R. Chris Gaynor

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

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 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.

This study compares clonal breeding strategies using stochastic simulation to determine their genetic gain per unit cost, finding that reciprocal recurrent selection (RRS) is optimal in diploids with high genomic selection intensity but not in autopolyploids.

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

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 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 develops a method to infer genotypes from high-throughput phenotypes to enable genomic selection in plant breeding programs, showing increased genetic gain in simulations.

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

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.

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.

This paper introduces AlphaPlantImpute, a heuristic method for phasing and imputing SNP data in bi-parental plant populations, showing better accuracy and efficiency than existing software.

This paper compares optimal cross selection with truncation selection in plant breeding programs, showing optimal cross selection achieves higher long-term genetic gain by improving efficiency and reducing genetic drift.