Daniel Gianola
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View article: Comparison of approximation methods for genomic estimated breeding values from observed to liability scales in dairy cattle health traits
Comparison of approximation methods for genomic estimated breeding values from observed to liability scales in dairy cattle health traits Open
The GEBV for health traits are typically published as probabilities obtained using threshold models. While these models benefit from theoretical properties, they require substantial computational resources and may face convergence issues. …
View article: Learning genetic values of individuals with incomplete pedigree, genomic and phenotypic data
Learning genetic values of individuals with incomplete pedigree, genomic and phenotypic data Open
Prediction of outcomes is important in personalized medicine, animal and plant breeding. Typical inputs for building prediction models in agriculture include genealogies, molecular markers and phenotypes. Information is seldom complete, i.…
View article: (Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers
(Quasi) multitask support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a case study with carcass traits in broilers Open
This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits (CT1 and CT…
View article: phenotypes and kernels
phenotypes and kernels Open
The G3_R1.RData file contains the necessary data to replicate the main results found in the paper titled: (Quasi) Multi-task support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits…
View article: Evaluation of Predictive Ability of Bayesian Regularized Neural Network Using Cholesky Factorization of Genetic Relationship Matrices for Additive and Non-additive Genetic Effects
Evaluation of Predictive Ability of Bayesian Regularized Neural Network Using Cholesky Factorization of Genetic Relationship Matrices for Additive and Non-additive Genetic Effects Open
This study aimed to explore the effects of additive and non-additive genetic effects on the prediction of complex traits using Bayesian regularized artificial neural network (BRANN). The data sets were simulated for two hypothetical pedigr…
View article: phenotypes and kernels
phenotypes and kernels Open
This rda file provides the necessary data to replicate the main results found in the paper titled: (Quasi) Multi-task support vector regression with heuristic hyperparameter optimization for whole-genome prediction of complex traits: a cas…
View article: Predictive assessment of single‐step BLUP with linear and non‐linear similarity RKHS kernels: A case study in chickens
Predictive assessment of single‐step BLUP with linear and non‐linear similarity RKHS kernels: A case study in chickens Open
Single‐step GBLUP (ssGBLUP) to obtain genomic prediction was proposed in 2009. Many studies have investigated ssGBLUP in genomic selection in animals and plants using a standard linear kernel (similarity matrix) called genomic relationship…
View article: Opinionated Views on Genome-Assisted Inference and Prediction During a Pandemic
Opinionated Views on Genome-Assisted Inference and Prediction During a Pandemic Open
OPINION article Front. Plant Sci., 06 August 2021 | https://doi.org/10.3389/fpls.2021.717284
View article: A Mixed Effects Model for Overdispersed Zero Inflated Poisson Data with an Application in Animal Breeding
A Mixed Effects Model for Overdispersed Zero Inflated Poisson Data with an Application in Animal Breeding Open
Response variables that are scored as counts, for example, number of mastitis cases in dairy cattle, often arise in quantitative genetic analysis. When the number of zeros exceeds the amount expected such as under the Poisson density, the …
View article: Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging Open
The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers…
View article: Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning
Feature Selection Stability and Accuracy of Prediction Models for Genomic Prediction of Residual Feed Intake in Pigs Using Machine Learning Open
Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allo…
View article: Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms
Machine Learning Prediction of Crossbred Pig Feed Efficiency and Growth Rate From Single Nucleotide Polymorphisms Open
This research assessed the ability of a Support Vector Machine (SVM) regression model to predict pig crossbred (CB) performance from various sources of phenotypic and genotypic information for improving crossbreeding performance at reduced…
View article: Genome-wide methylation prediction of biological age using reproducing kernel Hilbert spaces and Bayesian ridge regressions
Genome-wide methylation prediction of biological age using reproducing kernel Hilbert spaces and Bayesian ridge regressions Open
The use of DNA methylation signatures to predict chronological age and the aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation mar…
View article: Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs
Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigs Open
An important economic reason for the loss of local breeds is that they tend to be less productive, and hence having less market value than commercial breeds. Nevertheless, local breeds often have irreplaceable values, genetically and socio…
View article: ON THE UNCERTAINTY ABOUT HERD IMMUNITY LEVELS REQUIRED TO STOP COVID-19 EPIDEMICS
ON THE UNCERTAINTY ABOUT HERD IMMUNITY LEVELS REQUIRED TO STOP COVID-19 EPIDEMICS Open
COVID-19 evolved into a pandemic in 2020 affecting more than 150 countries. Given the absence of a vaccine, discussion has taken place on the strategy of allowing the virus to spread in a population, to increase population “herd immunity”.…
View article: Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials
Deep Kernel and Deep Learning for Genome-Based Prediction of Single Traits in Multienvironment Breeding Trials Open
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, …
View article: Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression
Inferring trait-specific similarity among individuals from molecular markers and phenotypes with Bayesian regression Open
Modeling covariance structure based on genetic similarity between pairs of relatives plays an important role in evolutionary, quantitative and statistical genetics. Historically, genetic similarity between individuals has been quantified f…
View article: A multiple-trait Bayesian Lasso for genome-enabled analysis and prediction of complex traits
A multiple-trait Bayesian Lasso for genome-enabled analysis and prediction of complex traits Open
1 Abstract A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on poss…
View article: Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments
Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments Open
Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is …
View article: New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes
New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes Open
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed…
View article: A certain invariance property of <scp>BLUE</scp> in a whole‐genome regression context
A certain invariance property of <span>BLUE</span> in a whole‐genome regression context Open
A curious result from mixed linear models applied to genome‐wide association studies was expanded. In particular, a model in which one or more markers are considered as fixed but are allowed to contribute to the covariance structure by tre…
View article: A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding
A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding Open
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the…