Genomic analysis of Nigerian indigenous chickens reveals their genetic diversity and adaptation to heat-stress

Rachman, Mifta P.; Bamidele, Oladeji; Dessie, Tadelle; Smith, Jacqueline; Hanotte, Olivier; Gheyas, Almas A.

Abstract

Indigenous poultry breeds from Africa can survive in harsh tropical environments (such as long arid seasons, excessive rain and humidity, and extreme heat) and are resilient to disease challenges, but they are not productive compared to their commercial counterparts. Their adaptive characteristics are in response to natural selection or to artificial selection for production traits that have left selection signatures in the genome. Identifying these signatures of positive selection can provide insight into the genetic bases of tropical adaptations observed in indigenous poultry and thereby help to develop robust and high-performing breeds for extreme tropical climates. Here, we present the first large-scale whole-genome sequencing analysis of Nigerian indigenous chickens from different agro-climatic conditions, investigating their genetic diversity and adaptation to tropical hot climates (extreme arid and extreme humid conditions). The study shows a large extant genetic diversity but low level of population differentiation. Using different selection signature analyses, several candidate genes for adaptation were detected, especially in relation to thermotolerance and immune response (e.g., cytochrome P450 2B4-like, TSHR, HSF1, CDC37, SFTPB, HIF3A, SLC44A2, and ILF3 genes). These results have important implications for conserving valuable genetic resources and breeding improvement of chickens for thermotolerance.

Citation

Rachman, M. P., Bamidele, O., Dessie, T., Smith, J., Hanotte, O., & Gheyas, A. A. (2024). Genomic analysis of Nigerian indigenous chickens reveals their genetic diversity and adaptation to heat-stress. Scientific Reports, 14(1), Article 2209. https://doi.org/10.1038/s41598-024-52569-4

• Journal Article Type: Article
• Acceptance Date: Jan 20, 2024
• Online Publication Date: Jan 26, 2024
• Publication Date: 2024
• Deposit Date: Apr 18, 2024
• Publicly Available Date: Apr 19, 2024
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 1
• Article Number: 2209
• DOI: https://doi.org/10.1038/s41598-024-52569-4
• Keywords: Computational biology and bioinformatics; Evolution; Genetics; Zoology
• Public URL: https://nottingham-repository.worktribe.com/output/30412041
• Publisher URL: https://www.nature.com/articles/s41598-024-52569-4
• Additional Information: Received: 25 September 2023; Accepted: 20 January 2024; First Online: 26 January 2024; : The authors declare no competing interests.