Skip to main content
Download PDF

Population structure, genetic diversity, and selective signature of Chaka sheep revealed by whole genome sequencing

BMC Genomics volume 21, Article number: 520 (2020) Cite this article

Abstract

Background

Chaka sheep, named after Chaka Salt Lake, are adapted to a harsh, highly saline environment. They are known for their high-grade meat quality and are a valuable genetic resource in China. Furthermore, the Chaka sheep breed has been designated a geographical symbol of agricultural products by the Chinese Ministry of Agriculture.

Results

The genomes of 10 Chaka sheep were sequenced using next-generation sequencing, and compared to that of additional Chinese sheep breeds (Mongolian: Bayinbuluke and Tan; Tibetan: Oula sheep) to explore its population structure, genetic diversity and positive selection signatures. Principle component analysis and a neighbor-joining tree indicated that Chaka sheep significantly diverged from Bayinbuluke, Tan, and Oula sheep. Moreover, they were found to have descended from unique ancestors (K = 2 and K = 3) according to the structure analysis. The Chaka sheep genome demonstrated comparable genetic diversity from the other three breeds, as indicated by observed heterozygosity (Ho), expected heterozygosity (He), runs of homozygosity (ROH), linkage disequilibrium (LD) decay. The enrichment analysis revealed that in contrast to Mongolian or Tibetan lineage groups, the genes annotated by specific missense mutations of Chaka sheep were enriched with muscle structure development (GO:0061061) factors including insulin-like growth factor 1 (IGF1), growth differentiation factor 3 (GDF3), histone deacetylase 9 (HDAC9), transforming growth factor beta receptor 2 (TGFBR2), and calpain 3 (CAPN3), among others. A genome-wide scan using Fst and XP-CLR revealed a list of muscle-related genes, including neurofibromin 1 (NF1) and myomesin 1 (MYOM1), under potential selection in Chaka sheep compared with other breeds.

Conclusions

The comprehensive genome-wide characterization provided the fundamental footprints for breeding and management of the Chaka sheep and confirmed that they harbor unique genetic resources.

Background

Chaka Salt Lake, a hypersaline lake, was formed nearly 11.4 ka BP (kilo years before present) in Wulan county, Qinghai province in northwestern China. Salinity in the lake increased by 7.2 cal ka BP (calibrated kilo years before present) [1]. Chaka sheep, a natural inhabitant of the lake, have naturally adapted to the hypersaline environment and plateau habitat. Also called Qinghai plateau half-fine wool sheep for fur and meat, the breed has been designated a geographical symbol of agricultural products by the Chinese Ministry of Agriculture.

Sheep are ruminants and one of the main sources of wool, hide, and meat for humans. Chinese indigenous sheep breeds include Mongolian, Kazakh, and Tibetan lineage groups according to their geographical distribution and genetic relationships (China National Commission of Animal Genetic Resources 2011) [2]. Geographically, the Kazakh lineage is mainly concentrated in the Xinjiang area, while the Tibetan lineage (TL) group inhabits the Yunnan-Guizhou plateau. However, the Mongolian lineage (ML) has the largest population and is widely distributed in northern, central, and eastern areas of China. Among these populations, Chaka (CKA), Tan (TAN), Bayinbuluke (BYK), and Oula (OLA) sheep are widespread in Qinghai, Ningxia, Xinjiang, and Qinghai, respectively, in China (Supplementary Figure 1). TAN and BYK sheep are within the Mongolian lineage, OLA is within the Tibetan lineage, and the lineage of CKA sheep has not yet been identified.

To characterize the Chaka sheep’s genome, it could be compared to the genomes of Mongolian (TAN and BYK sheep) and Tibetan lineage sheep (OLA sheep). It is possible that artificial selection positively drives breed diversity. Most Chinese domestic sheep are reared for meat, wool, or body growth purposes while some varieties are multipurpose. TAN sheep are mainly reared for lambskin, BYK sheep are raised for mutton, and OLA sheep grow rapidly and exhibit good stress resistance. The CKA breed, known as tributary sheep in ancient times, is well-known for high mutton quality and exceptional wool production, making it an excellent multipurpose breed.

Although SNP arrays have been extensively used to identify genetic variations [3,4,5,6], the results are limited by low probe density. High-throughput sequencing has allowed for improved characterization of genome characteristics and population structure at a genome-wide level [7]. Several studies have explored the genome diversity of goat [8], cattle [9], and pigs [10] by next-generation sequencing. However, to our knowledge, a survey of the genome-wide genetic features of CKA sheep has not been conducted. To investigate genetic diversity and population structure of CKA sheep, genomes of 40 sheep, including 10 each of the CKA, TAN, BAK, and OLA breeds, were assembled to the sheep reference genome (REF). Our analyses provided new insights into the genetic diversity, population structure, and selective signature of the CKA breed compared to the other three breeds, which will improve the conservation programs for CKA sheep.

Results

Whole-genome sequencing and genetic variation

A total of 40 individual sheep, including CKA, TAN, BAK, and OLA (for each, n = 10) breeds were used to call 14.9 million autosomal SNPs and 1.54 million indels, focusing mainly on the intronic region by GATK (Supplementary Table 1). Moreover, 90,870 missense variants and 184,319 synonymous variants were found, and 1154 deletions and 791 insertions caused frameshift mutations.

Three metrics were used to estimate the genetic diversities of the sheep breeds in the current study. The range of expected heterozygosity (He) and observed heterozygosity (Ho) were 0.226–0.316 and 0.238–0.240, respectively (Supplementary Table 2). The expected heterozygosity was observed to be slightly higher than the observed heterozygosity in all populations. The expected heterozygosity of CKA sheep was much higher than that of other breeds on average. Furthermore, approximately 65% of the single nucleotide polymorphisms (SNPs) were highly variable with a minor allele frequency (MAF) > 0.3.

Demographic inferences in sheep population were based on the analyses of ROH, FROH, LD decay, and effective population size. The ROH number varied between the sheep populations, ranging from ~ 43.37 Mb to ~ 172.76 Mb, while the average ROH lengths in CKA, BYK, OLA, and TAN sheep were approximately 83.80 Mb, 84.56 Mb, 104.60 Mb, and 69.00 Mb, respectively, suggesting the following order of breed genetic diversity: TAN > CKA > BYK > OLA (Table 1, detail in Supplementary Table 2 and Supplementary Table 3). The total length of ROH in CKA sheep, in the range of < 0.5 Mb, was lowest, suggesting CKA sheep have higher genetic diversity (Fig. 1a, b). Moreover, the result from LD decay (Fig. 1c) was nearly consistent with those from the ROH profile, in which the lowest genetic diversity was found in the OLA breed. Inbreeding coefficients (FROH) among the sheep populations ranged from 0.01658–0.06606 (Table 1), while lower inbreeding coefficients were observed in TAN (FROH = 0.0264) and CKA breeds (FROH = 0.032) on average (Fig. 1d). The effective population sizes of the four breeds 1000 years before present were OLA sheep > TAN sheep > CKA sheep > BYK sheep (Fig. 1e).

Fig. 1
figure 1

Information about ROH, inbreeding coefficient (FROH), LD decay, and effective population size. a. The total length of ROH in different length categories. b. The distribution of the length and number of ROH in different individuals. c. Linkage disequilibrium decay (LD decay) in four breeds. d. Violin plot of genomic inbreeding coefficient in four sheep populations. e. Effective population sizes of different generations in four breeds. CKA, Chaka sheep; OLA, Oula sheep; BYK, Bayinbuluke sheep and TAN, Tan sheep

Full size image

Population structure

Most of the sheep breeds included in this study originated from western China. The PCA showed that the first component (Fig. 2a) separated the CKA breed from the other breeds (Fig. 2a), explaining 4.43% of the total variance, which indicated a considerable genetic distance between the CKA breed and the other three breeds. Among these four breeds, OLA sheep were differentiated from the other breeds in the second component, explaining 3.30% of the total variance (Fig. 2a).

Fig. 2
figure 2

Analysis of the population structure in four sheep breeds. a Principal component analysis of 40 individuals. b Neighbor-joining tree of 40 individuals. c Bar plot of ancestry compositions by ADMIXTURE with the assumed number of ancestries (K = 2 and 3). CKA, Chaka sheep; OLA, Oula sheep; BYK, Bayinbuluke sheep and TAN, Tan sheep

Full size image

We explored the phylogenetic relationships using the autosomal genome data. The NJ tree demonstrated that CKA and OLA sheep grouped separately (Fig. 2b) while BYK and TAN sheep grouped in a single clade, consistent with PCA.

Clustering models were used for estimating ancestral populations with K = 2 and K = 3 by ADMIXTURE [11] (Fig. 2c). K changed progressively from 02 to 03 in the four analyzed breeds, providing evidence of admixture, the extent of which varied between the different breeds. The results suggested K = 2 as the most likely number of genetically distinct groups for our sample, reflecting the divergence of CKA from other breeds. At K = 3, TAN sheep represented clear evidence of genetic heterogeneity with shared genome ancestry with BYK sheep, which both belong to the Mongolian lineage.

Specific SNPs and Indels in CKA sheep

We detected 1,193,639 and 162,911 specific SNPs and indels in CKA sheep, respectively (Fig. 3a, b). Moreover, 2759 host genes were annotated by missense mutations, including SNPs and indels for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the genes annotated with missense mutations enriched muscle structure development (GO:0061061), the fibroblast growth factor receptor signaling pathway (GO:0008543), and skeletal myofibril assembly (GO:0014866) (Fig. 3c). Furthermore, 117 genes were identified to be associated with muscle structure development such as insulin-like growth factor 1 (IGF1), growth differentiation factor 3 (GDF3), histone deacetylase 9 (HDAC9), transforming growth factor beta receptor 2 (TGFBR2), and calpain 3 (CAPN3), among others. (Supplementary Table 4). Some genes also enriched nerve-related gene ontology shown in Fig. 3c.

Fig. 3
figure 3

Specific SNP and indel in CKA sheep and the GO and KEGG analysis of its host gene. a The SNPs b The indel distribution in different breeds. c Heatmap for Top 44 GO and KEGG analysis of its host gene. CKA, Chaka sheep; ML, Mongolian lineage (TAN, Tan and BYK, Bayinbuluke sheep) and TL, Tibetan lineage (OLA, Oula sheep)

Full size image

Genetic signature of positive selection in Chaka

The cross-population composite likelihood ratio (XP-CLR) was used to detect the selective signal by comparing CKA with other three breeds grouped together, and Fst detected positive selection signatures in CKA sheep compared with the other three sheep breeds as per the results of population structure (Fig. 4a, b).

Fig. 4
figure 4

Genetic signature of positive selection in Chaka and GO and KEGG analysis. a The XP-CLR of CKA sheep based on other three sheep b The Fst on four breeds c. GO and KEGG heatmap selected analyzed by XP-CLR. d GO and KEGG heatmap selected analyzed by Fst. The top 5% was chosen as the significant threshold for Fst and XP-CLR

Full size image

In XP-CLR, 521 genes were selected for the downstream analyses (Fig. 4a and Supplementary Table 5). Notably, the genes were significantly enriched in the MAPK signaling pathway (hsa04010) and supramolecular fiber organization (GO:0097435) (Fig. 4c and Supplementary Table 6). The results suggested the enrichment of muscle-related genes including MDS1 and EVI1 complex locus (MECOM), Neurofibromin 1 (NF1), transforming growth factor beta 2 (TGFB2), phospholipase A2 group IVF (PLA2G4F), phosphatidylinositol-3,4,5-trisphosphate dependent Rac exchange factor 1 (PRex1) and myomesin 1 (MYOM1) which are crucial in the genome selection of CKA sheep.

Furthermore, 3619 genes showed a putatively high selection signature above the threshold by Fst (Fig. 4b and Supplementary Table 7). GO and KEGG analysis indicated its enrichment in muscle structure development (GO:0061061) (Fig. 4d and Supplementary Table 8). Furthermore, 62 genes were identified to be associated with the muscle structure development, such as 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), lysine acetyltransferase 2A (KAT2A), RAR-related orphan receptor alpha (RORA), ubiquitin-specific peptidase 2 (USP2), and SRY-box transcription factor 6 (SOX6).

Lastly, NF1 and MYOM1 genes were positively selected in CKA sheep, indicating Fst and XP-CLR revealed adaptive and beneficial selection. In addition, some genes also enriched nerve-related genes, as shown in Fig. 4c and d.

Discussion

CKA sheep are well known for their diverse alleles, more so than any of their commercial counterparts, and they have been a valuable genetic resource, potentially harboring unique gene pools resulting from long-term adaptation to the Chaka Salt Lake environment. To explore the genetic resources and to preserve the diverse gene pools of CKA sheep, we reported the genome-wide population structure, genetic diversity, and candidate signatures of positive selection in CKA sheep breeds for the first time using next-generation sequencing.

Combining the length of the ROH with LD decay, the CKA and TAN sheep were found to have higher genetic diversity than OLA and BYK sheep. FROH showed that CKA and TAN sheep have a lower inbreeding coefficient, which suggested their inbreeding depression was not severe. Since OLA sheep showed the highest inbreeding coefficient, definitive measures should be taken to prevent inbreeding depression. The estimated relative effective population size of the breeds 1000 years before present was OLA > TAN>CKA > BYK. Apart from the OLA sheep, the genetic diversity determined by effective population size was consistent with ROH and LD decay results (TAN>CKA > BYK). However, OLA sheep represented the largest effective population size, which may result from the fact that OLA sheep are within semi-feral populations that did not receive extensive human interventions [12].

The results of PCA, NJ-tree, and STRUCTURE suggest that the four sheep breeds could be subdivided into three genetic clusters: the CKA group, the Mongolian lineage group, and the Tibetan lineage group. In general, the partitioning of the breeds was consistent with their geographic distributions and breed properties. The PCA showed that CKA sheep completely separated from OLA, TAN, and BYK sheep. While OLA sheep, a famous Tibetan lineage breed, is completely separated from TAN and BYK. This result is consistent with previous reports [13]. For the NJ phylogenetic tree, the CKA and OLA sheep were clustered separately, while, BYK and TAN sheep clustered together. Furthermore, CKA sheep represented distinctive ancestry in the structure analysis. OLA sheep separated at K = 3, which shared its ancestry with TAN and BYK sheep (K = 2), a result also consistent with previous studies [13]. However, TAN sheep appeared to share some composition with CKA and OLA sheep, which requires further research.

Population structure analysis can distinguish geographical origins. In the current analysis, BYK and TAN sheep were observed to originate from Xinjiang and Ningxia provinces, respectively. Moreover, structural and phylogenetic analysis revealed that these breeds originated from the Mongolian lineage, possessing similar physiological characteristics. Besides, the Mongolian lineage group has a widespread distribution in China, which may be the result of Genghis Khan’s expedition during the Yuan dynasty, highlighting the superior adaptability and performance of these sheep populations [14]. The current data set provides strong evidence of the different population structures between CKA and the other three breeds. Further analyses are needed to develop an in-depth understanding of the relationships between the CKA sheep and Mongolian or Tibetan lineage groups.

Additionally, CKA sheep accounted for 8.25% (1,356,550/16,443,030) of total mutations. The specific missense mutations, including SNPs and indels of CKA sheep, annotated 2686 genes for functional annotation. The muscle structure development (GO:0061061) pathway was enriched by 117 genes, including IGF1, GDF3, HDAC9, TGFBR2, and CAPN3. The IGF1 plays key roles in skeletal muscle physiology, including the promotion of essential protein synthesis for muscle repair or hypertrophic adaptation and the competition between cellular proliferation and differentiation [15]. GDF3 is a regulator of myoblast proliferation, differentiation, and muscle regeneration [16]. HDAC9 could suppress the transcriptional activity of MEF2 and thus impair the transcriptional circuitry of muscle differentiation through the negative-feedback loop [17]. The expression of the CAPN3 gene is involved in progressive muscular dystrophies during early human development [18]. These gene functions determining muscle development help to provide a solid foundation for sheep breeding.

Various statistical tools such as Fst and XP-CLR are accepted tools in selection signature identification for livestock [19] as they can indicate the selective direction needed to identify a list of novel regions as potential selection targets. Meat quality is a quantitative trait regulated by complex factors such as glycolysis, stress reaction, cell cycle, proteolysis, protein ubiquitination, and apoptosis, among others [20]. GO and KEGG analysis indicated that the genes above the Fst threshold enriched muscle structure development (GO:0061061). Genes in selective signals identified by XP-CLR enriched the MAPK signaling pathway (hsa04010) and supramolecular fiber organization (GO:0097435). Combining Fst and XP-CLR, we were able to identify NF1 and MYOM1 genes positively selected for the development of muscles. NF1 gene is required for skeletal muscle development [21] and essential for normal muscle function and survival [22]. Whereas, the MYOM1 gene encodes a highly specific protein marker in cell differentiation of cross-striated muscle and might function in myofibril assembly and/or maintenance [23]. The subsarcolemmal cytoskeleton can be generally grouped into junctional and non-junctional domains [24]. For adult skeletal muscle fibers, the supramolecular organization of the subsarcolemmal cytoskeleton is also a crucial factor in influencing meat quality [25].

Conclusion

The PCA, STRUCTURE, and NJ-tree analyses revealed that CKA sheep are distinct from BYK, TAN, and OLA sheep, and this breed has descended from a unique ancestor. The host genes annotated by specific missense mutations, including SNPs and indels of CKA sheep enriched muscle structure development (GO:0061061), which included IGF1, GDF3, HDAC9, TGFBR2, and CAPN3, genes, among others. Besides, strong selection signals were detected by Fst, which also enriched muscle structure development (GO:0061061). Strong selection by XP-CLR enriched MAPK signaling pathway (hsa04010) and supramolecular fiber organization (GO:0097435), identifying NF1 and MYOM1, two common genes related to muscle development.

Methods

Ethics statement

This study was conducted according to the Faculty Animal Policy and Welfare Committee of Northwest A&F University (FAPWC-NWAFU).

Sample collection and sequencing

We randomly selected CKA sheep (n = 10) for the study from 305 individuals scattered on the prairie [26]. Genomic DNA was extracted without euthanasia from 1 mL whole blood samples in 2% heparin [27]. DNA quality, purity, and concentration were detected by electrophoresis on 1% agarose gels, NanoPhotometer® spectrophotometry (IMPLEN, CA, USA), and the Qubit® DNA Assay Kit using the Qubit®2.0 Fluorometer (Life Technologies, CA, USA). After the purification of PCR products (AMPure XP system), the Agilent Bioanalyzer 2100 system was used to assess the library quality. Finally, the qualified libraries were subjected to HiSeq 4000 next-generation sequencing with paired-end mode. Trimmomatic (Leading:20 Trailing:20 Slidingwindow:3–15 Avgqual:20 Minlen:35 Tophred:33) was used to trim the sequencing reads, and FASTQC was used to assess the quality of the raw sequencing data. The qualified reads were then mapped, sorted, and deduped to the sheep reference genome (Oar_v4.0) by BWA-MEM (0.7.13-r1126) and Picard v2.18.2 (http://broadinstitute.github.io/picard/). The average sequencing depth was obtained for the 10 CKA sheep was 7.68×. The overall alignment rate and read coverage were 98.56 and 96.38%, respectively, across all samples. Detailed information about the mapping rate, duplication, insert size, mean depth, and amount of sequence was provided in Supplementary Table 9. Data for BAK (n = 10), TAN (n = 10), OLA (n = 10) sheep were downloaded from NCBI (SRR shown in Supplementary Table 10 with accession number SRP066883) [12]. Amongst these populations in China, CKA, TAN, BYK, and OLA sheep are widespread to central Qinghai (36.90°N, 98.46°E), Ningxia (37.78°N, 107.41°E), Xinjiang (43.03°N, 84.15°E), and eastern Qinghai (35.52°N, 102.02°E), respectively (Supplementary Figure 1 and Supplementary Table 10).

Variant identification

To assess the genetic diversity of CKA sheep, the genomes of the four sheep breeds (Table 2) were assembled and genotyped using GATK (version 3.6–0-g89b7209) [28]. A total of 15,021,174 SNPs were retained after filtering for population structure and selection signature analyses.

Table 1 Average length, average number of ROH and average genomic inbreeding coefficient of four sheep groups
Full size table
Table 2 Sample information about Chinese sheep breeds
Full size table

Observed heterozygosity (Ho) and expected heterozygosity (He) were used as indicators of genetic diversity. The level of runs of homozygosity (ROH) was estimated using PLINK v1.9 [29,30,31] with the following parameters: chr-set 26 --maf 0.05 --homozyg-window-snp 50 --homozyg-snp 50 --homozyg-kb 300 --homozyg-density 50 --homozyg-gap 1000 --homozyg-window-missing 5 --homozyg-window-threshold 0.05 --homozyg-window-het 03. The inbreeding coefficient (FROH) was calculated as the sum length of ROH divided by the full length of the reference genome (2,615,499,683 bp). Furthermore, linkage disequilibrium (LD) decay between pairwise SNPs was calculated by popLDdecay software [32]. The effective population size was estimated with a mutation rate of 1e-8 base per generation, minor allele frequency (MAF) < 0.2, and segment size was 2,000,000 and 100,000 iterations using PopSizeABC [33].

Principal component analysis, phylogenetic tree, and STRUCTURE

The principal component analysis (PCA), phylogenetic tree, and STRUCTURE were analyzed using vcftools v0.1.12 (https://vcftools.github.io/index.html) to convert vcf into plink format. The minimum allele frequency (MAF) > 0.0057 was used to ensure that at least two alleles at each site in all groups were retained, while plink was used to remove the linkage sites in genomic data with parameters of “--indep-pairwise 50 5 0.2 “, and the filtered data were used for the subsequent analyses.

The PCA was conducted using EIGENSOFT (version 4.2), implementing the Tracy–Widom statistic to assess the significance of each principal component. PC1 and PC2 were plotted using the ggplot2 package in the R 3.6.1 software.

To establish the evolutionary relationship among the four sheep breeds, we constructed a phylogenetic tree with the neighboring (neighbor-joining, NJ) method using plink software with the matrix of pairwise genetic distances. The result was visualized by FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/).

To accurately identify the ancestral components of the four sheep breeds, the study used ADMIXTURE to estimate the ancestral composition of each individual with genome-wide unlinked sites. Optimization for population differentiation was conducted according to CV errors, and only two-three ancestors (K = 2/3) were used and visualized using R 3.6.1 (Supplementary Table 11). Because the results of K = 4 distorted the population differentiation, analysis with four ancestors (K = 4) was not conducted.

Selection signatures

Interpopulation Wright’s Fst analyses were conducted between the four sheep breeds. Selection signature refers to genomic regions under artificial or natural selection, which are mainly characterized by certain regions of DNA fragment polymorphism, a more homozygous locus, and the increasing linkage disequilibrium extent. The fixation index (FST) values [34] were calculated in sliding 50-kb windows with 20-kb steps along the autosomes using VCFtools [35]. The top 5% was chosen as the significance threshold for Fst.

The cross-population composite likelihood ratio (XP-CLR) is another method for the determination of the selection signal based on the differentiation of allele frequency across populations. It can avoid ascertainment biases and successfully detect older signals and the selections on standing variation [36]. The XP-CLR is a likelihood method for selection signature, which means the allele frequency changed very rapidly due to random drift between two populations [36]. Non-overlapping sliding windows of 50 kb were used, and the maximum number of SNPs within each window was 600. The top 5% was chosen as the significance threshold for XP-CLR.

GO, KEGG pathway analysis

Annovar and Snpeff were deployed for gene annotations of ~ 15 million SNP, ~ 1.6 million indels [37, 38], and the selective signatures. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were completed using metascape online software (http://metascape.org/gp/index.html#/main/step1) [39].

Availability of data and materials

Raw sequence data of Tan, Bayinbuluke, and Oula sheep were downloaded from the National Center for Biotechnology Information (NCBI) Sequence Read Archive under accession number SRP066883 (https://www.ncbi.nlm.nih.gov/search/all/?term=SRP066883). Sequencing reads of Chaka sheep have been submitted to NCBI with accession number SRP237252 (https://www.ncbi.nlm.nih.gov/search/all/?term=SRP237252) (Supplementary Table 10). The sheep reference genome is Oar_v4.0 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000298735.2/.)

Abbreviations

Ho:

Observed heterozygosity

He:

Expected heterozygosity

ROH:

Runs of homozygosity

LD:

Linkage disequilibrium

SNPs:

Single nucleotide polymorphisms

Indels:

Insertion and deletion

TL:

Tibetan lineage

ML:

Mongolian lineage

CKA:

Chaka

TAN:

Tan

BYK:

Bayinbuluke

OLA:

Oula

GATK:

Genome Analysis Toolki

MAF:

Minimum allele frequency

PCA:

Principal component analysis

NJ:

Neighbor-joining

XP-CLR:

Cross-population composite likelihood ratio

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FROH :

Inbreeding coefficients

IGF1 :

Insulin-like growth factor 1

GDF3 :

Growth/differentiation factor 3

HDAC9 :

Histone deacetylase 9

TGFBR2 :

Transforming growth factor beta receptor 2

CAPN3 :

Calpain 3

NF1 :

Neurofibromin 1

TGFB2 :

Transforming growth factor beta 2

PLA2G4F :

Phospholipase A2 group IVF

PRex1 :

Phosphatidylinositol-3,4,5-trisphosphate dependent Rac exchange factor 1

MYOM1 :

Myomesin 1

HMGCR :

3-hydroxy-3-methylglutaryl-CoA reductase

KAT2A :

Lysine acetyltransferase 2A

RORA :

RAR-related orphan receptor alpha

USP2 :

Ubiquitin specific peptidase 2

SOX6 :

SRY-box transcription factor 6

References

  1. Xingqi L, Dong H, Rech JA, Matsumoto R, Bo Y, Yongbo W. Evolution of Chaka salt Lake in NW China in response to climatic change during the latest Pleistocene–Holocene. Quat Sci Rev. 2008;27(7–8):867–79.

    Article  Google Scholar 

  2. Du L. Animal genetic resources in China: sheep and goats. Beijing: China Agriculture Press; 2011.

    Google Scholar 

  3. Xu P, Wang X, Ni L, Zhang W, Lu C, Zhao X, Zhao X, Ren J. Genome-wide genotyping uncovers genetic diversity, phylogeny, signatures of selection, and population structure of Chinese Jiangquhai pigs in a global perspective. J Anim Sci. 2019;97(4):1491–500.

    Article  PubMed  PubMed Central  Google Scholar 

  4. ALSHAWI AF, Essa A, Al-Bayatti S, Hanotte OH. Genome analysis reveals genetic admixture and signature of selection for productivity and environmental traits in Iraqi cattle. Front Genet. 2019;10:609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mastrangelo S, Saura M, Tolone M, Salces-Ortiz J, Di Gerlando R, Bertolini F, Fontanesi L, Sardina M, Serrano M, Portolano B. The genome-wide structure of two economically important indigenous Sicilian cattle breeds. J Anim Sci. 2014;92(11):4833–42.

    Article  CAS  PubMed  Google Scholar 

  6. Brito LF, Kijas JW, Ventura RV, Sargolzaei M, Porto-Neto LR, Cánovas A, Feng Z, Jafarikia M, Schenkel FS. Genetic diversity and signatures of selection in various goat breeds revealed by genome-wide SNP markers. BMC Genomics. 2017;18(1):229.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bickhart DM, Hou Y, Schroeder SG, Alkan C, Cardone MF, Matukumalli LK, Song J, Schnabel RD, Ventura M, Taylor JF. Copy number variation of individual cattle genomes using next-generation sequencing. Genome Res. 2012;22(4):778–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Benjelloun B, Alberto FJ, Streeter I, Boyer F, Coissac E, Stucki S, BenBati M, Ibnelbachyr M, Chentouf M, Bechchari A. Characterizing neutral genomic diversity and selection signatures in indigenous populations of Moroccan goats (Capra hircus) using WGS data. Front Genet. 2015;6:107.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kim J, Hanotte O, Mwai OA, Dessie T, Bashir S, Diallo B, Agaba M, Kim K, Kwak W, Sung S. The genome landscape of indigenous African cattle. Genome Biol. 2017;18(1):34.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wang Z, Chen Q, Yang Y, Liao R, Zhao J, Zhang Z, Chen Z, Zhang X, Xue M, Yang H. Genetic diversity and population structure of six Chinese indigenous pig breeds in the Taihu Lake region revealed by sequencing data. Anim Genet. 2015;46(6):697–701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19(9):1655–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pan Z, Li S, Liu Q, Wang Z, Zhou Z, Di R, Miao B, Hu W, Wang X, Hu X. Whole-genome sequences of 89 Chinese sheep suggest role of RXFP2 in the development of unique horn phenotype as response to semi-feralization. GigaScience. 2018;7(4):giy019.

    Article  PubMed Central  Google Scholar 

  13. Wei C, Wang H, Liu G, Wu M, Cao J, Liu Z, Liu R, Zhao F, Zhang L, Lu J. Genome-wide analysis reveals population structure and selection in Chinese indigenous sheep breeds. BMC Genomics. 2015;16(1):194.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Juvaini A-M, Qazvini MM. Genghis Khan: the history of the world conqueror. Manchester: Manchester University Press; 1997.

    Google Scholar 

  15. Philippou A, Maridaki M, Halapas A, Koutsilieris M. The role of the insulin-like growth factor 1 (IGF-1) in skeletal muscle physiology. In Vivo. 2007;21(1):45–54.

    CAS  PubMed  Google Scholar 

  16. Varga T, Mounier R, Patsalos A, Gogolák P, Peloquin M, Horvath A, Pap A, Daniel B, Nagy G, Pintye E. Macrophage PPARγ, a lipid activated transcription factor controls the growth factor GDF3 and skeletal muscle regeneration. Immunity. 2016;45(5):1038–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Haberland M, Arnold MA, McAnally J, Phan D, Kim Y, Olson EN. Regulation of HDAC9 gene expression by MEF2 establishes a negative-feedback loop in the transcriptional circuitry of muscle differentiation. Mol Cell Biol. 2007;27(2):518–25.

    Article  CAS  PubMed  Google Scholar 

  18. Fougerousse F, Durand M, Suel L, Pourquie O, Delezoide A-L, Romero NB, Abitbol M, Beckmann JS. Expression of genes (CAPN3, SGCA, SGCB, and TTN) involved in progressive muscular dystrophies during early human development. Genomics. 1998;48(2):145–56.

    Article  CAS  PubMed  Google Scholar 

  19. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci. 2014;166:133–43.

    Article  Google Scholar 

  20. Mullen A, Stapleton P, Corcoran D, Hamill R, White A. Understanding meat quality through the application of genomic and proteomic approaches. Meat Sci. 2006;74(1):3–16.

    Article  CAS  PubMed  Google Scholar 

  21. Kossler N, Stricker S, Rödelsperger C, Robinson PN, Kim J, Dietrich C, Osswald M, Kühnisch J, Stevenson DA, Braun T. Neurofibromin (Nf1) is required for skeletal muscle development. Hum Mol Genet. 2011;20(14):2697–709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sullivan K, El-Hoss J, Quinlan KG, Deo N, Garton F, Seto JT, Gdalevitch M, Turner N, Cooney GJ, Kolanczyk M. NF1 is a critical regulator of muscle development and metabolism. Hum Mol Genet. 2013;23(5):1250–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Eppenberger HM, Perriard J-C, Rosenberg UB, Strehler EE. The Mr 165,000 M-protein myomesin: a specific protein of cross-striated muscle cells. J Cell Biol. 1981;89(2):185–93.

    Article  CAS  PubMed  Google Scholar 

  24. Berthier C, Blaineau S. Supramolecular organization of the subsarcolemmal cytoskeleton of adult skeletal muscle fibers. A review. Biol Cell. 1997;89(7):413–34.

    Article  CAS  PubMed  Google Scholar 

  25. Rutigliano M, Picariello G, Trani A, Di Luccia A, La Gatta B. Protein aggregation in cooked pork products: new details on the supramolecular organization. Food Chem. 2019;294:238–47.

    Article  CAS  PubMed  Google Scholar 

  26. Cheng J, Jiang R, Yang Y, Cao X, Huang Y, Lan X, Lei C, Hu L, Chen H. Association analysis of KMT2D copy number variation as a positional candidate for growth traits. Gene. 2020;753:144799.

    Article  CAS  PubMed  Google Scholar 

  27. Sambrook J, Russell D, Russell D. Molecular cloning, a laboratory manual (3-volume set). New York: Cold Spring Harbor Laboratory Press Cold Spring Harbor; 2001.

    Google Scholar 

  28. Chen N, Cai Y, Chen Q, Li R, Wang K, Huang Y, Hu S, Huang S, Zhang H, Zheng Z. Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia. Nat Commun. 2018;9(1):2337.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bosse M, Megens H-J, Madsen O, Paudel Y, Frantz LA, Schook LB, Crooijmans RP, Groenen MA. Regions of homozygosity in the porcine genome: consequence of demography and the recombination landscape. PLoS Genet. 2012;8(11):e1003100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture. Nat Rev Genet. 2018;19(4):220.

    Article  CAS  PubMed  Google Scholar 

  32. Zhang C, Dong S-S, Xu J-Y, He W-M, Yang T-L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics. 2018;35(10):1786–8.

    Article  Google Scholar 

  33. Boitard S, Rodriguez W, Jay F, Mona S, Austerlitz F. Inferring population size history from large samples of genome-wide molecular data - an approximate Bayesian computation approach. PLoS Genet. 2016;12(3):e1005877.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38(6):1358–70.

    CAS  PubMed  Google Scholar 

  35. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps. Genome Res. 2010;20(3):393–402.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yang H, Wang K. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Nat Protoc. 2015;10(10):1556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain. Fly. 2012;6(2):80–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

Thanks to the Laboratory for Agricultural Genomic Big Data in the College of Animal Science and Technology, Northwest A&F University.

Funding

The funders had a role in study design. This work was supported by the Qinghai Province Major Science and Technology Projects (2017-NK-153).

Author information

Authors and Affiliations

  1. Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Jie Cheng, Huangqing Zhao, Ningbo Chen, Xiukai Cao, Yongzhen Huang, Xianyong Lan, Chuzhao Lei & Hong Chen

  2. National Institute for Biotechnology and Genetic Engineering, Jhang Road, 577, Faisalabad, Pakistan

    Quratulain Hanif

  3. Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Quratulain Hanif

  4. Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, 810001, Qinghai, China

    Li Pi & Linyong Hu

  5. Animal Disease Control Center of Haixi Mongolian and Tibetan Autonomous Prefecture, Delingha, 817000, Qinghai, China

    Buren Chaogetu

Authors
  1. Jie Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huangqing Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ningbo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiukai Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quratulain Hanif
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Pi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Linyong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Buren Chaogetu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yongzhen Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xianyong Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chuzhao Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JC designed the work and wrote the manuscript. HZ and NC were responsible for data analysis and the creation of new software used in the analyses. XC and QH assisted in the interpretation of data and modifying the manuscript. LP, LH, and BC made substantial contributions to the acquisition data of CKA sheep and performed preliminary analysis. YH, XL, CL, and HC have drafted the work and substantively revised it. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hong Chen.

Ethics declarations

Ethics approval and consent to participate

The protocols used in this study and animals were approved by the Faculty Animal Policy and Welfare Committee of Northwest A&F University (FAPWC-NWAFU) and Northwest Institute of Plateau Biology, Chinese Academy of Sciences.

Consent for publication

Not applicable.

Competing interests

The authors in this manuscript declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1: Supplementary Table 1.

The genetic variation information of SNPs and indels in sheep breeds. Supplementary Table 2. The level of genetic diversity in the four populations. Supplementary Table 3. Amount of ROH at different lengths. Supplementary Table 4. Host genes of CKA Specific missense mutations for GO and KEGG analysis. Supplementary Table 5. Summary of candidate genes in the Chaka sheep population detected by XP-CLR statistics. Supplementary Table 6. The list of GO and KEGG analyses for genes with high selection signature by XP-CLR. Supplementary Table 7. Summary of candidate genes in four sheep populations detected by Fst statistics. Supplementary Table 8. The list of GO and KEGG analyses for genes with high selection signature by Fst. Supplementary Table 9. The range and amount of sequence data. Supplementary Table 10. Sample collection and sequencing information. Supplementary Table 11. Optimum for population differentiation according to CV errors.

Additional file 2:

Supplementary Figure 1. Geographic distribution of the four Chinese sheep breeds. (We state that the map and CKA images depicted in Supplementary Figure 1 were our own. TAN, BYK and OLA images were from this paper [12] and we obtained written permission from the copyright holders to use and adapt these images depicted in Supplementary Figure 1.)

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, J., Zhao, H., Chen, N. et al. Population structure, genetic diversity, and selective signature of Chaka sheep revealed by whole genome sequencing. BMC Genomics 21, 520 (2020). https://doi.org/10.1186/s12864-020-06925-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-020-06925-z

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us