Rohan L. Fernando

This paper validates the linear regression method for assessing population bias and accuracy of predictions, showing it works for non-linear models when data are appropriately partitioned.

This paper introduces NN-Bayes, a Bayesian neural network method for genome-wide prediction and association studies, showing improved performance over linear models in simulations and real data.

The paper derives an exact distribution of linkage disequilibrium (LD) over generations in a finite population, accounting for mutation, selection, and minor allele frequency filtering.

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.

This paper develops and evaluates broad-based multi-trait Bayesian regression methods for genomic prediction, showing improved accuracy over single-trait and restricted multi-trait approaches.

This study evaluates the accuracy and bias of single-step genomic prediction under selection by simulating cattle populations and comparing models with and without fitting microg as a fixed effect.

This paper introduces a parallel computing approach to accelerate whole-genome Bayesian regression analyses by making marker effect sampling independent through orthogonal data augmentation.