New statistical tools sharpen the search for causal DNA changes in livestock

Researchers at North Carolina State University have developed a new suite of statistical methods that dramatically improves the ability to pinpoint DNA changes responsible for important traits in livestock. The work addresses long-standing challenges in fine-mapping – the process of identifying which DNA changes are responsible for trait differences between animals – especially in populations in which animals are closely related.

Fine-mapping works like searching a long book: initial genetic studies identify which chapter contains important information, but fine-mapping pinpoints the exact paragraphs or sentences that matter. This approach has been successful in human genetics, where studies typically involve large numbers of unrelated individuals. But livestock populations, including pigs and cattle, contain animals with complex pedigrees, causing standard human-genetics tools to perform poorly. 

A new study published in Briefings in Bioinformatics presents a comprehensive statistical framework designed specifically for these related populations. The framework introduces several new or adapted computational methods that correctly account for genetic relatedness and substantially improve 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 used large datasets of Duroc and Yorkshire pigs to show how relatedness distorts standard measures of so-called linkage disequilibrium – the correlations among genetic variants that many fine-mapping tools rely on.

To address this, the team developed tools that incorporate “relatedness-adjusted” genomic correlations, allowing popular fine-mapping platforms to perform correctly in animal populations. Across more than 40 simulated scenarios, the adjusted methods consistently outperformed existing approaches, often by several-fold. Performance was especially strong in multi-breed datasets, where additional genetic diversity improves the distinction between causal and merely correlated variants.

The study also introduces gene-level posterior inclusion probabilities, or PIPgene, which aggregate evidence across all variants within a gene. This approach strengthens biological interpretation and allows researchers to identify meaningful candidate genes even when single-variant signals are weak. In Duroc pig data, PIPgene highlighted genes such as MRAP2 and LEPR, both central to how the body uses and stores energy.

“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 released open-source software to support adoption of the new framework across livestock species.

Co-authors on the paper include Junjian Wang and Christian Maltecca from the Department of Animal Science at 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. 

This work is supported by the Agriculture and Food Research Initiative (AFRI) Foundational and Applied Science Program, project award no. 2023-67015-39260, and the Research Capacity Fund (HATCH), project award no. 7008128, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture.

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Note to editors: The abstract of the paper follows.

“Fine-mapping methods for complex traits: essential adaptations for samples of related individuals”

Authors:  Junjian Wang, Christian Maltecca, and Jicai Jiang, North Carolina State University; Francesco Tiezzi, University of Florence; Yijian Huang, Smithfield Premium Genetics; Garrett See and Clint Schwab, AcuFast LLC; Julong Wei, Wayne State University

Published: Nov. 21, 2025 in Briefings in Bioinformatics

DOI: 10.1093/bib/bbaf614 

Abstract: Fine-mapping causal variants from genome-wide association studies (GWAS) loci is challenging in populations with substantial relatedness, such as livestock, as standard methods often assume unrelatedness, leading to poor fine-mapping accuracy. Here, we introduce a comprehensive Bayesian framework to address this. Our approach features BFMAP-Shotgun Stochastic Search for individual-level data, which uses a linear mixed model (LMM) and shotgun stochastic search with simulated annealing. For summary statistics, we develop FINEMAP-adj and SuSiE-adj, novel strategies that directly use standard FINEMAP and SuSiE for samples of related individuals by employing LMM-derived inputs (particularly a relatedness-adjusted linkage disequilibrium matrix). Furthermore, genomic-feature posterior inclusion probability (PIP), implemented here as gene-level PIP (PIPgene), is proposed to enhance detection power by aggregating variant signals. Extensive simulations based on pig genotypes across diverse heritability levels and population structures (pure-breed and multi-breed) show our methods substantially outperform existing tools (FINEMAP, SuSiE, FINEMAP-inf, SuSiE-inf, and GCTA-COJO) in samples of related individuals, achieving notable improvements in fine-mapping accuracy (e.g. up to several-fold increases in the area under the precision-recall curve). Multi-breed populations greatly enhance fine-mapping accuracy compared to single-breed populations. Additionally, PIPgene markedly improves candidate gene identification. Application to Duroc pig traits demonstrates practical utility, with functional enrichment analysis confirming our methods’ superior identification of biologically relevant variants. This work provides robust, validated methods and associated software for accurate fine-mapping in populations with complex relatedness.