Gregor Gorjanc

The paper uses probabilistic inference in pedigreed canine populations to estimate disease-causing allele frequencies more comprehensively than genetic tests alone, showing improved monitoring of recessive diseases.

The paper models the impacts of imports and hybridisation in honeybees, finding that increased imports accelerate introgression and reduce fitness, with spatial spread and slow purging of introgressed material.

The paper presents high-quality genome assemblies for five African cattle breeds using PacBio HiFi sequencing, achieving high contiguity and completeness.

The paper develops a method to estimate variance components and predict genetic values using ancestral recombination graphs (ARGs) and linear mixed models on large datasets.

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

This paper introduces a TBLUP method using local DNA trees to estimate haplotype and mutation effects, showing improved accuracy and efficiency compared to traditional SNP-based approaches.

This paper introduces branch relatedness and efficient algorithms for computing branch GRMs using tree sequences, enabling large-scale genomic analysis.

The paper extends the stdpopsim simulation framework to include realistic selection models, enabling studies of selection's impact on genetic variation across species.

The paper introduces randPedPCA, an R package for rapid PCA of large pedigrees using sparse matrices and randomised linear algebra, demonstrating its efficiency on simulated and real datasets.

The paper develops an algorithm to accurately assign breed origin to alleles in crossbred dairy cattle, achieving high accuracy in simulated breeding programs.

This paper evaluates genotyping strategies for a service dog population using simulation, finding that low-pass sequencing outperforms SNP arrays in imputation accuracy and cost-effectiveness.

This study uses ancestral recombination graphs to improve quantitative genetic analysis of rice yield in indica and japonica subspecies, showing better predictive ability when combining both subspecies.

This paper introduces AlphaSimR, a software tool for stochastic simulations in plant breeding, providing scripts and vignettes for various breeding strategies.

The paper introduces a new formalism for representing ancestral recombination graphs that generalizes classical approaches and supports modern computational methods.

The paper simulates honeybee populations to compare genetic relatedness estimators and highlights methodological considerations for interpreting relatedness coefficients.

This study develops high-density linkage maps for Sitka spruce using SNP markers, enhancing genomic resources for conifers and supporting genome assembly and genetic management.

The paper compares genome-based (GBLUP) and pedigree-based (ABLUP) prediction methods in maritime pine, finding that GBLUP's within-family accuracy is low but can be improved with sufficient training data.

This study characterizes genomic and phenotypic variation in finger millet using SNP and trait data, revealing complex diversification patterns and geographic differentiation.

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.

This study compares genomic selection strategies in clonal plant breeding, finding that selecting parents based on genomic predicted cross performance leads to faster genetic gain and reduced inbreeding.

The paper expands the stdpopsim species catalog and improves genome simulations by adding non-crossover recombination and species-specific annotations, making simulations more realistic for non-model organisms.

This study models the potential of gene drives to suppress invasive social wasp populations and finds that only fertility-targeting drives could be effective under specific conditions.

SIMplyBee simulates honeybee populations and breeding programs, enabling testing of strategies for genetic gain and variability.

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.

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

This study evaluates the benefits of using whole-genome sequence data for genomic prediction in pigs, finding small improvements over marker arrays in some scenarios.

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

The paper identifies rare and population-specific functional variants in commercial pig lines and assesses their impact on production traits and genomic prediction.