G. Rosa

发表

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

K. Weigel, D. Gianola, F. Peñagaricano, 2013, Genetics.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

D. Gianola, G. Morota, G. Rosa, 2020, Heredity.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers

M. Craven, G. Rosa, T. Passafaro, 2020, BMC genomics.

A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans

D. Allison, D. Gianola, G. de los Campos, 2012, Genetics.

Radial basis function regression methods for predicting quantitative traits using SNP markers.

K. Weigel, D. Gianola, G. Rosa, 2010, Genetics research.

Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

K. Weigel, D. Gianola, G. Rosa, 2011, BMC Genetics.

Prediction of body mass index in mice using dense molecular markers and a regularized neural network.

K. Weigel, D. Gianola, G. Rosa, 2011, Genetics research.

Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data

D. Gianola, G. Morota, G. Rosa, 2015, Genetics.

Application of support vector regression to genome-assisted prediction of quantitative traits

Daniel Gianola, Kent A. Weigel, Nanye Long, 2011, Theoretical and Applied Genetics.

Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)

D. Gianola, G. Rosa, C. Schön, 2017, G3: Genes, Genomes, Genetics.

Marker-assisted prediction of non-additive genetic values

Daniel Gianola, Kent A. Weigel, Nanye Long, 2011, Genetica.

Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers

Kent A Weigel, Daniel Gianola, Nanye Long, 2008, Genetics.

Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Kent A Weigel, Daniel Gianola, Andreas Kranis, 2009, Genetics Selection Evolution.

Bayesian model averaging for evaluation of candidate gene effects

Xiao-Lin Wu, Daniel Gianola, Kent A. Weigel, 2010, Genetica.

Bayesian and Machine Learning Models for Genomic Prediction of Anterior Cruciate Ligament Rupture in the Canine Model

G. Rosa, R. Todhunter, M. Momen, 2020, G3.

Comparison of classification methods for detecting associations between SNPs and chick mortality

Kent A Weigel, Daniel Gianola, Nanye Long, 2009, Genetics Selection Evolution.

Genome-wide association analysis in dogs implicates 99 loci as risk variants for anterior cruciate ligament rupture

D. Gianola, B. Valente, G. Rosa, 2017, PloS one.

Zero-inflated Poisson regression models for QTL mapping applied to tick-resistance in a Gyr × Holstein F2 population

G. Rosa, F. Silva, M. A. Machado, 2011, Genetics and molecular biology.

Enviromics in breeding: applications and perspectives on envirotypic-assisted selection

H. Piepho, G. Rosa, D. Grattapaglia, 2020, Theoretical and Applied Genetics.

for genetical genomics studies Review of microarray experimental design strategies

Jingyuan Fu, R. Jansen, G. Rosa, 2013 .

Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models

D. Gianola, B. Valente, G. Morota, 2018, bioRxiv.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers

M. Craven, G. Rosa, T. Passafaro, 2020, BMC genomics.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

D. Gianola, G. Rosa, Yaodong Hu, 2015, BMC Genomics.

A predictive assessment of genetic correlations between traits in chickens using markers

D. Gianola, B. Valente, G. Rosa, 2017, Genetics Selection Evolution.

Selective transcriptional profiling and data analysis strategies for eQTL mapping in outbred F 2 populations

J. Steibel, R. Tempelman, C. Ernst, 2008 .

Would large reference populations unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

Mark W. Craven, G. Rosa, T. Passafaro, 2020 .

Selection signature analyses and genome‐wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis

G. Rosa, M. Momen, Susannah J Sample, 2022, G3.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

D. Gianola, G. Rosa, Yaodong Hu, 2015, BMC Genomics.

Design of Microarray Experiments for Genetical Genomics Studies

Steven G. Gilmour, G. Rosa, S. Gilmour, 2006, Genetics.

Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts

K. Weigel, D. Gianola, A. Vazquez, 2012, Front. Gene..

Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods.

José Crossa, Kent A Weigel, Guilherme J M Rosa, 2010, Genetics research.

Review of microarray experimental design strategies for genetical genomics studies.

Guilherme J M Rosa, G. Rosa, N. de Leon, 2006, Physiological genomics.

Integrating biological information into the statistical analysis and design of microarray experiments.

A. Vazquez, G. Rosa, G J M Rosa, 2010, Animal : an international journal of animal bioscience.

On Reference Designs For Microarray Experiments

Juan P Steibel, Guilherme J M Rosa, J. Steibel, 2005, Statistical applications in genetics and molecular biology.

Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models

Juan P. Steibel, Robert J. Tempelman, Guilherme J. M. Rosa, 2005, Comparative and functional genomics.

An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.

Chuanyu Sun, Xiao-Lin Wu, Kent A Weigel, 2012, Genetics research.

Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

D. Gianola, G. Morota, G. Rosa, 2020, Heredity.

Modeling relationships between calving traits: a comparison between standard and recursive mixed models

Xiao-Lin Wu, Kent A Weigel, Daniel Gianola, 2010, Genetics Selection Evolution.

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

B. Valente, G. Rosa, H. Bovenhuis, 2014, Genetics Selection Evolution.

Defining window-boundaries for genomic analyses using smoothing spline techniques

Timothy M. Beissinger, D. Gianola, G. Rosa, 2015, Genetics Selection Evolution.

Is Structural Equation Modeling Advantageous for the Genetic Improvement of Multiple Traits?

Xiao-Lin Wu, Daniel Gianola, Bruno D. Valente, 2013, Genetics.

Searching for phenotypic causal networks involving complex traits: an application to European quail

B. Valente, G. Rosa, R. A. Torres, 2011, Genetics Selection Evolution.

Searching for Recursive Causal Structures in Multivariate Quantitative Genetics Mixed Models

Guilherme J M Rosa, Daniel Gianola, Bruno D Valente, 2010, Genetics.

Exploring Biological Relationships Between Calving Traits in Primiparous Cattle with a Bayesian Recursive Model

K. Weigel, D. Gianola, G. Rosa, 2009, Genetics.

Inferring Quantitative Trait Pathways Associated with Bull Fertility from a Genome-Wide Association Study

K. Weigel, F. Peñagaricano, H. Khatib, 2013, Front. Gene..

Meta-Analysis of Candidate Gene Effects Using Bayesian Parametric and Non-Parametric Approaches

Xiao-Lin Wu, Daniel Gianola, Guilherme J. M. Rosa, 2014, Journal of genomics.

Across-breed genetic investigation of canine hip dysplasia, elbow dysplasia, and anterior cruciate ligament rupture using whole-genome sequencing

G. Rosa, B. Davis, M. Momen, 2022, Frontiers in Genetics.

Multivariate genome-wide association analysis identifies novel and relevant variants associated with anterior cruciate ligament rupture risk in the dog model

G. Rosa, P. Muir, A. Piazza, 2018, BMC Genetics.

Effect of genotype imputation on genome-enabled prediction of complex traits: an empirical study with mice data

Daniel Gianola, Hayrettin Okut, D. Gianola, 2014, BMC Genetics.

L2-Boosting algorithm applied to high-dimensional problems in genomic selection.

Kent A Weigel, Daniel Gianola, Hugo Naya, 2010, Genetics research.

Inferring causal phenotype networks using structural equation models

Xiao-Lin Wu, Daniel Gianola, Gustavo de los Campos, 2011, Genetics Selection Evolution.

Relationship between gestation length, calving difficulty, and perinatal mortality in primiparous Holstein cows

K. Weigel, E. Maturana, D. Gianola, 2008 .

A Vision for Development and Utilization of High-Throughput Phenotyping and Big Data Analytics in Livestock

Guilherme J. M. Rosa, James M. Reecy, Ryan Reuter, 2019, Front. Genet..

Robust Linear Mixed Models with Normal/Independent Distributions and Bayesian MCMC Implementation

Daniel Gianola, Carlos Roberto Padovani, D. Gianola, 2003 .

Running title: Trait connections in maize with Bayesian networks

D. Gianola, G. Rosa, C. Schön, 2017 .

Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models

Daniel Gianola, Andreas Kranis, Gota Morota, 2018, bioRxiv.

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

B. Valente, G. Rosa, H. Bovenhuis, 2014, Genetics Selection Evolution.

Hierarchical Modeling of Structural Coefficients for Heterogeneous Networks with an Application to Animal Production Systems

Guilherme J. M. Rosa, K Chitakasempornkul, Abigail Jager, 2020, Journal of Agricultural, Biological and Environmental Statistics.

Applying family analyses to electronic health records to facilitate genetic research

Xiayuan Huang, David Page, Robert C. Elston, 2018, Bioinform..

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

D. Gianola, G. Rosa, R. Schnabel, 2013, Genetics Selection Evolution.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Xiao-Lin Wu, Kent A Weigel, Daniel Gianola, 2012, Genetics Selection Evolution.

A Primer on High-Throughput Computing for Genomic Selection

Xiao-Lin Wu, Stewart Bauck, Daniel Gianola, 2011, Front. Gene..

Advances in Bayesian methods for quantitative genetic analysis.

Daniel Gianola, Guilherme J. M. Rosa, R. Rekaya, 2002 .

Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics

Mariza de Andrade, Tiago M Fragoso, Alexandre C Pereira, 2016, Genetic epidemiology.

Multivariate genome-wide association analysis identifies novel and relevant variants associated with anterior cruciate ligament rupture risk in the dog model

G. Rosa, P. Muir, A. Piazza, 2018, BMC genetics.

Predictive assessment of single-step BLUP with linear and non-linear similarity RKHS kernels: A case study in chickens.

D. Gianola, G. Rosa, M. Momen, 2021, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse

E. G. Cothran, G. Rosa, B. Davis, 2023, Frontiers in genetics.

Predictive ability of genome-assisted statistical models under various forms of gene action

Daniel Gianola, Andreas Kranis, Gota Morota, 2018, Scientific Reports.

Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts

Daniel Gianola, Kent A. Weigel, Ana I. Vazquez, 2012, Front. Gene..

Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers.

K. Weigel, D. Gianola, G. Rosa, 2007, Developments in biologicals.

A Bayesian approach for constructing genetic maps when markers are miscoded

B. Yandell, D. Gianola, G. Rosa, 2002, Genetics Selection Evolution.

A mixture model approach for the analysis of small exploratory microarray experiments

S. D. Rider, G. Rosa, W. Muir, 2009 .

Bayesian Variable Selection in Multilevel Item Response Theory Models with Application in Genomics

G. Rosa, M. de Andrade, J. Soler, 2016, Genetic epidemiology.

Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation.

D Gianola, G J M Rosa, D. Gianola, 2009, Journal of animal science.

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models

K. Weigel, D. Gianola, F. Peñagaricano, 2013, Genetics.

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models

Stewart Bauck, Brent W Woodward, Robert D Schnabel, 2013, Genetics Selection Evolution.

Defining window-boundaries for genomic analyses using smoothing spline techniques

Daniel Gianola, Timothy M Beissinger, Timothy M. Beissinger, 2015, Genetics Selection Evolution.

Long-term impacts of genome-enabled selection

Daniel Gianola, Kent A. Weigel, Nanye Long, 2011, Journal of Applied Genetics.

An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network.

D Gianola, K A Weigel, K. Weigel, 2012, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.