| Literature DB >> 30572824 |
Wengang Zhang1, Xue Gao1, Yang Zhang2, Yumin Zhao3, Jiabao Zhang4, Yutang Jia5, Bo Zhu1, Lingyang Xu1, Lupei Zhang1, Huijiang Gao1, Junya Li6, Yan Chen7.
Abstract
BACKGROUND: China exhibits a great diversity of ecosystems and abundant cattle resources, with nearly 30 million cattle from 53 indigenous breeds reared in specific geographic regions. To explore the genetic diversity and population structure of Chinese indigenous cattle, a population genetic analysis at both the individual and population levels was conducted and the admixture analysis was performed. We genotyped 572 samples from 20 Chinese indigenous cattle breeds using GeneSeek Genomic Profiler Bovine LD (GGP-LD, 30 K) and downloaded the published data of 77 samples from 4 worldwide commercial breeds genotyped with Illumina BovineSNP50 Beadchip (SNP50, 50 K).Entities:
Keywords: Chinese indigenous cattle; Genetic diversity; Population structure
Mesh:
Year: 2018 PMID: 30572824 PMCID: PMC6302425 DOI: 10.1186/s12863-018-0705-9
Source DB: PubMed Journal: BMC Genet ISSN: 1471-2156 Impact factor: 2.797
Fig. 1The distribution of indigenous cattle breeds on a map of China. A total of 20 Chinese cattle breeds were sampled in this study, including 17 indigenous cattle breeds and 3 improved breeds. Red, blue, and grey plots represent Northern-distribution, Central-distribution, and Southern-distribution breeds, respectively. (Map is downloaded from Wikimedia Commons https://commons.wikimedia.org/wiki/File:China_location_map.svg)
Proportion of polymorphic SNPs, observed and expected heterozygosities, inbreeding coefficient and effective population size in Chinese and worldwide breeds
| Breed | Breed abbr. |
|
| Regionc | Combined data seti |
| |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
| ||||||||||
| Chinese indigenous cattle breeds | Beisha | BSC | 19 | 18 | South | 0.21 | 0.848 | 0.289 | 0.285 | −0.015(0.015) | 10 |
| Tiantai | TTC | 21 | 21 | South | 0.21 | 0.859 | 0.280 | 0.284 | 0.016(0.031) | 59 | |
| Wenling Humped | WHC | 20 | 19 | South | 0.22 | 0.893 | 0.300 | 0.297 | −0.009(0.11) | 72 | |
| Nandan | NDC | 19 | 19 | South | 0.21 | 0.834 | 0.275 | 0.279 | 0.013(0.078) | 67 | |
| Longlin | LLC | 15 | 15 | South | 0.23 | 0.906 | 0.310 | 0.307 | −0.011(0.406) | 59 | |
| Dianzhong | DZC | 30 | 30 | South | 0.26 | 0.924 | 0.331 | 0.339 | 0.024(0.005) | 29 | |
| Wenshan | WSC | 47 | 47 | South | 0.26 | 0.939 | 0.333 | 0.335 | 0.008(0.212) | 152 | |
| Zhaotong | ZTC | 43 | 42 | South | 0.28 | 0.965 | 0.366 | 0.365 | −0.004(0.511) | 85 | |
| Dabieshan | DBS | 44 | 44 | South | 0.25 | 0.951 | 0.342 | 0.336 | −0.019(0.001) | 259 | |
| Nanyang | NYC | 15 | 13 | Central | 0.27 | 0.943 | 0.356 | 0.349 | −0.021(0.02) | 105 | |
| Luxi | LXC | 14 | 14 | Central | 0.28 | 0.962 | 0.361 | 0.368 | −0.020(0.001) | 221 | |
| Qinchuan | QCC | 30 | 30 | Central | 0.28 | 0.972 | 0.370 | 0.364 | −0.018(0.001) | 235 | |
| Jinnan | JNC | 55 | 55 | Central | 0.28 | 0.968 | 0.374 | 0.360 | −0.038(0.001) | 37 | |
| Tibetan | TIC | 20 | 7 | Plateau | 0.25 | 0.904 | 0.362 | 0.335 | – | – | |
| Fuzhou | FZC | 13 | 13 | North | 0.22 | 0.803 | 0.305 | 0.290 | −0.051(0.05) | 47 | |
| Mongolian | MGC | 15 | 15 | North | 0.25 | 0.904 | 0.331 | 0.322 | −0.026(0.001) | 206 | |
| Yanbian Yellow | YYC | 59 | 58 | North | 0.24 | 0.848 | 0.325 | 0.315 | −0.032(0.001) | 85 | |
| Improved cattle breeds | Chinese Caoyuan Red | CCR | 26 | 26 | North | 0.25 | 0.849 | 0.331 | 0.316 | −0.050(0.001) | 63 |
| Liaoyu White | LWC | 20 | 20 | North | 0.26 | 0.867 | 0.334 | 0.326 | −0.023(0.001) | 422 | |
| Xinjiang Brown | XJB | 47 | 47 | North | 0.22 | 0.769 | 0.313 | 0.288 | −0.085(0.001) | 29 | |
| Worldwide cattle breeds | Angus | AN | – | 20 | Scotland | 0.30 | 0.971 | 0.395 | 0.386 | −0.025(0.001) | |
| Hereford | HFD | – | 20 | Wales | 0.29 | 0.967 | 0.384 | 0.376 | −0.022(0.001) | – | |
| GIR | GIR | – | 20 | India | 0.16 | 0.737 | 0.227 | 0.225 | −0.010(0.005) | – | |
| Sahiwal | SAHW | – | 17 | Pakistan | 0.16 | 0.729 | 0.225 | 0.221 | −0.019(0.012) | – | |
| Mean | 0.24 | 0.888 | 0.326 | 0.320 | −0.019 | 118 | |||||
aTotal number of individuals sampled in each breed
bNumber of individuals remaining after quality control, with a call rate > 0.95
cSouth refers to south China, Central refers to central China, North refers to north China and Plateau refers to Tibet
dMinor Allele Frequency
eProportion of polymorphic loci within a breed
fObserved heterozygosity
gExpected heterozygosity
hInbreeding coefficient (significant level)
iPn, Ho, He, and F are calculated based on 7003 SNPs using bootstrapping method
jRecent effective population size
Fig. 2Principal component analysis of 630 individuals based on 7003 independent SNPs. PC1 explained 9.56% of global variation, PC2 explained 1.65% of global variation, and PC3 explained 1.34% of global variation. Light-red plots represent improved breeds in northern China, deep-red plots represent indigenous breeds in northern China, blue plots represent breeds in central China and grey plots represent breeds in southern China. For worldwide breeds, Bos taurus breeds and Bos indicus breeds are represented by purple-red plots and purple plots, respectively
Fig. 3Neighbour-joining tree relating to the 630 individuals from twenty breeds of Chinese cattle. The tree was constructed using the allele sharing distance averaged over 7003 SNPs. Edges are coloured according to the individual breed of origin
Fig. 4Model-based population assignment for 630 individuals based on 7003 SNPs using STRUCTURE (K = 2–6) and plotted with Distruct software. Eurasian Bos taurus represents worldwide breeds with European taurine ancestry, and Bos indicus represents worldwide breeds with zebu ancestry. For K = 2, red descent represents Asian indicine (Bos indicus) ancestry, and yellow descent represents European taurine (Bos taurus) ancestry
Fig. 5Localities of cattle breeds and the frequency of Eurasian taurine and Asian indicine lineages. The Qinling Mountains and Taihang Mountains are represented with green lines. (Map is downloaded from https://www.mapsofworld.com/asia/)
Fig. 6Maximum likelihood tree inferred from 18 cattle populations with migration events. a, no migration events; b-f, one to five migration events, respectively. Migration arrows are coloured according to their weight
Fig. 7Maximum likelihood tree inferred from 13 cattle breeds with one migration event
Results of the f3 for Chinese indigenous cattle1
| X population | Y population | Z population | Z-score from | |
|---|---|---|---|---|
| QCC2 | NDC | MGC | − 0.0017 | −4.8663 |
| QCC | TTC | MGC | −0.0014 | −4.33679 |
| NYC | NDC | MGC | −0.0064 | −18.3264 |
| NYC | TTC | MGC | −0.0047 | −12.8435 |
| LXC | TTC | FZC | −0.0053 | −13.0848 |
| LXC | TTC | MGC | −0.0080 | −32.1615 |
| LXC | TTC | JNC | −0.0084 | −56.7148 |
| LXC | TTC | QCC | −0.0082 | −62.5641 |
| JNC | TTC | MGC | 0.0005 | 1.6375 |
| JNC | NDC | MGC | 0.0014 | 6.2444 |
1Three populations are included in the topology structure. If the f3 statistics result is significant negative, the X population may have descended from an admixture event of the Y and Z populations
2See Table 1 for breed abbreviations