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Ana I. Vazquez
Researcher at Michigan State University
Publications - 84
Citations - 2383
Ana I. Vazquez is an academic researcher from Michigan State University. The author has contributed to research in topics: Population & Heritability. The author has an hindex of 23, co-authored 78 publications receiving 1971 citations. Previous affiliations of Ana I. Vazquez include University of Alabama at Birmingham & University of Wisconsin-Madison.
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Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor
TL;DR: It is shown how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects, and for the analysis of data from unrelated individuals, the asymptotic upper bound on R2 may be of the order of 20% of the trait heritability.
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Beyond Missing Heritability: Prediction of Complex Traits
Robert Makowsky,Nicholas M. Pajewski,Yann C. Klimentidis,Ana I. Vazquez,Christine W. Duarte,David B. Allison,Gustavo de los Campos +6 more
TL;DR: Using data from the Framingham Heart Study, the genomic prediction of human height in training and validation samples is explored while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number and type of subjects used to train the model.
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Technical note: an R package for fitting generalized linear mixed models in animal breeding.
TL;DR: The pedigreemm package of R was developed as an extension of the lme4 package, and allows mixed models with correlated random effects to be fitted for Gaussian, binary, and count responses.
Journal ArticleDOI
Accurate Genomic Prediction of Human Height
Louis Lello,Steven G. Avery,Laurent C. A. M. Tellier,Laurent C. A. M. Tellier,Ana I. Vazquez,Gustavo de los Campos,Stephen D. H. Hsu +6 more
TL;DR: The authors constructed genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning).
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Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle
Kent A. Weigel,G. de los Campos,Ana I. Vazquez,Guilherme J. M. Rosa,Daniel Gianola,C.P. Van Tassell,C.P. Van Tassell +6 more
TL;DR: Results of this study indicate that if a suitable reference population with high-density genotypes is available, a low-density chip comprising 3,000 equally spaced SNP may provide approximately 95% of the predictive ability observed with the BovineSNP50 Beadchip in Jersey cattle.