Person:
Cerón-Rojas, J.J.

Profile Picture
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Cerón-Rojas
First Name
J.J.
Name
Cerón-Rojas, J.J.
ORCID ID
0000-0003-2885-6831

Filters

2014 - 2019182020 - 20227
Reset filters

Settings

Search Results

Now showing 1 - 10 of 25
  • Thumbnail Image
    A linear profit function for economic weights of linear phenotypic selection indices in plant breeding
    (CSSA, 2022) Cerón-Rojas, J.J.; Gowda, M.; Toledo, F.H.; Beyene, Y.; Bentley, A.R.; Crespo Herrera, L.A.; Gardner, K.A.; Crossa, J.
    Publication
  • Thumbnail Image
    Identification of disease resistance parents and genome-wide association mapping of resistance in spring wheat
    (MDPI, 2022) Iqbal, M.; Semagn, K.; Jarquin, D.; Randhawa, H.S.; McCallum, B.D.; Howard, R.; Aboukhaddour, R.; Ciechanowska, I.; Strenzke, K.; Crossa, J.; Cerón-Rojas, J.J.; N’Diaye, A.; Pozniak, C.; Spaner, D.
    Publication
  • Thumbnail Image
    Chapter 32. Theory and practice of phenotypic and genomic selection indices
    (Springer Nature, 2022) Crossa, J.; Cerón-Rojas, J.J.; Martini, J.W.R.; Covarrubias, E.; Alvarado Beltrán, G.; Toledo, F.H.; Velu, G.
    Publication
  • Thumbnail Image
    Identification of spring wheat with superior agronomic performance under contrasting nitrogen managements using linear phenotypic selection indices
    (MDPI, 2022) Iqbal, M.; Semagn, K.; Cerón-Rojas, J.J.; Crossa, J.; Jarquin, D.; Howard, R.; Beres, B.L.; Strenzke, K.; Ciechanowska, I.; Spaner, D.
    Publication
  • Thumbnail Image
    Publication
  • Thumbnail Image
    Publication
  • Thumbnail Image
    Combined multistage linear genomic selection indices to predict the net genetic merit in plant breeding
    (Genetics Society of America, 2020) Cerón-Rojas, J.J.; Crossa, J.
    Publication
  • Thumbnail Image
    Efficiency of a constrained linear genomic selection index to predict the net genetic merit in plants
    (Genetics Society of America, 2019) Cerón-Rojas, J.J.; Crossa, J.
    The constrained linear genomic selection index (CLGSI) is a linear combination of genomic estimated breeding values useful for predicting the net genetic merit, which in turn is a linear combination of true unobservable breeding values of the traits weighted by their respective economic values. The CLGSI is the most general genomic index and allows imposing constraints on the expected genetic gain per trait to make some traits change their mean values based on a predetermined level, while the rest of them remain without restrictions. In addition, it includes the unconstrained linear genomic index as a particular case. Using two real datasets and simulated data for seven selection cycles, we compared the theoretical results of the CLGSI with the theoretical results of the constrained linear phenotypic selection index (CLPSI). The criteria used to compare CLGSI vs. CLPSI efficiency were the estimated expected genetic gain per trait values, the selection response, and the interval between selection cycles. The results indicated that because the interval between selection cycles is shorter for the CLGSI than for the CLPSI, CLGSI is more efficient than CLPSI per unit of time, but its efficiency could be lower per selection cycle. Thus, CLGSI is a good option for performing genomic selection when there are genotyped candidates for selection.
    Publication
  • Thumbnail Image
    Optimum and decorrelated constrained multistage linear phenotypic selection indices theory
    (Crop Science Society of America (CSSA), 2019) Cerón-Rojas, J.J.; Toledo, F.H.; Crossa, J.
    Some authors have evaluated the unconstrained optimum and decorrelated multistage linear phenotypic selection indices (OMLPSI and DMLPSI, respectively) theory. We extended this index theory to the constrained multistage linear phenotypic selection index context, where we denoted OMLPSI and DMLPSI as OCMLPSI and DCMLPSI, respectively. The OCMLPSI (DCMLPSI) is the most general multistage index and includes the OMLPSI (DMLPSI) as a particular case. The OCMLPSI (DCMLPSI) predicts the individual net genetic merit at different individual ages and allows imposing constraints on the genetic gains to make some traits change their mean values based on a predetermined level, while the rest of them remain without restrictions. The OCMLPSI takes into consideration the index correlation values among stages, whereas the DCMLPSI imposes the restriction that the index correlation values among stages be null. The criteria to evaluate OCMLPSI efficiency vs. DCMLPSI efficiency were that the total response of each index must be lower than or equal to the single-stage constrained linear phenotypic selection index response and that the expected genetic gain per trait values should be similar to the constraints imposed by the breeder. We used one real and one simulated dataset to validate the efficiency of the indices. The results indicated that OCMLPSI accuracy when predicting the selection response and expected genetic gain per trait was higher than DCMLPSI accuracy when predicting them. Thus, breeders should use the OCMLPSI when making a phenotypic selection.
    Publication
  • Thumbnail Image
    The relative efficiency of two multistage linear phenotypic selection indices to predict the net genetic merit
    (Crop Science Society of America (CSSA), 2019) Cerón-Rojas, J.J.; Toledo, F.H.; Crossa, J.
    Multistage linear phenotypic selection indices predict the individual net genetic merit at different individual ages or stages and are cost-saving strategies for improving several traits. In a two-stage context, we compared the relative efficiency of the optimum multistage linear phenotypic selection index (OMLPSI) and the decorrelated multistage linear phenotypic selection index (DMLPSI) theory to predict the individual net genetic merit and selection response using a real and a simulated dataset. In addition, we described a method for obtaining the OMLPSI selection intensity in a two-stage context. The criteria used to compare the relative efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage linear phenotypic selection index (LPSI) selection response, similar to the accuracy of each index to predict the net genetic merit. Using four different total proportions (p = 0.05, 0.10, 0.20, and 0.30) for the real dataset, the total DMLPSI selection response was 22.80% higher than the estimated single-stage LPSI selection response, whereas the total OMLPSI selection response was only 2.21% higher than the estimated single-stage LPSI selection response. In addition, at Stage 2, OMLPSI accuracy was 62.24% higher than the DMLPSI accuracy for predicting the net genetic merit. We found similar results for the simulated data. Thus, we recommend using OMLPSI when performing the multistage phenotypic selection.
    Publication