Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation

Genomic basis of the differences between cider and dessert apple varieties

Unravelling 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 FST 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

Reconstruction of multi-generation pedigrees involving numerous old apple cultivars thanks to whole-genome SNP data

A number of European apple cultivars are old, some of them dating back to the Renaissance, Middle Ages or even earlier. Many other cultivars have been developed during subsequent times. In order to decipher the relationships that link some of these old cultivars, whole-genome SNP data (~ 250K) for over 1400 genotypes were analyzed to infer first-degree relationships and reconstruct pedigrees. We used simple exclusion tests based on a count of Mendelian error to identify up to a thousand potential parent-offspring duos, including 295 complete parent-offspring trios and a hundred duos that could be oriented. grand-parents for some missing parents could also be inferred. Combining all this information allowed us to reconstruct pedigrees (up to 6 generations) highlighting the central role of major founders such as ‘Reinette Franche’, ‘Margil’, and ‘Alexander’. Haplotypes were deduced from genotypic data and pedigrees, and used to measure haplotype sharing between supposedly unrelated cultivars, allowing investigating further links between them.To our knowledge, such a large analysis to reconstruct multigeneration pedigrees involving (very) old cultivars selected over such time has never before been performed in perennial fruit species

High-quality de novo assembly of the apple genome and methylome dynamics of early fruit development

Using the latest sequencing and optical mapping technologies, we have produced a high-quality de novo assembly of the apple (Malus domestica Borkh.) genome. Repeat sequences, which represented over half of the assembly, provided an unprecedented opportunity to investigate the uncharacterized regions of a tree genome; we identified a new hyper-repetitive retrotransposon sequence that was over-represented in heterochromatic regions and estimated that a major burst of different transposable elements (TEs) occurred 21 million years ago. Notably, the timing of this TE burst coincided with the uplift of the Tian Shan mountains, which is thought to be the center of the location where the apple originated, suggesting that TEs and associated processes may have contributed to the diversification of the apple ancestor and possibly to its divergence from pear. Finally, genome-wide DNA methylation data suggest that epigenetic marks may contribute to agronomically relevant aspects, such as apple fruit development

An integrated approach for increasing breeding efficiency in apple and peach in Europe

Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA). This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond

Genomic selection in commercial perennial crops: applicability and improvement in oil palm (Elaeis guineensis Jacq.)

Genomic selection (GS) uses genome-wide markers to select individuals with the desired overall combination of breeding traits. A total of 1,218 individuals from a commercial population of Ulu Remis x AVROS (UR x AVROS) were genotyped using the OP200K array. The traits of interest included: shellto- fruit ratio (S/F, %), mesocarp-to-fruit ratio (M/F, %), kernel-to-fruit ratio (K/F, %), fruit per bunch (F/B, %), oil per bunch (O/B, %) and oil per palm (O/P, kg/palm/year). Genomic heritabilities of these traits were estimated to be in the range of 0.40 to 0.80. GS methods assessed were RR-BLUP, Bayes A (BA), Cπ (BC), Lasso (BL) and Ridge Regression (BRR). All methods resulted in almost equal prediction accuracy. The accuracy achieved ranged from 0.40 to 0.70, correlating with the heritability of traits. By selecting the most important markers, RR-BLUP B has the potential to outperform other methods. The marker density for certain traits can be further reduced based on the linkage disequilibrium (LD). Together with in silico breeding, GS is now being used in oil palm breeding programs to hasten parental palm selection

QTL detection by multi-parent linkage mapping in oil palm (Elaeis guineensis Jacq.)

A quantitative trait locus (QTL) analysis designed for a multi-parent population was carried out and tested in oil palm (Elaeis guineensis Jacq.), which is a diploid cross-fertilising perennial species. A new extension of the MCQTL package was especially designed for crosses between heterozygous parents. The algorithm, which is now available for any allogamous species, was used to perform and compare two types of QTL search for small size families, within-family analysis and across-family analysis, using data from a 2 × 2 complete factorial mating experiment involving four parents from three selected gene pools. A consensus genetic map of the factorial design was produced using 251 microsatellite loci, the locus of the Sh major gene controlling fruit shell presence, and an AFLP marker of that gene. A set of 76 QTLs involved in 24 quantitative phenotypic traits was identified. A comparison of the QTL detection results showed that the across-family analysis proved to be efficient due to the interconnected families, but the family size issue is just partially solved. The identification of QTL markers for small progeny numbers and for marker-assisted selection strategies is discussed