Submit or Track your Manuscript LOG-IN

Genetic Diversity and Population Structure of Seven Tibet Yak Ecotype Populations using Microsatellite Markers

PJZ_51_5_1979-1982

 

 

Genetic Diversity and Population Structure of Seven Tibet Yak Ecotype Populations using Microsatellite Markers

Yan-Bin Zhu1, Wang-Dui Basang1, Zhan-Dui Pingcuo1, Yang-Ji Cidan1, Sang Luo1, Dun-Zhu Luosang1, Yang-La Dawa1 and Guang-Xin E2,3,*

1Institute of Animal Husbandryand Veterinary Medicine, Tibet Academy of Agriculture and Animal Husandry Science, Lhasa, 850009, China

2State Key Laboratory of Barley and Yak Germplasm Resources and Genetic Improvement (Tibet Academy of Agricultural and Animal Husbandry Sciences (TAAAS)), Lhasa, 850002, China

3Chongqing Key Laboratory of Forage & Herbivore, Chongqing Engineering Research Centre for Herbivores Resource Protection and Utilization, College of Animal Science and Technology, Southwest University, Chongqing, China

Yan-Bin Zhu and Wang-Dui Basang contributed equally to this article.

ABSTRACT

The aim of the present study was to perform genetic diversity and population structure estimation on 382 individuals from seven Chinese Tibetan ecotype yak population using twenty-one microsatellites. The results revealed that the HO ranged from 0.4854±0.0194 in NNV to 0.6086±0.0267 in YRY, and the NA ranged from 3.86±1.98 in XMY to 6.05±3.37 in NNV. The least number of markers which deviated from Hardy-Weinberg equilibrium within the population was W, and largest number of deviations was in the NNV population. Consistent of phylogenetic relationships between the seven populations was identified by Phylogenetic N-J network (Reynold’s genetic distance), FST and Principal components factor (PCA) analysis. These analyses inferred that the population cluster data was not only consistent with the populations’ geographic habits but could also be influenced by artificial selection and feeding style. Lastly, two credible genetic backgrounds were identified from the yak populations in this study using STRUCTURE software, which corresponded to previous knowledge about different molecular genetic markers. Therefore, unexpectedly, our study indicated that the diversity of some of the populations was decreased, leaving us to improve and refine our conversional strategy. In addition, this resulted in a greater understanding of human yak phylogenetic differentiation as well as providing data support for understanding the evolution and migration of yak population in future studies.


Article Information

Received 04 February 2019

Revised 11 April 2019

Accepted 14 May 2019

Available online 27 June 2019

Authors’ Contributions

W-DB, Y-BZ and G-XE conceived and designed the experiments. Y-BZ, Z-DP, Y-JC, D-ZL and SL performed the lab work. Y-LD, Y-BZ and G-XE analyzed the data and wrote the paper.

Key words

Domestic Yak, Diversity, Microsatellite, Population Structure, Tibet

DOI: http://dx.doi.org/10.17582/journal.pjz/2019.51.5.sc6

* Corresponding author: [email protected]

0030-9923/2019/0005-1979 $ 9.00/0

Copyright 2019 Zoological Society of Pakistan



Yak (Bos grunniens) are found on the Qinghai-Tibetan Plateau (Altitude > 3500m) and have more than 5,000 years of domesticated history (Zhang et al., 2014). They are an important farm animal species of Qinghai-Tibet Plateau both economically and culturally. Recently, a number of studies have estimated the diversity of yak populations using different genetic markers, such as the Y-Chromosome marker (Ma et al., 2015, 2018), mitochondrial DNA (Song et al., 2015; Basang et al., 2018), microsatellite (Zhang et al., 2008; Pei et al., 2018), and wide-genome sequences (Qiu et al, 2015; Lan et al., 2018).

Naqu is a city under the jurisdiction of the Tibet Autonomous Region. It is located in the north of Tibet, between the Tanggula Mountains, the Nyainqentanglha Mountains and the Gangdise Mountains. It is about 1156 kilometers long from east to west and 760 kilometers wide from north to south. It is connected to Changdu City in the east, Ali in the west, and Lhasa and Linzhi in the south. The city is adjacent to the north, bordering Xinjiang Uygur Autonomous Region and Qinghai Province. The total area is 369,674 square kilometers, with a total population of 501,300. At the same time, the existing yak population of Nagqu is about 3 million, which is the main producing area of yak in Qinghai-Tibet Plateau.

Here, to promote the development of local domestic Yak breeding in Naqu region, it is urgent that the genetic diversity be evaluated. In this study, we aim to estimate the genetic diversity of seven local yak population (six from

 

Table I.- Original distribution and sample information for the seven Tibetan yak populations.

Name

Code

SZ

N

E

Native Location

Xiama Population

XMY

20

30°47′51.61″

92°40′36.28″

Xiama No.3 villege, Jiali, Naqu

Jiali Population

JLY

111

30°38′36.76″

93°13′51.74″

Jiali Farm, Naqu

Neirong Farm Population

NRF

61

32°06′36.73″

92°18′7.68″

Neirong Farm, Neirong, Naqu

Neirong nine village Population

NNV

47

31°44′44.43″

92°04′4.36″

Nima No.9 Villege, Neirong, Naqu

Neirong eleven village Population

NEV

59

31°44′46.32″ 

92°04′2.03″

Nima No.11 Villege, Neirong, Naqu

Yare Population

YRY

23

31°29′42.03″

82°19′59.05″

Yare, Geji, Arli

Dangxiong Population

DXY

61

30°31′39.23″

91°17′20.96″

Longren, Dangxiong, Naqu

SZ, Sample size; N, North latitude; E, East longitude and Code, short name of breed.

 

Naqu, and one from Ali region) using 21 microsatellite markers, and the evolution of their genetic divergence and population structure.

 

Material and methods

The experimental conditions used in this study were approved by the Committee on the Ethics of Animal Experiments of Southwest University (No. [2007] 3) and accordant with the Animal Protection Law of China. DNA of 382 individuals from seven Tibet yak populations (Table I) was extracted by a standard phenol-chloroform protocol from vein blood samples.

All individuals were genotyped at the 21 microsatellite markers (Supplementary Table I) as well as with genotyping being performed on a Genetic Analyzer 3130xl (Applied Biosystems, US), a detailed description of the genotyping can be found in E et al. (2018). Conventional genetic diversity parameters, including observed (Ho) heterozygosity, expected heterozygosity (HE), mean number of alleles (NA), and polymorphism information content (PIC), were estimated with the Microsatellite Toolkit (Park, 2008). Deviations of markers from Hardy-Weinberg equilibrium (HWE) within populations were identified using GENEPOP 3.4 software (Raymond and Rousset, 1995). The inbreeding coefficient (FIS) was estimated, and Fisher’s exact test with Bonferroni correction was performed using FSTAT 2.9.3.2 (Goudet, 1995). Pairwise differences between populations (FST) and loci under selection were identified using Arlequin software version 3.5.1.3 (Excoffier and Lischer, 2010).

Principal components factor analysis (PCA) performed with R-Script. Bayesian clustering algorithm was implemented in STRUCTURE 2.3.3 (Pritchard et al., 2000; Falush et al., 2003) with 50000 burn, and 100000 Markov Chain Monte Carlo (MCMC) in 100 iterations from K=1 to K=7. To estimate the most optimal K reference as deta K = m|L”(K)|/s|L(K)| by Structure Harvester (Earl and vonHoldt, 2012).

 

Results and discussion

In total, 193 alleles were identified in 7 local yak populations across 21 microsatellites. Across populations, an average of 9.2 alleles of each marker were observed, ranging from 2 in BGR3001 to 21 in TGLA53. The HO and HE of each marker within all individuals was 0.5239 (from 0.0304 (BGR3012) to 0.735 (CSRD247)) and 0.555 (from 0.034 (BGR3012) to 0.7986 (SPS115)), respectively. The mean PIC across markers was 0.4969 and ranged from 0.0323 (BGR3012) to 0.8091 (TGLA73) across populations (Supplementary Table II). Our analysis not only indicated that most of the microsatellites which were used to estimate the diversity of yak populations would show high levels of polymorphism, but also indicated that the markers used in this study are qualified to represent the genetic diversity of these yak populations.

Across markers, the HE within a breed ranged from 0.5333±0.0646 in XMY to 0.5832±0.0524 in YRY. The HO ranged from 0.4854±0.0194 in NNV to 0.6086±0.0267 in YRY, and the NA ranged from 3.86±1.98 in XMY to 6.05±3.37 in NNV (Table II). This indicated that those yak ecotype populations were still existing with a high level of diversity within the population.

Based on the Hardy-Weinberg equilibrium (HWE) analysis, nearly all markers in YRY populations were at HWE, expected BM2113. Only two markers deviated from HWE (dHWE) in B (BGR3004, ILSTS008), and DXY (BM2113, AGLA293) and other four populations exhibited 6 to 9 markers of dHWE. The range of FIS within populations was from 0.153 in NNV to -0.059 in YRY population. Three of these populations (JLY, NEV, NNV) had P-value for FIS considered significant at the adjusted nominal level (5%, P < 0.00034). According to the HWE and FIS results, inbreeding did not occur in DWY, YRY and XMY population. This indicates that the other four populations are under potential risk of becoming endangered. This should be a reminder to the government and management units involved that they should pay more attention to the protection of the genetic diversity in these populations and improve their current protection and conservation methods.

In addition, highest average number of pairwise differences within populations was observed in XMY

 

Table II.- Genetic diversity and pairwise differences of seven Tibet ecotype yak populations.

Population genetic diversity

Pair-wise differences analysis

Popul ation

HO (±SD)

HE (±SD)

NA (±SD)

FIS

P-Value

dH WE

XMY

JLY

NRF

NNV

NEV

YRY

DXY

XMY

0.51± 0.03

0.53± 0.07

3.86± 1.98

0.04

0.17

2

1.49

1.46*

1.57*

1.43*

1.60*

1.59*

1.57*

JLY

0.51± 0.01

0.54± 0.06

5.76± 3.63

0.06

0.0003#

8

0.09*

1.23

1.32*

1.22

1.34*

1.32*

1.31*

NRF

0.51± 0.02

0.55± 0.06

4.95± 2.87

0.07

0.001

8

0.16*

0.03*

1.34

1.30*

1.36

1.35

1.32

NNV

0.49± 0.02

0.57± 0.06

6.05± 3.37

0.15

0.0003#

9

0.09*

0.006

0.03*

1.21

1.31*

1.29*

1.29*

NEV

0.49± 0.02

0.56± 0.06

5.19± 3.14

0.11

0.0003#

6

0.17*

0.04*

0.004

0.01*

1.37

1.36

1.34

YRY

0.61± 0.03

0.58± 0.05

4.24± 2.21

-0.06

0.78

1

0.19*

0.04*

0.01

0.03*

0.02

1.32

1.33

DXY

0.54± 0.02

0.55± 0.06

4.95± 2.94

0.01

0.30

2

0.17*

0.04*

-0.006

0.03*

0.002

0.02

1.31

Pa, number of private alleles; dHWE, number of populations that deviated (P < 0.01) from Hardy-Weinberg equilibrium; #, indicates the adjusted nominal level (5%) for one table is 0.0006 based on 1680 randomizations of P-values for FIS.


 

(πX=1.48846) and the lowest was observed in NNV (πX=1.20773). The results of genetic divergence between populations (FST) based on corrected average pairwise differences (πXY-(πX+πY) and average number of pairwise differences between populations (πXY) indicated that the XMY population was carrying a higher divergence than the others (Table II, Fig. 1).

The genetic pattern of those populations from the phylogenetic Network tree can be seen in Figure 2A. The phylogenetic network revealed that XMY, and JLY population separated into Cluster I, which was easily understood being that the habitat of these three populations were around Jiali county. However, four populations, NRF, DXY, YRY, NEV population were separated into Cluster II, which was inconsistent with their geographic location, the YRY being from Ali region and others from the Naqu region of Tibet. In addition, the smaller sample size of this population (23, YRY) was not enough to present its correct population structure.

Regarding the genetic investigation of PCA (Fig. 2B) the investigation indicated that there could be a popular exchange of gene flow among those populations due to nomadic behavior, regular activity and human migration. Strangely, there are three populations NRF, NEV and NNV that had large divergences between each other. This could easily be caused by local artificial or cross breeding. What’s more, due to the high occurrence of irregular breedable livestock selection human intervention was a possible reason leading to inbreeding within this population which was revealed from FIS and dHWD in this study, this could also contribute to unreasonable population phylogenetic relationships within the populations.

Furthermore, we used STRUCTURE software to cluster the 7 Tibet ecotype populations into 2 ≤ K ≤7 (Fig. 2C). The result revealed that the most credible K value was 2 by ΔK = m| L” (K)|/s|L(K)|, which was consistent with current knowledge that there are two genetic backgrounds within Chinese yak with different molecular markers, such as mitochondrial DNA (e.g., Basang et al., 2018), and Y –chromosome (e.g., Ma et al., 2018).

 

Conclusion

Seven Chinese Tibetan ecotype populations were genotyped using 21 microsatellites. The results indicated that the current genetic diversity in some of these populations is decreasing and conservation strategies should be improved What’s more, the gene flow exchanged among those populations could be due to nomadic behavior and normal activity etc. Last but not least two divergence genetic backgrounds were identified in this study which further corroborates the current knowledge of human yak phylogenetic differentiation as well as provides data support for understanding the evolution and migration of yak populations in future studies.


 

Acknowledgements

This work was supported by National Technical System for Beef and Yak Industry (CARS-38), and the Open Project Program of State Key Laboratory of Barley and Yak Gemplasm Resources and Genetics Improvement (Tibet Academy of Agricultural and Animal Husbandry Sciences (TAAAS)), Lhasa Tibet 850002, China.

 

Conflict of interest

Authors declare no conflict of interest.

 

Supplementary material

There is supplementary material associated with this article. Access the material online at: http://dx.doi.org/10.17582/journal.pjz/2019.51.5.sc6

 

References

Basang, W.D., An, T.W., Danjiu, L.B., Zhu, Y.B., He, S.C., Luo, X.L., Ni, W.W., Wang, X., Cheng, S.Z., Wang, J. and E, G.X., 2018. Pakistan J. Zool., 50: 2051-2057. https://doi.org/10.17582/journal.pjz/2018.50.6.2051.2057

E, G.X., Zhao, Y.J., Chen, L.P., Ma, Y.H., Chu, M.X., Li, X.L., Hong, Q.H., Li, L.H., Guo, J.J., Zhu, L., Han, Y.G., Gao, H.J., Zhang, J.H., Jiang, H.Z., Jiang, C.D., Wang, G.F., Ren, H.X., Jin, M.L., Sun, Y.Z., Zhou, P. and Huang, Y.F., 2018. Ecol Evol., 8: 5111-5123.

Earl, D.A. and vonHoldt, B.M., 2012. Conserv. Genet. Resour., 4: 359-361.

Excoffier, L. and Lischer, H.E., 2010. Mol. Ecol. Resour., 10: 564-567. https://doi.org/10.1111/j.1755-0998.2010.02847.x

Falush, D., Stephens, M. and Pritchard, J.K., 2003. Genetics, 164: 1567–1587.Goudet, J., 1995. J. Hered., 86: 485-486.

Felsenstein, J., 2005. PHYLIP (Phylogeny Inference Package) version 3.6 Distributed by the author. Department of Genome Sciences, University of Washington, Seattle.

Kovach, W.L., and Services, K.C., 2004. MVSP- A multi-variate statistical package for Windows. ver 3.13m. http://www.kovcomp.co.uk/mvsp/index.html

Lan, D., Xiong, X., Mipam, T.D., Fu, C., Li, Q., Ai, Y., Hou, D., Chai, Z., Zhong, J. and Li, J., 2018. G3 (Bethesda), 28:945-952. https://doi.org/10.1534/g3.118.300572

Ma, Z.J., Xia, X.T., Chen, S.M., Zhao, X.C., Zeng, L.L., Xie, Y.L., Chao, S.Y., Xu, J.T., Sun, Y.G., Li, R.Z., Guanque, Z.X., Han, J.L. and Lei, C.Z., 2018. Anim. Genet., 49:618-622.

Ma, Z.J., Chen, S.M., Sun, Y.G., Xi, Y.L., Li, R.Z., Xu, J.T. and Lei, C.Z., 2015. Genet. mol. Res., 14:18859-62.

Pei, J., Bao, P., Chu, M., Liang, C., Ding, X., Wang, H., Wu, X., Guo, X. and Yan, P., 2018. Peer J., 6: e5946. https://doi.org/10.7717/peerj.5946

Pritchard, J.K., Stephens, M. and Donnely, P., 2000. Genetics, 155: 945–959.

Qiu, Q., Wang, L., Wang, K., Yang, Y., Ma, T., Wang, Z., Zhang, X., Ni, Z., Hou, F., Long, R., Abbott, R., Lenstra, J. and Liu, J., 2015. Nat. Commun., 22:10283.

Raymond, M. and Rousset, F., 1995. Heredity, 86: 248-249. https://doi.org/10.1093/oxfordjournals.jhered.a111573

Song, Q.Q., Chai, Z.X., Xin, J.W., Zhao, S.J., Ji, Q.M., Zhang, C.F., Ma, Z.J. and Zhong, J.C., 2015. Genet. Mol. Res., 14:1763-70. https://doi.org/10.4238/2015.March.13.3

Zhang, G., Chen, W., Xue, M., Wang, Z., Chang, H., Han, X., Liao, X. and Wang, D.J., 2008. J. Genet. Genom., 35: 233-238.

To share on other social networks, click on any share button. What are these?

Pakistan Journal of Zoology

December

Pakistan J. Zool., Vol. 56, Iss. 6, pp. 2501-3000

Featuring

Click here for more

Subscribe Today

Receive free updates on new articles, opportunities and benefits


Subscribe Unsubscribe