Ivan Pocrnić

This paper improves genomic evaluation in Tanzanian smallholder dairy cattle by incorporating spatial modelling to better separate genetic and environmental effects.

This paper reviews genetic diversity estimates of rare UK livestock breeds, finding gaps in DNA-based measures and discrepancies between census and DNA-based Ne estimates.

This study simulates dairy cattle breeding programs to assess how accounting for mitochondrial DNA variation affects the accuracy of breeding values and genetic gain.

This study simulates the long-term effects of genomic selection on genetic mean and variance in layer chicken breeding programs, finding that optimal contribution selection balances genetic gain and variance loss best.

The paper investigates SNP profiles around QTN in small Ne populations and shows how these profiles affect genomic prediction accuracy and QTN detection.

This study simulates the impact of importing genetic material on small cattle breeding programs, finding that benefits depend on genetic correlation, import extent, and breeding scheme.

The paper optimizes the core subset selection in the APY approximation to improve stability and repeatability of genomic predictions.

The paper introduces a method to partition genetic mean and variance contributions in breeding programs to better understand selection paths and optimize breeding strategies.

This paper introduces a framework for temporal and genomic analysis of additive genetic variance in breeding programs, showing good concordance between simulated and estimated variances.

This paper introduces HD-ClvR, a CRISPR-based gene drive system that eliminates resistance and localizes spread, demonstrating its effectiveness in controlling grey squirrel populations.

This paper introduces metafounders as pseudo-individuals to reduce bias in genomic evaluations by incorporating ancestral relationships estimated from marker genotypes and pedigrees.