Microevolutionary Dynamics of Chicken Genomes under Divergent Selection for Adiposity

Hui Zhang, Qiqi Liang, Ning Wang, Qigui Wang, Li Leng, Jie Mao, Yuxiang Wang, Shouzhi Wang, Jiyang Zhang, Hao Liang, Xun Zhou, Yumao Li, Zhiping Cao, Peng Luan, Zhipeng Wang, Hui Yuan, Zhiquan Wang, Xuming Zhou, Susan J. Lamont, Yang DaRuiqiang Li, Shilin Tian, Zhiqiang Du, Hui Li

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Decades of artificial selection have significantly improved performance and efficiency of animal production systems. However, little is known about the microevolution of genomes due to intensive breeding. Using whole-genome sequencing, we document dynamic changes of chicken genomes under divergent selection on adiposity over 19 generations. Directional selection reduced within-line but increased between-line genomic differences. We observed that artificial selection tended to result in recruitment of preexisting variations of genes related to adipose tissue growth. In addition, novel mutations contributed to divergence of phenotypes under selection but contributed significantly less than preexisting genomic variants. Integration of 15 generations genome sequencing, genome-wide association study, and multi-omics data further identified that genes involved in signaling pathways important to adipogenesis, such as autophagy and lysosome (URI1, MBL2), neural system (CHAT), and endocrine (PCSK1) pathways, were under strong selection. Our study provides insights into the microevolutionary dynamics of domestic animal genomes under artificial selection.

Original languageEnglish (US)
Article number101193
JournaliScience
Volume23
Issue number6
DOIs
StatePublished - Jun 26 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s)

Keywords

  • Biological Sciences
  • Evolutionary Biology
  • Genetics
  • Genomics

Fingerprint

Dive into the research topics of 'Microevolutionary Dynamics of Chicken Genomes under Divergent Selection for Adiposity'. Together they form a unique fingerprint.

Cite this