Researchers at North Carolina State University have introduced a new set of statistical methods aimed at improving the identification of DNA changes that influence important traits in livestock. The work addresses persistent challenges in fine-mapping, which is the process of pinpointing specific genetic variants responsible for differences in traits among animals, particularly in populations where animals are closely related.
Fine-mapping has been effective in human genetics studies involving large groups of unrelated individuals. However, standard tools often fail when applied to livestock populations such as pigs and cattle due to their complex pedigrees.
A recent study published in Briefings in Bioinformatics details a statistical framework tailored for these related animal populations. The new approach introduces computational methods that take genetic relatedness into account, leading to significant improvements in fine-mapping accuracy.
“Our work provides tools that finally make fine-mapping reliable in real livestock populations, where animals are related and standard human-genetics methods fail,” said Jicai Jiang, corresponding author and assistant professor of animal science at NC State. “These methods hold promise to provide livestock researchers and breeding companies with a more reliable path for identifying variants that influence important traits such as growth, fat deposition, reproduction, feed efficiency and milk production.”
The research utilized large datasets from Duroc and Yorkshire pigs to demonstrate how relatedness can distort standard measures used by many fine-mapping tools. To address this issue, the team developed techniques that use “relatedness-adjusted” genomic correlations. These adjustments allow existing fine-mapping platforms to function properly within animal populations.
Testing across more than 40 simulated scenarios showed that the adjusted methods consistently outperformed current approaches, especially when analyzing multi-breed datasets where greater genetic diversity helps distinguish between causal and correlated variants.
Additionally, the study introduces gene-level posterior inclusion probabilities (PIPgene), which combine evidence from all variants within a gene. This makes it easier for researchers to identify candidate genes even if signals from individual variants are weak. In data from Duroc pigs, PIPgene highlighted genes such as MRAP2 and LEPR—both involved in energy usage and storage.
“By making fine-mapping accurate in populations with complex relatedness, we can now move from broad genomic signals to specific genes with much greater confidence,” Jiang said.
The research team has made open-source software available so other scientists can apply these new methods across different livestock species.
Co-authors on the paper include Junjian Wang and Christian Maltecca from NC State; Francesco Tiezzi from the University of Florence; Yijian Huang from Smithfield Premium Genetics; Garrett See and Clint Schwab from AcuFast LLC; and Julong Wei from Wayne State University.
Funding for this project was provided by the Agriculture and Food Research Initiative (AFRI) Foundational and Applied Science Program through project award no. 2023-67015-39260, as well as by the Research Capacity Fund (HATCH), project award no. 7008128, both administered by the U.S. Department of Agriculture’s National Institute of Food and Agriculture.
