Gregor Gorjanc

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.

The paper introduces msprime 1.0, a high-performance simulation tool for ancestry and mutation in population genetics, showing it is faster and more memory efficient than existing alternatives.

This study investigates the feasibility of using gene drive technology to control Varroa destructor mites, finding that fertility-targeting drives fail to spread but toxin or resistance-removing drives may be viable.

This study estimates recombination rate variation in pigs using multiocus iterative peeling across 150,000 pigs, identifies heritability, and finds three genomic regions associated with recombination.

The paper compares index selection and independent culling in genomic selection for multi-trait breeding programs, showing index selection is more efficient when economic weights are known.

AlphaPart partitions genetic trends to identify sources of genetic gain in breeding programs using a simulated pig example.

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 study shows that dairy breeding programs can maximize genetic gain by reallocating resources from phenotyping to genotyping, without increasing overall investment.

This paper shows genomic selection in intercrop breeding programs leads to greater genetic gain than phenotypic selection, regardless of genetic correlation with monocrop yield.

This paper introduces a Bayesian method that improves genomic modeling by incorporating expert knowledge through prior distributions, enhancing robustness and selection accuracy in plant breeding.

AlphaSimR is an R package for simulating plant and animal breeding programs, offering flexibility in modeling complex breeding strategies.

The paper introduces a method to phase and impute genotypes of missing parents in bi-parental plant populations, achieving high accuracy for both parents and their descendants.

This study shows spatial modelling improves genetic evaluation in smallholder dairy cattle systems by better separating genetic and environmental effects.

This paper improves long-range phasing and haplotype library imputation algorithms to handle large and heterogeneous datasets, achieving high accuracy with reduced computational time.

AlphaFamImpute improves genotype imputation in full-sib families using low-coverage GBS data by leveraging shared haplotype segments between siblings.

This study shows that a two-part hybrid crop breeding strategy with genomic selection and a maximum avoidance of inbreeding crossing scheme maximizes genetic gain and long-term genetic variance.

This paper introduces Origin Specific Genomic Selection (OSGS) to improve the introgression of beneficial exotic alleles in crop breeding by partitioning marker effects based on allele origin.

This study evaluates sequencing strategies for whole-genome imputation using hybrid peeling, finding that 2% of the population sequenced at 2x coverage provides high accuracy.

This study demonstrates high accuracy of whole-genome sequence imputation in large livestock populations using hybrid peeling, with median individual-wise correlation of 0.97.

This paper introduces a hierarchical model to estimate haplotype effects using phylogenetic relationships, showing improved estimates with sparse modeling and better performance in low-observation scenarios.

This study shows genomic data can improve genetic evaluation accuracy in smallholder dairy farms with small herds and weak genetic connections.

This paper compares spatial models for agricultural field trials using INLA, showing AR1⊗AR1 and SPDE models provide best predictive performance with or without genomic data.

This paper simulates genomic selection strategies in a small dairy population to evaluate their impact on genetic gain, variation, and sustainability.

This study shows that family-specific low-density genotype arrays improve pedigree-based imputation accuracy, especially at very low marker densities.

This study simulates genome editing and selection strategies to reduce deleterious load in animal breeding, finding that editing multiple recessive variants is more effective than selection against carriers.

This study evaluates how training population design affects genomic prediction accuracy in wheat, finding that larger training sets and related crosses improve prediction accuracy.

The paper identifies three haplotypes with putatively recessive lethal alleles in Irish beef cattle and shows their pleiotropic effects on economically important traits.

AlphaFamSeq is a family-based phasing algorithm that minimizes errors and maximizes allele phasing, achieving high accuracy in simulated data.

This study simulates strategies for using surrogate sire technology to enhance genetic merit in livestock breeding, finding a three-stage approach most effective for maximizing genetic gain.