Source: National Hog Farmer
Researchers at North Carolina State University have developed new statistical methods that greatly improve fine-mapping, the process of identifying specific DNA variants responsible for important traits in livestock. Traditional fine-mapping tools, designed for human populations of mostly unrelated individuals, perform poorly in livestock such as pigs and cattle, where animals are often closely related. Using large datasets from Duroc and Yorkshire pigs, the researchers showed that genetic relatedness distorts standard measures of linkage disequilibrium, reducing accuracy.
Their newly published framework corrects for this by incorporating relatedness-adjusted genomic correlations, allowing existing fine-mapping platforms to work effectively in livestock populations. Across more than 40 simulations, the new methods consistently outperformed standard approaches, especially in multi-breed datasets. The study also introduces gene-level posterior inclusion probabilities, which strengthen biological interpretation by identifying candidate genes even when individual variant signals are weak. Open-source software has been released to support adoption, offering breeders more precise tools to improve traits such as growth, reproduction, and feed efficiency.