Dataset.

Table_6_Searching for Abiotic Tolerant and Biotic Stress Resistant Wild Lentils for Introgression Breeding Through Predictive Characterization.XLSX

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/330855
Digital.CSIC. Repositorio Institucional del CSIC
  • Rubio Teso, María Luisa
  • Lara-Romero, Carlos
  • Rubiales, Diego
  • Parra-Quijano, Mauricio
  • Iriondo, José M.
Supplementary Material 6: List of top variables chosen by the Random Forest algorithm (in blue font), shown per group of variables and ordered by Mean Decrease Accuracy Value. Variables in red are correlated according to the bivariate correlations (See Supplementary Material 7)., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed
 
DOI: http://hdl.handle.net/10261/330855
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/330855

HANDLE: http://hdl.handle.net/10261/330855
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/330855
 
Ver en: http://hdl.handle.net/10261/330855
Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/330855

Digital.CSIC. Repositorio Institucional del CSIC
oai:digital.csic.es:10261/330855
Dataset. 2022

TABLE_6_SEARCHING FOR ABIOTIC TOLERANT AND BIOTIC STRESS RESISTANT WILD LENTILS FOR INTROGRESSION BREEDING THROUGH PREDICTIVE CHARACTERIZATION.XLSX

Digital.CSIC. Repositorio Institucional del CSIC
  • Rubio Teso, María Luisa
  • Lara-Romero, Carlos
  • Rubiales, Diego
  • Parra-Quijano, Mauricio
  • Iriondo, José M.
Supplementary Material 6: List of top variables chosen by the Random Forest algorithm (in blue font), shown per group of variables and ordered by Mean Decrease Accuracy Value. Variables in red are correlated according to the bivariate correlations (See Supplementary Material 7)., Crop wild relatives are species related to cultivated plants, whose populations have evolved in natural conditions and confer them valuable adaptive genetic diversity, that can be used in introgression breeding programs. Targeting four wild lentil taxa in Europe, we applied the predictive characterization approach through the filtering method to identify populations potentially tolerant to drought, salinity, and waterlogging. In parallel, the calibration method was applied to select wild populations potentially resistant to lentil rust and broomrape, using, respectively, 351 and 204 accessions evaluated for these diseases. An ecogeographic land characterization map was used to incorporate potential genetic diversity of adaptive value. We identified 13, 1, 21, and 30 populations potentially tolerant to drought, soil salinity, waterlogging, or resistance to rust, respectively. The models targeting broomrape resistance did not adjust well and thus, we were not able to select any population regarding this trait. The systematic use of predictive characterization techniques may boost the efficiency of introgression breeding programs by increasing the chances of collecting the most appropriate populations for the desired traits. However, these populations must still be experimentally tested to confirm the predictions., Peer reviewed




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