One Health: New statistical methods improve gene mapping in livestock
Researchers at North Carolina State University have developed new statistical methods that significantly refine the fine mapping of DNA changes in livestock. The study, published in the journal Briefings in Bioinformatics, solves challenges in identifying trait variants in closely related animal populations.
While fine mapping in human genetics is successful in unrelated individuals, standard methods fail in farm animals such as pigs or cattle due to complex relationships. The new statistical framework takes this relationship into account and improves the accuracy of locating variants that affect traits such as growth, fat deposition or milk production.
Using data from Duroc and Yorkshire pigs, the team showed how kinship distorts the linkage imbalance. The developed “kinship-adjusted” methods outperformed existing approaches in over 40 scenarios, especially for multiracial datasets. A new measure, posterior inclusion probability at the gene level (PIPgene), facilitates the identification of candidate genes such as MRAP2 and LEPR that regulate energy metabolism.
The methods are available as open-source software and promise applications for all livestock species. The research was supported by the U.S. Department of Agriculture.
Original Paper:
Editor: X-Press Journalistenbüro GbR
Gender Notice. The personal designations used in this text always refer equally to female, male and diverse persons. Double/triple naming and gendered designations are used for better readability. ected.
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