Genomic selection for fruit quality traits in apple (Malus×domestica Borkh.)

Chromosome level high-density integrated genetic maps improve the Pyrus bretschneideri 'DangshanSuli' v1.0 genome

Background

Chromosomal level reference genomes provide a crucial foundation for genomics research such as genome-wide association studies (GWAS) and whole genome selection. The chromosomal-level sequences of both the European (Pyrus communis) and Chinese (P. bretschneideri) pear genomes have not been published in public databases so far.

Results

To anchor the scaffolds of P. bretschneideri ‘DangshanSuli’ (DS) v1.0 genome into pseudo-chromosomes, two genetic maps (MH and YM maps) were constructed using half sibling populations of Chinese pear crosses, ‘Mantianhong’ (MTH) × ‘Hongxiangsu’ (HXS) and ‘Yuluxiang’ (YLX) × MTH, from 345 and 162 seedlings, respectively, which were prepared for SNP discovery using genotyping-by-sequencing (GBS) technology. The MH and YM maps, each with 17 linkage groups (LGs), were constructed from 2606 and 2489 SNP markers and spanned 1847 and 1668 cM, respectively, with average marker intervals of 0.7. The two maps were further merged with a previously published genetic map (BD) based on the cross ‘Bayuehong’ (BYH) × ‘Dangshansuli’ (DS) to build a new integrated MH-YM-BD map. By using 7757 markers located on the integrated MH-YM-BD map, 898 scaffolds (400.57 Mb) of the DS v1.0 assembly were successfully anchored into 17 pseudo-chromosomes, accounting for 78.8% of the assembled genome size. About 88.31% of them (793 scaffolds) were directionally anchored with two or more markers on the pseudo-chromosomes. Furthermore, the errors in each pseudo-chromosome (especially 1, 5, 7 and 11) were manually corrected and pseudo-chromosomes 1, 5 and 7 were extended by adding 19, 12 and 14 scaffolds respectively in the newly constructed DS v1.1 genome. Synteny analyses revealed that the DS v1.1 genome had high collinearity with the apple genome, and the homologous fragments between pseudo-chromosomes were similar to those found in previous studies. Moreover, the red-skin trait of Asian pear was mapped to an identical locus as identified previously.

Conclusions

The accuracy of DS v1.1 genome was improved by using larger mapping populations and merged genetic map. With more than 400 MB anchored to 17 pseudo-chromosomes, the new DS v1.1 genome provides a critical tool that is essential for studies of pear genetics, genomics and molecular breeding.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5224-6) contains supplementary material, which is available to authorized users.

Multivariate GBLUP Improves Accuracy of Genomic Selection for Yield and Fruit Weight in Biparental Populations of Vaccinium macrocarpon Ait

The development of high-throughput genotyping has made genome-wide association (GWAS) and genomic selection (GS) applications possible for both model and non-model species. The exploitation of genome-assisted approaches could greatly benefit breeding efforts in American cranberry (Vaccinium macrocarpon) and other minor crops. Using biparental populations with different degrees of relatedness, we evaluated multiple GS methods for total yield (TY) and mean fruit weight (MFW). Specifically, we compared predictive ability (PA) differences between univariate and multivariate genomic best linear unbiased predictors (GBLUP and MGBLUP, respectively). We found that MGBLUP provided higher predictive ability (PA) than GBLUP, in scenarios with medium genetic correlation (8-17% increase with corg~0.6) and high genetic correlations (25-156% with corg~0.9), but found no increase when genetic correlation was low. In addition, we found that only a few hundred single nucleotide polymorphism (SNP) markers are needed to reach a plateau in PA for both traits in the biparental populations studied (in full linkage disequilibrium). We observed that higher resemblance among individuals in the training (TP) and validation (VP) populations provided greater PA. Although multivariate GS methods are available, genetic correlations and other factors need to be carefully considered when applying these methods for genetic improvement.

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.

Apple fruit acidity is genetically diversified by natural variations in three hierarchical epistatic genes: MdSAUR37, MdPP2CH and MdALMTII

Many efforts have been made to map quantitative trait loci (QTLs) to facilitate practical marker-assisted selection (MAS) in plants. In the present study, using MapQTL and BSA-seq (bulk segregant analysis using next generation sequencing) with two independent pedigree-based populations, we identified four major genome-wide QTLs responsible for apple fruit acidity. Candidate genes were screened in major QTL regions, and three functional gene markers, including a non-synonymous A/G single-nucleotide polymorphism (SNP) in the coding region of MdPP2CH, a 36-bp insertion in the promoter of MdSAUR37 and a previously reported SNP in MdALMTII, were validated to influence the malate content of apple fruits. In addition, MdPP2CH inactivated three vacuolar H⁺ -ATPases (MdVHA-A3, MdVHA-B2 and MdVHA-D2) and one aluminium-activated malate transporter (MdALMTII) via dephosphorylation and negatively influenced fruit malate accumulation. The dephosphotase activity of MdPP2CH was suppressed by MdSAUR37, which implied a higher hierarchy of genetic interaction. Therefore, the MdSAUR37/MdPP2CH/MdALMTII chain cascaded hierarchical epistatic genetic effects to precisely determine apple fruit malate content. An A/G SNP (-1010) on the MdMYB44 promoter region from a major QTL (qtl08.1) was closely associated with fruit malate content. The predicted phenotype values (PPVs) were estimated using the tentative genotype values of the gene markers, and the PPVs were significantly correlated with the observed phenotype values. Our findings provide an insight into plant genome-based selection in apples and will aid in conducting research to understand the fundamental physiological basis of quantitative genetics.

Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana

Improving the efficiency of selection in conventional crossbreeding is a major priority in banana ( spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47- 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.

Generation of advanced fire blight-resistant apple (Malus × domestica) selections of the fifth generation within 7 years of applying the early flowering approach

The approach presented here can be applied to reduce the time needed to introduce traits from wild apples into null segregant advanced selections by one-fourth. Interesting traits like resistances to pathogens are often found within the wild apple gene pool. However, the long juvenile phase of apple seedlings hampers the rapid introduction of these traits into new cultivars. The rapid crop cycle breeding approach used in this paper is based on the overexpression of the birch (Betula pendula) MADS4 transcription factor in apple. Using the early flowering line T1190 and 'Evereste' as source of the fire blight resistance (Fb_E locus), we successfully established 18 advanced selections of the fifth generation in the greenhouse within 7 years. Fifteen individuals showed the habitus expected of a regular apple seedling, while three showed very short internodes. The null segregants possessing a regular habitus maintained the high level of fire blight resistance typical for 'Evereste'. Using SSR markers, we estimated the percentage of genetic drag from 'Evereste' still associated with Fb_E on linkage group 12 (LG12). Eight out of the 18 selections had only 4% of 'Evereste' genome left. Since genotypes carrying the apple scab resistance gene Rvi6 and the fire blight resistance QTL Fb_F7 were used as parents in the course of the experiments, these resistances were also identified in some of the null segregants. One seedling is particularly interesting as, beside Fb_E, it also carries Fb_F7 heterozygously and Rvi6 homozygously. If null segregants obtained using this method will be considered as not genetically modified in Europe, as is already the case in the USA, this genotype could be a very promising parent for breeding new fire blight and scab-resistant apple cultivars in European apple breeding programs.

Genotyping-by-sequencing through transcriptomics: implementation in a range of crop species with varying reproductive habits and ploidy levels

The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker-dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost-effectiveness and sample throughput. Genotyping-by-sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS-transcriptomics (GBS-t) approach developed to be broadly applicable, cost-effective and high-throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS-t was validated as a simple and largely automated, cost-effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS-t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity.

A Genome-Wide Association Study of Apple Quality and Scab Resistance

The apple ( × Borkh.) is an economically and culturally important crop grown worldwide. Growers of this long-lived perennial must produce fruit of adequate quality while also combatting abiotic and biotic stress. Traditional apple breeding can take up to 20 yr from initial cross to commercial release, but genomics-assisted breeding can help accelerate this process. To advance genomics-assisted breeding in apple, we performed genome-wide association studies (GWAS) and genomic prediction in a collection of 172 apple accessions by linking over 55,000 single nucleotide polymorphisms (SNPs) with 10 phenotypes collected over 2 yr. Genome-wide association studies revealed several known loci for skin color, harvest date and firmness at harvest. Several significant GWAS associations were detected for resistance to a major fungal pathogen, apple scab ( [Cke.] Wint.), but we demonstrate that these hits likely represent a single ancestral source. Using genomic prediction, we show that most phenotypes are sufficiently predictable using genome-wide SNPs to be candidates for genomic selection. Finally, we detect a signal for firmness retention after storage on chromosome 10 and show that it may not stem from variation in , a gene repeatedly identified in bi-parental mapping studies and widely believed to underlie a major QTL for firmness on chromosome 10. We provide evidence that this major QTL is more likely due to variation in a neighboring ethylene response factor (ERF) gene. The present study showcases the superior mapping resolution of GWAS compared to bi-parental linkage mapping by identifying a novel candidate gene underlying a well-studied, major QTL involved in apple firmness.

Realistic texture synthesis for point-based fruitage phenotype

Although current 3D scanner technology can acquire textural images from a point model, visible seams in the image, inconvenient data acquisition and occupancy of a large space during use are points of concern for outdoor fruit models. In this paper, an SPSDW (simplification and perception based subdivision followed by down-sampling weighted average) method is proposed to balance memory usage and texture synthesis quality using a crop fruit, such as apples, as a research subject for a point-based fruit model. First, the quadtree method is improved to make splitting more efficient, and a reasonable texton descriptor is defined to promote query efficiency. Then, the color perception feature is extracted from the image for all pixels. Next, an advanced sub-division scheme and down-sampling strategy are designed to optimize memory space. Finally, a weighted oversampling method is proposed for high-quality texture mixing. This experiment demonstrates that the SPSDW method preserves the mixed texture more realistically and smoothly and preserves color memory up to 94%, 84.7% and 85.7% better than the two-dimesional processing, truncating scalar quantitative and color vision model methods, respectively.

Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives

There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype-phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.

Genome-wide association study and genomic prediction in citrus: Potential of genomics-assisted breeding for fruit quality traits

Novel genomics-based approaches such as genome-wide association studies (GWAS) and genomic selection (GS) are expected to be useful in fruit tree breeding, which requires much time from the cross to the release of a cultivar because of the long generation time. In this study, a citrus parental population (111 varieties) and a breeding population (676 individuals from 35 full-sib families) were genotyped for 1,841 single nucleotide polymorphisms (SNPs) and phenotyped for 17 fruit quality traits. GWAS power and prediction accuracy were increased by combining the parental and breeding populations. A multi-kernel model considering both additive and dominance effects improved prediction accuracy for acidity and juiciness, implying that the effects of both types are important for these traits. Genomic best linear unbiased prediction (GBLUP) with linear ridge kernel regression (RR) was more robust and accurate than GBLUP with non-linear Gaussian kernel regression (GAUSS) in the tails of the phenotypic distribution. The results of this study suggest that both GWAS and GS are effective for genetic improvement of citrus fruit traits. Furthermore, the data collected from breeding populations are beneficial for increasing the detection power of GWAS and the prediction accuracy of GS.

Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies

Background

Highly polygenic traits such as fruit weight, sugar content and acidity strongly influence the agroeconomic value of peach varieties. Genomic Selection (GS) can accelerate peach yield and quality gain if predictions show higher levels of accuracy compared to phenotypic selection. The available IPSC 9K SNP array V1 allows standardized and highly reliable genotyping, preparing the ground for GS in peach.

Results

A repeatability model (multiple records per individual plant) for genome-enabled predictions in eleven European peach populations is presented. The analysis included 1147 individuals derived from both commercial and non-commercial peach or peach-related accessions. Considered traits were average fruit weight (FW), sugar content (SC) and titratable acidity (TA). Plants were genotyped with the 9K IPSC array, grown in three countries (France, Italy, Spain) and phenotyped for 3–5 years. An analysis of imputation accuracy of missing genotypic data was conducted using the software Beagle, showing that two of the eleven populations were highly sensitive to increasing levels of missing data. The regression model produced, for each trait and each population, estimates of heritability (FW:0.35, SC:0.48, TA:0.53, on average) and repeatability (FW:0.56, SC:0.63, TA:0.62, on average). Predictive ability was estimated in a five-fold cross validation scheme within population as the correlation of true and predicted phenotypes. Results differed by populations and traits, but predictive abilities were in general high (FW:0.60, SC:0.72, TA:0.65, on average).

Conclusions

This study assessed the feasibility of Genomic Selection in peach for highly polygenic traits linked to yield and fruit quality. The accuracy of imputing missing genotypes was as high as 96%, and the genomic predictive ability was on average 0.65, but could be as high as 0.84 for fruit weight or 0.83 for titratable acidity. The estimated repeatability may prove very useful in the management of the typical long cycles involved in peach productions. All together, these results are very promising for the application of genomic selection to peach breeding programmes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3781-8) contains supplementary material, which is available to authorized users.

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

Genetic Diversity, Population Structure, and Linkage Disequilibrium of a Core Collection of Ziziphus jujuba Assessed with Genome-wide SNPs Developed by Genotyping-by-sequencing and SSR Markers

Chinese jujube (Ziziphus jujuba Mill) is an economically important fruit species native to China with high nutritious and medicinal value. Genotyping-by-sequencing was used to detect and genotype single nucleotide polymorphisms (SNPs) in a core collection of 150 Chinese jujube accessions and further to characterize their genetic diversity, population structure, and linkage disequilibrium (LD). A total of 4,680 high-quality SNPs were identified, of which 38 sets of tri-allelic SNPs were detected. The average polymorphism information content (PIC) values based on bi-allelic SNPs and tri-allelic SNPs were 0.27 and 0.38, respectively. STRUCTURE and principal coordinate analyses based on SNPs revealed that the 150 accessions could be clustered into two groups. However, neighbor-joining trees indicated the accessions should be grouped into three major clusters. Our data confirm that the resolving power for genetic diversity was similar for the SSRs and SNPs. In contrast, regarding population structure, the resolving power was higher for SSRs than for SNPs. The LD pattern in Chinese jujube was investigated for the first time. We observed a relatively rapid LD decay with a short range (∼10 kb) for all pseudo-chromosomes and for individual pseudo-chromosomes. Our findings provide important information for future genome-wide association analyses and marker-assisted selective breeding of Chinese jujube.

Exploiting Wild Relatives for Genomics-assisted Breeding of Perennial Crops

Perennial crops are vital contributors to global food production and nutrition. However, the breeding of new perennial crops is an expensive and time-consuming process due to the large size and lengthy juvenile phase of many species. Genomics provides a valuable tool for improving the efficiency of breeding by allowing progeny possessing a trait of interest to be selected at the seed or seedling stage through marker-assisted selection (MAS). The benefits of MAS to a breeder are greatest when the targeted species takes a long time to reach maturity and is expensive to grow and maintain. Thus, MAS holds particular promise in perennials since they are often costly and time-consuming to grow to maturity and evaluate. Well-characterized germplasm that breeders can tap into for improving perennials is often limited in genetic diversity. Wild relatives are a largely untapped source of desirable traits including disease resistance, fruit quality, and rootstock characteristics. This review focuses on the use of genomics-assisted breeding in perennials, especially as it relates to the introgression of useful traits from wild relatives. The identification of genetic markers predictive of beneficial phenotypes derived from wild relatives is hampered by genomic tools designed for domesticated species that are often ill-suited for use in wild relatives. There is therefore an urgent need for better genomic resources from wild relatives. A further barrier to exploiting wild diversity through genomics is the phenotyping bottleneck: well-powered genetic mapping requires accurate and cost-effective characterization of large collections of diverse wild germplasm. While genomics will always be used in combination with traditional breeding methods, it is a powerful tool for accelerating the speed and reducing the costs of breeding while harvesting the potential of wild relatives for improving perennial crops.

Reduced representation genome sequencing reveals patterns of genetic diversity and selection in apple

Identifying DNA sequence variations is a fundamental step towards deciphering the genetic basis of traits of interest. Here, a total of 20 cultivated and 10 wild apples were genotyped using specific-locus amplified fragment sequencing, and 39,635 single nucleotide polymorphisms with no missing genotypes and evenly distributed along the genome were selected to investigate patterns of genome-wide genetic variations between cultivated and wild apples. Overall, wild apples displayed higher levels of genetic diversity than cultivated apples. Linkage disequilibrium (LD) decays were observed quite rapidly in cultivated and wild apples, with an r² -value below 0.2 at 440 and 280 bp, respectively. Moreover, bidirectional gene flow and different distribution patterns of LD blocks were detected between domesticated and wild apples. Most LD blocks unique to cultivated apples were located within QTL regions controlling fruit quality, thus suggesting that fruit quality had probably undergone selection during apple domestication. The genome of the earliest cultivated apple in China, Nai, was highly similar to that of Malus sieversii, and contained a small portion of genetic material from other wild apple species. This suggested that introgression could have been an important driving force during initial domestication of apple. These findings will facilitate future breeding and genetic dissection of complex traits in apple.

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

In crops, optimizing target traits in breeding programs can be fostered by selecting appropriate combinations of architectural traits which determine light interception and carbon acquisition. In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a 'Starkrimson' × 'Granny Smith' cross, were used. The phyllochron was estimated as a function of thermal time and sylleptic branching was modeled subsequently depending on phyllochron. From a genetic map built with SNPs, marker effects were estimated on four MAppleT parameters with rrBLUP, using 2007 data. These effects were then considered in MAppleT to simulate tree development in the two climatic conditions. The genome wide prediction model gave consistent estimations of parameter values with correlation coefficients between observed values and estimated values from SNP markers ranging from 0.79 to 0.96. However, the accuracy of the prediction model following cross validation schemas was lower. Three integrative traits (the number of leaves, trunk length, and number of sylleptic laterals) were considered for validating MAppleT simulations. In 2007 climatic conditions, simulated values were close to observations, highlighting the correct simulation of genetic variability. However, in 2004 conditions which were not used for model calibration, the simulations differed from observations. This study demonstrates the possibility of integrating genome-based information in a FSPM for a perennial fruit tree. It also showed that further improvements are required for improving the prediction ability. Especially temperature effect should be extended and other factors taken into account for modeling GxE interactions. Improvements could also be expected by considering larger populations and by testing other genome wide prediction models. Despite these limitations, this study opens new possibilities for supporting plant breeding by in silico evaluations of the impact of genotypic polymorphisms on plant integrative phenotypes.

An experimental validation of genomic selection in octoploid strawberry

The primary goal of genomic selection is to increase genetic gains for complex traits by predicting performance of individuals for which phenotypic data are not available. The objective of this study was to experimentally evaluate the potential of genomic selection in strawberry breeding and to define a strategy for its implementation. Four clonally replicated field trials, two in each of 2 years comprised of a total of 1628 individuals, were established in 2013-2014 and 2014-2015. Five complex yield and fruit quality traits with moderate to low heritability were assessed in each trial. High-density genotyping was performed with the Affymetrix Axiom IStraw90 single-nucleotide polymorphism array, and 17 479 polymorphic markers were chosen for analysis. Several methods were compared, including Genomic BLUP, Bayes B, Bayes C, Bayesian LASSO Regression, Bayesian Ridge Regression and Reproducing Kernel Hilbert Spaces. Cross-validation within training populations resulted in higher values than for true validations across trials. For true validations, Bayes B gave the highest predictive abilities on average and also the highest selection efficiencies, particularly for yield traits that were the lowest heritability traits. Selection efficiencies using Bayes B for parent selection ranged from 74% for average fruit weight to 34% for early marketable yield. A breeding strategy is proposed in which advanced selection trials are utilized as training populations and in which genomic selection can reduce the breeding cycle from 3 to 2 years for a subset of untested parents based on their predicted genomic breeding values.

DNA-informed breeding of rosaceous crops: promises, progress and prospects

Crops of the Rosaceae family provide valuable contributions to rural economies and human health and enjoyment. Sustained solutions to production challenges and market demands can be met with genetically improved new cultivars. Traditional rosaceous crop breeding is expensive and time-consuming and would benefit from improvements in efficiency and accuracy. Use of DNA information is becoming conventional in rosaceous crop breeding, contributing to many decisions and operations, but only after past decades of solved challenges and generation of sufficient resources. Successes in deployment of DNA-based knowledge and tools have arisen when the 'chasm' between genomics discoveries and practical application is bridged systematically. Key steps are establishing breeder desire for use of DNA information, adapting tools to local breeding utility, identifying efficient application schemes, accessing effective services in DNA-based diagnostics and gaining experience in integrating DNA information into breeding operations and decisions. DNA-informed germplasm characterization for revealing identity and relatedness has benefitted many programs and provides a compelling entry point to reaping benefits of genomics research. DNA-informed germplasm evaluation for predicting trait performance has enabled effective reallocation of breeding resources when applied in pioneering programs. DNA-based diagnostics is now expanding from specific loci to genome-wide considerations. Realizing the full potential of this expansion will require improved accuracy of predictions, multi-trait DNA profiling capabilities, streamlined breeding information management systems, strategies that overcome plant-based features that limit breeding progress and widespread training of current and future breeding personnel and allied scientists.

On the Accuracy of Genomic Selection

Genomic selection is focused on prediction of breeding values of selection candidates by means of high density of markers. It relies on the assumption that all quantitative trait loci (QTLs) tend to be in strong linkage disequilibrium (LD) with at least one marker. In this context, we present theoretical results regarding the accuracy of genomic selection, i.e., the correlation between predicted and true breeding values. Typically, for individuals (so-called test individuals), breeding values are predicted by means of markers, using marker effects estimated by fitting a ridge regression model to a set of training individuals. We present a theoretical expression for the accuracy; this expression is suitable for any configurations of LD between QTLs and markers. We also introduce a new accuracy proxy that is free of the QTL parameters and easily computable; it outperforms the proxies suggested in the literature, in particular, those based on an estimated effective number of independent loci (M_e). The theoretical formula, the new proxy, and existing proxies were compared for simulated data, and the results point to the validity of our approach. The calculations were also illustrated on a new perennial ryegrass set (367 individuals) genotyped for 24,957 single nucleotide polymorphisms (SNPs). In this case, most of the proxies studied yielded similar results because of the lack of markers for coverage of the entire genome (2.7 Gb).

Distinct transcriptome profiles reveal gene expression patterns during fruit development and maturation in five main cultivated species of pear (Pyrus L.)

The transcriptomes of five pear cultivars, 'Hosui' (P. pyrifolia), 'Yali' (P. bretschneideri), 'Kuerlexiangli' (P. sinkiangensis), 'Nanguoli' (P. ussuriensis), and 'Starkrimson' (P. communis) were sequenced at seven key fruit developmental stages, from fruit setting to maturation and fruit senescence after harvesting. In total, 33,136 genes that could be mapped by reads, were analyzed. Most gene expression cluster models showed a steadily decreasing trend. Gene expression patterns had obvious differences according to maturity type, that is, post-ripening cultivars were still vigorous at maturity, and showed a higher proportion of up-regulated genes; non post-ripening cultivars had a gradually decreasing tendency during fruit maturation. Meanwhile, differentially expressed genes related to fruit quality and development, such as stone cells, sugar, acid and hormones, were identified. Co-expression analysis revealed that several ethylene synthesis genes and polyphenoloxidase-related genes interacted with each other directly, and an indirect relationship was reflected between ethylene synthesis genes and ethylene response genes. In addition, the highly diverse SNPs represented the great differences between oriental and occidental pears. Understanding how RNA-seq based gene-expression patterns and differential gene expression contribute to fruit quality allows us to build models for gene-expression for fruit development of Pyrus species.

Establishment and Optimization of Genomic Selection to Accelerate the Domestication and Improvement of Intermediate Wheatgrass

Intermediate wheatgrass (IWG) is a perennial species and has edible and nutritious grain and desirable agronomic traits, including large seed size, high grain yield, and biomass. It also has the potential to provide ecosystem services and an economic return to farmers. However, because of its allohexaploidy and self-incompatibility, developing molecular markers for genetic analysis and molecular breeding has been challenging. In the present study, using genotyping-by-sequencing (GBS) technology, 3436 genome-wide markers discovered in a biparental population with 178 genets, were mapped to 21 linkage groups (LG) corresponding to 21 chromosomes of IWG. Genomic prediction models were developed using 3883 markers discovered in a breeding population containing 1126 representative genets from 58 half-sib families. High predictive ability was observed for seven agronomic traits using cross-validation, ranging from 0.46 for biomass to 0.67 for seed weight. Optimization results indicated that 8 to 10 genets from each half-sib family can form a good training population to predict the breeding value of their siblings, and 1600 genome-wide markers are adequate to capture the genetic variation in the current breeding population for genomic selection. Thus, with the advances in sequencing-based marker technologies, it was practical to perform molecular genetic analysis and molecular breeding on a new and challenging species like IWG, and genomic selection could increase the efficiency of recurrent selection and accelerate the domestication and improvement of IWG.

Evaluation of the 2b-RAD method for genomic selection in scallop breeding

The recently developed 2b-restriction site-associated DNA (2b-RAD) sequencing method provides a cost-effective and flexible genotyping platform for aquaculture species lacking sufficient genomic resources. Here, we evaluated the performance of this method in the genomic selection (GS) of Yesso scallop (Patinopecten yessoensis) through simulation and real data analyses using six statistical models. Our simulation analysis revealed that the prediction accuracies obtained using the 2b-RAD markers were slightly lower than those obtained using all polymorphic loci in the genome. Furthermore, a small subset of markers obtained from a reduced tag representation (RTR) library presented comparable performance to that obtained using all markers, making RTR be an attractive approach for GS purpose. Six GS models exhibited variable performance in prediction accuracy depending on the scenarios (e.g., heritability, sample size, population structure), but Bayes-alphabet and BLUP-based models generally outperformed other models. Finally, we performed the evaluation using an empirical dataset composed of 349 Yesso scallops that were derived from five families. The prediction accuracy for this empirical dataset could reach 0.4 based on optimal GS models. In summary, the genotyping flexibility and cost-effectiveness make 2b-RAD be an ideal genotyping platform for genomic selection in aquaculture breeding programs.

Biotechnology and apple breeding in Japan

Apple is a fruit crop of significant economic importance, and breeders world wide continue to develop novel cultivars with improved characteristics. The lengthy juvenile period and the large field space required to grow apple populations have imposed major limitations on breeding. Various molecular biological techniques have been employed to make apple breeding easier. Transgenic technology has facilitated the development of apples with resistance to fungal or bacterial diseases, improved fruit quality, or root stocks with better rooting or dwarfing ability. DNA markers for disease resistance (scab, powdery mildew, fire-blight, Alternaria blotch) and fruit skin color have also been developed, and marker-assisted selection (MAS) has been employed in breeding programs. In the last decade, genomic sequences and chromosome maps of various cultivars have become available, allowing the development of large SNP arrays, enabling efficient QTL mapping and genomic selection (GS). In recent years, new technologies for genetic improvement, such as trans-grafting, virus vectors, and genome-editing, have emerged. Using these techniques, no foreign genes are present in the final product, and some of them show considerable promise for application to apple breeding.

Next generation breeding

The genomic revolution of the past decade has greatly improved our understanding of the genetic make-up of living organisms. The sequencing of crop genomes has completely changed our vision and interpretation of genome organization and evolution. Re-sequencing allows the identification of an unlimited number of markers as well as the analysis of germplasm allelic diversity based on allele mining approaches. High throughput marker technologies coupled with advanced phenotyping platforms provide new opportunities for discovering marker-trait associations which can sustain genomic-assisted breeding. The availability of genome sequencing information is enabling genome editing (site-specific mutagenesis), to obtain gene sequences desired by breeders. This review illustrates how next generation sequencing-derived information can be used to tailor genomic tools for different breeders' needs to revolutionize crop improvement.

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.

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 approaches to selection in outcrossing perennials: focus on essential oil crops

The yield of essential oil in commercially harvested perennial species (e.g. 'Oil Mallee' eucalypts, Tea Trees and Hop) is dependent on complex quantitative traits such as foliar oil concentration, biomass and adaptability. These often show large natural variation and some are highly heritable, which has enabled significant gains in oil yield via traditional phenotypic recurrent selection. Analysis of transcript abundance and allelic diversity has revealed that essential oil yield is likely to be controlled by large numbers of quantitative trait loci that range from a few of medium/large effect to many of small effect. Molecular breeding techniques that exploit this information could increase gains per unit time and address complications of traditional breeding such as genetic correlations between key traits and the lower heritability of biomass. Genomic selection (GS) is a technique that uses the information from markers genotyped across the whole genome in order to predict the phenotype of progeny well before they reach maturity, allowing selection at an earlier age. In this review, we investigate the feasibility of genomic selection (GS) for the improvement of essential oil yield. We explore the challenges facing breeders selecting for oil yield, and how GS might deal with them. We then assess the factors that affect the accuracy of genomic estimated breeding values, such as linkage disequilibrium (LD), heritability, relatedness and the genetic architecture of desirable traits. We conclude that GS has the potential to significantly improve the efficiency of selection for essential oil yield.

Genes Encoding Aluminum-Activated Malate Transporter II and their Association with Fruit Acidity in Apple

A gene encoding aluminum-activated malate transporter (ALMT) was previously reported as a candidate for the Ma locus controlling acidity in apple (Malus × domestica Borkh.). In this study, we found that apple ALMT genes can be divided into three families and the Ma1 gene belongs to the ALMTII family. Duplication of ALMTII genes in apple is related to the polyploid origin of the apple genome. Divergence in expression has occurred between the Ma1 gene and its homologs in the ALMTII family and only the Ma1 gene is significantly associated with malic acid content. The Ma locus consists of two alleles, Ma1 and ma1. Ma1 resides in the tonoplast and its ectopic expression in yeast was found to increase the influx of malic acid into yeast cells significantly, suggesting it may function as a vacuolar malate channel. In contrast, ma1 encodes a truncated protein because of a single nucleotide substitution of G with A in the last exon. As this truncated protein resides within the cell membrane, it is deemed to be nonfunctional as a vacuolar malate channel. The frequency of the Ma1Ma1 genotype is very low in apple cultivars but is high in wild relatives, which suggests that apple domestication may be accompanied by selection for the Ma1 gene. In addition, variations in the malic acid content of mature fruits were also observed between accessions with the same genotype in the Ma locus. This suggests that the Ma gene is not the only genetic determinant of fruit acidity in apple.

The distribution and impact of common copy-number variation in the genome of the domesticated apple, Malus x domestica Borkh

Background

Copy number variation (CNV) is a common feature of eukaryotic genomes, and a growing body of evidence suggests that genes affected by CNV are enriched in processes that are associated with environmental responses. Here we use next generation sequence (NGS) data to detect copy-number variable regions (CNVRs) within the Malus x domestica genome, as well as to examine their distribution and impact.

Methods

CNVRs were detected using NGS data derived from 30 accessions of M. x domestica analyzed using the read-depth method, as implemented in the CNVrd2 software. To improve the reliability of our results, we developed a quality control and analysis procedure that involved checking for organelle DNA, not repeat masking, and the determination of CNVR identity using a permutation testing procedure.

Results

Overall, we identified 876 CNVRs, which spanned 3.5 % of the apple genome. To verify that detected CNVRs were not artifacts, we analyzed the B- allele-frequencies (BAF) within a single nucleotide polymorphism (SNP) array dataset derived from a screening of 185 individual apple accessions and found the CNVRs were enriched for SNPs having aberrant BAFs (P < 1e-13, Fisher’s Exact test). Putative CNVRs overlapped 845 gene models and were enriched for resistance (R) gene models (P < 1e-22, Fisher’s exact test). Of note was a cluster of resistance gene models on chromosome 2 near a region containing multiple major gene loci conferring resistance to apple scab.

Conclusion

We present the first analysis and catalogue of CNVRs in the M. x domestica genome. The enrichment of the CNVRs with R gene models and their overlap with gene loci of agricultural significance draw attention to a form of unexplored genetic variation in apple. This research will underpin further investigation of the role that CNV plays within the apple genome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2096-x) contains supplementary material, which is available to authorized users.

Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments

The nonadditive genetic effects may have an important contribution to total genetic variation of phenotypes, so estimates of both the additive and nonadditive effects are desirable for breeding and selection purposes. Our main objectives were to: estimate additive, dominance and epistatic variances of apple (Malus × domestica Borkh.) phenotypes using relationship matrices constructed from genome-wide dense single nucleotide polymorphism (SNP) markers; and compare the accuracy of genomic predictions using genomic best linear unbiased prediction models with or without including nonadditive genetic effects. A set of 247 clonally replicated individuals was assessed for six fruit quality traits at two sites, and also genotyped using an Illumina 8K SNP array. Across several fruit quality traits, the additive, dominance, and epistatic effects contributed about 30%, 16%, and 19%, respectively, to the total phenotypic variance. Models ignoring nonadditive components yielded upwardly biased estimates of additive variance (heritability) for all traits in this study. The accuracy of genomic predicted genetic values (GEGV) varied from about 0.15 to 0.35 for various traits, and these were almost identical for models with or without including nonadditive effects. However, models including nonadditive genetic effects further reduced the bias of GEGV. Between-site genotypic correlations were high (>0.85) for all traits, and genotype-site interaction accounted for <10% of the phenotypic variability. The accuracy of prediction, when the validation set was present only at one site, was generally similar for both sites, and varied from about 0.50 to 0.85. The prediction accuracies were strongly influenced by trait heritability, and genetic relatedness between the training and validation families.

A dense SNP genetic map constructed using restriction site-associated DNA sequencing enables detection of QTLs controlling apple fruit quality

Background

Genetic map based quantitative trait locus (QTL) analysis is an important method for studying important horticultural traits in apple. To facilitate molecular breeding studies of fruit quality traits in apple, we aim to construct a high density map which was efficient for QTL mapping and possible to search for candidate genes directly in mapped QTLs regions.

Methods

A total of 1733 F1 seedlings derived from ‘Jonathan’ × ‘Golden Delicious’ was used for the map constructionand QTL analysis. The SNP markers were developed by restriction site-associated DNA sequencing (RADseq). Phenotyping data of fruit quality traits were calculated in 2008-2011. Once QTLs were mapped, candidate genes were searched for in the corresponding regions of the apple genome sequence underlying the QTLs. Then some of the candidate genes were validated using real-time PCR.

Results

A high-density genetic map with 3441 SNP markers from 297 individuals was generated. Of the 3441 markers, 2017 were mapped to ‘Jonathan’ with a length of 1343.4 cM and the average distance between markers was 0.67 cM, 1932 were mapped to ‘Golden Delicious’ with a length of 1516.0 cM and the average distance between markers was 0.78 cM. Twelve significant QTLs linked to the control of fruit weight, fruit firmness, sugar content and fruit acidity were mapped to seven linkage groups. Based on gene annotation, 80, 64 and 17 genes related to fruit weight, fruit firmness and fruit acidity, respectively, were analyzed.Among the 17 candidate genes associated with control of fruit acidity, changes in the expression of MDP0000582174 (MdMYB4) were in agreement with the pattern of changes in malic acid content in apple during ripening, and the relative expression of MDP0000239624 (MdME) was significantly correlated withfruit acidity.

Conclusions

We demonstrated the construction of a dense SNP genetic map in apple using next generation sequencing and that the increased resolution enabled the detection of narrow interval QTLs linked to the three fruit quality traits assessed. The candidate genes MDP0000582174 and MDP0000239624 were found to be related to fruit acidity regulation. We conclude that application of RADseq for genetic map construction improved the precision of QTL detection and should be utilized in future studies on the regulatory mechanisms of important fruit traits in apple.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1946-x) contains supplementary material, which is available to authorized users.

Uncovering co-expression gene network modules regulating fruit acidity in diverse apples

Background

Acidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates.

Results

Guided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P < 0.001) correlation with malate. Of these, the Ma1 containing module (Turquoise) of 336 genes showed the highest correlation (0.79). We also identified 12 intramodular hub genes from each of the five modules and 18 enriched gene ontology (GO) terms and MapMan sub-bines, including two GO terms (GO:0015979 and GO:0009765) and two MapMap sub-bins (1.3.4 and 1.1.1.1) related to photosynthesis in module Turquoise. Using Lemon-Tree algorithms, we identified 12 regulator genes of probabilistic scores 35.5–81.0, including MDP0000525602 (a LLR receptor kinase), MDP0000319170 (an IQD2-like CaM binding protein) and MDP0000190273 (an EIN3-like transcription factor) of greater interest for being one of the 18 MSAGs or one of the 12 intramodular hub genes in Turquoise, and/or a regulator to the cluster containing Ma1.

Conclusions

The most relevant finding of this study is the identification of the MSAGs, intramodular hub genes, enriched photosynthesis related processes, and regulator genes in a WGCNA module Turquoise that not only encompasses Ma1 but also shows the highest modular correlation with acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1816-6) contains supplementary material, which is available to authorized users.

Genomic Prediction of Biomass Yield in Two Selection Cycles of a Tetraploid Alfalfa Breeding Population

Alfalfa (Medicago sativa L.) is a widely planted perennial forage legume grown throughout temperate and dry subtropical regions in the world. Long breeding cycles limit genetic improvement of alfalfa, particularly for complex traits such as biomass yield. Genomic selection (GS), based on predicted breeding values obtained using genome-wide molecular markers, could enhance breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic selection using genotyping-by-sequencing (GBS). We then developed prediction equations using yield data from three locations. Approximately 10,000 single nucleotide polymorphism (SNP) markers were used for GS modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the location. The GS model developed using Cycle 0 as the training population in predicting total biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype × environment interaction and the number of harvests and years used to generate yield phenotypes had effects on prediction accuracy across generations and locations, Based on our results, the selection efficiency per unit time for GS is higher than phenotypic selection, although accuracies will likely decline across multiple selection cycles. This study provided evidence that GS can accelerate genetic gain in alfalfa for biomass yield.

A major QTL controlling apple skin russeting maps on the linkage group 12 of 'Renetta Grigia di Torriana'

BACKGROUND

Russeting is a disorder developed by apple fruits that consists of cuticle cracking followed by the replacement of the epidermis by a corky layer that protects the fruit surface from water loss and pathogens. Although influenced by many environmental conditions and orchard management practices, russeting is under genetic control. The difficulty in classifying offspring and consequent variable segregation ratios have led several authors to conclude that more than one genetic determinant could be involved, although some evidence favours a major gene (Ru).

RESULTS

In this study we report the mapping of a major genetic russeting determinant on linkage group 12 of apple as inferred from the phenotypic observation in a segregating progeny derived from 'Renetta Grigia di Torriana', the construction of a 20 K Illumina SNP chip based genetic map, and QTL analysis. Recombination analysis in two mapping populations restricted the region of interest to approximately 400 Kb. Of the 58 genes predicted from the Golden Delicious sequence, a putative ABCG family transporter has been identified. Within a small set of russeted cultivars tested with markers of the region, only six showed the same haplotype of 'Renetta Grigia di Torriana'.

CONCLUSIONS

A major determinant (Ru_RGT) for russeting development putatively involved in cuticle organization is proposed as a candidate for controlling the trait. SNP and SSR markers tightly co-segregating with the Ru_RGT locus may assist the breeder selection. The observed segregations and the analysis of the 'Renetta Grigia di Torriana' haplotypic region in a panel of russeted and non-russeted cultivars may suggest the presence of other determinants for russeting in apple.

Genomic basis of the differences between cider and dessert apple varieties

Unraveling the genomic processes at play during variety diversification is of fundamental interest for understanding evolution, but also of applied interest in crop science. It can indeed provide knowledge on the genetic bases of traits for crop improvement and germplasm diversity management. Apple is one of the most important fruit crops in temperate regions, having both great economic and cultural values. Sweet dessert apples are used for direct consumption, while bitter cider apples are used to produce cider. Several important traits are known to differentiate the two variety types, in particular fruit size, biennial versus annual fruit bearing, and bitterness, caused by a higher content in polyphenols. Here, we used an Illumina 8k SNP chip on two core collections, of 48 dessert and 48 cider apples, respectively, for identifying genomic regions responsible for the differences between cider and dessert apples. The genome-wide level of genetic differentiation between cider and dessert apples was low, although 17 candidate regions showed signatures of divergent selection, displaying either outlier F_ST values or significant association with phenotypic traits (bitter versus sweet fruits). These candidate regions encompassed 420 genes involved in a variety of functions and metabolic pathways, including several colocalizations with QTLs for polyphenol compounds.

Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa

BACKGROUND

A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry Fragaria × ananassa. Short-read sequences from one diploid and 19 octoploid accessions were aligned to the diploid Fragaria vesca 'Hawaii 4' reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.

RESULTS

About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%). The remaining SNPs included those discovered in the diploid progenitor F. iinumae (3.9%), and speculative "codon-based" SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix's "SNPolisher" R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family 'Holiday' × 'Korona' with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.

CONCLUSIONS

The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

Development and preliminary evaluation of a 90 K Axiom® SNP array for the allo-octoploid cultivated strawberry Fragaria × ananassa

BACKGROUND

A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry Fragaria × ananassa. Short-read sequences from one diploid and 19 octoploid accessions were aligned to the diploid Fragaria vesca 'Hawaii 4' reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.

RESULTS

About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing "haploSNPs" (11.7%). The remaining SNPs included those discovered in the diploid progenitor F. iinumae (3.9%), and speculative "codon-based" SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix's "SNPolisher" R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family 'Holiday' × 'Korona' with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.

CONCLUSIONS

The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array's high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.

Genomic selection prediction accuracy in a perennial crop: case study of oil palm (Elaeis guineensis Jacq.)

Genomic selection empirically appeared valuable for reciprocal recurrent selection in oil palm as it could account for family effects and Mendelian sampling terms, despite small populations and low marker density. Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this is expected mainly through shortened breeding cycles and increased selection intensity, which requires sufficient GS accuracy in selection candidates, despite often small training populations. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), the major world oil crop. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated within-population GS accuracies when predicting breeding values of non-progeny-tested individuals for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that GS could account for family effects and Mendelian sampling terms in Group B but only for family effects in Deli. Presumably, this difference between populations originated from their contrasting breeding history. The GS accuracy ranged from -0.41 to 0.94 and was positively correlated with the relationship between training and test sets. Training sets optimized with the so-called CDmean criterion gave the highest accuracies, ranging from 0.49 (pulp to fruit ratio in Group B) to 0.94 (fruit weight in Group B). The statistical methods did not affect the accuracy. Finally, Group B could be preselected for progeny tests by applying GS to key yield traits, therefore increasing the selection intensity. Our results should be valuable for breeding programs with small populations, long breeding cycles, or reduced effective size.

Accuracy and responses of genomic selection on key traits in apple breeding

The application of genomic selection in fruit tree crops is expected to enhance breeding efficiency by increasing prediction accuracy, increasing selection intensity and decreasing generation interval. The objectives of this study were to assess the accuracy of prediction and selection response in commercial apple breeding programmes for key traits. The training population comprised 977 individuals derived from 20 pedigreed full-sib families. Historic phenotypic data were available on 10 traits related to productivity and fruit external appearance and genotypic data for 7829 SNPs obtained with an Illumina 20K SNP array. From these data, a genome-wide prediction model was built and subsequently used to calculate genomic breeding values of five application full-sib families. The application families had genotypes at 364 SNPs from a dedicated 512 SNP array, and these genotypic data were extended to the high-density level by imputation. These five families were phenotyped for 1 year and their phenotypes were compared to the predicted breeding values. Accuracy of genomic prediction across the 10 traits reached a maximum value of 0.5 and had a median value of 0.19. The accuracies were strongly affected by the phenotypic distribution and heritability of traits. In the largest family, significant selection response was observed for traits with high heritability and symmetric phenotypic distribution. Traits that showed non-significant response often had reduced and skewed phenotypic variation or low heritability. Among the five application families the accuracies were uncorrelated to the degree of relatedness to the training population. The results underline the potential of genomic prediction to accelerate breeding progress in outbred fruit tree crops that still need to overcome long generation intervals and extensive phenotyping costs.

Methods for the design, implementation, and analysis of illumina infinium™ SNP assays in plants

The advent of Next-Generation sequencing-by-synthesis technologies has fuelled SNP discovery, genotyping, and screening of populations in myriad ways for many species, including various plant species. One technique widely applied to screening a large number of SNP markers over a large number of samples is the Illumina Infinium™ assay.

Genomic selection accuracies within and between environments and small breeding groups in white spruce

BACKGROUND

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.

RESULTS

Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.

CONCLUSIONS

Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Development and validation of a 20K single nucleotide polymorphism (SNP) whole genome genotyping array for apple (Malus × domestica Borkh)

High-density SNP arrays for genome-wide assessment of allelic variation have made high resolution genetic characterization of crop germplasm feasible. A medium density array for apple, the IRSC 8K SNP array, has been successfully developed and used for screens of bi-parental populations. However, the number of robust and well-distributed markers contained on this array was not sufficient to perform genome-wide association analyses in wider germplasm sets, or Pedigree-Based Analysis at high precision, because of rapid decay of linkage disequilibrium. We describe the development of an Illumina Infinium array targeting 20K SNPs. The SNPs were predicted from re-sequencing data derived from the genomes of 13 Malus × domestica apple cultivars and one accession belonging to a crab apple species (M. micromalus). A pipeline for SNP selection was devised that avoided the pitfalls associated with the inclusion of paralogous sequence variants, supported the construction of robust multi-allelic SNP haploblocks and selected up to 11 entries within narrow genomic regions of ±5 kb, termed focal points (FPs). Broad genome coverage was attained by placing FPs at 1 cM intervals on a consensus genetic map, complementing them with FPs to enrich the ends of each of the chromosomes, and by bridging physical intervals greater than 400 Kbps. The selection also included ∼3.7K validated SNPs from the IRSC 8K array. The array has already been used in other studies where ∼15.8K SNP markers were mapped with an average of ∼6.8K SNPs per full-sib family. The newly developed array with its high density of polymorphic validated SNPs is expected to be of great utility for Pedigree-Based Analysis and Genomic Selection. It will also be a valuable tool to help dissect the genetic mechanisms controlling important fruit quality traits, and to aid the identification of marker-trait associations suitable for the application of Marker Assisted Selection in apple breeding programs.

Genomics: a potential panacea for the perennial problem

Perennial crops represent important fresh and processed food sources worldwide, but advancements in breeding perennials are often impeded due to their very nature. The perennial crops we rely on most for food take several years to reach production maturity and require large spaces to grow, which make breeding new cultivars costly compared with most annual crops. Because breeding perennials is inefficient and expensive, they are often grown in monocultures consisting of small numbers of elite cultivars that are vegetatively propagated for decades or even centuries. This practice puts many perennial crops at risk for calamity since they remain stationary in the face of evolving pest and disease pressures. Although there is tremendous genetic diversity available to them, perennial crop breeders often struggle to generate commercially successful cultivars in a timely and cost-effective manner because of the high costs of breeding. Moreover, consumers often expect the same cultivars to be available indefinitely, and there is often little or no incentive for growers and retailers to take the risk of adopting new cultivars. While genomics studies linking DNA variants to commercially important traits have been performed in diverse perennial crops, the translation of these studies into accelerated breeding of improved cultivars has been limited. Here we explain the "perennial problem" in detail and demonstrate how modern genomics tools can significantly improve the cost effectiveness of breeding perennial crops and thereby prevent crucial food sources from succumbing to the perils of perpetual propagation.

Fast and cost-effective genetic mapping in apple using next-generation sequencing

Next-generation DNA sequencing (NGS) produces vast amounts of DNA sequence data, but it is not specifically designed to generate data suitable for genetic mapping. Recently developed DNA library preparation methods for NGS have helped solve this problem, however, by combining the use of reduced representation libraries with DNA sample barcoding to generate genome-wide genotype data from a common set of genetic markers across a large number of samples. Here we use such a method, called genotyping-by-sequencing (GBS), to produce a data set for genetic mapping in an F1 population of apples (Malus × domestica) segregating for skin color. We show that GBS produces a relatively large, but extremely sparse, genotype matrix: over 270,000 SNPs were discovered but most SNPs have too much missing data across samples to be useful for genetic mapping. After filtering for genotype quality and missing data, only 6% of the 85 million DNA sequence reads contributed to useful genotype calls. Despite this limitation, using existing software and a set of simple heuristics, we generated a final genotype matrix containing 3967 SNPs from 89 DNA samples from a single lane of Illumina HiSeq and used it to create a saturated genetic linkage map and to identify a known QTL underlying apple skin color. We therefore demonstrate that GBS is a cost-effective method for generating genome-wide SNP data suitable for genetic mapping in a highly diverse and heterozygous agricultural species. We anticipate future improvements to the GBS analysis pipeline presented here that will enhance the utility of next-generation DNA sequence data for the purposes of genetic mapping across diverse species.

The draft genome sequence of European pear (Pyrus communis L. 'Bartlett')

We present a draft assembly of the genome of European pear (Pyrus communis) 'Bartlett'. Our assembly was developed employing second generation sequencing technology (Roche 454), from single-end, 2 kb, and 7 kb insert paired-end reads using Newbler (version 2.7). It contains 142,083 scaffolds greater than 499 bases (maximum scaffold length of 1.2 Mb) and covers a total of 577.3 Mb, representing most of the expected 600 Mb Pyrus genome. A total of 829,823 putative single nucleotide polymorphisms (SNPs) were detected using re-sequencing of 'Louise Bonne de Jersey' and 'Old Home'. A total of 2,279 genetically mapped SNP markers anchor 171 Mb of the assembled genome. Ab initio gene prediction combined with prediction based on homology searching detected 43,419 putative gene models. Of these, 1219 proteins (556 clusters) are unique to European pear compared to 12 other sequenced plant genomes. Analysis of the expansin gene family provided an example of the quality of the gene prediction and an insight into the relationships among one class of cell wall related genes that control fruit softening in both European pear and apple (Malus × domestica). The 'Bartlett' genome assembly v1.0 (http://www.rosaceae.org/species/pyrus/pyrus_communis/genome_v1.0) is an invaluable tool for identifying the genetic control of key horticultural traits in pear and will enable the wide application of marker-assisted and genomic selection that will enhance the speed and efficiency of pear cultivar development.