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. A Europe-led effort to bridge the gap between genomics and breeding offers a new resource to support apple and peach production. A team led by François Laurens from the Research Institute in Horticulture and Seeds (IRHS) in Angers, France, review the tools, methodologies and scientific discoveries made through FruitBreedomics, a project that ran between 2011 and 2015 and involved 28 different research and private companies from around the world. These new molecular and bioinformatics tools include DNA arrays for mapping genetic diversity, computer software for analyzing genetic information and novel methods for understanding commercially important traits such as fruit quality and resistance to stresses. The project’s materials should help apple and peach producers better plan their breeding programs so as to meet grower and consumer demand for high-quality fruit with desired characteristics. Fruit industry is facing economic challenges imposed by increasingly fierce international competition and decreasing fruit consumption, societal demand for a more sustainable production, and biological problems caused by climate changes. Releasing new cultivars that meet these challenges is a major goal of all breeding programs. For decades, response to address them has been slow due to the nature of fruit tree breeding: long term; low efficiency; and hence high cost. For the past 20 years, international networks of fruit geneticists and genomicists have been built to develop research on fruit genetics aimed at enhancing fruit quality traits as well as resistance to biotic stresses. The commonly applied aim of these programs was to release molecular markers associated to major agronomic traits to help and improve the efficiency of the breeding programs.1: at this time, the most common molecular markers used to build fruit genetic maps were SSRs3; their high quality and polymorphism had made them very helpful for diversity studies but high cost and low throughput made them unsuitable to build dense genetic maps; this resulted in big gaps in genetic maps and limited resolution in quantitative trait loci (QTL) mapping. In addition, all the genetic studies were based on bi-parental progenies leading to a lack of information on the genetic diversity which was practically used in commercial breeding programs3. To overcome these challenges, the European Union thanks to the Seventh Framework Programme (2007–2013) funded the FruitBreedomics consortium from March 2011 to August 2015 to take advantage of the first initiatives to decipher fruit species genomes . This European large collaborative project aimed at making use of the latest methodologies, technologies and knowledge to improve the efficiency of current fruit breeding programmes. It focused on apple and peach, two major fruits in Europe, keeping in mind that many tools and much knowledge gained could also be of benefit to other species of the Rosaceae family via the strong genomic and functional ancestral relatedness among these species.Despite all these efforts, in 2010 very few breeding programmes were adopting the output of these researches. From this premise, a European consortium listed the main bottlenecks that hampered the use of new technologies in breeding programmesFruitBreedomics, with researchers from 28 research institutes and private companies  and acoustic (crispness and crunchiness). A TA-XTplus Texture Analyzer equipped with an Acoustic Envelop Detector (AED) device 7. Specific alleles at these two loci explained each >20% of the variability for SMF and this trait was, in turn, associated with maturity date, late maturing individuals having an increased SMF. A third unlinked QTL was also found that affected maturity time but not flesh texture, allowing for genotype combinations that are early maturing and have the SMF phenotype7.In peach, slow melting flesh (SMF) varieties, like ‘BIG TOP’ have become de ‘facto’ standard in the industry because of their high fruit quality and better handling after harvest. However, this trait, characterised by a slower firmness loss than melting flesh fruit after harvest, is quite difficult to phenotype. Two methodologies tested in FruitBreedomics showed promising results: (i) time-course postharvest evaluation with a hand-held penetrometer (ii) rheograms acquired from digital penetrometer . Both of them are now recommended to breeders who develop this type of varieties. Two peach progenies having ‘Big Top’ as one of the parents were genotyped with the 9K single-nucleotide polymorphism (SNP) chip (see below) and phenotyped with a hand-held penetrometer at different stages of the postharvest process. After mapping two major independent QTL were detected, one exclusively found in ‘Big Top’Monilinia spp. induces severe damage in the orchard but also during transport and storage8. Efforts outstretched during the life of the project participated in compiling the information currently available on this disease9, developing new artificial infection assessment to screen progenies and exploring fruit physical and biochemical characteristics as potential resistance factors (publication submitted). Specifically, surface compounds, waxes and epidermis phenolic compounds may contribute to the resistance of the fruits in the middle stage of growth (pit hardening) (upcoming publication). Though environmental control may be important, the variability of susceptibility to M. laxa and fruit characteristics observed within progenies indicated a quantitative inheritance of these traits, further studied via QTL analyses10. Some genotypes that displayed infection probability equal to zero for different years, sites or tests are currently used to develop peach cultivars resistant to brown rot.Peach brown rot caused by 11. Second, the role of plant morphology was explored using high-throughput phenotyping technologies (PhenoArch platform). For the first time in fruit trees, the variability of water use efficiency (WUE) and the respective roles of plant biomass and transpiration were explored within a French core collection. The total plant biomass was highly correlated to WUE, and two of its components (maximum internode length and plant height) were highly heritable. Groups of morphotypes were identified based on organogenesis and organ dimensions  under severe drought was confirmed13. The variability among genotypes of morphologic and functioning responses to soil water contents illustrates a promising genetic diversity and opens the way for improving apple plant materials for the use of water.Global climate change predictions indicate decreased water availability in many important fruit growing areas. Selection of apple and peach cultivars that might endure water stress is going to be in the ‘to do list’ of fruit breeding programs as it is in other plant species. However, the assessment of water stress resistance is neither simple nor fast. FruitBreedomics has explored two methods to assess tolerance/resistance to water scarcity. First, a multivariate statistical approach was developed to assess leaf photo-assimilation performance using a portable photosynthesis system. Water stress tolerance of genotypes could be discriminated by the IPL index, correlated with photosynthetic activity and variables representing their morphological responses14. Objectives may focus on the selection of cultivars with either lower chilling requirements or higher heat requirements of the floral buds to prevent from spring frost risk or excessive flowering advances.Besides water availability concerns, erratic phenology may also hamper production in some regions in the future. Besides forcing tests that proved relevant to select cultivars with desired chilling and heating requirements, phenological models that requires observed data easier to screen allowed to progress in the dissection of budbreak and flowering time genetic architecture15. All data are accessible through a stable and well-maintained interface, available at the address http://bioinformatics.tecnoparco.org/fruitbreedomics.Bioinformatics infrastructures were designed and deployed to provide efficient storage and access of phenotypic data and genome sequence variation information. We developed and managed web-based relational data banks specifically created for apple and peach data, and provided specific bioinformatics tools for data integration and visualizationhttp://gmod.org/wiki/Chado), the same underlying many installations including The Genome Database of Rosaceae (GDR—http://rosaceae.org/). With this infrastructure, Fruibreedomics data landscape were aligned to GDR, easing data exchange between the two projects.The database contains a shared nomenclature for phenotypes attributes and descriptors, and a list of consensus cultivar names from several countries. It also stores genotypes and phenotypes collected during several years of observations on collections of plants, characterised by cultivars and geographical locations. Two separate database systems have been developed, along with their two interfaces. The first one stores phenotype data relying on an experiment-based model. This database reflects, in its structure, the natural data shape of the phenotyping information: trees, experimental orchards, phenotypes and genotypes are represented as separate and interconnected tables, in a classic relational data model; this facilitates standardization and importing/integration procedures, as well as update of this particular type of data. While an automatic procedure allows to upload large amounts of data, performing data validation in the meantime, a second interface, constituted by web pages containing modules and forms, allows researchers to manually input information (ensuring integrity and correctness of data) and to surf and update database content. All the information included in this part of the system is periodically copied, together with other types of data  to the final database, into a Chado-like structure , and Marker Based Analysis (MBA), with the development of two custom online tools, namely 16 and one for apple with 384 SNPs used for gene diversity and linkage mapping studies17, followed a few months later by a 8K SNP markers produced by the IRSC . The FruitBreedomics consortium, after evaluation of the efficiency of the apple SNP arrays available, decided to develop two higher density chips for apple in order to fulfil the objectives of the project in particular to fine map QTLs for main horticultural traits. Firstly, a new array with a target size of 20K SNPs, was developed in order to better cover chromosome ends and other gaps within chromosomes. A number of robust SNP markers (3.7K) from the available IRSC apple 8K Infinium SNP array were included in the new 20K Illumina Infinium array, while an additional 16.3K SNPs were predicted from re-sequencing data derived from 14 apple genotypes and two double-haploids from ‘Golden Delicious’19. A haplotype-targeting strategy similar to that adopted to design the IRSC array was implemented for the design of the array, in order to combine the information from individual SNPs into haploblocks and provide fully informative multi-allelic markers. An ad hoc pipeline for SNP selection was devised to reach the target of 20K SNPs, while at the same time avoiding the pitfalls presented by paralogous sequence variants. The performance of the SNP array was assessed using data from >2000 seedlings from 25 full-sib families20 and ~400 cultivars and breeding selections. This evaluation revealed that >75% of the SNP assays were polymorphic and segregated in a Mendelian fashion. The genetic linkage maps constructed on individual families had an average of 6.8K SNPs uniformly distributed along the genome19, which provided ~7000 high-quality markers suitable for pedigree-based QTL mapping on multi-parent populations (see below).At the beginning of the project, three DNA arrays were available: one for peach with 9K SNP markers from the IPSC 21 both of which are available through the GDR databasewww.rosaceae.org. Using the information obtained from the 20K array on wide germplasm it was also possible to study the decay of linkage disequilibrium (LD) for each apple chromosome. As expected, LD decays quite rapidly, at an average distance of 55 kb . A much higher density array was therefore necessary to perform Genome Wide Association studies (GWAS) in apple.The genetic position of ca. 16K SNPs was used for the re-ordering of contigs from the first apple genome sequence into an improved version of the apple genome (v3.0) now available in a recent de novo assembly of a ‘Golden Delicious’ doubled-haploid whole genome sequence22. For SNP discovery a high coverage whole-genome re-sequencing strategy across a panel of highly diverse apple cultivars was adopted to call individual genotypes and reduce the number of false-positive SNPs included in the array design. This also allowed the selection of sets of polymorphic markers that together represented the diversity of the discovery panel. All the robust SNP markers from the previous 20K Illumina Infinium array were included together with 1.5K SNPs derived from a Genotyping By Sequencing experiment, while the remaining 465K SNPs were newly predicted from a re-sequencing of 63 apple genotypes and two doubled haploids (available upon request). The Axiom Apple480K Chip was successfully produced and it is now public available. Using a quality prediction model based on logistic regression, a total of 275,223 highly informative and robust SNPs (56% of the 487K) were identified22. This newly developed array was used for apple GWAS on 1324 apple accessions from six European germplasm collections and for the construction of four high-density parental maps23.Thus, a 487K SNP array, the largest ever produced for a fruit plant and among the largest for higher plants, was developed using Affymetrix technology16. Since LD in peach was estimated at about 13–15 cM24, the density of the array is sufficient to perform optimal GWAS in peach with the goal of obtaining SNP closely linked to horticultural traits of interest. Among the 8144 included in the array, 4271 were scored as polymorphic in a collection of 1580 peach accessions  and an average of 2166 validated SNPs were obtained for each of 18 peach progenies (derived from intra- and interspecific crosses). In addition, the genotyping of several segregating progenies, allowed the construction of dense SNP maps for accurate QTL detection, as demonstrated by a first application for the identification of QTLs for volatile organic compounds involved in peach fruit aroma26.For peach, the IPSC  9K SNP array was successfully used for genotyping both segregating populations and germplasm collections. The average space between SNPs in the array is 26.7 kb, which corresponds to 16.5 SNPs per cM28 and GWAS based on the analysis of populations of unrelated cultivars. Both approaches aimed to decipher the genetic control of major agronomic traits and provide DNA diagnostic tools to support breeders in the selection of new cultivars.The second main aim of FruitBreedomics was to improve knowledge on the inheritance of important traits and to discover and characterize marker trait associations that could be made applicable for MAB through the development of DNA diagnostic tools. These genetic studies have been performed thanks to two complementary strategies both based on examining and exploring a wide genetic diversity: the Pedigree Based Analysis (PBA), which is based on the integrated analysis of multiple pedigreed full-sib familieshttp://www.illumina.com)29.Software packages were developed to provide an efficient workflow for genetic studies using SNP markers from the filtering and calling of markers through the phasing of SNP marker alleles, the building of haploblocks and haplotypes to PBA-based QTL discovery software. Firstly, the ASSIsT software allows a smooth, fast and reliable filtering and calling of Infinium derived SNP markers. The format of its input files are compatible with output files from GenomeStudio  that usually have a major negative impact on map quality. This included the use of multiple full-sib families (21), reduction in the amount of missing data through a bin-mapping strategy, and the transformation of the data from a cross pollination design to a back cross design, as outlined by Di Pierro et al.20. The dedicated software HapAg was developed in support to this approach, which has been made public available at http://www.wageningenur.nl/en/show/HaploblockAggregator.htm.The accuracy by which gene positions can be localised is affected by many factors, including the order of the markers used in the analyses. Ideally, this order is identical to their true order on the chromosomes. For peach, this order can be adequately deduced from the known reference genome, due to the high quality of the sequence, which was further improved also with the use of genetic maps produced in the FruitBreedomics project21 as a reference for the completion of other genetic linkage maps in apple47.Next, this map was used in FruitBreedomics for PBA based QTL discovery studies and in further works to improve the apple reference genome sequenceIn peach, the ability to position a target gene to a very confined region is regularly limited due to shortage of informative markers. Here, informative means that a marker reveals a difference (polymorphism) between two homologous chromosomes. In FruitBreedomics over 1.3 million new polymorphisms have been identified by examining the genome sequence of four important parents through a re-sequencing experiment. Next, some new SNP markers were designed and applied on some specific families, allowing to narrow down the potential position of a ‘Maturity Date’ gene by 27% to only 159 kb. The new polymorphisms have also been used for the design of an higher density Illumina peach SNP chip (18k peach SNP chip) in the context of an international collaboration involving FruitBreedomics and RosBREED partners .50.In peach seven key horticultural traits  measured over several years were analysed in 18 progenies (1467 plants) from five European breeding programs. These were located in INRA-Avignon (France), INRA-Bordeaux (France), IRTA-Lleida (Spain), MAS.PES program  and CREA-Rome . The progenies derived from both commercial and non-commercial parents (from peach or peach related species). As no individuals were duplicated or common between locations, to remove the effect of the environment in the analysis, phenotypic data was standardized. The progenies were genotyped with the 9K IPSC array. After filtering the SNPs for quality as described in the ASSIsT pipeline, polymorphic SNPs were grouped in haploblocks of 1 cM. Phenotypic (including raw and standardized data) and genotypic data were analysed with PBA software. We identified 47 QTLs, 22 coinciding with major genes and QTLs found in previous analyses and 25 new. Nearly half of the QTLs (47%) were detected only when analysing non-commercial progenies. This strategy also provided estimations on the narrow sense heritability of each trait and the estimation of the QTL genotype and breeding value of each parent51 and EU-ISAFRUIT and some national projects. In FruitBreedomics, this corresponding germplasm has been genotyped with the 20K SNP array19. Marker trait associations have been found, and their stability over storage have been evaluated. Papers to report on this in detail are in progress for fruit firmness and storability, acidity and total sugars, fruit size, fruit color, and pre-harvest fruit drop. Presentations on part of the results on these are accessible through the FruitBreedomics website. Findings on the genetics of budbreak, flowering time and regular bearing have been published44.In apple a series of fruit quality traits have been studied on 25 mapping populations from seven breeding programs from six European countries for which the phenotypic data were collected as part of the previous EU-HiDRAS28 and facilitates composing germplasm sets for PBA studies52. This information is also useful in clarifying the origin and mode of action of discovered QTL alleles and extrapolation of QTL results to other, genetically linked individuals. In FruitBreedomics over 800 apple cultivars and breeding selections were SNP genotyped. The data allowed validation of historic pedigree records, tracing parentages of over 50 individuals for which pedigree records were lacking or false. This included major founding cultivars world-wide  as well as ancestors from cultivars of specific interest . Moreover, the 20 most produced cultivars show close genetic relationships. This tendency towards mono-culture could be ‘vulnerable to a catastrophe’, e.g., in case a new disease would show up. In 1991, Way et al. 54 already alerted that ‘careful consideration of pedigrees and increased size of the genetic base are needed in future apple breeding strategies’. FruitBreedomics surveyed the genetic diversity that is currently used in apple breeding. Seventeen European programs participated, as well as programs from New Zealand and the USA. Over 3200 crosses and over 3000 ancestors were examined for their founder composition. Usually, programs build on a genetically narrow framework of a few founders. However, overall a major part of the genetic make-up of modern germplasm is of highly diverse origin coming from up to 90 founders within a single breeding program. The data document the huge efforts of breeders to include new traits from very distinct genetic resources, including wild species. The results will be useful to breeders and policymakers in the EU and worldwide to define an agenda on breeding strategies and research priorities related to genetic diversity.In Europe, apple production has a narrow genetic base: half of the production comes from just four cultivars: ‘Golden Delicious’, ‘Gala‘, ‘Idared‘, ‘Red Delicious‘ (A work package of FruitBreedomics was dedicated to the study of germplasm. The objectives were to: (i) improve the knowledge of genetic variability in apple and peach collections by exploring the phenotypic and allelic diversity available in apple and peach germplasm collections  and defining potential national and European Core collections (CC); (ii) identify genomic regions contributing to the genetic control of horticultural traits through GWAS; (iii) supply breeders with tightly linked molecular markers for implementation into their MAB pipelines.Considering that a large number of apple and peach accessions had been maintained and evaluated for several traits and years in European repositories, we first surveyed and made available a list of the accessions existing within a number of key European genbanks (5477 apples and 2885 peaches), together with a short description of the traits and phenotypic records available, with their respective protocols. Common protocols and descriptors were also defined for both apple and peach for further phenotypic assessments. Afterwards subsets of accessions were selected for each collection (1560 apples and 1296 peaches) and their phenotypic records transferred to the FruitBreedomics database following the scales agreed. The peach collections considered were those maintained by the University of Milano, CRA , INRA-Avignon (France) and IRTA (Spain) plus a valuable Chinese collection representing a large portion of peach variability absent in the European germplasm collections . The apple collections were those maintained by CRA-W-Gembloux (Belgium), INRA-Angers (France), RBIP-Holovouzy (Czech Republic), SLU-Balsgard (Sweden), University of Bologna , and NFC-Brogdale (UK). Phenotypic records for phenology, fruit quality, tree architecture, and some disease resistance data were uploaded to the FruitBreedomics database. Thereafter subsets of accessions were selected as representatives of the collections based on curators’ knowledge and/or available genotypic (SSR) data. This subset of accessions was phenotyped for key selected traits during at least two additional seasons in their respective location.57. Thanks to the FruitBreedomics project, a large number of additional apple accessions from various European germplasm collections were genotyped for diversity analysis with 16 common SSRs. Altogether, >2400 accessions were jointly analysed. The final sample included accessions from germplasm collections located in Belgium, France, Italy, United Kingdom, Czech Republic, Sweden, Switzerland, Spain, Russia and Kyrgyzstan. SSR data were used to identify synonyms and investigate the genetic structure of the whole gene pool, detecting differentiation according to the geographic origin of the accessions , these results suggest that the monomorphic regions are identical by descent as a consequence of a high level of co-ancestry between peach cultivars.Genetic diversity of >1500 peach accessions was evaluated with the Illumina 9K peach SNP array. Data allowed the detection of synonyms, detecting 173 groups encompassing 473 cultivars with at least two identical cultivars ; consequently 300 cultivars were removed. The 1240 unique peach accessions grouped in three main populations, which correlated with breeding strategies  and also with geographic origin Genotypic data together with phenotypic information obtained before and during FruitBreedomics allowed the definition of core collection (CC) representative for partner’s repositories as well as a first tentative European CC per species intended for future research studies. Such data have been extremely useful to identify clones and synonyms within and between collections, which will aid in a more efficient management of the apple and peach collections. These combined and co-ordinated datasets represent a significant development in the co-ordination of European genetic resource collections for both apple and peach, and will offer valuable base for continued efforts in bringing further germplasm collections together in the future.59. A detailed analysis of the most frequent SNP haplotypes in European breeding programmes for the loci G (peach/nectarine), Y (white/yellow flesh), S (flat/round fruit shape) and D (acid/subacid fruits) allowed markers to be designed for MAB60. In general, the success in predicting the phenotype was high (>80%). As an example, Fig. 59. Twenty-three SNPs located at the top of chromosome 5 showed significant association with the trait. These SNPs were organized in 4 highly frequent haplotypes  and on chromosomes 3, 10 and 16 (ripening period) 23, consistently with already published studies62.For apple, GWAS analyses were also engaged by combining the previously described phenotypic data and SNP genotypic data newly gained thanks to the development of the Axiom_Apple480k arrayBased on the assessment made in 2010, the FruitBreedomics consortium decided to tackle the issue of the efficiency of breeding programs through two main ways, (1) improving and developing pre-breeding strategies, (2) implementing molecular markers in commercial breeding programs by making available the tools and genetic knowledge gained in the project.63.To face the very narrow genetic basis FruitBreedomics aimed at the establishment of pre-breeding materials in apple and peach considering new traits in a more efficient way and further development and application in case studies of the early flowering approach in appleRvi2, Rvi4, Rvi6, Pl1, Pl2, Pld and Plm have been used for this process. Additionally, crosses were performed to introduce scab resistance from M. baccata jackii (Rvi11) and Hansens baccata No. 2 (Rvi12) as well as low chilling, low allergenic potential, specific aroma (litchi and mango flavour) and extraordinary fruit shape. The targets for peach were resistance to powdery mildew , green peach aphid  and brown rot; combined with specific traits involved in fruit quality such as slow ripening, non-acid flesh (D) and flat shape (S) as well as combinations of resistance to brown rot and powdery mildew. The efficiency of pre-breeding was increased by the choice of donors and the development of strategies to shorten the generation cycle in apple (fast-track approach). A list of crosses done and the availability of plant materials can be searched on the FruitBreedomics web page (www.fruitbreedomics.com).Pre-breeding materials can be defined as materials carrying new traits or trait combinations, which can be used as donors to improve cultivar breeding by the introduction of these traits. The development of pre-breeding materials, a time-consuming and expensive process, is necessary if desired traits or combinations are not present in existing advanced breeding materials, i.e., if the donors are wild apple species or new combinations of desired traits contain too much genetic drag. Apple pre-breeding materials carrying pyramids of apple scab resistance or powdery mildew resistance genes or combinations of both have been developed by conventional breeding. Resistance genes BpMADS4 inserted on the chromosome 464. Genotypes carrying this ‘early flowering’ gene show slender shoots and their flowering time cannot be controlled. Therefore, within FruitBreedomics we tried to produced new lines using a heat shock promoter to induce flowering by the repression of TFL1 (terminate flower 1)65. Unfortunately, this approach failed, but the development of transgenic lines carrying the early flowering gene (BpMADS4) on different linkage groups was successful. BpMADS4 lines carrying the flowering gene on linkage groups 2, 4, 5, 7, 8, 9, 10, 14, 15, 16 are now available and allow the application of the approach on traits also present on chromosome 466.The current early flowering approach in apple is based on the line T1190 which constitutively expresses the transcription factor FB_E fire blight resistance locus from ‘Evereste’67 were developed in <7 years68  and for FB_MR569 starting from an F1 hybrid in only four years. By classical breeding the production of the same generation would have taken 25 years or longer. Performing artificial inoculations with E. amylovora demonstrated that the final products, i.e., pre-breeding materials (at least BC’4) without the early flowering gene but carrying the fire blight resistance gene, possess a high level of fire blight resistance. Therefore, very valuable pre-breeding materials for the breeding of new fire blight resistant cultivars is available. In the USA such pre-breeding genotypes are not considered as genetically modified, US apple breeders can readily use them, but this chance will be precluded to European apple breeders if these genotypes will not be deregulated.The proof of concept of the early flowering approach clearly confirmed that using this approach one generation in around 1 year is possible. BC’4 plants carrying only the Another aim of FruitBreedomics was to develop more efficient breeding programmes applying marker assisted breeding. To reach this goal, the project aimed at designing and developing (i) molecular markers (SNPs) tightly linked to specific major horticultural traits, (ii) cost-efficient marker-assisted breeding (MAB) pipeline(s); and (iii) it also made a proof of concept of Genome Wide Selection on apple and peach.Rvi2, Rvi4, Rvi5, Rvi6, Rvi11, Rvi12, Rvi13, Rvi15 and QTLs on LG17 of ‘Fiesta’ and ‘Discovery’, the fire blight resistance genes FB_MR5, FB_E and the QTL FB_F7, powdery mildew resistance gene Pl2 and QTLs on LG 2 and 13 of TN10–8 and the rosy apple aphid resistance gene Dp_Fl71. For peach, SNP markers were found for fruit low acidity (locus D); fruit shape  glabrous fruit epidermis (peach/nectarine locus G); fruit flesh colour (white/yellow locus Y), non-melting (locus F) and for the green peach aphid resistance gene Rm2 and QTLs on LG3 and LG5 and finally for the powdery mildew resistance gene Vr2 and QTLs on LG6 and LG860.Prior to the practical development of MAB, the identification of SNP markers associated to specific apple and peach traits/loci started. The work led to the identification of several SNP markers. For apple, SNP markers were found for the apple scab resistance genes 72).Then a protocol reporting all the optimized steps necessary to perform MAB was developed together with a very efficient tracking system for seedlings avoiding the time consuming single labelling of each seedling. Moreover, a genotyping platform that can be used also by breeders without an own DNA lab was identified and tested in the apple and peach pilot studies. The apple and peach pilot studies had two aims: to validate the predictions done using the SNP markers, and to test the optimized MAB protocol and the selected genotyping platform. For the loci that could be validated, the predictions done with the SNP markers were correct in close or above 90% of the cases. The selection strategy tested in an apple pilot study allowed identifying 89 seedlings among over 5000 with pyramided resistance genes, best growth characteristics and potential best fruit quality traits  is a promising and innovative strategy to select among genotyped individuals when their phenotypes are not yet available, provided a related population was genotyped and phenotyped to estimate marker effects used for accurate prediction of breeding or genotypic value or phenotype. As for many other large perennials, this strategy is highly desirable for apple and peach breeding where phenotyping is lengthy, time-consuming and costly and concerns many traits. In FruitBreedomics project, an apple GWS experiment was pursued with the objectives to (1) assess the accuracy of prediction and (2) study realized response to selection. Twenty pedigree-related families, initially gathered for PBA, and their marker and phenotypic data, were used to build a genome-wide prediction model57. This model was applied to predict Genomic Breeding Values (GBV) in five full-sib families retrieved from commercial apple breeding programs of two FruitBreedomics breeders. These families were phenotyped over two consecutive years in breeders’ orchards and genotyped using TaqMan OpenArray plates preloaded with 512 SNPs uniformly distributed over the apple genome that were a subset of the 20K SNP of the Illumina array. SNP genotypes were completed through imputation up to the same number of SNPs as in the training population50, indicating GWS as a promising approach in peach breeding programmes too.In peach, a set including >1100 seedlings  was used as a basis to assess the feasibility of GWS for fruit weight, sugar content and titratable acidity. SNP data (9K IPSC array) were integrated with phenotypic data collected over 3–5 years in Italy, France, and Spain. Although results varied among different populations and traits, in general predictive abilities were greater than 60% on averageFruitBreedomics was a very important endeavour! It was an integrative project gathering 28 institutes and companies representing 14 countries and 367 stakehoders.FruitBreedomics made significant breakthroughs in the development of tools, methodologies and knowledge for the fruit scientific community. The most significant ones are (i) the new genomic tools (20K and 487K apple SNP chips) which are the foundations of further genetic and genomic studies, (ii) softwares like ASSIsT, FlexQTL which helped identifying robust and informative molecular markers and building genetic maps, (iii) new discovery and fine mapping approaches like Pedigree-Based Analysis (PBA) and GWAS that allowed the discovery of additional molecular markers closely linked to important traits such as fruit quality and biotic and abiotic resistances to be used for genetic studies and marker assisted breeding.The project also gave a deeper insight on the European apple and peach germplasm and breeding population structure that allows breeders to choose for new parent material to grasp new variability and introduce it into their breeding programs.Monilinia in peach and drought tolerance in apple. FruitBreedomics further developed pipelines for MAB on apple and peach.FruitBreedomics also provided the breeders with a huge number of pre-breeding materials and new phenotypic methods to assess traits of interest for apple and peach, in particular methods to assess fruit texture, resistance to Although FruitBreedomics ended in 2015 still with important results to be published, it has generated many short-term impacts for both fruit scientific and breeding communities. For example, peach resequencing data have generated a new add-on Illumina peach 18K SNP chip integrating ca 9000 SNPs in the already existing 9K IPSC chip. This new chip is currently being used by RosBreed partners for extensive genotyping. The decisive input of the dense SNP maps has widely applied for improving the quality of genome sequences for both apple and peach. Information on genetic diversity within European germplasm collections and breeding populations has helped to set up a new European experimental design on apple and peach to perform GWAS and genomic prediction with consideration of the Genotype × Environment × Management interaction. The first assessments will start in 2018.Preliminary studies are ongoing to set up a common European Fruit Breeding Platform that will provide various practical services and commodities for breeders including an efficient breeding interface application to help breeders to better plan their crosses, new pre-breeding materials and MAB services.Sup Table 1(DOCX 17 kb)


Cucumis melo L.) is one of the most important horticultural species, which includes several taxonomic groups. With the advent of next-generation sequencing, single nucleotide polymorphism (SNP) markers are widely used in the study of genetic diversity and genomics.Melon . Genetic analysis within each subpopulation revealed patterns of diversity associated with fruit phenotype and geographical origin. We used SNP data to describe, for each subpopulation, the average linkage disequilibrium (LD) decay, and to highlight genomic regions possibly resulting from directional selection and associated with phenotypic variation.We report the first successful application of genotyping-by-sequencing (GBS) technology in melon. We detected 25,422 SNPs by the analysis of 72 accessions collected in Apulia, a secondary centre of diversity in Southern Italy. Analyses of genetic structure, principal components, and hierarchical clustering support the identification of three distinct subpopulations. One of them includes accessions known with the folk name of ‘C. melo. We provide useful information to preserve endangered gene pools and to guide the use of germplasm in breeding. Finally, our findings lay a foundation for molecular breeding approaches and the identification of genes underlying phenotypic traits.We used GBS to characterize patterns of genetic diversity and genomic features within The online version of this article (doi:10.1186/s12864-016-3429-0) contains supplementary material, which is available to authorized users. Cucumis melo L., 2n = 2 × = 24) is one of the most important vegetables worldwide. Current world production of melon is over 31 million tons  = 1 and FST [C vs I] = 1) , while the latter is located in an intergenic region. Two significant associations were also found for SW , seed length (SL), seed width (SW) and 100 seed weight (SWe). FLTM was significantly associated with two SNP loci, 2:689400-SNV and 8:24132601-SNV, on chromosome 2 and 8, respectively  is higher than the distance observed between melons belonging to the different varietaschate (‘carosello’) and inodorus (winter melon). Possibly, the confusion between the two types arose from their common use as unripen fruits. It would be extremely interesting to compare, at the genomic level, ‘barattiere’ with all the intraspecific varietas reported for C. melo [C. melo and its cultivation history.Prior to this study, landraces known as ‘varietas . The res C. melo . This miinodorus group is traditionally cultivated in Southern Italy, where it is appreciated for its long storability that allows consumption during the whole autumn and winter [inodorus accessions is associated with the rind phenotype. This finding is consistent with a previous cluster analysis carried out on Spanish and Eurasian germplasm [inodorus germplasm, which might be unveiled by future studies, are likely to occur as Southern Italy experienced a long Spanish domination during the Early Modern period.The d winter . Here, wermplasm . Yellow C. melo germplasm, an information of great importance for GWAS. Tomason et al. [C. melo. However, these authors pooled several taxonomic groups and used simple sequence repeat (SSR) markers, therefore their results are not comparable to those reported in this study. The subpopulation I displayed a much slower LD decay than C and B. This might be explained by the reasonable assumption that winter melon experienced a stronger anthropic selection pressure than ‘carosello’ and ‘barattiere’, which led to the fixation of a higher number of LD blocks. The slow LD decay in the inodorus group is ideal to identify markers associated with favourable traits, and thus assisted selection, as it allows for an efficient coverage of the genome with a relatively low number of markers. On the other hand, long LD blocks may represent a limiting factor when association studies are aimed to fine-map genes of interest.Another aim of this work was to study the average LD decay in n et al.  previousC. melo subpopulations characterized by structure analysis. Thus, several highly divergent SNP loci were identified by the estimation of pairwise FST values. As we showed that LD decays at a low rate in C. melo, the chance is high that these loci are not the real targets of selection, but are rather in association with them. Unfortunately, knowledge on the molecular basis underlying phenotypic variation in C. melo is still scarce. Moreover, the B and C groups, which are of local commercial interest, lack a thoroughly phenotypic characterization. Filling these gaps might thus help to associate high FST loci identified in this study with specific events of selection.An additional goal of this study was to detect putative selection signatures in the genome of the three GID1c gibberellin receptor (MELO3C015362) maps 400Kbp far from the marker 2:689400-SNV, associated with FLTM. In Arabidopsis, GID1 homolog mutants are extremely late flowering under long-day conditions and fails to flower under short-day conditions [CLEAVAGE STIMULATION FACTOR (MELO3C008872), whose mutation leads to late flowering in Arabidopsis [MULTICOPY SUPRESSOR OF IRA1 (MSI1) gene (MELO3C006243), previously associated with seed development [Previous works successfully used populations of moderate size for GWAS –23. Thernditions , 31. In bidopsis , is locaelopment , residesC. melo. We provide useful information to understand the genetic structure of this species and to protect minor gene pools from genetic erosion. Finally, our results might prompt molecular breeding approaches and be a resource for future studies aiming to link genomic variation with evolutionary and phenotypic traits.GBS was for the first time applied in C. melo was obtained by local donors, with their prior informed consent, within the framework of the project “Biodiversity of Apulian vegetable species” , aimed at the safeguard and characterization of Apulian rural biodiversity. The accessions, corresponding to winter melons (25) and the commercial types ‘carosello’ (28) and ‘barattiere’ (19)  and the FAO Genebank Standards for Plant Genetic Resources for Food and Agriculture (http://www.fao.org/3/a-i3704e.pdf).A set of 72 accessions of ApeKI as described by Elshire et al. [Genomic DNA was isolated from young leaf samples using the DNeasy Plant Mini Kit (Qiagen). A reduced representation GBS library was prepared using the restriction enzyme e et al.  and seque et al.  and the e et al.  were usee et al. . Additioe et al. . The fil2 equal to 0.5. Each K was run ten times with a burn-in period of 100,000 and 100,000 Markov chain Monte Carlo (MCMC) repeats after burn-in. The value of the ΔK parameter, based on the second order rate of change of the likelihood function (ln Pr (X|K)), was used as criterion to estimate the true K [The admixture-based clustering model implemented in the software STRUCTURE 2.3.4  was usede true K . GenotypLD-pruned SNPs were also used to study molecular diversity among individual accessions. Principal component analysis (PCA) was performed using SVS v8.4.0. Furthermore, a Neighbor-Joining cladogram was obtained using the Archaeopteryx visualization tool implemented in TASSEL v5.2.20.C. melo subpopulations identified by structure analysis. Pairwise r2 values, estimated using the expectation-maximization (EM) algorithm implemented in SVS v8.4.0, were plotted against the distance (kb) between adjacent SNP loci, and a regression curve was fit to the data.LD decay was evaluated within the three ST) estimates were obtained using the formula of Weir and Cockerham [ST value were estimated using the percentile-t bootstrapping technique reported by Leviyang and Hamilton [ST provided a measure of genetic distance between subpopulations. FST values at individual loci were plotted against the melon 3.5.1. genome assembly to highlight genomic regions putatively subjected to directional selection. SNP loci associated with FST estimates higher than 0.9 were used to draw a custom Venn diagram.For each subpopulation pair, fixation index  were collected on plant material sown at the experimental farm “P. Martucci” of the University of Bari (41°01′22.1″N 16°54′21.0″E), according to a randomized block design with 3 replicates. FLTM and FLTP were recorded as the number of days from the sowing date to the date when 50% of the plants showed the first flower completely open. Cross-pollination among accessions was prevented using net cages in which bumble bees were introduced. SL, SW and SWe were determined on 100 seeds collected on 10 randomly chosen individuals. Association between SNP loci with a minimum MAF of 10% and phenotypes was determined using the weighted mixed linear model (MLM) implemented in TASSEL v5, taking into account kinship and genetic structure (K + Q). Significant associations were inferred using an adjusted p-value (Bonferroni correction).


Lactuca sativa genome assembly. To exemplify the utility of these resources, we screened the collection for putative sources of resistance to currant-lettuce aphid (Nasonovia ribisnigri) and carried out association analyses to look for potential SNPs linked to resistance.Domesticated lettuce varieties encompass much morphological variation across a range of crop type groups, with large collections of cultivars and landrace accessions maintained in genebanks. Additional variation not captured during domestication, present in ancestral wild relatives, represents a potentially rich source of alleles that can deliver to sustainable crop production. However, these large collections are difficult and costly to screen for many agronomically important traits. In this paper, we describe the generation of a diversity collection of 96 lettuce and wild species accessions that are amenable to routine phenotypic analysis and their genotypic characterization with a panel of 682 newly developed expressed sequence tag (EST)-linked KASP™ single nucleotide polymorphism (SNP) markers that are anchored to the draft The online version of this article (doi:10.1007/s11032-016-0606-4) contains supplementary material, which is available to authorized users. Lactuca sativa L.) is a high-value horticultural crop in many countries, e.g. UK lettuce production/imports had an estimated farm gate value of £266 million in 2011   is the most significant pest infesting lettuce in northern Europe  that is able to thrive on ‘resistant’ plants possessing Nr   and different morphotypes of the ‘wild species’ L. serriola that are inter-crossable producing fertile F1 progeny. The secondary gene pool includes the wild species Lactuca saligna and L. virosa ) significantly linked to the observed resistance. The diversity set and its genomic tools are then placed in the context of how to take these data forward.In this paper, we describe the publicly available UK Vegetable Genetic Improvement Network lettuce diversity set, with an associated panel of next-generation sequencing-derived single nucleotide polymorphism (SNP) markers. The SNP panel was converted to breeder-friendly ‘Kompetitive Allele Specific PCR’ (KASP™) markers and used to assess population structure and phylogenetic relationships in the diversity set. In addition, we screened the diversity set for resistance to the currant-lettuce aphid Lactuca collection at Centre for Genetic Resources Netherlands (CGN) using the core selector tool . Restrictions selected 17 wild species that were sexually compatible with L. sativa. This resulted in a DS of 96 accessions, representing diversity in crop type, geographical origin, and phenotype. The DS contains four parents of two mapping populations. The 96 accessions are described in detail in Supplementary Table The 96 accessions of the lettuce DS are an extension of 19 accessions selected from the lettuce collection at UK Vegetable Genebank, Wellesbourne, UK, used previously to quantify nitrate content  and kept at 18 °C 16L:8D in a randomized order in insect-proof growth chambers. After 2 weeks, seedlings were transplanted into 9-cm pots for aphid screening. This process was repeated on five occasions; sowing dates are as follows: 1 February 2011, 9 March 2011, 4 April 2011, 4 May 2011, and 16 June 2011. The experiment was arranged in an alpha design with ten replicates, each containing 8 blocks of 12 pots. This design ensured pairs of lines occurred together in the same block at most twice, allowing adjustments to be made in the analysis for spatial variation in levels of infestation. However, on the first occasion, variable germination was experienced resulting in two or more seedlings only being available for 28 lines, with no seedlings for 48 lines. A revised alpha design was generated using 12 blocks of either 6 or 7 pots each, the 12 blocks being grouped into 2 replicates of 6 blocks with 20 single replicate lines allocated across blocks. Eight replicates of the original alpha design (replicates containing 8 blocks of 12 pots each) were used for the four subsequent occasions, two replicates being used on each occasion, with spaces left in blocks where the allocated line failed to germinate. Lines were therefore replicated between 4 and 10 times within the experiment.N. ribisnigri Nr:0 clone 4850a , cultured previously on L. sativa cv. Pinokio (lacking Nr). Clone 4850a does not reproduce on cultivars possessing Nr. Inoculated plants were covered individually with micro-perforated polypropylene bags (200 mm × 500 mm) and arranged in an alpha design on a single shelf in a controlled environment room maintained at 20 °C, 16L:8D. After 3 weeks, aphid numbers on each plant were recorded ) over a 2-day period.Five-week-old plants were inoculated with five newborn nymphs (1–2-day-old) of For each accession, variance components were estimated and predicted means calculated for aphid counts , and total aphids), using restricted maximum likelihood (REML) (Patterson and Thompson −1 using TE (pH 8.0) and stored at −20 °C.Total DNA was isolated from young true leaves using DNeasy plant Maxi Kits  following manufacturer’s guidelines and diluted to 100 ng μlL. sativa cultivars Iceberg and Saladin. These accessions are parental lines of a recombinant-inbred line population  selection performed twice using Dynal magnetic beads . Illumina libraries were prepared using TruSeq Stranded mRNA kit v5  following manufacturer’s protocol (15018818 A). Cultivar-specific libraries were multiplexed using six-nucleotide barcoded adapters and randomly assigned to two lanes. Seventy base paired-end sequence reads were generated for these libraries using an Illumina Genome Analyzer IIx and score read quality assessed using CASAVA v1.8 .http://cgpdb.ucdavis.edu/cgpdb2/est_info_assembly.php) supplementary Table n = 678) and internal control for matching pairs of sequence that aligned to different EST contigs and contain alternate SNP bases (n = 4).A reference sequence database was constructed comprising 76,043 ESTs from the CLS_S3_Sat.assembly (L. sativa|CAP3:100/95) database (L. sativa pseudo-chromosome assembly ‘Lsat_1_v4’ (accessed 24 September 2012) from U.C. Davis Lettuce Genome Resource (https://lgr.genomecenter.ucdavis.edu/) with SNPs positioned in unassembled genomic regions being assigned to an arbitrary group 10.The selected 682 SNP sequences were converted to KASP™ marker assays using the LGC Genomics  ‘KASP™-On-Demand’ assay design service Table S and are r2 . Phylogenetic analyses were performed using maximum likelihood (1000 bootstrap iterations each) implemented in MEGA v5.2.2  and K), a burn-in period of 100,000 Markov Chain Monte Carlo iterations and 300,000 run-length were implemented using an admixture model following Hardy-Weinberg equilibrium and correlated allele frequencies. For each simulated value of K (for K = 1–10), four runs were repeated independently. To explore population structure in the L. sativa accessions, the STRUCTURE analyses were repeated with wild Lactuca species omitted. The same parameter settings were implemented for each simulated value of K (for K = 1–6) with four runs repeated independently. The python script structureHarvester.py v0.6.92  = average of LnP(D) from STRUCTURE runs per K, L′K = L(K)n − L(K)n−1, L″(K) = L′(K)n − L′(K)n−1, ΔK = [L″(K)]/Stdev. Principal component analyses were performed using the R package Genome Association and Prediction Tool (GAPIT)  was generated for each test, acting as a guide to SNP effect. Values for K are distributed approximately as a chi-square distribution with 1 df for the two-genotype classes. A stringent P value of ≤0.005 was used to minimize type I error  were used for genome-wide association studies (GWAS) using GAPIT  in R , and ß is a vector of fixed effects fitting X to Y and includes SNP, population structure (from Q matrix), and the intercept. The vector u contains the random effect of individual on phenotype and Z is a design matrix  but comprising 1’s and 0’s linking u to Z, with e as the residual error . Accessions are inbred and produce good seed yields, suitable for use in replicated trials for phenotype analyses.The lettuce DS comprises 96 accessions sourced from Warwick Genetic Resources Unit and the Centre for Genetic Resources Netherlands. The collection represents variation in Lactuca collections such as those previously assembled , and 1393 EST sequences containing SNPs were selected, from which 682 were converted to LKAMs (Supplementary Table L. sativa pseudo-chromosome assembly (http://lgr.genomecenter.ucdavis.edu) with 586 assigned to pseudo-chromosomes 1–9, and 96 located on genomic contigs not assembled in pseudo-chromosomes being assigned to an arbitrary group 10  for multi-allelic pairwise comparisons  were monomorphic in the wild species, yet polymorphic in the crop types, and 6 LKAMs (0.92 %) were polymorphic in the wild species and monomorphic in the crop types, 2 LKAMs having one base monomorphic in the crop compared to the alternate base being monomorphic in the wild species. Overall levels of heterozygous calls in the collection were low, ranging between 0.59 and 25.37 % in the Russian cv. Mestyni (possibly derived from a landrace), The dendrogram separated the wild species (cluster I) from domesticated cultivars (cluster II) with strong support, and the cultivars were roughly clustered into morphological groupings in a weakly supported ‘comb’ with some strongly supported sub-clades. Iceberg and Saladin have the highest apparent genetic dissimilarity among cultivars, as a consequence of all LKAMs being polymorphic between them. Genotype data were then used to construct a split decomposition Neighbour-Net phylogenetic network Fig.  showing K peak at K = 3, suggesting three main sub-populations in the diversity set , a clade containing three Batavian and three crisp-head types, and a group of accessions encompassing Butterhead and Latin types. The remaining cultivars appear to be admixtures between these populations to varying degrees.The three inferred sub-populations are broadly the wild species (9). Calmar is Great Lakes type with tip-burn resistance and downy mildew resistance acquired from a distant cross in its pedigree involving L. serriola  × L. sativa hybrid with an L. virosa accession (Thompson and Ryder L. serriola derived downy mildew resistance) was selected . At 3 weeks’ post-inoculation, plants were inspected and numbers of alate (winged) and apterous (non-winged) N. ribisnigri were counted; the total number of aphids on each accession was the sum of the two groupings. Figure To determine the degree of resistance to N. ribisnigri per line. However, a significant effect of plant line was seen on the total numbers of aphids , numbers of alates  and numbers of apterae  and are considered the most sensitive to host plant differences, due partly to increased numbers of receptors on their antennae compared to apterous individuals  was most resistant, having the lowest mean total number of N. ribisnigri (P = 0.001) to all other accessions, Fig. L. sativa, white cutting lettuce) was most susceptible, with 78.66 alates and 380.7 aphids in total (back-transformed data), supporting 634 times more aphids compared to line 95.To compare resistance of lettuce lines to The proportions of phenotypic variance explained by genetic variance (heritability) were reasonably high for each trait: alate (0.562), apterous (0.636), and total aphid count (0.674), suggesting aphid numbers are indeed influenced by host plant genotype  test and a linear mixed model test. The Kruskal-Wallis test was first used to estimate the effect of genotype at each SNP on mean phenotype value for numbers of alate, apterous and total N. ribisnigri. A value for the test statistic (H) was generated for each test with an associated χ2P value, acting as a guide to the SNP effect. A stringent P value was used as a cutoff for putative associations, since no account for population sub-structure was implemented for this method (Table SP ≤ 0.005) associated with counts for alate, apterous and total aphids were different, with 15 SNPs for alate, 10 for apterous and 13 for total aphid number, with 9 SNPs shared between the three-phenotype groupings. The alate group had three unique SNPs and shared three SNPs with the total aphid group. By contrast, the apterous group had one SNP shared with total aphid count but this was absent from the alate group. Interestingly, for each phenotype, it was the Iceberg-like SNP that was associated with a decrease in mean count, apart from markers LS1_242 and LS1_98, with the Saladin-like SNP associated with a decrease in mean counts.To explore the genetic basis of variation in d Table S. NumbersP values for each SNP were plotted in relation to the genome assembly, clear patterns were observed, with seven SNPs being significant for all three-phenotype classes , belonging to the Catharanthus roseus RLK1 (CrRLK1)-like protein family  but not with SNPs on either side of this interval, suggesting that gene(s) affecting the number of alate morphs maybe linked to this interval. LS1_381 has sequence homology with At3g12750, a Zinc transporter (ZIP1), whereas LKAM LS1_695 aligns to At1g37130, encoding nitrate reductase 2 (NR2) (Supplementary Table r2 = 0.71) as significantly associated with all aphid phenotype groups. These two markers delimit a 643,928-bp interval containing LS1_666 ; however, LS1_666 was not significant for the KW or mixed model analyses, although the mixed-model associations were just above significance (P = 0.01), with alates P = 0.055, apterae P = 0.061 and total count P = 0.045.LKAMs LS1_381 and LS1_695 were both significant for the number of alate aphids and are 58,371 bp apart, with strong linkage to each other  with genes in the vicinity of the markers; however, it is clear that resistance to L. sativa domesticated accessions and the more diverse wild Lactuca spp. The wild ‘unadapted’ germplasm provides the opportunity to explore the presence of alleles not represented in cultivated germplasm that may be introgressed into domesticated species using conventional breeding strategies  now provide a means to re-sequence mapping parents, aiding the discovery of large numbers of SNPs which can be physically positioned in genome sequence  was the most resistant to all aphid morph stages, consistent with L. virosa being the original source of Nr .The SNPs significantly associated with aphid count data and the LD in the vicinity of those SNPs together define genomic intervals to look for candidate genes. Linkage disequilibrium in lettuce has been estimated as Nr, and its genomic location has not been described. The diversity set is anticipated to include lines that possess Nr. Since we found SNPs associated with resistance positioned across different chromosomes, we are unable to determine which SNPs may be linked to or represent Nr. In fact, it is possible that Nr has not been detected at all and a much higher density of SNPs may be required to do so. This is likely as it would have been desirable for breeders to select for recombination around Nr to remove non-crop-adapted L. virosa flanking sequence. It would be interesting to compare the observed resistance of lines to original cultivar release dates, i.e. pre- and post-introgression of the Nr allele, but we found this information to be not readily available. The associations on different groups also imply that, in addition to Nr, the diversity set contains lines with other resistance alleles that have not previously been described; these alleles therefore represent putative targets to pyramid for enhanced resistance.Patents protect the use of N. ribisnigri count data using segregating populations that have been genotyped using the LKAMs, for example the Saladin × Iceberg RIL population , N. ribisnigri infests a number of alternative herbaceous s including other commercial Asteraceae such as chicory (Cichorium intybus L.), endive (Cichorium endive L.) and some common weed species  (PDF 59 kb)Supplementary Table S2Features of the CGP EST assembly CLS_S3_ESTs_Sat, EST library.pdf (EMS2) (PDF 40 kb)Supplementary Table S3Lettuce Specific KASP™ assay primer pairs.xlsx (ESM3) (XLSX 217 kb)Supplementary Table S4Lactuca sativa Lsat_1_v4 pseudo-chromosome assembly.xlsx (ESM4) (PDF 24 kb)Supplementary Fig. S1Position of LKAMS in assembly.pdf (EMS5) (PDF 202 kb)Supplementary Fig. S2Heterozygozity and PIC of LKAMS.pdf (EMS6) (PDF 274 kb)Supplementary Fig. S3LD decay with distance.pdf (EMS7) (PDF 166 kb)Supplementary Fig. S4Pairwise rsq in assembly.pdf (EMS8) (PDF 381 kb)Supplementary Table S5REML variance components.xlsx (EMS9) (PDF 30 kb)Supplementary Table S6Kruskal-Wallis association tests.xlsx (EMS10) (XLSX 49 kb)Supplementary Fig. S5Manhattan and Q-Q plots with genomic control.pdf (EMS11) (PDF 219 kb)Supplementary Table S7GAPIT GWAS.xlsx (EMS12) (XLSX 56 kb)Supplementary Table S8GAPIT GWAS other associations.xlsx (EMS13) (XLSX 55 kb)Supplementary Table S9Putative roles of the LKAM SNPs significant in both association tests.xlsx (EMS14) (PDF 67 kb)


WPBR), caused by the non‐native invasive fungus Cronartium ribicola (J.C. Fisch.), has significantly reduced wild populations of these conifers in North America. Major resistance (R) genes against specific avirulent pathotypes have been found in several five‐needle pine species. In this study, we screened genic SNP markers by comparative transcriptome and genetic association analyses and constructed saturated linkage maps for the western white pine (Pinus monticola) R locus (Cr2). Phenotypic segregation was measured by a hypersensitive reaction (HR)‐like response on the needles and disease symptoms of cankered stems post inoculation by the C. ribicola avcr2 race. SNP genotypes were determined by HRM‐ and TaqMan‐based SNP genotyping. Saturated maps of the Cr2‐linkage group (LG) were constructed in three seed families using a total of 34 SNP markers within 21 unique genes. Cr2 was consistently flanked by contig_2142 (encoding a ruvb‐like protein) and contig_3772 (encoding a delta‐fatty acid desaturase) across the three seed families. Cr2 was anchored to the Pinus consensus LG‐1, which differs from LGs where other R loci of Pinus species were mapped. GO annotation identified a set of NBS‐LRR and other resistance‐related genes as R candidates in the Cr2 region. Association of one nonsynonymous SNP locus of an NBS‐LRR gene with Cr2‐mediated phenotypes provides a valuable tool for marker‐assisted selection (MAS), which will shorten the breeding cycle of resistance screening and aid in the restoration of WPBR‐disturbed forest ecosystems.Molecular breeding incorporates efficient tools to increase rust resistance in five‐needle pines. Susceptibility of native five‐needle pines to white pine blister rust ( Pinus monticola Douglas ex D. Don) is highly susceptible to white pine blister rust (WPBR). This rust disease is caused by an exotic fungal pathogen Cronartium ribicola J.C. Fisch., which was introduced into North America around 1910. Since then, C. ribicola has spread to most regions where native five‐needle pines (subgenus Strobus) are naturally distributed. Due to a variety of factors, including high susceptibility and mortality to WPBR, the frequency of P. monticola in natural stands has been reduced by up to 95% , developing rust hazard zones, pruning infected branches and breeding for host resistance  in four five‐needle species: sugar pine (P. lambertiana Doug.), western white pine, south‐western white pine (P. strobiformis Engelm.) and limber pine (P. flexilis James) ‐like defence response on needles post basidiospore infection , amplified fragment length polymorphism (AFLP), sequence‐characterized amplified region (SCAR) and resistance gene analogs (RGA)‐based AFLP  with DNA markers at high density is commonly used as one of the most valuable research approaches to address the need for high‐throughput selection of superior plant traits of economical or ecological interest among various germplasms. Molecular mapping of plant R genes has been used to dissect the genetic architecture of host resistance, providing an effective research strategy to develop diagnostic markers in plant breeding  was mapped to LG‐2 , which can be detected across the whole genome by next‐generation sequencing (NGS), provide the richest resource for detection of genic variations. A large number of SNP and satellite markers were discovered in et al., P. taeda (v1.1) and P. lambertiana (v1.0) through the PineRefSeq project  and receptor‐like protein kinase (RLK) families during white pine blister rust interactions  analysis and gene annotation demonstrated that the Cr2 region contains multiple genes with putative functions in disease resistance, including those encoding NBS‐LRR proteins, F‐box proteins and fatty acid desaturase.To develop MAS tools for y Figure . High‐reCr2/−) seed families and resistant segregating (Cr2/cr2) seed families  were evaluated for disease resistance phenotypes after C. ribicola inoculation  seed families  seed families, G × P association analysis . Among all tested SNP loci, snp41490‐1778M showed the strongest association with cr2‐mediated susceptibility (R2 = 0.9). The 12 SNP loci showing phenotypic association were distributed in eight functional genes: one gene with three SNP loci (contig_3772), two genes each with two SNP loci (contig_41490 and contig_11322) and five other genes each with one SNP locus  seed families by haploid segregation analysis. SNP genotypes of each individual megagametophyte sample were determined by the HRM‐based SNP genotyping technology  and susceptible (cr2/cr2) seed families, as well as between P. monticola transcripts and P. taeda genomic contigs  seed families used for Cr2 mapping. In total, 21 functional genes were genotyped by HRM‐based analysis  seed families  and used to construct genetic maps.To enrich the s Figure d. Among s Figure . Genotypin silico SNPs detected by RNA‐seq analysis, the Sequenom and HRM genotyping methods.To further confirm genotypes of SNP loci, Sanger sequencing was performed on PCR fragments encompassing SNP loci from genomic DNA of haploid tissues Figure . AlignmeCr2‐LG in the seed families #3592 (LOD ≥9), #3566 (LOD ≥10) and #1974 (LOD ≥10), respectively. The Cr2‐LG lengths were about 20 cM in all three seed families, with a mean locus density of <1 cM per SNP marker  and one with a highly homologous hit (1.651E−82) , and the distal segment containing five SNP loci, from four unigene, with R = 0.77 (P = 0.12). When Cr2‐LG was compared with the P. taeda genetic map developed by Neves et al. (R = 0.97 (P < 0.00001), where nine SNP loci from six unigenes were covered  encoded putative NBS‐LRR proteins. Among well‐characterized plant R proteins, contig_41490 showed the highest homology to the tobacco TMV R protein N (U15605) by BLASTp analysis with an E value of 2e−86. Sequence alignment using Clustal Omega revealed 29.4% amino acid identity across the full‐length sequences between these two proteins , contig_2142 encoding a ruvb‐like protein, contig_13194 encoding bromodomain‐containing protein, contig_13287 encoding a proline‐rich protein, contig_573 encoding mitochondrial pyruvate dehydrogenase (PDH) and contig_3772 encoding delta‐fatty acid desaturase‐like protein. Genes homologous to these have previously been reported to be involved in the plant defence response against pathogen attacks or to be a component in plant immune systems.Cr2 region: contig_2711 (a homolog of methylthioribose kinase), contig_34906  and contig_186209 (a tyrosine sulfotransferase‐like). These genes have not yet been reported to be involved in disease resistance. Our study did not detect a significant change in transcript levels for these three genes during white pine blister rust interaction, suggesting that they may not directly contribute to disease resistance against C. ribicola. Identification of NBS‐LRR genes and other disease resistance‐related genes in the Cr2 region could be valuable in breeding new rust‐resistant seed families and gaining a deeper understanding of disease response mechanisms in western white pine.Three other genes were mapped within the C. ribicola inoculation. All of these genes were up‐regulated except for contig_9279 which was down‐regulated in resistant seedlings  showed significant regulation of their mRNA expression in the primary needle tissues after C. ribicola infection  showed defence‐responsive expression in stem tissues during et al., Two SNP loci (snp41490‐1618R and snp41490‐1778M) were used to develop TaqMan SNP genotyping arrays Figure . TaqMan Cr2‐resistant parental trees used in western white pine breeding programmes in Oregon (OR) and British Columbia (BC) as well as seed families with partial resistance (bark reaction) (Table Cr2/−) were detected as a mixture from approximately 71 full‐sib families  seed families represented a wide geographical distribution in western North America  Table , S3. AmoCr2‐resistant parental trees were selected to represent three unrelated populations without confirmed phenotypic data. Because no MGR was observed across BC landscapes in all screening tests conducted in the BC breeding programmes  and 40 BC es Hunt, , the locCr1 to Cr4) were proposed as major R genes, with each conferring a species of five‐needle pines  complete resistance against a specific avirulent pathotype of C. ribicola  and susceptible (cr2/cr2) seed families of P. monticola, or between P. monticola and P. taeda  thes Figure b; (ii) gy Figure e and enra Figure d; (iii) es Table . Genic Set al., Cr1, Cr2 and Cr4 using various types of DNA markers  on the Pinus consensus LG‐2  using R gene enrichment sequencing (RenSeq)  confers enhanced resistance to pathogens  cycle and a delta‐fatty acid desaturase, respectively. Both genes may not be Cr2 controls MGR is unclear, identification of multiple genes on the Cr2‐LG as functional candidates with defence‐responsive expression opens new approaches to studying the Cr2‐avcr2 interaction   and heterozygous (Cr2/cr2) genotypes due to codominant nature of SNP markers. The first parent trees identified with Cr2 originated from the Champion Mine area in the Cottage Grove Ranger District of the Umpqua National Forest, OR, USA. This resistant germplasm was planted in British Columbia, Canada, but is not well adapted to the region. The BC breeding programme is currently using conventional breeding to transfer Cr2 into local seed orchard parents. The local parent trees are highly susceptible to WPBR but have favourable traits, such as fast growth and resistance to root‐rot and needle blight disease. It usually takes 2~3 years for phenotypic confirmation of stem canker development under controlled inoculations in the glasshouse, and much longer in a field test. Using the MAS tools we have developed, we are able to select Cr2‐seedlings during seed germination stages. This was confirmed using composite‐resistant (Cr2/−) seed families , seed families  were used as mapping populations. The seedlings from these three maternal trees were confirmed with Cr2‐controlled phenotypic segregation in previous inoculation tests, and the Cr2 locus in these seed families was descended from parents or grandparents from the Champion Mine area in the Cottage Grove Ranger District of the Umpqua National Forest or the Bear Pass planting site on the Willamette National Forest in Oregon  leaves infected by avcr2 race. Diseased Ribes leaves were collected from geographical areas where no virulent race (vcr2) was detected. After infection with avcr2 race under controlled conditions  in the inoculation room, seedlings were moved back to the glasshouse. Symptoms of blister rust disease and phenotypic traits were evaluated for each seedling in 2011.Megagametophyte samples of three western white pine resistant, segregating  composite seed families used in the breeding programmes were analysed for 432 SNP loci, and the resistant phenotypes mediated by Cr2 were evaluated for each individual seedling  and susceptible (cr2/cr2) seed families as outlined in a previous report  in BLASTn analysis and potential physical linkage in P. taeda genomic contigs, those highly conserved Pinus genes were assessed for their potential linkage to Cr2 and SNP loci distributed within related genes were then selected from P. monticola needle and stem transcriptomes  and 10 ng of genomic DNA. PCR was run on an ABI 7500 Fast Real‐Time PCR system with programme settings as recommended by the manufacturer. Genotypic data were collected and analysed using HRM Software v2.0 (Applied Biosystems).Genomic DNA was extracted from megagametophyte or needle tissues using a Plant DNeasy kit (Qiagen), in accordance with the manufacturer's protocol. SNP loci selected from the above approaches were genotyped using HRM analysis  and cloned into the pGEM‐T easy vector . After selection of recombinant plasmids, genomic DNA insert sequences were determined on both strands with an ABI310 DNA sequencer (Applied Biosystems) using a Thermo‐cycle sequence kit (Amersham) with vector primers T7 and SP6. DNA sequence data were assembled and analysed using BLAST, Clustal Omega and ORF finder network services at the National Center for Biotechnology Information (NCBI) and the ExPASy Proteomics Server at the Swiss Institute of Bioinformatics .Cr2‐LG by haploid segregation analysis as previously described . Markers showing significant (P < 0.05) segregation distortions were initially eliminated from the LG construction and were then added later as accessory markers. A logarithm of the odds ratio (LOD) threshold of 6 and a distance threshold of 30 cM were used to define a linkage group (LG). The robustness of the data sets for each LG was confirmed by the grouping module of JoinMap using a LOD threshold of 10. Only megagametophyte samples of the susceptible phenotype were counted because the resistant phenotype could be conferred by Cr2 from pollen. The Kosambi mapping function was used to calculate genetic distances. The integrated maps were constructed by combining individual maps of three seed families. P. taeda consensus maps published previously  or orthologs by BLAST analysis and conservation of relative order and location of the mapped genes.The JoinMap software version 3.0  from previous studies  seedlings (stem canker‐free) and susceptible (cr2/cr2) seedlings (stem‐cankered), respectively. In both statistical tests, P‐values were corrected by the false discovery rate (FDR).Kal's test and The authors declare no conflicts of interest.Figure S1 Sanger sequencing for confirmation of SNP loci.Click here for additional data file.Figure S2 Comparative mapping of Pinus conserved genes between Pinus monticola Cr2 linkage group (LG) and P. taeda consensus LG‐1.Click here for additional data file.Figure S3 Alignment analysis of full‐length sequences of Pinus monticola contig_41490 and TMV resistance protein N  from tobacco (Nicotiana glutinosa).Click here for additional data file.Table S1 Primer sequences used for qPCR in genotyping by high‐resolution melting (HRM) analysis.Table S2 Primer and probe sequences used for qPCR in SNP genotyping by TaqMan arrays.Table S3 Geographical locations of western white pine seed families with open pollination.Click here for additional data file.


Hypoglycemia is a common side effect of insulin therapy in type 1 and type 2 diabetes. Limited data exist on the frequency of hypoglycemic events in type 2 diabetic patients in Turkey. Our study investigated self-reported hypoglycemic events and awareness of hypoglycemia in Turkish patients with type 2 diabetes.People with type 2 diabetes older than 18 years of age were recruited from the two university hospital diabetes clinics. The frequency and severity of hypoglycemia and awareness of hypoglycemia during the preceding year were determinated using questionnaires by the face-to-face interview method.In this study of 187 patients with type 2 diabetes, 83.4% had impaired awareness of their hypoglycemia, and 62% reported that they had missed some of the symptoms of hypoglycemia. Of the patients reporting hypoglycemic symptoms and severity level, 84.1% experienced mild hypoglycemia, 60% moderate, and 15.5% severe hypoglycemia in the past year. No significant association was made between hypoglycemia awareness and age, body-mass index (BMI), years of diabetes, dose of insulin, duration of insulin use, number of meals, or amount of snacking. A significant correlation was found between A1c levels and hypoglycemia awareness and severity of hypoglycemia. A significant correlation was found between dose of insulin, amount of snacking, and severity of hypoglycemia. No significant association was made between severity of hypoglycemia and age, BMI, years of diabetes, duration of insulin use, or the number of meals. However, the group with severe hypoglycemia had diabetes longer, and the average daily dose of insulin use was higher than in other groups.According to the study results, the percentage of patients with impaired awareness of hypoglycemia is high, and 62% of patients reported that they had missed some of the symptoms of hypoglycemia in type 2 diabetes. In addition, the percentage of severe hypoglycemic events is not low. Impaired awareness of hypoglycemia is a major risk factor for severe hypoglycemic events. Patients should be educated about the danger of hypoglycemia. Education should be improved, and a determined attempt should be made to eradicate the problem. Hypoglycemia presents a barrier to optimum diabetes management for type 1 and type 2 diabetes mellitus. Hypoglycemia is one of the most feared acute complications of diabetes. In particular, patients who experience severe and recurrent hypoglycemia have many fears about their inability to recognize the symptoms of hypoglycemia. When suffering from hypoglycemia, they seem stupid or drunk, engage in embarrassing behavior, and experience hypoglycemia while driving or sleeping . HypoglyThere are limited data on the prevalence of hypoglycemia awareness and its relationship with hypoglycemic events, and, furthermore, there are few studies reporting data for hypoglycemia in people with type 2 diabetes mellitus in Turkey. Previously, the literature has not demonstrated the correlation between hypoglycemic events and hypoglycemia awareness in individuals with type 2 diabetes in Turkey. We investigated the frequency of self-reported severity hypoglycemic events and levels of awareness of hypoglycemia in Turkey.This study is a descriptive and relational study to determine self-reported hypoglycemic event frequency and levels of awareness in individuals with type 2 diabetes in Turkey.Research data were collected from individuals diagnosed with diabetes who sought treatment at the diabetes polyclinics of two university hospitals between July 2013 and January 2014.n = t2pq∕d2) was used to calculate the minimum sample size required for inclusion in the study. The research sample included 187 individuals over 18 years of age who had been diagnosed with type 2 diabetes for at least one year.Sampling formula   q = 1 − p  t = Z score (standard)  d =  Standard of deviation   n = (1.96)2∗0.14∗0.86∕(0.05)2 = 187.All 187 patients had no disabilities, good vision, hearing, perception and cognition. All have also voluntarily consented to full participation.During the data collection process, the Questionnaire Form, prepared by researchers, was used. The data were collected from patients in face-to-face interviews.The questionnaire form, which was prepared by researchers based on a literature review  was usedThe classification system for awareness of hypoglycemia was based on a prospectively validated study by t-test for continuous variables and the chi-square test for categorical variables.To quantify the frequency and severity of hypoglycemia, patients were asked to read a list of hypoglycemic symptoms and record the frequency of such symptoms by level of severity. Hypoglycemic severity was categorized as (1) mild ; (2) moderate ; (3) severe . Patients reported the frequency of hypoglycemia for each level of hypoglycemic symptom severity  during the preceding one year. The frequency data collected were: never; 1–2 episodes; one episode per month; more than one per month; more than one per week; or daily. Characteristics of patients with and without reported hypoglycemic symptoms were compared using the t-test, correlation, one-way analysis of variance (one-way ANOVA), and chi-square test were used to analyze the data.Data were analyzed using the Statistical Package for the Social Sciences  version 15.0. Percentage distributions, mean, Researchers obtained permission from the ethics committee and the hospital where the study was conducted. The approval reference/number “2013/33-11” from the Non-invasive research ethics committee of Dokuz Eylul University. A written voluntary consent was provided by each patients.A total of 187 individuals  participated in the study. Among these individuals, 52.9% were primary school graduates, 95.7% were married, 97.9% were living with their spouse, 20.3% were employed, 87.7% had income equal to their expenses, 5.3% were users of alcohol, and 17.1% were smokers.Mean age (SD) of the study population was 56.7 (11.3) years. Individuals’ average length of time since diabetes diagnosis was 10.21 (6.92) years. Average duration of oral anti-diabetic drug (OAD) use was 6.6 (5.23) years, and average duration of insulin use was 6.4 (4.4) years. Average duration of combined use of OAD and insulin was 7.4 (3.9) years. Average of daily dose of insulin use was 48.75 (29.46) IU, average A1c value was 7.86 (1.71), average BMI value was 28.16 (5.37), average number of meals was 4.25 (1.57), and average number of snacks was 1.79 (.76). Among the participants, 59.4% used insulin as the most commonly used medication for diabetes treatment; 67.9% of the individuals had other chronic diseases, and 64.7% had complications of diabetes. All of the participants received training in diabetes management and, of these individuals, 50.8% receive their last training one month ago. More than half (51.3%) of the individuals had irregular eating habits, and 31% had snacking habits; 23% were exercising; 61.5% were monitoring blood sugar one to four times daily; and the health of 70.6% was perceived to be good .The awareness of hypoglycemia, frequency of hypoglycemic events and their perception by diabetic patients are given in Of the patients reporting hypoglycemic symptoms and severity level, 94.1% of all patients experienced mild hypoglycemia, 59.9% of all patients experienced moderate hypoglycemia, and 15.5% of all the patients experienced severe hypoglycemia in the past year . For thoIn the present study, there was significant but weak negative correlation between awareness of hypoglycemia and severity of hypoglycemia .There was no significant association between hypoglycemia awareness and age, BMI, years of diabetes, dose of insulin, duration of insulin use, the number of meals, or the number of snacks per day. A significant relationship was found between A1c levels and hypoglycemia awareness. The group with impaired awareness of hypoglycemia had significantly higher A1c values than other groups .A significant relationship was also found between A1c levels and severity of hypoglycemia. The groups that had experienced mild and moderate hypoglycemia symptoms had significantly higher A1c values than other groups. At the same time, the groups that had experienced severe hypoglycemia symptoms had lower A1c values than other groups. In addition, in present study, A1c value of the majority of the groups that had experienced mild and moderate hypoglycemia symptoms was between 6 and 9% . A significant relation was found between dose of insulin, amount of snacking, and severity of hypoglycemia. The group that had experienced moderate hypoglycemia symptoms used significantly higher doses of insulin and snacked more times per day than other groups. The group that had experienced severe hypoglycemia symptoms used significantly higher doses of insulin per day than other groups .This study identifies frequency and severity of hypoglycemic events and awareness of hypoglycemia in people with type 2 diabetes in Turkey. A minority of patients in our study reported awareness of hypoglycemia (5.9%); the majority however reported either impaired awareness (83.4%) or unawareness (10.7%). Similar findings were found in previous studies in different countries ranging from 43% to 75% . In our These findings were similar to insulin-treated diabetes reported in the literature. In these patients, the incidence of mild and severe hypoglycemia ranged from 30% to 73% and 2.3% to 26%, respectively .r =  − .23, r =  − .22, r =  − .20; mild, moderate, severe) (p < 0.01). The study of In the present study, there was significant but weak negative correlation between awareness of hypoglycemia and severity of hypoglycemia (In this study, no significant association between hypoglycemia awareness and age, BMI, years of diabetes, dose of insulin, duration of insulin use, number of meals, or amount of snacking was found. A significant relation, however, was found between A1c levels and hypoglycemia awareness. The group with impaired awareness of hypoglycemia had significantly higher A1c values than other groups. In this study, a significant relation was found between A1c levels and severity of hypoglycemia. The groups that had experienced mild and moderate hypoglycemia symptoms showed significantly higher A1c values than other groups. At the same time, the groups that had experienced severe hypoglycemia symptoms had lower A1c values than other groups. In addition, in present study, A1c value of the majority of the groups that had experienced mild and moderate hypoglycemia symptoms was between 6% and 9% (this finding not showed in table-cuttof values A1c A significant relationship was found among the dose of insulin, the number of daily snacks, and the severity of hypoglycemia. The group that had experienced moderate hypoglycemia symptoms used significantly higher doses of insulin and snacked more often than other groups. The group that had experienced severe hypoglycemia symptoms used significantly higher doses of insulin than other groups. Limitations of our study include the reliance on patients’ self-reported data and their interpretation of the symptoms of hypoglycemia. Our study was also limited because we were unable to compare the differences of hypoglycemia rates and awareness of hypoglycemia between demographics and disease-related factors because the numbers in each group were too small.This study shows that mild and moderate hypoglycemic events and impaired awareness of hypoglycemia are common characteristics in patients with type 2 diabetes in Turkey. Impaired awareness of hypoglycemia is the strongest risk factor for severe hypoglycemia events. A patient who has impaired awareness of hypoglycemia will not achieve optimum glycemic control. Appropriate education includes an emphasis on regular snacks at the right times, regular mealtimes, and advice about the amount of carbohydrate needed to control symptoms. All patients should receive education regarding hypoglycemia. Education about the recognition of hypoglycemic events is necessary, and patients should be encouraged to speak about hypoglycemia with health care professionals.10.7717/peerj.2700/supp-1Data S1Click here for additional data file.


Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model.The BayesC model assumes a priori that markers have normally distributed effects with probability SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP effects (SNP-BLUP model). When reducing marker density from WGS data to 30 K, SNP-BLUP tended to yield the highest accuracies, at least in the short term.Based on SVD of the genotype matrix, we developed a direct method for the calculation of BayesC estimates of marker effects. Although SVD- and MCMC-based marker effects differed slightly, their prediction accuracies were similar. Assuming that the SVD of the marker genotype matrix is already performed for other reasons (e.g. for SNP-BLUP), computation times for the BayesC predictions were comparable to those of SNP-BLUP. Singular value decomposition (SVD) is commonly used in multi-variate statistics to study the explanatory variables and to decompose the design matrix into independent components . In prinmentclass2pt{minimt-distribution in BayesB [In genomic selection (GS), matrix entclass1pt{miniman BayesB  or the nn BayesB ) and withe trait . EspeciaComputationally, variable selection models are mostly implemented by using Markov chain Monte Carlo (MCMC) algorithms –4, whichGenerally, we will assume that we are dealing with WGS data. Polymorphisms in the sequence will be called SNPs, although extension to other types of polymorphisms is straightforward, as long as their genotypes can be translated into covariates in a regression model. We will briefly describe the application of ils, see . In GS, pes (see ); \docummentclass2pt{minimmentclasspt{minimaThus, the above regression model can be rewritten as:ns (MME)  become:1The BayesC prior distribution is a mixture distribution :emes see ).Consider estimation of the effect of SNP sing Eq. . And, sientclass1pt{minimaIn order to write the likelihood under the model without an effect at SNP from Eq.  and the LLR is combined with the log-prior-ratio, The In the BayesC analysis, the variance of the components 8 bp) each. Simulation of WGS data followed the approach of [−8, and mutations followed the infinite sites model. This resulted in 531,836 SNPs with minor allele frequencies (MAF) higher than 0.01 in the reference population, in which SNP effects were estimated. Per chromosome, 200 SNPs with MAF higher than 0.01 were randomly sampled as causative SNPs, i.e. 4000 causative SNPs in total. Genotypes were standardized to the values of WGS data were simulated to demonstrate the calculations and evaluate their results. The simulated species had 20 chromosomes of 1 Morgan , which provides parallel algorithms for this task . The Table An When 30 K SNP-chip data were used, the MCMC and r et al. .The difference between the posterior probabilities, g in Eq.  to prediWe assumed that the value of cument}π . Howeverlidation . \documeThe computational speed of the In situations where the family structure is not strong (as was the case in our simulated data), the per chromosome components are approximately unrelated and the final nts, Eq.  holds on use Eq.  (knowing use Eq.  can be umentclass2pt{minimAfter performing the