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The fine‐scale genetic structure and selection signals of Chinese indigenous pigs.
- Source :
- Evolutionary Applications; Feb2020, Vol. 13 Issue 2, p458-475, 18p
- Publication Year :
- 2020
-
Abstract
- Genome‐wide SNP profiling has yielded insights into the genetic structure of China indigenous pigs, but has focused on a limited number of populations. Here, we present an analysis of population structure and signals of positive selection in 42 Chinese pig populations that represent the most extensive pig phenotypic diversity in China, using genotype data of 1.1 million SNPs on customized Beadchips. This unravels the fine‐scale genetic diversity, phylogenic relationships, and population structure of these populations, which shows remarkably concordance between genetic clusters and geography with few exceptions. We also reveal the genetic contribution to North Chinese pigs from European modern pigs. Furthermore, we identify possible targets of selection in the Tibetan pig, including the well‐characterized hypoxia gene (EPAS1) and several previously unrecognized candidates. Intriguingly, the selected haplotype in the EPAS1 gene is associated with higher hemoglobin contents in Tibetan pigs, which is different from the protective role of EPAS1 in the high‐altitude adaptation in Tibetan dogs and their owners. Additionally, we present evidence for the causality between EDNRB variants and the two‐end‐black (TEB) coat color phenotype in all Chinese pig populations except the Jinhua pig. We hypothesize that distinct targets have been independently selected for the formation of the TEB phenotype in Chinese pigs of different geographic origins. This highlights the importance of characterizing population‐specific genetic determinants for heritable phenotype in diverse pig populations. [ABSTRACT FROM AUTHOR]
- Subjects :
- SWINE
ANIMAL coloration
SINGLE nucleotide polymorphisms
SWINE breeding
DOG owners
Subjects
Details
- Language :
- English
- ISSN :
- 17524563
- Volume :
- 13
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Evolutionary Applications
- Publication Type :
- Academic Journal
- Accession number :
- 141356381
- Full Text :
- https://doi.org/10.1111/eva.12887