Mapping and Introgression of QTL Involved in Fruit Shape Transgressive Segregation into 'Piel de Sapo' Melon (Cucucumis melo L.)

A mapping F-2 population from the cross 'Piel de Sapo' x PI124112 was selectively genotyped to study the genetic control of morphological fruit traits by QTL (Quantitative Trait Loci) analysis. Ten QTL were identified, five for FL (Fruit Length), two for FD (Fruit Diameter) and three for FS (Fruit Shape). At least one robust QTL per character was found, flqs8.1 (LOD = 16.85, R-2 = 34%), fdqs12.1 (LOD = 3.47, R-2 = 11%) and fsqs8.1 (LOD = 14.85, R-2 = 41%). flqs2.1 and fsqs2.1 cosegregate with gene a (andromonoecious), responsible for flower sex determination and with pleiotropic effects on FS. They display a positive additive effect (a) value, so the PI124112 allele causes an increase in FL and FS, producing more elongated fruits. Conversely, the negative a value for flqs8.1 and fsqs8.1 indicates a decrease in FL and FS, what results in rounder fruits, even if PI124112 produces very elongated melons. This is explained by a significant epistatic interaction between fsqs2.1 and fsqs8.1, where the effects of the alleles at locus a are attenuated by the additive PI124112 allele at fsqs8.1. Roundest fruits are produced by homozygous for PI124112 at fsqs8.1 that do not carry any dominant A allele at locus a (PiPiaa). A significant interaction between fsqs8.1 and fsqs12.1 was also detected, with the alleles at fsqs12.1 producing more elongated fruits. fsqs8.1 seems to be allelic to QTL discovered in other populations where the exotic alleles produce elongated fruits. This model has been validated in assays with backcross lines along 3 years and ultimately obtaining a fsqs8.1-NIL (Near Isogenic Line) in 'Piel de Sapo' background which yields round melons.This work was supported by grants AGL2009-12698-C02-02 and AGL2012-40130-C02-02 from the Spanish Ministry of Economy and Competitiveness to AJM. AD was supported by a JAE-Doc contract from CSIC, MF by a Postdoctoral contract from GRAG, IE by a fellowship from the former Spanish Ministry of Education and BZ by a fellowship from Instituto Agronomico Mediterraneo de Zaragoza (IAMZ), Spain. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Díaz Bermúdez, A.; Zarouri, B.; Fergany, M.; Eduardo, I.; Álvarez, JA.; Picó Sirvent, MB.; Monforte Gilabert, AJ. (2014). Mapping and Introgression of QTL Involved in Fruit Shape Transgressive Segregation into 'Piel de Sapo' Melon (Cucucumis melo L.). PLoS ONE. 9(8):104188-104188. https://doi.org/10.1371/journal.pone.0104188S10418810418898FAO (2014) Food and Agricultural Organization of the United Nations: FAOSTAT http://faostat3.fao.org/faostat-gateway/go/to/download/Q/QC/EFerguson, A. R. (1999). KIWIFRUIT CULTIVARS: BREEDING AND SELECTION. 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Whole-genome genotyping of grape using a panel of microsatellite

The use of microsatellite markers in large-scale genetic studies is limited by its low throughput and high cost and labor requirements. Here, we provide a panel of 45 multiplex PCRs for fast and cost-efficient genome-wide fluorescence-based microsatellite analysis in grapevine. The developed multiplex PCRs panel (with up to 15-plex) enables the scoring of 270 loci covering all the grapevine genome (9 to 20 loci/chromosome) using only 45 PCRs and sequencer runs. The 45 multiplex PCRs were validated using a diverse grapevine collection of 207 accessions, selected to represent most of the cultivated Vitis vinifera genetic diversity. Particular attention was paid to quality control throughout the whole process (assay replication, null allele detection, ease of scoring). Genetic diversity summary statistics and features of electrophoretic profiles for each studied marker are provided, as are the genotypes of 25 common cultivars that could be used as references in other studies

Association study of phenology, yield and quality related traits in table grapes using SSR and SNP markers

The advent of cheaper high throughput genotyping technologies and the availability of large germplasm collections encouraged the extension of Genome-Wide Association Studies (GWAS) to crop plants. However, to date these strategies have not yet been tested in grapevine (Vitis vinífera L.). Taking advantage of the availability of a large grapevine germplasm collection maintained at the germplasm bank of El Encín (Alcalá de Henares, Madrid, Spain) and the relatively affordable genotyping tools, in this study we proposed to assess the feasibility of GWAS in grapevine, using both a high density Single Nucleotide Polymorphisms (SNPs) dataset and a sparse Simple Sequence Repeats (SSRs) coverage. A diversity panel of 274 grapevine accessions representing most of the cultivated grapevine gene pool was genotyped using the Genotyping by Sequencing strategy (GBS). A total of 358,454 genome-wide distributed SNPs were identified. After filtering, a reliable dataset of 36,332 SNPs across 242 individuals, with a minimum minor allele frequency (MAF) of 0.01 and an average missingness of 12%, was obtained. On the other hand, we developed a panel of multiplex PCRs (Polymerase Chain Reaction) that allowed to genotype the mapping collection for 264 genome-wide distributed SSR markers using only 45 PCRs and sequencer runs. The genetic information generated with this panel (allele size ranges, diversity parameters, presence of null alleles and ease of scoring) will serve as the basis for the identification of the most suitable loci for future microsatellite-based genetic studies in grapevine. GWAS relies on Linkage Disequilibrium (LD) to detect genotype-phenotype associations. LD across the grapevine genome was examined using both GBS-SNPs and SSR markers. SNP-based LD decayed to r2 = 0.2 within an average distance of 1 kb, with considerable variation among chromosomes (from 0.03 to 6.79 kb). Therefore, millions of SNPs would be needed to perform efficient GWAS in grapevine. Although the set of SSR markers did not allow to obtain reliable estimates of LD decay throughout the genome because of insufficient marker density, the results suggest that SSR-based LD would not extend much more than SNP-based LD. SNP-based GWAS across 21 phenotypic and agronomic traits permitted to identify genetic associations at the genome-wide Bonferroni corrected threshold for simply to moderately inherited traits such as berry color, flower sex, muscat flavor, seed content or berry size. Most of these associations overlapped with previously known quantitative trait loci (QTLs), validating hence the effectiveness of this approach, whereas others correspond to new loci that have not been identified before. Putative QTLs were also found for some traits such as ripening time, cluster architecture or berry shape and firmness. However no associations were detected for others such as flowering time or cluster length and diameter. The SSR-based association analysis allowed to detect consistent associations at the nominal significance level of 0.01 for more than one year that agree with SNP-based associations found in the present research and/or with QTLs reported in the literature, but also provided some evidence of new associations. Overall, the present research demonstrates the potential of association studies in grapevine, highlighting ad discussing strengths and weaknesses that would need to be taken into consideration in future GWAS projects. RESUMEN Las últimas innovaciones en tecnologías de genotipado de alto rendimiento y la disponibilidad de grandes colecciones de germoplasma han impulsado la realización de estudios de asociación conocidos como “Genome-Wide Association Studies” (GWAS) en las especies cultivadas. Sin embargo, hasta la fecha no se han llevado a cabo trabajos que evalúen la utilidad de este tipo de estrategias en la vid (Vitis vinífera L.). En este estudio nos propusimos comprobar la utilidad del GWAS para análisis genético en la vid, empleando para ello la colección del banco de germoplasma de vid de El Encín (Alcalá de Henares, Madrid, España) y dos aproximaciones: utilizando por un lado marcadores SNPs (Single Nucleotide Polymorphism) en alta densidad; y por otro una baja cobertura de marcadores microsatélites (SSR; Simple Sequence Repeat). Una colección de 274 accesiones de vid, que representa gran parte de la diversidad genética existente en la vid cultivada fue genotipada empleando la estrategia de Genotyping by Sequencing (GBS), lo que permitió identificar un total de 358.454 SNPs distribuidos a lo largo de todo el genoma. Tras un proceso de filtrado de datos se obtuvo un set de 36.332 SNPs de alta calidad para 242 individuos, con una frecuencia mínima del alelo minoritario (MAF) de 0,01 y una proporción media de datos perdidos del 12%. Por otro lado, se desarrolló un panel de PCRs (Polymerase Chain Reaction) multiplex que permitió analizar 264 marcadores SSRs distribuidos a lo largo del genoma de la vid empleando sólo 45 reacciones de PCR. La información genética y alélica generada con este panel (rangos de amplificación, parámetros de diversidad, presencia de alelos nulos, facilidad de genotipado) serán de gran utilidad para seleccionar los marcadores SSRs más adecuados para futuros estudios genéticos en la vid. Los estudios de tipo GWAS se fundamentan en los patrones de distribución del desequilibrio de ligamiento (LD) a lo largo del genoma. En este trabajo hemos estudiado los patrones de distribución del LD a lo largo del genoma de la vid empleando tanto marcadores de tipo SNPs como microsatélites. Los resultados demostraron que el LD basado en SNPs decae para un r2 de 0.2, dentro de una distancia media de 1 kb, aunque se observó una variación considerable entre cromosomas (de 0,03 a 6,79 kb). Por lo tanto, para desarrollar GWAS eficientemente en la vid sería necesario utilizar millones de SNPs. Aunque el análisis con SSRs no permitió obtener estimas fiables del decaimiento del LD a lo largo de todo el genoma debido a la insuficiente densidad de marcadores, los resultados indican que el LD basado en SSRs no se extendería mucho más que el LD basado en SNPs. El GWAS para 21 caracteres fenotípicos e agronómicos basado en SNPs permitió detectar asociaciones con una significación muy alta (corrección de Bonferroni) para caracteres de herencia sencilla o moderadamente compleja como el color de la baya, el sexo de la flor, el sabor amoscatelado, contenido en semillas o el tamaño de bayas. Muchas de estas asociaciones se localizan en regiones en las que estudios anteriores habían ya identificado QTLs (Quantitative Trait Loci), confirmando así la validez de los resultados obtenidos, mientras que otras corresponden a nuevos QTLs no identificados hasta la fecha. Se detectaron también posibles asociaciones para algunos caracteres, como la fecha de maduración, arquitectura del racimo o la forma y firmeza de la baya. Sin embargo, no se detectaron asociaciones para otros caracteres, como fecha de floración o longitud y diámetro del racimo. El análisis de asociación basado en SSRs permitió detectar asociaciones consistentes al nivel de significación del 0.01 para más de un año, coincidiéndose con asociaciones encontradas en el análisis con SNPs y/o con QTLs identificados en estudios anteriores. El análisis de asociación basado en SSRs también proporcionó algunas pruebas de nuevos QTLs. Como conclusión, el trabajo desarrollado demuestra el potencial de los estudios de asociación en la vid, poniendo de manifiesto sus fortalezas, pero también puntos débiles, que deberían ser objeto de especial consideración en futuros proyectos de GWAS

Genetic diversity in Vicia faba L. populations cultivated in Tunisia revealed by simple sequence repeat analysis

Faba bean (Vicia faba L.) is one of the most important legumes in the world. Little is known about the genetic resources of faba bean in Southern Tunisia. In the present study, genetic diversity within Tunisian faba bean germplasms was investigated using 16 simple sequence repeat markers. In total, 50 alleles were detected. The number of alleles per marker ranged from 2 to 6, with an average of 3. Genetic diversity and polymorphism information content values averaged, respectively, 0.43 (range 0.34–0.51) and 0.36 (range 0.28–0.43). The mean heterozygosity value was 0.27. A model-based structure analysis based on neighbour-joining tree and factorial correspondence analysis revealed the presence of two subpopulations, consistent with the clustering based on genetic distance (GD). The overall Fis value was 0.36, indicating the importance of selfing in these populations. Analysis of molecular variance revealed that the within-population genetic variance component was much higher than the between-population or between-subpopulation variance component. The genetic relationships based on Nei’s GD revealed that AGD (Aguadulce) and SAG (Super Aguadulce) and TF1 and TF2 (Tafartassa-Gafsa) were the most closely related populations. Assessment of genetic variation within faba bean populations will be informative for the conservation of germplasms and the implementation of effective breeding programmes in Tunisia.The authors thank Professor Francisco Madueno from Instituto de Biologia Molecular y Celular de Plantas (IBMCP), Universidad Politecnica de Valencia (UPV)-Consejo Superior de Investigaciones Cientificas (CSIC), for providing financial support for this study. The authors also thank Professor Robert L Jarret (Bob) from United States Department of Agriculture (USDA/ARS/PGRU), Griffin, USA, for providing help with the language aspects of the manuscript.Yahia, Y.; Hannachi, H.; Monforte Gilabert, AJ.; Cockram, J.; Loumerem, M.; Zarouri, B.; Ferchichi, A. (2014). Genetic diversity in Vicia faba L. populations cultivated in Tunisia revealed by simple sequence repeat analysis. Plant Genetic Resources. 12(3):278-285. doi:10.1017/S1479262114000021S27828512

Frequency distribution of the traits across the F₂ population derived from the cross ‘PS’ × PI124112.

<p>(A) FL (Fruit Length), (B) FD (Fruit Diameter) and (C) FS (Fruit Shape). Both parents and F₁ values are marked. In the upper box, 25^th, 50^th and 75^th quartiles are displayed; the sample mean and the 95% confidence interval are represented by a diamond, and the outlier values as dots. The bracket along the edge of the box stands for the part of the graph in which 50% of the observations are gathered together. Mean and standard deviation estimates for a normal distribution are also shown.</p

Significant digenic interactions between all the FS QTL.

<p>Significant digenic interactions between all the FS QTL.</p

Fruit Shape (FS) polymorphism at each stage of the crossing program to obtain the <i>fsqs8.1</i>-NIL.

<p>Fruits of (A) ‘PS’; (B) PI124112; (C) 2M158-3 (F₁); (D) different F₂ plants to show the transgressive segregation of FS; (E) 3M70-47 (F₂); (F) 5M113-1 × ‘PS’ (BC1); (G) 6M59-13 × ‘PS’ (BC2); (H) 7M36-1 × ‘PS’ (BC3); (I) 8M42-24 × ‘PS’ (BC4); (J) 9M7-15 ∶ (BC4S1); (K) 10M2-30 × ‘PS’ (BC5); (L) 11M27 ∶ (BC5S1), (L.1) 11M27-20: heterozygous at <i>fsqs8.1</i> and (L.2) 11M27-11: homozygous at <i>fsqs8.1</i>; (M) the <i>fsqs8.1</i>-NIL 12M57-3 OP (BC5S2).</p

Mean and Standard Deviation (SD) values of the traits for the parents, the F₁ and the whole F₂.

<p>Mean and Standard Deviation (SD) values of the traits for the parents, the F₁ and the whole F₂.</p

QTL detected for FL, FD and FS using the merged data from two locations of the F₂ population from the cross ‘PS’ × PI124112.

<p>QTL detected for FL, FD and FS using the merged data from two locations of the F₂ population from the cross ‘PS’ × PI124112.</p

Digenic interactions studied by two-way ANOVA between the FS QTL using the markers significantly linked to them.

<p>(A) <i>fsqs8.1</i> and <i>fsqs2.1</i> (<i>fsqs2.1</i> × <i>fsqs8.1</i>); (B) <i>fsqs12.1</i> (<i>fsqs8.1</i> × <i>fsqs12.1</i>). Alleles at <i>locus flqs2.1</i> are named A and a since this QTL has been previously identified as gene <i>a</i> <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104188#pone.0104188-Prin1" target="_blank">[12]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104188#pone.0104188-Noguera1" target="_blank">[49]</a>. PsPs: homozygous for the allele PS (solid line); PsPi: heterozygous (dashed line); PiPi: homozygous for the allele PI124112 (dotted line).</p