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Lopez-Cruz, M.

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Lopez-Cruz
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Lopez-Cruz, M.

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Now showing 1 - 7 of 7
  • Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data
    (John Wiley & Sons Inc., 2022) Lopez-Cruz, M.; Dreisigacker, S.; Crespo Herrera, L.A.; Bentley, A.R.; Singh, R.P.; Poland, J.; Shrestha, S.; Huerta-Espino, J.; Velu, G.; Juliana, P.; Mondal, S.; Pérez-Rodríguez, P.; Crossa, J.
    Publication
  • Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
    (Springer Nature, 2021) Lopez-Cruz, M.; Beyene, Y.; Gowda, M.; Crossa, J.; Pérez-Rodríguez, P.; De Los Campos, G.
    Publication
  • META-R: a software to analyze data from multi-environment plant breeding trials
    (Elsevier, 2020) Alvarado Beltrán, G.; Rodríguez, F.M.; Pacheco Gil, Rosa Angela; Burgueño, J.; Crossa, J.; Vargas Hernández, M.; Pérez-Rodríguez, P.; Lopez-Cruz, M.
    Publication
  • Regularized selection indices for breeding value prediction using hyper-spectral image data
    (Nature Publishing Group, 2020) Lopez-Cruz, M.; Olson, E.; Rovere, G.; Crossa, J.; Dreisigacker, S.; Mondal, S.; Singh, R.P.; De Los Campos, G.
    Publication
  • Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs
    (Springer Nature, 2015) Xuecai Zhang; Pérez-Rodríguez, P.; Semagn, K.; Beyene, Y.; Babu, R.; Lopez-Cruz, M.; San Vicente Garcia, F.M.; Olsen, M.; Buckler, E.; Jannink, J.L.; Prasanna, B.M.; Crossa, J.
    One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.
    Publication
  • Increased prediction accuracy in wheat breeding trials using a marker x environment interaction Genomic Selection model
    (Genetics Society of America, 2015) Lopez-Cruz, M.; Crossa, J.; Bonnett, D.; Dreisigacker, S.; Poland, J.; Jannink, J.L.; Singh, R.P.; Autrique, E.; De Los Campos, G.
    Genomic selection (GS) models use genome-wide genetic information to predict genetic values of candidates of selection. Originally, these models were developed without considering genotype · environment interaction( G·E). Several authors have proposed extensions of the single-environment GS model that accommodate G·E using either covariance functions or environmental covariates. In this study, we model G·E using a marker · environment interaction (M·E) GS model; the approach is conceptually simple and can be implemented with existing GS software.We discuss how themodel can be implemented by using an explicit regression of phenotypes on markers or using co-variance structures (a genomic best linear unbiased prediction-type model). We used the M·E model to analyze three CIMMYT wheat data sets (W1, W2, and W3), where more than 1000 lines were genotyped using genotyping-by-sequencing and evaluated at CIMMYT’s research station in Ciudad Obregon, Mexico, under simulated environmental conditions that covered different irrigation levels, sowing dates and planting systems.We compared the M·E model with a stratified (i.e., within-environment) analysis and with a standard (across-environment) GS model that assumes that effects are constant across environments (i.e., ignoring G·E). The prediction accuracy of the M·E model was substantially greater of that of an across-environment analysis that ignores G·E. Depending on the prediction problem, the M·E model had either similar or greater levels of prediction accuracy than the stratified analyses. The M·E model decomposes marker effects and genomic values into components that are stable across environments (main effects) and others that are environment-specific (interactions). Therefore, in principle, the interaction model could shed light over which variants have effects that are stable across environments and which ones are responsible for G·E. The data set and the scripts required to reproduce the analysis are publicly available as Supporting Information.
    Publication
  • Genomic prediction in maize breeding populations with genotyping-by sequencing
    (Genetics Society of America, 2013) Crossa, J.; Beyene, Y.; Semagn, K.; Pérez-Rodríguez, P.; Hickey, J.; Charles Chen; De Los Campos, G.; Burgueño, J.; Windhausen, V.S.; Buckler, E.; Jannink, J.L.; Lopez-Cruz, M.; Babu, R.
    Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction using GBS is currently undergoing investigation in several crops, including maize, wheat, and cassava. The main objective of this study was to evaluate various methods for incorporating GBS information and compare them with pedigree models for predicting genetic values of lines from two maize populations evaluated for different traits measured in different environments (experiments 1 and 2). Given that GBS data come with a large percentage of uncalled genotypes, we evaluated methods using nonimputed, imputed, and GBS-inferred haplotypes of different lengths (short or long). GBS and pedigree data were incorporated into statistical models using either the genomic best linear unbiased predictors (GBLUP) or the reproducing kernel Hilbert spaces (RKHS) regressions, and prediction accuracy was quantified using cross-validation methods. The following results were found: relative to pedigree or marker-only models, there were consistent gains in prediction accuracy by combining pedigree and GBS data; there was increased predictive ability when using imputed or nonimputed GBS data over inferred haplotype in experiment 1, or nonimputed GBS and information-based imputed short and long haplotypes, as compared to the other methods in experiment 2; the level of prediction accuracy achieved using GBS data in experiment 2 is comparable to those reported by previous authors who analyzed this data set using SNP arrays; and GBLUP and RKHS models with pedigree with nonimputed and imputed GBS data provided the best prediction correlations for the three traits in experiment 1, whereas for experiment 2 RKHS provided slightly better prediction than GBLUP for drought-stressed environments, and both models provided similar predictions in well-watered environments.
    Publication