Whole-genome selective scans detect genes associated with important phenotypic traits in goat (Capra hircu s)

Xing Wan; Jia-Nan Jing; Dong-Feng Wang; Feng-Hua Lv

doi:10.3389/fgene.2023.1173017

Whole-genome selective scans detect genes associated with important phenotypic traits in goat (Capra hircu s)

Front Genet. 2023 Apr 18:14:1173017. doi: 10.3389/fgene.2023.1173017. eCollection 2023.

Authors

Xing Wan¹, Jia-Nan Jing¹, Dong-Feng Wang^{2

3}, Feng-Hua Lv¹

Affiliations

¹ College of Animal Science and Technology, China Agricultural University, Beijing, China.
² CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China.
³ University of Chinese Academy of Sciences (UCAS), Beijing, China.

Abstract

Goats with diverse economic phenotypic traits play an important role in animal husbandry. However, the genetic mechanisms underlying complex phenotypic traits are unclear in goats. Genomic studies of variations provided a lens to identify functional genes. In this study, we focused on the worldwide goat breeds with outstanding traits and used whole-genome resequencing data in 361 samples from 68 breeds to detect genomic selection sweep regions. We identified 210-531 genomic regions with six phenotypic traits, respectively. Further gene annotation analysis revealed 332, 203, 164, 300, 205, and 145 candidate genes corresponding with dairy, wool, high prolificacy, poll, big ear, and white coat color traits. Some of these genes have been reported previously (e.g., KIT, KITLG, NBEA, RELL1, AHCY, and EDNRA), while we also discovered novel genes, such as STIM1, NRXN1, LEP, that may be associated with agronomic traits like poll and big ear morphology. Our study found a set of new genetic markers for genetic improvement in goats and provided novel insights into the genetic mechanisms of complex traits.

Keywords: artificial selection; candidate genes; goat; phenotypic traits; whole-genome scan.

Grants and funding

This work was supported by the Natural Science Foundation of China (Nos.32061133010, 31972527) and the National Key Research and Development Program-Key Projects (2021YFD1300900). We thank Meng-Hua Li for his helpful comments and revisions.