Daniel Gianola B

The paper introduces Hy-BLUP, a Bayesian prediction method for handling incomplete pedigree, genomic, and phenotypic data in genome-enabled prediction.

The paper predicts biological age using DNA methylation data with RKHS and Bayesian ridge regression, finding higher accuracy with unadjusted methylation data and sex-specific aging patterns.

The paper introduces a multiple-trait Bayesian Lasso (MBL) for genome-based analysis and prediction of complex traits, showing improved predictive performance in Pinus trees compared to existing methods.

The paper applies structural equation models to GWAS in chickens to decompose SNP effects into direct, indirect, and total effects, showing greater insight into causal relationships than traditional multi-trait methods.