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Andres Legarra
Researcher at Institut national de la recherche agronomique
Publications - 201
Citations - 10166
Andres Legarra is an academic researcher from Institut national de la recherche agronomique. The author has contributed to research in topics: Population & Computer science. The author has an hindex of 45, co-authored 170 publications receiving 7870 citations. Previous affiliations of Andres Legarra include University of Toulouse & University of Georgia.
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Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.
TL;DR: A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure.
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A relationship matrix including full pedigree and genomic information
TL;DR: This work proposes a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H, which is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.
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Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
Gustavo de los Campos,Hugo Naya,Daniel Gianola,José Crossa,Andres Legarra,Eduardo Manfredi,Kent A. Weigel,José Miguel Cotes +7 more
TL;DR: This article adapts the Bayesian least absolute shrinkage and selection operator (LASSO) to arrive at a regression model where markers, pedigrees, and covariates other than markers are considered jointly, and results indicate that inclusion of markers in the regression further improved the predictive ability of models.
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Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information
TL;DR: The proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information when the information attributable to genomics can be expressed as modifications to the numerator relationship matrix.
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Genome-wide association mapping including phenotypes from relatives without genotypes.
TL;DR: The single-step GBLUP (ssGBLUP) method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.