 trees from 16 full-sib families, which were phenotyped for 18 traits and genotyped for 1,506 single nucleotide polymorphisms (SNPs). The power of GWAS and accuracy of genomic prediction were improved when we combined data from the breeding populations and the parental population. The accuracy of genomic prediction was improved further when full-sib data of the target family were available. The results suggest that phenotype data collected in breeding programs can be beneficial for GWAS and GS when they are combined with genome-wide marker data. The potential of GWAS and GS will be further extended if we can build a system for routine collection of the phenotype and marker genotype data for breeding populations.

The genome sequence of sweet cherry (Prunus avium) for use in genomics-assisted breeding

We determined the genome sequence of sweet cherry (Prunus avium) using next-generation sequencing technology. The total length of the assembled sequences was 272.4 Mb, consisting of 10,148 scaffold sequences with an N50 length of 219.6 kb. The sequences covered 77.8% of the 352.9 Mb sweet cherry genome, as estimated by k-mer analysis, and included >96.0% of the core eukaryotic genes. We predicted 43,349 complete and partial protein-encoding genes. A high-density consensus map with 2,382 loci was constructed using double-digest restriction site–associated DNA sequencing. Comparing the genetic maps of sweet cherry and peach revealed high synteny between the two genomes; thus the scaffolds were integrated into pseudomolecules using map- and synteny-based strategies. Whole-genome resequencing of six modern cultivars found 1,016,866 SNPs and 162,402 insertions/deletions, out of which 0.7% were deleterious. The sequence variants, as well as simple sequence repeats, can be used as DNA markers. The genomic information helps us to identify agronomically important genes and will accelerate genetic studies and breeding programs for sweet cherries. Further information on the genomic sequences and DNA markers is available in DBcherry (http://cherry.kazusa.or.jp (8 May 2017, date last accessed)).

Efficiency of genomic selection for breeding population design and phenotype prediction in tomato

Genomic selection (GS), which uses estimated genetic potential based on genome-wide genotype data for a breeding selection, is now widely accepted as an efficient method to improve genetically complex traits. We assessed the potential of GS for increasing soluble solids content and total fruit weight of tomato. A collection of big-fruited F1 varieties was used to construct the GS models, and the progeny from crosses was used to validate the models. The present study includes two experiments: a prediction of a parental combination that generates superior progeny and the prediction of progeny phenotypes. The GS models successfully predicted a better parent even if the phenotypic value did not vary substantially between candidates. The GS models also predicted phenotypes of progeny, although their efficiency varied depending on the parental cross combinations and the selected traits. Although further analyses are required to apply GS in an actual breeding situation, our results indicated that GS is a promising strategy for future tomato breeding design.

De-novo transcriptome assembly for gene identification, analysis, annotation, and molecular marker discovery in Onobrychis viciifolia

BACKGROUND

Sainfoin (Onobrychis viciifolia) is a highly nutritious tannin-containing forage legume. In the diet of ruminants sainfoin can have anti-parasitic effects and reduce methane emissions under in vitro conditions. Many of these benefits have been attributed to condensed tannins or proanthocyanidins in sainfoin. A combination of increased use of industrially produced nitrogen fertilizer, issues with establishment and productivity in the first year and more reliable alternatives, such as red clover led to a decline in the use of sainfoin since the middle of the last century. In recent years there has been a resurgence of interest in sainfoin due to its potential beneficial nutraceutical and environmental attributes. However, genomic resources are scarce, thus hampering progress in genetic analysis and improvement. To address this we have used next generation RNA sequencing technology to obtain the first transcriptome of sainfoin. We used the library to identify gene-based simple sequence repeats (SSRs) and potential single nucleotide polymorphisms (SNPs).

RESULTS

One genotype from each of five sainfoin accessions was sequenced. Paired-end (PE) sequences were generated from cDNA libraries of RNA extracted from 7 day old seedlings. A combined assembly of 92,772 transcripts was produced de novo using the Trinity programme. About 18,000 transcripts were annotated with at least one GO (gene ontology) term. A total of 63 transcripts were annotated as involved in the tannin biosynthesis pathway. We identified 3786 potential SSRs. SNPs were identified by mapping the reads of the individual assemblies against the combined assembly. After stringent filtering a total of 77,000 putative SNPs were identified. A phylogenetic analysis of single copy number genes showed that sainfoin was most closely related to red clover and Medicago truncatula, while Lotus japonicus, bean and soybean are more distant relatives.

CONCLUSIONS

This work describes the first transcriptome assembly in sainfoin. The 92 K transcripts provide a rich source of SNP and SSR polymorphisms for future use in genetic studies of this crop. Annotation of genes involved in the condensed tannin biosynthesis pathway has provided the basis for further studies of the genetic control of this important trait in sainfoin.

A simulation-based breeding design that uses whole-genome prediction in tomato

Efficient plant breeding methods must be developed in order to increase yields and feed a growing world population, as well as to meet the demands of consumers with diverse preferences who require high-quality foods. We propose a strategy that integrates breeding simulations and phenotype prediction models using genomic information. The validity of this strategy was evaluated by the simultaneous genetic improvement of the yield and flavour of the tomato (Solanum lycopersicum), as an example. Reliable phenotype prediction models for the simulation were constructed from actual genotype and phenotype data. Our simulation predicted that selection for both yield and flavour would eventually result in morphological changes that would increase the total plant biomass and decrease the light extinction coefficient, an essential requirement for these improvements. This simulation-based genome-assisted approach to breeding will help to optimise plant breeding, not only in the tomato but also in other important agricultural crops.

Genomics-assisted breeding in fruit trees

Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.

Advances in Japanese pear breeding in Japan

The Japanese pear (Pyrus pyrifolia Nakai) is one of the most widely grown fruit trees in Japan, and it has been used throughout Japan's history. The commercial production of pears increased rapidly with the successive discoveries of the chance seedling cultivars 'Chojuro' and 'Nijisseiki' around 1890, and the development of new cultivars has continued since 1915. The late-maturing, leading cultivars 'Niitaka' and 'Shinko' were released during the initial breeding stage. Furthermore, systematic breeding by the Horticultural Research Station (currently, NARO Institute of Fruit Tree Science, National Agriculture and Food Research Organization (NIFTS)) began in 1935, which mainly aimed to improve fruit quality by focusing on flesh texture and black spot disease resistance. To date, 22 cultivars have been released, including 'Kosui', 'Hosui', and 'Akizuki', which are current leading cultivars from the breeding program. Four induced mutant cultivars induced by gamma irradiation, which exhibit some resistance to black spot disease, were released from the Institute of Radiation Breeding. Among these cultivars, 'Gold Nijisseiki' has become a leading cultivar. Moreover, 'Nansui' from the Nagano prefectural institute breeding program was released, and it has also become a leading cultivar. Current breeding objectives at NIFTS mainly combine superior fruit quality with traits related to labor and cost reduction, multiple disease resistance, or self-compatibility. Regarding future breeding, marker-assisted selection for each trait, QTL analyses, genome-wide association studies, and genomic selection analyses are currently in progress.

Genomics of pear and other Rosaceae fruit trees

The family Rosaceae includes many economically important fruit trees, such as pear, apple, peach, cherry, quince, apricot, plum, raspberry, and loquat. Over the past few years, whole-genome sequences have been released for Chinese pear, European pear, apple, peach, Japanese apricot, and strawberry. These sequences help us to conduct functional and comparative genomics studies and to develop new cultivars with desirable traits by marker-assisted selection in breeding programs. These genomics resources also allow identification of evolutionary relationships in Rosaceae, development of genome-wide SNP and SSR markers, and construction of reference genetic linkage maps, which are available through the Genome Database for the Rosaceae website. Here, we review the recent advances in genomics studies and their practical applications for Rosaceae fruit trees, particularly pear, apple, peach, and cherry.

Genomic prediction of biological shape: elliptic Fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.)

Shape is an important morphological characteristic both in animals and plants. In the present study, we examined a method for predicting biological contour shapes based on genome-wide marker polymorphisms. The method is expected to contribute to the acceleration of genetic improvement of biological shape via genomic selection. Grain shape variation observed in rice (Oryza sativa L.) germplasms was delineated using elliptic Fourier descriptors (EFDs), and was predicted based on genome-wide single nucleotide polymorphism (SNP) genotypes. We applied four methods including kernel PLS (KPLS) regression for building a prediction model of grain shape, and compared the accuracy of the methods via cross-validation. We analyzed multiple datasets that differed in marker density and sample size. Datasets with larger sample size and higher marker density showed higher accuracy. Among the four methods, KPLS showed the highest accuracy. Although KPLS and ridge regression (RR) had equivalent accuracy in a single dataset, the result suggested the potential of KPLS for the prediction of high-dimensional EFDs. Ordinary PLS, however, was less accurate than RR in all datasets, suggesting that the use of a non-linear kernel was necessary for accurate prediction using the PLS method. Rice grain shape can be predicted accurately based on genome-wide SNP genotypes. The proposed method is expected to be useful for genomic selection in biological shape.

Identification of QTLs controlling harvest time and fruit skin color in Japanese pear (Pyrus pyrifolia Nakai)

Using an F1 population from a cross between Japanese pear (Pyrus pyrifolia Nakai) cultivars 'Akiakari' and 'Taihaku', we performed quantitative trait locus (QTL) analysis of seven fruit traits (harvest time, fruit skin color, flesh firmness, fruit weight, acid content, total soluble solids content, and preharvest fruit drop). The constructed simple sequence repeat-based genetic linkage map of 'Akiakari' consisted of 208 loci and spanned 799 cM; that of 'Taihaku' consisted of 275 loci and spanned 1039 cM. Out of significant QTLs, two QTLs for harvest time, one for fruit skin color, and one for flesh firmness were stably detected in two successive years. The QTLs for harvest time were located at the bottom of linkage group (LG) Tai3 (nearest marker: BGA35) and at the top of LG Tai15 (nearest markers: PPACS2 and MEST050), in good accordance with results of genome-wide association study. The PPACS2 gene, a member of the ACC synthase gene family, may control harvest time, preharvest fruit drop, and fruit storage potential. One major QTL associated with fruit skin color was identified at the top of LG 8. QTLs identified in this study would be useful for marker-assisted selection in Japanese pear breeding programs.