Mapping QTLs associated with fruit quality traits in peach Prunus persica (L.) Batsch using SNP maps

[EN] Fruit quality is an essential criterion used to select new cultivars in peach breeding programs and is determined based on a combination of organoleptic and nutritional traits. The aim of this study was to identify quantitative trait loci (QTLs) for fruit quality traits in an F-1 nectarine population derived from 'Venus' and 'Big Top' cultivars. The progeny were evaluated over 4 years for agronomical and biochemical characteristics and genotyped using simple sequence repeat (SSR) markers and 'IPSC 9K peach SNP array v1'. Two genetic maps were constructed using 411 markers. The 'Venus' map spanned 259 cM on nine linkage groups (LGs) with 104 markers. The 'Big Top' map spanned 464 cM on 10 LGs with 122 markers. Single or Multiple QTL models mapping was applied separately for each year and all years combined. A total of 54 QTLs mapped over 12 LGs belonged to seven peach chromosomes. Most of the QTLs were consistent over the 4 years of study and were validated with the multi-year analysis. QTLs for total phenolic, flavonoid, and anthocyanin contents were reported for the first time in peach. LG 4 in 'Venus' and LG 5 in 'Big Top' showed the highest numbers of QTLs. This work represents the first study in an F-1 nectarine family to identify peach genomic regions that control fruit quality traits using 'IPSC 9K SNP array v1' and provides useful information for marker-assisted breeding to produce peaches with better antioxidant content and healthy attributes.We are grateful to C.H. Crisosto (University of California, Davis) for providing SSR markers (UCDCH15 and BINEPPCU6377). We thank E. Sierra and S. Segura for the technical assistance and plant management in the field and N. Ksouri for the bioinformatic assistance. We are grateful to A. Casas and E. Igartua for the assistance and support with the statistical analysis using JoinMap (R) 4 software. This study was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) grants AGL-2008-00283, AGL2011-24576, and AGL2014-52063-R and was co-funded by the FEDER and the Regional Government of Aragon (A44) with European Social Fund. W. Abidi was supported by a JAE-Pre fellowship from the Consejo Superior de Investigaciones Cientificas (CSIC), which enabled him to visit the University of California, Davis, and the IBMCP, Valencia, Spain. J.L. Zeballos received a master fellow funded by the Spanish Agency for International Cooperation and Development (AECID).Zeballos, JL.; Adibi, W.; Giménez Millán, R.; Monforte Gilabert, AJ.; Moreno, MA.; Gogorcena, Y. (2016). Mapping QTLs associated with fruit quality traits in peach Prunus persica (L.) Batsch using SNP maps. Tree Genetics and Genomes. 12(3):1-17. https://doi.org/10.1007/s11295-016-0996-9S117123Abbott AG, Rajapakse S, Sosinski B, Lu ZX, Sossey-Alaoui K, Gannavarapu M, Reighard G, Ballard RE, Baird WV, Scorza R, Callahan A (1998) Construction of saturated linkage maps of peach crosses segregating for characters controlling fruit quality, tree architecture and pest resistance. Acta Hortic 465:41–50Abidi W, Jiménez S, Moreno MÁ, Gogorcena Y (2011) Evaluation of antioxidant compounds and total sugar content in a nectarine [Prunus persica (L.) Batsch] progeny. Int J Mol Sci 12:6919–6935Abidi W, Cantín C, Gonzalo MJ, Moreno MA, Gogorcena Y (2012) Genetic control and location of QTLs involved in antioxidant capacity and fruit quality traits in peach [Prunus persica (L.) Batch]. Acta Hortic 962:129–134Abidi W, Cantín CM, Jiménez S, Giménez R, Moreno MA, Gogorcena Y (2015) Effect of antioxidant compounds and total sugars and genetic background on the chilling injury susceptibility of a non-melting peach [Prunus persica (L.) Batsch] progeny. J Sci Food Agric 95:351–358Albrechtsen A, Nielsen FC, Nielsen R (2010) Ascertainment biases in SNP chips affect measures of population divergence. Mol Biol Evol 11:2534–2547Arús P, Verde I, Sosinski B, Zhebentyayeva T, Abbott AG (2012) The peach genome. Tree Genet Genomes 8:531–547Boeing H, Bechthold A, Bub A, Ellinger S, Haller D, Kroke A, Leschik-Bonnet E, Müller MJ et al (2012) Critical review: vegetables and fruit in the prevention of chronic diseases. Eur J Nutr 51:637–663Brem RB, Kruglyak L (2005) The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc Natl Acad Sci U S A 102:1572–1577Broman KW, Saunak S (2009) A guide to QTL mapping with R/qtl. Statistic for biology and health. Springer, New York. doi: 10.1007/978-0-387-92125-9Broman KW, Wu H, Sen Ś, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889–890Byrne DH, Raseira MB, Bassi D, Piagnani MC, Gasic K, Moreno MA, Pérez S (2012) Peach. In: Badenes ML, Byrne DH (eds) Fruit breeding, vol 8. Handbook of plant breeding, vol 8. Springer, New York, pp 505–569Cantín CM, Gogorcena Y, Moreno MA (2009a) Analysis of phenotypic variation of sugar profile in different peach and nectarine [Prunus persica (L.) Batsch] breeding progenies. J Sci Food Agric 89:1909–1917Cantín CM, Moreno MA, Gogorcena Y (2009b) Evaluation of the antioxidant capacity, phenolic compounds, and vitamin C content of different peach and nectarine [Prunus persica (L.) Batsch] breeding progenies. J Agric Food Chem 57:4586–4592Cantín CM, Crisosto CH, Ogundiwin EA, Gradziel T, Torrents J, Moreno MA, Gogorcena Y (2010a) Chilling injury susceptibility in an intra-specific peach [Prunus persica (L.) Batsch] progeny. Postharvest Biol Technol 58:79–87Cantín CM, Gogorcena Y, Moreno MA (2010b) Phenotypic diversity and relationships of fruit quality traits in peach and nectarine [Prunus persica (L.) Batsch] breeding progenies. Euphytica 171:211–226Crisosto CH (2002) How do we increase peach consumption? Acta Hortic 592:601–605Chagné D, Krieger C, Rassam M, Sullivan M, Fraser J, André C, Pindo M, Troggio M, Gardiner SE, Henry RA, Allan AC, McGhie TK, Laing WA (2012) QTL and candidate gene mapping for polyphenolic composition in apple fruit. BMC Plant Biol 12:1–16Chaparro JX, Werner DJ, O’Malley D, Sederoff RR (1994) Targeted mapping and linkage analysis of morphological isozyme, and RAPD markers in peach. Theor Appl Genet 87:805–815Da Silva Linge C, Bassi D, Bianco L, Pacheco I, Pirona R, Rossini L (2015) Genetic dissection of fruit weight and size in an F2 peach (Prunus persica (L.) Batsch) progeny. Mol Breed 35:71Davey MW, Kenis K, Keulemans J (2006) Genetic control of fruit vitamin C contents. Plant Physiol 142:343–351Dirlewanger E, Pronier V, Parvery C, Rothan C, Guye A, Monet R (1998) Genetic linkage map of peach [Prunus persica (L.) Batsch] using morphological and molecular markers. Theor Appl Genet 97:888–895Dirlewanger E, Moing A, Rothan C, Svanella L, Pronier V, Guye A, Plomion C, Monet R (1999) Mapping QTLs controlling fruit quality in peach (Prunus persica (L.) Batsch). Theor Appl Genet 98(1):18–31Dirlewanger E, Graziano E, Joobeur T, Garriga-Calderé F, Cosson P, Howad W, Arús P (2004) Comparative mapping and marker-assisted selection in Rosaceae fruit crops. Proc Natl Acad Sci U S A 101:9891–9896Dirlewanger E, Cosson P, Renaud C, Monet R, Poëssel JL, Moing A (2006) New detection of QTLs controlling major fruit quality components in peach. Acta Hortic 713:65–72Dirlewanger E, Quero-García J, Le Dantec L, Lambert P, Ruiz D, Dondini L, Illa E, Quilot-Turion B, Audergon JM, Tartarini S, Letourmy P, Arús P (2012) Comparison of the genetic determinism of two key phenological traits, flowering and maturity dates, in three Prunus species: peach, apricot and sweet cherry. Heredity 109:280–292Eduardo I, Pacheco I, Chietera G, Bassi D, Pozzi C, Vecchietti A, Rossini L (2011) QTL analysis of fruit quality traits in two peach intraspecific populations and importance of maturity date pleiotropic effect. Tree Genet Genomes 7:323–335Eduardo I, Chietera G, Pirona R, Pacheco I, Troggio M, Banchi E, Bassi D, Rossini L, Vecchietti A, Pozzi C (2013) Genetic dissection of aroma volatile compounds from the essential oil of peach fruit: QTL analysis and identification of candidate genes using dense SNP maps. Tree Genet Genomes 9:189–204Eduardo I, López-Girona E, BatlIe I, Reig G, Iglesias I, Howad W, Arús P, Aranzana MJ (2014) Development of diagnostic markers for selection of the subacid trait in peach. Tree Genet Genomes 10:1695–1709Eduardo I, Picañol R, Rojas E, BatlIe I, Howad W, Aranzana MJ, Arús P (2015) Mapping of a major gene for the slow ripening character in peach: co-location with the maturity date gene and development of a candidate gene-based diagnostic marker for its selection. Euphytica 205:627–636Etienne C, Rothan C, Moing A, Plomion C, Bodénès C, Svanella-Dumas L, Cosson P, Pronier V, Monet R, Dirlewanger E (2002) Candidate genes and QTLs for sugar and organic acid content in peach [Prunus persica (L.) Batsch]. Theor Appl Genet 105:145–159FAOSTAT (2015) Food and Agriculture Organization of the United Nations. http://faostat.fao.org/site/291/default.aspx . Accessed 1 August 2015Font i Forcada C, Oraguzie N, Igartua E, Moreno MA, Gogorcena Y (2013) Population structure and marker-trait associations for pomological traits in peach and nectarine cultivars. Tree Genet Genomes 9:331–349Fresnedo-Ramírez J, Bink MCAM, van der Weg E, Famula TR, Crisosto CH, Frett TJ, Gasic K, Peace CP, Gradziel TM (2015) QTL mapping of pomological traits in peach and related species breeding germplasm. Mol Breed 35:166Frett T, Reighard G, Okie W, Gasic K (2014) Mapping quantitative trait loci associated with blush in peach [Prunus persica (L.) Batsch]. Tree Genet Genomes 10:367–381GDR (2015) Genome database for Rosaceae. http://www.rosaceae.org/species/prunus_persica/genome_v1.0 . Accessed 13 Nov 2015Grattapaglia D, Sederoff R (1994) Genetic linkage maps of Eucalyptus grandis and Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers. Genetics 137:1121–1137Illa E, Eduardo I, Audergon JM, Barale F, Dirlewanger E, Li X, Moing A, Lambert P, Le Dantec L, Gao Z, Poëssel JL, Pozzi C, Rossini L, Vecchietti A, Arús P, Howad W (2011) Saturating the Prunus (stone fruits) genome with candidate genes for fruit quality. Mol Breed 28(4):667–682Infante R, Farcuh M, Meneses C (2008) Monitoring the sensorial quality and aroma through an electronic nose in peaches during cold storage. J Sci Food Agric 88:2073–2078Jáuregui B, De Vicente MC, Messeguer R, Felipe A, Bonnet A, Salesses G, Arús P (2001) A reciprocal translocation between ‘Garfi’ almond and ‘Nemared’ peach. Theor Appl Genet 102:1169–1176Martin C, Zhang Y, Tonelli C, Petroni K (2013) Plants, diet, and health. Annu Rev Plant Biol 64:19–46Martínez-García PJ, Fresnedo-Ramírez J, Parfitt DE, Gradziel TM, Crisosto CH (2013a) Effect prediction of identified SNPs linked to fruit quality and chilling injury in peach [Prunus persica (L.) Batsch]. Plant Mol Biol 81:161–174Martínez-García PJ, Parfitt DE, Ogundiwin EA, Fass J, Chan HM, Ahmad R, Lurie S, Dandekar A, Gradziel TM, Crisosto CH (2013b) High density SNP mapping and QTL analysis for fruit quality characteristics in peach (Prunus persica L.). Tree Genet Genomes 9:19–36Nielsen R, Hubisz MJ, Clark AG (2004) Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics 168:2373–2382Nuñez-Lillo G, Cifuentes-Esquivel A, Troggio M, Micheletti D, Rodrigo Infante R, Campos-Vargas R, Orellana A, Blanco-Herrera F, Meneses C (2015) Identification of candidate genes associated with mealiness and maturity date in peach [Prunus persica (L.) Batsch] using QTL analysis and deep sequencing. Tree Genet Genomes 1:86Ogundiwin EA, Peace CP, Gradziel TM, Parfitt DE, Bliss FA, Crisosto CH (2009) A fruit quality gene map of Prunus. BMC Genomics 10:587Orazem P, Stampar F, Hudina M (2011) Quality analysis of ‘Redhaven’ peach fruit grafted on 11 rootstocks of different genetic origin in a replant soil. Food Chem 124(4):1691–1698Pacheco I, Bassi D, Eduardo I, Ciacciulli A, Pirona R, Rossini L, Vecchietti A (2014) QTL mapping for brown rot (Monilinia fructigena) resistance in an intraspecific peach (Prunus persica L. Batsch) F1 progeny. Tree Genet Genomes 10:1223–1242. doi: 10.1007/s11295-014-0756-7Peace C, Norelli J (2009) Genomics approaches to crop improvement in the Rosaceae. In: Folta KM, Gardiner SE (eds) Genetics and genomics of Rosaceae, vol 6. Plant genetics and genomics: crops and models. Springer, New York, pp 19–53Pirona R, Eduardo I, Pacheco I, Da Silva Linge C, Miculan M, Verde I, Tartarini S, Dondini L, Giorgio Pea G, Daniele Bassi D, Rossini L (2013) Fine mapping and identification of a candidate gene for a major locus controlling maturity date in peach. BMC Plant Biol 3:166Quarta R, Dettori MT, Sartori A, Verde I (2000) Genetic linkage map and QTL analysis in peach. Acta Hortic 521:233–242Quilot B, Wu BH, Kervella J, Génard M, Foulongne M, Moreau K (2004) QTL analysis of quality traits in an advanced backcross between Prunus persica cultivars and the wild relative species P. davidiana. Theor Appl Genet 109:884–897Romeu J, Monforte AJ, Sánchez G, Granell A, García-Brunton J, Badenes M, Ríos G (2014) Quantitative trait loci affecting reproductive phenology in peach. BMC Plant Biol 14:52Ru S, Main D, Evans K, Peace C (2015) Current applications, challenges, and perspectives of marker-assisted seedling selection in Rosaceae tree fruit breeding. Tree Genet Genomes 11:8Salazar JA, Ruiz D, Egea J, Martínez-Gómez P (2013) Transmission of fruit quality traits in apricot (Prunus armeniaca L.) and analysis of linked quantitative trait loci (QTLs) using simple sequence repeat (SSR) markers. Plant Mol Biol Rep 31:1506–1517. doi: 10.1007/s11105-013-0625-9Sánchez G, Besada C, Badenes ML, Monforte AJ, Granell A (2012) A non-targeted approach unravels the volatile network in peach fruit. PLoS One 7(6), e38992Sánchez G, Romeu J, García J, Monforte AJ, Badenes M, Granell A (2014) The peach volatilome modularity is reflected at the genetic and environmental response levels in a QTL mapping population. BMC Plant Biology 14:137Steemers FJ, Chang W, Lee G, Barker DL, Shen R, Gunderson KL (2006) Whole-genome genotyping with the single-base extension assay. Nat Methods 3(1):31–33. doi: 10.1038/nmeth842Van Ooijen JW (1992) Accuracy of mapping quantitative trait loci in autogamous species. Theor Appl Genet 84:803–811Van Ooijen JW (2006) JoinMap 4, software for the calculation of genetic linkage maps in experimental populations. Kyazma B.V., Wageningen, NetherlandsVerde I, Quarta R, Cedrola C, Dettori MT (2002) QTL analysis of agronomic traits in a BC1 peach population. Acta Hortic 592:291–297Verde I, Bassil N, Scalabrin S, Gilmore B, Lawley CT, Gasic K, Micheletti D, Rosyara UR, Cattonaro F, Vendramin E, Main D, Aramini V, Blas AL, Mockler TC, Bryant DW, Wilhelm L, Troggio M, Sosinski B, Aranzana MJ, Arús P, Iezzoni A, Morgante M, Peace C (2012) Development and evaluation of a 9k SNP array for peach by internationally coordinated SNP detection and validation in breeding germplasm. PLoS One 7(4), e35668Verde I, Abbott AG, Scalabrin S, Jung S, Shu SQ, Marroni F, Zhebentyayeva T, et al. (Int Peach Genome I) (2013) The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat Genet 45:487-U447Verdu CF, Guyot S, Childebrand N, Bahut M, Celton JM, Gaillard S, Lasserre-Zuber P, Troggio M, Guilet D, Laurens F (2014) QTL analysis and candidate gene mapping for the polyphenol content in cider apple. PLoS One 9(10), e107103Vizzotto M, Porter W, Byrne D, Luis Cisneros-Zevallos L (2014) Polyphenols of selected peach and plum genotypes reduce cell viability and inhibit proliferation of breast cancer cells while not affecting normal cells. Food Chem 164:363–370Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78Wargovich MJ, Morris J, Moseley V, Weber R, Byrne DH (2012) Developing fruit cultivars with enhanced health properties, in fruit breeding. In: Badenes ML, Byrne DH (eds) Fruit breeding, vol 8, Handbook of plant breeding. Springer, New York, pp 37–68Yang J, Hu Ch, Hu H, Yu R, Xia Z, Ye X, Zhu J (2008) QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24(5):721–723Yang N, Reighard G, Ritchie D, Okie W, Gasic K (2013) Mapping quantitative trait loci associated with resistance to bacterial spot (Xanthomonas arboricola pv. pruni) in peach. Tree Genet Genomes 9:573–586Zeballos J (2012) Identification of genomic region related to fruit quality traits in peach. Universidad de Lleida, Zaragoza, SpainZeballos J, Abidi W, Giménez R, Monforte AJ, Moreno MA, Gogorcena Y (2015) QTL analysis of fruit quality traits in peach [Prunus persica (L.) Batsch] using dense SNP maps. Acta Hortic 1084:703–710Zhebentyayeva TN, Swire-Clark G, Georgi LL, Garay L, Jung S, Forrest S, Blenda AV, Blackmon B, Mook J, Horn R, Howad W, Arús P, Main D, Tomkins JP, Sosinski B, Baird WV, Reighard GL, Abbott AG (2008) A framework physical map for peach, a model Rosaceae species. Tree Genet Genomes 4:745–756Zhebentyayeva TN, Fan S, Chandra A, Bielenberg DG, Reighard GL, Okie WR, Abbott AG (2014) Dissection of chilling requirement and bloom date QTLs in peach using a whole genome sequencing of sibling trees from an F2 mapping population. Tree Genet Genomes 10:35–5

European traditional tomatoes galore: a result of farmers' selection of a few diversity-rich loci

A comprehensive collection of 1254 tomato accessions, corresponding to European traditional and modern varieties, early domesticated varieties, and wild relatives, was analyzed by genotyping by sequencing. A continuous genetic gradient between the traditional and modern varieties was observed. European traditional tomatoes displayed very low genetic diversity, with only 298 polymorphic loci (95% threshold) out of 64 943 total variants. European traditional tomatoes could be classified into several genetic groups. Two main clusters consisting of Spanish and Italian accessions showed higher genetic diversity than the remaining varieties, suggesting that these regions might be independent secondary centers of diversity with a different history. Other varieties seem to be the result of a more recent complex pattern of migrations and hybridizations among the European regions. Several polymorphic loci were associated in a genome-wide association study with fruit morphological traits in the European traditional collection. The corresponding alleles were found to contribute to the distinctive phenotypic characteristic of the genetic varietal groups. The few highly polymorphic loci associated with morphological traits in an otherwise a low-diversity population suggests a history of balancing selection, in which tomato farmers likely maintained the morphological variation by inadvertently applying a high selective pressure within different varietal types

European traditional tomatoes galore: a result of farmers' selection of a few diversity-rich loci

[EN] The high phenotypic diversity observed among European traditional tomato varieties was created by traditional farmer-driven selection by inadvertently combining a very few polymorphic loci subjected to balancing selection. A comprehensive collection of 1254 tomato accessions, corresponding to European traditional and modern varieties, early domesticated varieties, and wild relatives, was analyzed by genotyping by sequencing. A continuous genetic gradient between the traditional and modern varieties was observed. European traditional tomatoes displayed very low genetic diversity, with only 298 polymorphic loci (95% threshold) out of 64 943 total variants. European traditional tomatoes could be classified into several genetic groups. Two main clusters consisting of Spanish and Italian accessions showed higher genetic diversity than the remaining varieties, suggesting that these regions might be independent secondary centers of diversity with a different history. Other varieties seem to be the result of a more recent complex pattern of migrations and hybridizations among the European regions. Several polymorphic loci were associated in a genome-wide association study with fruit morphological traits in the European traditional collection. The corresponding alleles were found to contribute to the distinctive phenotypic characteristic of the genetic varietal groups. The few highly polymorphic loci associated with morphological traits in an otherwise a low-diversity population suggests a history of balancing selection, in which tomato farmers likely maintained the morphological variation by inadvertently applying a high selective pressure within different varietal types.This work was supported by the European Commission H2020 research and innovation program through TRADITOM grant agreement no. 634561, G2P-SOL, grant agreement no. 677379, and HARNESSTOM grant agreement no. 101000716. MP is grateful to the Spanish Ministerio de Ciencia e Innovacion for a postdoctoral grant (IJC2019-039091-I/AEI/10.13039/501100011033).Blanca Postigo, JM.; Pons Puig, C.; Montero-Pau, J.; Sánchez-Matarredona, D.; Ziarsolo, P.; Fontanet, L.; Fisher, J.... (2022). European traditional tomatoes galore: a result of farmers' selection of a few diversity-rich loci. Journal of Experimental Botany. 73(11):3431-3445. https://doi.org/10.1093/jxb/erac07234313445731

Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato

[EN] The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity.We thank Universitat Illes Balears, the Greek Gene Bank (GGB-NAGREF), Universita degli Studi Mediterranea Reggio Calabria, the CRB-Leg (INRA-GAFL)", the Genebank of CNR-IBBR (Bari, Italy) and ARCA 2010 for seed sharing. CNR-IBBR also acknowledges the seed donors, the Leibniz Institute of Plant Genetics and Crop Plant Research, Maria Cristina Patane (CNR-IBE, Catania, Italy) and La Semiorto Sementi SRL, as well as Mrs. Roberta Nurcato for technical assistance. IBMCP-UPV acknowledges Maurizio Calduch (ALCALAX) for technical assistance and Mario Fon for English grammar editing. This work was supported by European Commission H2020 research and innovation program through TRADITOM grant agreement No.634561, G2P-SOL, grant agreement No. 677379, and HARNESSTOM grant agreement No. 101000716. Clara Pons and Mariola Plazas are grateful to Spanish Ministerio de Ciencia e Innovacion for postdoctoral grants FJCI-2016-29118 and IJC2019-039091I/AEI/10.13039/501100011033; Joan Casals to a Serra Hunter Fellow at Universitat Politècnica de Catalunya.Pons Puig, C.; Casals, J.; Palombieri, S.; Fontanet, L.; Riccini, A.; Rambla Nebot, JL.; Ruggiero, A.... (2022). Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato. Horticulture Research. 9:1-16. https://doi.org/10.1093/hr/uhac112116

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Parthenogenic haploids in melon (Cucumis melo L.): generation and molecular characterization of a doubled haploid line population

[EN] Melon (Cucumis melo) is one of the principal vegetable crops for fresh market, for which a large number of breeding programs, oriented to generate inbred pure lines and hybrids, is established worldwide. The process to obtain and select these lines has been highly accelerated by the use of biotechnological techniques such as the generation of doubled haploid line (DHL) populations andmolecular markers.Moreover, the use of DHLs in genetic studies is a useful tool because of their complete homozygosity and the permanent availability of plant material perpetuated by seed. In this work, the parthenogenetic response of 17 melon genotypes and the F1 hybrid PI 161375 · Spanish cultivar Piel de Sapo (PS) was studied considering three stages along the in vitro DHL generation process. The response of the analyzed melon cultivars was heterogeneous through the DHL generation with different limiting steps for each genotype. The response of the PI 161375 · PS hybrid was more similar to the male (PS) than the female parent (PI 161375), although the response of the maternal genotype was higher for some stages. This points to the important role of alleles from both parents in the different steps of theDHL generation process, and it could explain the identification of six genomic regions with distorted allelic segregation skewed toward PS or PI 161375. This hybrid was used to generate a population of 109 DHLs, the gametophytic origin of which was confirmed by flow cytometry and molecular markers.This work was supported in part by the Spanish seed company Semillas Fit´o S.A. (Barcelona, Spain) and the research projects AGL2000-03602, AGL2003-09175-C02-01, and 2FD1997-0325 financed by the Spanish Ministry of Science and Innovation and with the support of the Ministry of Innovation, Universities and Enterprise of Catalonia.Gonzalo, MJ.; Claveria, E.; Monforte Gilabert, AJ.; Dolcet-Sanjuan, R. (2011). Parthenogenic haploids in melon (Cucumis melo L.): generation and molecular characterization of a doubled haploid line population. Journal of the American Society for Horticultural Science. 136(2):145-154. http://hdl.handle.net/10251/28324S145154136

A chemical genetic roadmap to improved tomato flavor

[EN] Modern commercial tomato varieties are substantially less flavorful than heirloom varieties. To understand and ultimately correct this deficiency, we quantified flavor-associated chemicals in 398 modern, heirloom, and wild accessions. A subset of these accessions was evaluated in consumer panels, identifying the chemicals that made the most important contributions to flavor and consumer liking. We found that modern commercial varieties contain significantly lower amounts of many of these important flavor chemicals than older varieties. Whole-genome sequencing and a genome-wide association study permitted identification of genetic loci that affect most of the target flavor chemicals, including sugars, acids, and volatiles. Together, these results provide an understanding of the flavor deficiencies in modern commercial varieties and the information necessary for the recovery of good flavor through molecular breeding.This work was supported by the NSF (grant IOS-0923312 to H.K.), the China National Key Research and Development Program for Crop Breeding (grant 2016YFD0100307 to S.H.), the Leading Talents of Guangdong Province Program (grant 00201515 to S.H.), the National Natural Science Foundation of China (grant 31601756 to G.Z.), the European Research Council (grant ERC-2011-AdG 294691 YIELD to D.Z.), and the European Commission Horizon 2020 program (TRADITOM grant 634561 to A.G. and D.Z.) This work was also supported by the Chinese Academy of Agricultural Science (ASTIP-CAAS) and the Shenzhen municipal and Dapeng district governments. We acknowledge the assistance of L. Kates in fieldwork and volatile, sugar, and acid quantification.Tieman, D.; Zhu, G.; Resende, MFR.; Lin, T.; Nguyen, C.; Bies, D.; Rambla Nebot, JL.... (2017). A chemical genetic roadmap to improved tomato flavor. Science. 355(6323):391-394. https://doi.org/10.1126/science.aal1556S3913943556323Food and Agriculture Organization of the United Nations; http://faostat.fao.org/site/339/default.aspx.Tieman, D., Bliss, P., McIntyre, L. M., Blandon-Ubeda, A., Bies, D., Odabasi, A. Z., … Klee, H. J. (2012). The Chemical Interactions Underlying Tomato Flavor Preferences. Current Biology, 22(11), 1035-1039. doi:10.1016/j.cub.2012.04.016R. G. Buttery, R. Teranishi, R. A. Flath, L. C. Ling, Fresh tomato volatiles: Composition and sensory studies. Am. Chem. Soc. Symp. 388, 213–222 (1987).Baldwin, E. A., Scott, J. W., Shewmaker, C. K., & Schuch, W. (2000). Flavor Trivia and Tomato Aroma: Biochemistry and Possible Mechanisms for Control of Important Aroma Components. HortScience, 35(6), 1013-1022. doi:10.21273/hortsci.35.6.1013Vogel, J. T., Tieman, D. M., Sims, C. A., Odabasi, A. Z., Clark, D. G., & Klee, H. J. (2010). Carotenoid content impacts flavor acceptability in tomato (Solanum lycopersicum). Journal of the Science of Food and Agriculture, 90(13), 2233-2240. doi:10.1002/jsfa.4076Zhang, B., Tieman, D. M., Jiao, C., Xu, Y., Chen, K., Fei, Z., … Klee, H. J. (2016). Chilling-induced tomato flavor loss is associated with altered volatile synthesis and transient changes in DNA methylation. Proceedings of the National Academy of Sciences, 113(44), 12580-12585. doi:10.1073/pnas.1613910113Lin, T., Zhu, G., Zhang, J., Xu, X., Yu, Q., Zheng, Z., … Huang, S. (2014). Genomic analyses provide insights into the history of tomato breeding. Nature Genetics, 46(11), 1220-1226. doi:10.1038/ng.3117Fridman, E. (2004). Zooming In on a Quantitative Trait for Tomato Yield Using Interspecific Introgressions. Science, 305(5691), 1786-1789. doi:10.1126/science.1101666Zanor, M. I., Osorio, S., Nunes-Nesi, A., Carrari, F., Lohse, M., Usadel, B., … Fernie, A. R. (2009). RNA Interference of LIN5 in Tomato Confirms Its Role in Controlling Brix Content, Uncovers the Influence of Sugars on the Levels of Fruit Hormones, and Demonstrates the Importance of Sucrose Cleavage for Normal Fruit Development and Fertility. Plant Physiology, 150(3), 1204-1218. doi:10.1104/pp.109.136598Tieman, D., Zeigler, M., Schmelz, E., Taylor, M. G., Rushing, S., Jones, J. B., & Klee, H. J. (2010). Functional analysis of a tomato salicylic acid methyl transferase and its role in synthesis of the flavor volatile methyl salicylate. The Plant Journal, 62(1), 113-123. doi:10.1111/j.1365-313x.2010.04128.xZanor, M. I., Rambla, J.-L., Chaïb, J., Steppa, A., Medina, A., Granell, A., … Causse, M. (2009). Metabolic characterization of loci affecting sensory attributes in tomato allows an assessment of the influence of the levels of primary metabolites and volatile organic contents. Journal of Experimental Botany, 60(7), 2139-2154. doi:10.1093/jxb/erp086Zierler, B., Siegmund, B., & Pfannhauser, W. (2004). Determination of off-flavour compounds in apple juice caused by microorganisms using headspace solid phase microextraction–gas chromatography–mass spectrometry. Analytica Chimica Acta, 520(1-2), 3-11. doi:10.1016/j.aca.2004.03.084Deikman, J., Kline, R., & Fischer, R. L. (1992). Organization of Ripening and Ethylene Regulatory Regions in a Fruit-Specific Promoter from Tomato (Lycopersicon esculentum). Plant Physiology, 100(4), 2013-2017. doi:10.1104/pp.100.4.2013Penarrubia, L., Aguilar, M., Margossian, L., & Fischer, R. L. (1992). An Antisense Gene Stimulates Ethylene Hormone Production during Tomato Fruit Ripening. The Plant Cell, 681-687. doi:10.1105/tpc.4.6.681Lewinsohn, E., Sitrit, Y., Bar, E., Azulay, Y., Meir, A., Zamir, D., & Tadmor, Y. (2005). Carotenoid Pigmentation Affects the Volatile Composition of Tomato and Watermelon Fruits, As Revealed by Comparative Genetic Analyses. Journal of Agricultural and Food Chemistry, 53(8), 3142-3148. doi:10.1021/jf047927tOltman, A. E., Jervis, S. M., & Drake, M. A. (2014). Consumer Attitudes and Preferences for Fresh Market Tomatoes. Journal of Food Science, 79(10), S2091-S2097. doi:10.1111/1750-3841.12638Tieman, D. M., Zeigler, M., Schmelz, E. A., Taylor, M. G., Bliss, P., Kirst, M., & Klee, H. J. (2006). Identification of loci affecting flavour volatile emissions in tomato fruits. Journal of Experimental Botany, 57(4), 887-896. doi:10.1093/jxb/erj074Rambla, J. L., Alfaro, C., Medina, A., Zarzo, M., Primo, J., & Granell, A. (2015). Tomato fruit volatile profiles are highly dependent on sample processing and capturing methods. Metabolomics, 11(6), 1708-1720. doi:10.1007/s11306-015-0824-5Bartoshuk, L. ., Duffy, V. ., Fast, K., Green, B. ., Prutkin, J., & Snyder, D. . (2003). Labeled scales (e.g., category, Likert, VAS) and invalid across-group comparisons: what we have learned from genetic variation in taste. Food Quality and Preference, 14(2), 125-138. doi:10.1016/s0950-3293(02)00077-0A. B. Gilmour, B. Gogel, B. Cullis, R. Thompson R ASReml User Guide Release 3.0. VSN International. Hemel Hempstead, UK (2009).Li, R., Yu, C., Li, Y., Lam, T.-W., Yiu, S.-M., Kristiansen, K., & Wang, J. (2009). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25(15), 1966-1967. doi:10.1093/bioinformatics/btp336(2012). The tomato genome sequence provides insights into fleshy fruit evolution. Nature, 485(7400), 635-641. doi:10.1038/nature11119Li, Y., Chen, W., Liu, E. Y., & Zhou, Y.-H. (2012). Single Nucleotide Polymorphism (SNP) Detection and Genotype Calling from Massively Parallel Sequencing (MPS) Data. Statistics in Biosciences, 5(1), 3-25. doi:10.1007/s12561-012-9067-4Jombart, T., Devillard, S., & Balloux, F. (2010). Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics, 11(1), 94. doi:10.1186/1471-2156-11-94Zheng, X., Levine, D., Shen, J., Gogarten, S. M., Laurie, C., & Weir, B. S. (2012). A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics, 28(24), 3326-3328. doi:10.1093/bioinformatics/bts606Jombart, T. (2008). adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics, 24(11), 1403-1405. doi:10.1093/bioinformatics/btn129Kang, H. M., Sul, J. H., Service, S. K., Zaitlen, N. A., Kong, S., Freimer, N. B., … Eskin, E. (2010). Variance component model to account for sample structure in genome-wide association studies. Nature Genetics, 42(4), 348-354. doi:10.1038/ng.548Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C. (2011). Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Human Genetics, 131(5), 747-756. doi:10.1007/s00439-011-1118-2Barrett, J. C., Fry, B., Maller, J., & Daly, M. J. (2004). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21(2), 263-265. doi:10.1093/bioinformatics/bth457Craig, D. W., Pearson, J. V., Szelinger, S., Sekar, A., Redman, M., Corneveaux, J. J., … Huentelman, M. J. (2008). Identification of genetic variants using bar-coded multiplexed sequencing. Nature Methods, 5(10), 887-893. doi:10.1038/nmeth.1251Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760. doi:10.1093/bioinformatics/btp324Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., … Homer, N. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. doi:10.1093/bioinformatics/btp35

Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato

The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity