Julius H. J. van der Werf

This study compares metagenomic pipelines for predicting methane emissions in sheep using functional and taxonomic features, finding COG-based functional features provide the highest accuracy.

This paper introduces a new hyper-parameter, the scale factor, to improve the prediction accuracy of the HBLUP method in genetic evaluation using simulated and real cattle data.

This study evaluates the balance between power and false positive rate in genome-wide association studies using Bonferroni and Benjamini-Hochberg methods, showing their performance varies with marker correlation and experimental design.

The paper introduces a method to detect genotype-population interaction effects using ancestry principal components to analyze genetic heterogeneity across populations.

This study uses a whole-genome approach to identify genetic and non-genetic variance components in cardiovascular risk influenced by lifestyle factors, showing their modulating effects and implications for CVD prevention.

This paper introduces a multivariate reaction norm model to estimate genotype-covariate correlation and interaction in complex trait analyses, showing improved performance over existing methods.

This study evaluates bias and accuracy in genomic breeding value prediction under selection scenarios using different statistical models and finds that SSGBLUP with proper scaling reduces bias and improves accuracy.

The paper develops a theoretical framework to estimate genomic prediction accuracy using reference populations with varying relationships to the target, showing that close relatives significantly improve accuracy.