Summary of transcriptome sequencing data
Two cDNA libraries were constructed using mRNAs extracted from fat-rumped Altay sheep and thin-tailed XFW sheep, sequenced, and two sets of raw reads were obtained containing 51,943,518 and 51,770,440 raw reads, respectively. Low-quality raw reads and adapter sequences were then filtered, ultimately resulting in 46,614,192 and 46,646,110 clean reads. Approximately 84% and 81% clean reads could be mapped to the sheep reference genome Ovis aries v3.1. The clean reads were finally assembled into Unigenes, which were categorized to two classes, specifically, clusters and singletons. Clusters were labeled by the prefix 'CL', followed by the cluster id. A single cluster included several Unigenes with > 70% sequence similarity. Singletons were indicated by the prefix 'Unigene' (Additional File 1, Table S1). In total, 153,914 and 117,254 clusters and 78,065 and 56,293 singletons were obtained from the two sample sets, respectively. The mean lengths of clusters were 335 nt and 317 nt, while mean lengths of singletons were 696 nt and 629 nt for Altay and XFW groups, respectively (Additional File 2, Figure S1). Clusters and singletons were further analyzed and filtered, resulting in a final total of 48,894 Unigenes. Transcriptome sequencing data are summarized in Table 1.
Table 1
Transcriptome sequencing data from Altay and XFM sheep
Samples
|
Altay
|
XFM
|
Total raw reads
|
51,943,518
|
51,770,440
|
Total clean reads
|
46,614,192
|
46,646,110
|
Total clean nucleotides (nt)
|
4,661,419,200
|
4,664,611,000
|
Q20 percentage (%)
|
97.97
|
97.86
|
N percentage (%)
|
0.01
|
0.01
|
GC percentage (%)
|
48.41
|
47.15
|
Error rate (%)
|
0.01
|
0.01
|
Total mapped reads
|
39,155,921
|
37,783,349
|
Multiple mapped reads
|
1,370,457
|
1,435,767
|
Unique mapped reads
|
37,785,464
|
36,347,582
|
Unmapped reads
|
7,458,280
|
8,862,761
|
Mapping rate (%)
|
84
|
81
|
Total number of clusters
|
153,914
|
117,254
|
Total number of singletons
|
78,065
|
56,293
|
Total length of clusters (nt)
|
51,601,654
|
37,173,312
|
Total length of singletons (nt)
|
54,302,714
|
35,416,372
|
Mean length of clusters (nt)
|
335
|
317
|
Mean length of singletons (nt)
|
696
|
629
|
N50 length of clusters (nt)
|
573
|
519
|
N50 length of singletons (nt)
|
1114
|
917
|
Annotation and expression analysis of Unigenes
Comparison of the Unigenes obtained with known gene sequences of Bos taurus and Ovis aries revealed a total of 21,527 genes (E-value < 0.00001), which were subsequently matched to NR (RefSeq non-redundant proteins), Swiss-Prot, KEGG and COG (Cluster of Orthologous Groups of proteins) databases, leading to 57%, 53%, 61%, and 45% annotation, respectively (E-value < 0.00001). GO analysis was applied to clarify the biological functions of the above genes (Additional File 1, Table S1).
Calculation of gene coverage revealed that 65% (13,993/21,527) genes of Altay sheep and 68% (14,638/21,527) genes of XFW sheep had 90–100% coverage (Fig. 2A, B). In total, 19,878 annotated genes with FPKM > 0 were detected in the two samples. The FPKM trends of the two samples were comparable, indicating similar expression patterns of the majority of genes in tail fat tissues of Altay and XFM sheep (Fig. 2C). The largest proportions of genes were expressed at low (1 < FPKM < 10) and moderate (10 < FPKM < 100) levels and only a small fraction expressed at high levels (FPKM > 100). The results indicate that high-throughput sequencing technology has an obvious advantage in detection of low-abundance genes. Further analysis revealed 94.08% (18,701/19,878) of the total genes, including 384 uniquely expressed genes, in fat rump of Altay sheep and 93.25% (18,536/19,878) of total genes, including 219 uniquely expressed genes, in thin tail of XFW sheep. Overall, we detected 18,317 common genes in tail fat tissues of the Altay and XFW sheep (Fig. 2D).
Analysis of DEGs between tail fat tissues of the two sheep breeds
In total, 8,042 differentially expressed genes (DEGs) were identified between the two sheep breeds using FDR ≤ 0.001 and |Log2Ratio| ≥ 1 as filter criteria (Fig. 3A; Additional File 3, Table S2). Within this gene set, differences in levels of 3,965 DEGs in tail fat tissues of the two sheep breeds were highly significant (FDR ≤ 0.001 and |Log2Ratio| ≥ 2), including 707 highly upregulated and 3,258 highly downregulated genes in fat-rumped Altay, compared to XFW thin-tailed sheep (Fig. 3B; Additional File 3, Table S2).
To further clarify the functions of DEGs in tail fat metabolism of the two sheep breeds, we identified 198 DEGs (72 upregulated and 126 downregulated) closely related to adipose tissue development, deposition and mobilization. Among these genes, levels of ABCA1, PLIN1, SORBS1, ANGPTL4, LPIN1, ELOVL5, ACACA, FASN, CIDEC, FABP3, and SLC27A2 were significantly higher in Altay than XFW sheep. In contrast, CYP4A11, FADS2, PTPLB, ACAA1, PPCK1, PMP2, HSL, CPT1A, C1QTNF1, ACADL, and C1QTNF9 were more highly expressed in tail fat of XFW than Altay sheep (Additional File 3, Table S2).
Based on significant differences in expression of these genes between tail fat tissues of Altay and XFW breeds and their participation in regulation of fat metabolism, we speculate that the DEGs identified play potentially important roles in influencing phenotypes of different sheep breeds. We further focused on 22 DEGs showing highly significant up- or down-regulation in tail fat tissue of Altay sheep as candidate genes.
qRT-PCR validation of RNA-seq data
To further investigate expression patterns and validate the reliability of RNA-seq results, 22 candidate genes were selected and their relative expression levels in rump and tail fat tissue of Altay and XFW sheep, respectively, assessed via qRT-PCR using non-pooled RNA samples (n = 3 for each breed). qRT-PCR expression patterns of these genes were consistent with RNA-Seq data (Fig. 4; Additional File 4, Figure S2), supporting the reliability of the expression profile generated with RNA-Seq.
Among the 22 candidate genes, 10 (ABCA1, ACACA, PLIN1, FASN, FABP3, SORBS1, ANGPTL4, LPIN1, SLC27A, and CIDEC) were highly upregulated in rump fat tissue of Altay relative to tail fat tissue of XFW sheep (P value < 0.01). In particular, expression of ACACA, ABCA1 and CIDEC in rump fat of Altay sheep was 6.75, 5.37 and 5.86 times higher than that in tail fat of XFW sheep (Fig. 4A). Eleven other genes (ACADL, C1QTNF1, CPT1A, CYP4A11, HSL, FADS2, PTPLB, ACAA1, PPCK1, PMP2, and C1QTNF9) were more highly expressed in tail fat tissue of XFW than Altay sheep (P value < 0.01), particularly HSL and CPT1A, which were 7.45 and 8.15 times higher, respectively (Fig. 4B). In view of these findings, we hypothesize that these genes play critical roles in regulating tail fat metabolism of Altay and XFW sheep and ultimately influence tail type.
GO and KEGG analyses of DEGs between Altay and XFW sheep
GO was applied for functional analysis of the 8,042 DEGs (Additional File 3, Table S2). GO terms with Q values ≤ 0.05 were considered significantly enriched and DEGs classified based on 'cellular component', 'molecular function', and 'biological process' categories. In total, 847 terms were enriched in cellular component, of which 24 were significantly enriched, such as 'membrane', 'membrane part', and 'cell periphery' (Fig. 5A). Overall, 7,014 biological process terms were enriched, 71 to a significant extent, including 'cell communication', 'response to stimulus', and 'multicellular organismal process' (Fig. 5B). Among the 1,903 terms enriched in molecular function, 32 were significantly enriched, including 'substrate-specific transporter activity', 'insulin receptor binding', and 'protein kinase A regulatory subunit binding' (Fig. 5C).
To identify the biological pathways involved in fat deposition, DEGs were mapped to the KEGG pathway database. Pathways with Q values ≤ 0.05 were considered significantly enriched (Additional File 3, Table S2). In total, 256 pathways were enriched, among which 134 were significantly enriched. The top 20 pathways, including 'MAPK signaling', 'Insulin signaling', 'Jak-STAT signaling', and 'Phospatidylinositol signaling', are listed in Fig. 5D. We identified 245 and 187 DEGs enriched in the MAPK and insulin signaling pathways, respectively, indicating key roles of these mechanisms in tail/rump fat metabolism of Altay and XFW sheep.
GO and KEGG analyses of DEGs related to adipose metabolism
GO analysis of the 198 DEGs disclosed significant enrichment of the molecular function terms 'catalytic activity', 'molecular function', and 'transferase activity'. With regard to biological process terms, 'lipid metabolic process', 'small molecule metabolic process', 'oxidation-reduction process', and 'single-organism metabolic process' were significantly enriched. Among cellular component terms, 'cytoplasm', 'cytoplasmic part', and 'intracellular part' were significantly enriched (Fig. 6A, B). The top 30 GO enrichment terms of DEGs are presented in Figs. 6C and D.
To identify the biological pathways underlying adipose deposition, the 198 DEGs were mapped to the KEGG pathway database. The pathways with Q values ≤ 0.05 were considered significantly enriched. Several pathways related to lipid metabolism were identified (Table 2).
Table 2
Lipid metabolism-related DEG-enriched signaling pathways
Pathway
|
DEGs
|
Up
|
Down
|
Ether lipid metabolism
|
AGPS, PPAP2, SPLA2, CEPT1, PLD1, PLD2
|
ENPP2, PAFAH2, EPT1, PLA2G12A, PAFAH1B2, PAFAH1B1, MAPKBP1
|
Sphingolipid metabolism
|
GLA, SPTLC2, SPTLC3, SGMS, PPAP2, GLB1, KDSR, SGMS2, UGCG, SGPL1
|
CERK, ACER2, SGPP1, GALC, SPT, SPTLC1, ASAH1, SPHK1, DPL1, LACZASAH1, CER, ACER12, B4GALT6
|
alpha-Linolenic acid metabolism
|
ACOX1, BDHAB, CYP1A2, COX1, SPLA2
|
PLA2G12A, FADS2, FADA, FADIA
|
Linoleic acid metabolism
|
PTGS1, BDHAB
|
PLA2G12A, MAPKBP1, CYP2J2, CYP2C40, CYP2E1
|
Arachidonic acid metabolism
|
PTGS2, ATS2, EPHX2, COX1, CBR3, CBR1
|
CYP2U1, PLA2G12A, CYP4F3, GGT5, LTC4S, PTGES, CYP4A11, K15717, ALDH3A2
|
Glycerolipid metabolism
|
LPIN1, PLSC, LCLAT1, ALDH9A1, PPAP, GPAT12, SHROOM4, GLA, DGAT2
|
DGKA, DGKD, ALDH3A2, DGKH, AGPAT1, DGKQ, MOGAT3, GPAT4, GPAT3, ADH, LIP, ATS2, ACT2, DGAT1
|
Biosynthesis of unsaturated fatty acids
|
ACNAT, ELOVL5, ACOX1
|
ACOT7, FADS2, TECR, ACAA1, BAAT, RPB1, PTPLB
|
Steroid hormone biosynthesis
|
PGFS, UGT2B7
|
CYP7B1, CYP1B1, CYP1A1, AKR1C2, AKR1C4, AKR1C, HSD11B1, HSD11B2,
|
Steroid biosynthesis
|
SOAT1, ACNAT2, ACOT8, PODNL1, TM7SF2
|
CYP2R1, SC5DL, AKR1C4, HSD17B4, SCP2, ERG24, LBR, ERG3
|
MAPK signaling pathway
|
AKT2, AKT3
|
PLA2G12A, TNFRSF1A, CHUK, IKBKB, MAPK8
|
Fatty acid degradation
|
FADA, HELZ, ADIPOQ, C5ORF25, MFSD4, CPT, ACOX1, FADD, ACOX3, ACSL1, ACSL3, ACSL4, ACSL6
|
CPT1A, ALDH3A2, ACADL, ACAT1, HADHB, ECHS1, CYP4A11, ACAA1, PAAF, ECHDC3, HADHA
|
Fatty acid elongation
|
ELOVL5, C5ORF25
|
HADHB, ECHS1, ACAA2, SMR3A, HELZ, PTPLB, HACD, TECR, ACOT7
|
Fatty acid biosynthesis
|
FASN, ACACA, CBR4
|
TNS, TENC1
|
Fat digestion and absorption
|
ABCA1, DGAT2, AGPAT1, LPPR3
|
CD36, APOA1, PLA2G12A, SCARB1, MOGAT3, MAPKBP1, GOT2, CALB2, LPAAT
|
Adipocytokine signaling pathway
|
AKT2, AKT3, ACSL1, ACSL3, ACSL4, ACSL6, ACACA, ACACB, CD36, SLC27A2, FADD, G6PC, RXRB, OPTN,
|
MAPK8, PRKAB1, PRKAB2, IRS2,CPT1A, ADIPOR2, ADIPOQ, PPARA, PPCK1, C1QTNF9, RXRA, PTPN11, LEP, CAMKK2, JAK2, STAT3, TNFRSF1A, TNFRSF1B, CHUK, IKBKB, MTOR, SLC2A4, NFKBIA, NFKBIB, ACSBG2, PCK2, PEPCK, STK11, C1QTNF1, PRKAG2
|
PPAR signaling pathway
|
FABP3, FABP4, SLC27A2, SLC27A6, ACSL1, ACSL3, ACSL4, ACSL6, PLIN1, ANGPTL4, SORBS1, PDPK
|
ADIPOQ, ACADL, AQP7, APOA1, SCP2, APM-1, ACAA1, CPT1A, ACOX1, ACOX2, C1QTNF1, CYP4A11, FADS2, PPCK1, PMP2, RXRA, RXRB, DBI, C1QTNF9, TRIM56
|
Insulin signaling pathway
|
ACACA, SORBS1, FASN, AKT2, AKT3, G6PC, PRKAA1, PRKAB2
|
MAPK8, PPCK1, IRS4, IKBKB, PDPK1, PRKAG2, SLC2A4, MTOR
|
Metabolic pathways
|
FASN, LPIN1, CDS2, PLD1, AGPS, PLD2, UGCG, SGMS2, AGPAT9, G6PC, DGAT2, GPAM, SGPL1, AGPAT6, TM7SF2, CBR3, GLB1, DGKE, SPTLC2, SPTLC3, ACOX1, PTGS2, EPT1, ETNK1, ACACA, ACSL1, ACSL3, ACSL4, ACSL6
|
PGS1, ACADL, IRS2, CYP2U1, PPCK1, ALDH3A2, PAFAH2, ACAT1, CYP2R1, HADHB, PLA2G12A, PAFAH1B2, CYP4F3, PCYT1A, HSD11B1, KDSR, SC5DL, PAFAH1B1, BDH1, AGPAT1, GGT5, DGKQ, LTC4S, ASAH1, AKR1A1, ECHS1, HSD11B2, CYP1A1, UGT2B, SPHK1, CYP4A11, ACAA1, DGKA, DGKD, ACER2, GALC, DGKH, SPTLC1, PTGES
|
Glycerophospholipid metabolism
|
EPT1, CDS2, PLD1, GPD1L, AGPAT9, PLD2, GPD2, AGPAT6, ETNK1, LCLAT1, GPAM, DGKE
|
PGS1, DGKA, DGKD, PLA2G12A, LPGAT1, PCYT1A, LYPLA1, PCYT2, GPD1, DGKH, DGKQ, AGPAT1
|
In total, 32 DEGs were enriched in the PPAR signaling pathway, including 12 upregulated and 20 downregulated genes. Among these, 10 (FABP3, FABP4, SLC27A2, SLC27A6, ACSL1, ACSL3, ACSL4, ACSL6, ANGPTL4, and PLIN1) were highly expressed and 16 (ADIPOQ, ACAA1, ACADL, AQP7, APOA1, SCP2, APM-1, CPT1A, ACOX1, ACOX2, C1QTNF1, CYP4A11, FADS2, PPCK1, RXRA, and PMP2) were significantly downregulated in rump fat of Altay sheep.
Furthermore, 44 DEGs were enriched in the adipocytokine signaling pathway, including 14 upregulated and 30 downregulated genes. Ten of these genes (AKT2, AKT3, ACSL1, ACSL3, ACSL4, ACSL6, ACACA, ACACB, CD36, and SLC27A) were significantly upregulated and 11 genes (MAPK8, PRKAB1, PRKAB2, IRS2, CPT1A, ADIPOR2, ADIPOQ, PPARA, PPCK1, C1QTNF9, and RXRA) were significantly downregulated in rump fat of Altay sheep.
Other pathways, including 'Metabolic pathway', 'Insulin signaling pathway', 'Glycerolipid metabolism', additionally play important roles in fat metabolism. The top 30 enriched pathways are presented in Fig. 7A and C. Heatmaps were generated that clearly depicted significantly enriched pathways and DEGs (Fig. 7B, D)
Since the majority of the 198 DEGs were enriched in key pathways significantly related to fat metabolism, we propose that these genes are critical for sheep tail phenotype regulation and should be further investigated.
Interaction network analysis of proteins encoded by DEGs related to adipose metabolism
With the aid of STRING and Cytoscape software, an interaction network of proteins encoded by the 198 DEGs related to adipose metabolism was constructed (Fig. 8), resulting in the detection of existing interactions among 148 genes. KEGG data disclosed 94, 37, 31, 26, 21, 25, and 19 interacting proteins related to the terms 'Metabolic pathway' (FDR ≤ 2.67e-61), 'Adipocytokine signaling pathway' (FDR ≤ 1.07e-47), 'PPAR signaling pathway' (FDR ≤ 9.24e-38), 'Glycerophospholipid metabolism' (FDR ≤ 5.14e-27), 'Fatty acid metabolism' (FDR ≤ 2.17e-25), 'Insulin resistance' (FDR ≤ 9.38e-25), and 'Fatty acid degradation' (FDR ≤ 1.43e-23), respectively. In view of the important roles of these pathways in adipose metabolism, we speculated that they may also influence the tail types of different sheep breeds by regulating fat metabolism in tail tissue.
The interaction network of 22 proteins related to fat deposition was further analyzed, which revealed interactions among 19 of the proteins (Fig. 9A). The core nodes were identified as ACOX1, FASN, and ACAA1. ACOX1 interacted with ACADS, SLC27A2, CPT1A, FASN, and ACAA1. Interactions of FASN with CPT1A, ACACB, ACACA, ACLY, and ACOX1 were detected. ACAA1 showed interactions with PEX7, HADH, ACADS, ACOX1, and ACLY. GO analysis revealed that the majority of these proteins were related to the PPAR signaling pathway, Fatty acid metabolism, and Fatty acid biosynthetic process. The expression patterns of these 22 proteins in tail/rump fat of Altay and XFWs detected via qRT-PCR and RNA-seq support their critical roles in tail type regulation of sheep through formation of regulatory feedback loops, ultimately leading to a complex network.
To further clarify the functions of ABCA1 and SLC27A2 showing significant SNP differences in sheep populations with different tail types, their interaction networks with other proteins were analyzed (Fig. 9B). ABCA1 showed interactions with 10 proteins, including APOA1, UGP2, ARHGEF12, AOX1 and ARHGEF11 while SLC27A2 interacted with 8 proteins, including ABCD1, AGPAT1, and HSD17B7. Based on their identification as DEGs of Altay and XFW sheep and significant association of mutations with tail type of sheep, we hypothesize that these gene variations potentially affect expression of ABCA1 and SLC27A2 and thus the functions of interacting proteins, leading to alterations in metabolism of tail fat that ultimately influence the tail type in sheep breeds.
Detection of variants in candidate genes related to tail-fat metabolism
Using GATK software package and SOAPsnp, a total of 41,724 and 42,193 SNPs were detected in tail fat tissue transcriptomes of Altay and XFW sheep, respectively (Additional File 5, Table S3; Additional File 6, Table S4). We specifically focused on the 22 candidate genes related to tail fat metabolism, which led to the identification of 13 SNPs in the coding regions of 9 genes, among which 12 induced amino acid alterations (Table 3).
Table 3
The SNPs in candidate genes related to tail fat metabolism
Gene
|
Position
|
Basic
|
FR-chr base
|
FR-chr reads
|
TT-chr base
|
TT-chr reads
|
Style of amino
acid mutation
|
chromosome
|
ACACA
|
13081041
|
T
|
T
|
255
|
C;T
|
216;39
|
Glu-Lys
|
11
|
13028657
|
A
|
G
|
255
|
A;G
|
226;28
|
Leu-Pro
|
11
|
PPCK1
|
57902435
|
C
|
C
|
94
|
TC
|
235;20
|
Glu-Lys
|
13
|
ABCA1
|
18100859
|
G
|
T;G
|
27;16
|
G
|
4
|
Pro-Leu
|
2
|
18167532
|
G
|
G
|
166
|
A;G
|
48;14
|
Lys-Glu
|
2
|
SLC27A2
|
57036072
|
C
|
C
|
2
|
A;C
|
13;5
|
Met-Ile
|
7
|
CPT1A
|
45468209
|
T
|
T;G
|
13;11
|
G
|
4
|
Ser-Arg
|
21
|
45468249
|
G
|
A;G
|
13;12
|
G
|
1
|
Ser-Ser
|
21
|
FBP2
|
31747535
|
G
|
A
|
4
|
G;A
|
27;7
|
Ile-Val
|
2
|
FADS2
|
31762518
|
C
|
C
|
2
|
T;C
|
37;16
|
Arg-Gly
|
21
|
39768783
|
C
|
C
|
11
|
T;C
|
29;15
|
Arg-Trp
|
21
|
39774594
|
A
|
G
|
20
|
G;A
|
35;31
|
Arg-Gly
|
21
|
PLIN1
|
20197576
|
C
|
C;G
|
251;2
|
T;C
|
189;64
|
Ala-Tyr
|
18
|
Note: FR, fat rump; TT, thin tail |
Using PCR-RFLP and PCR-SSCP, the distribution of seven SNPs that induced amino acid substitutions was further investigated in Altay, XFW, and Hu sheep populations with different tail phenotype (Table 4).
Table 4
Distribution of 7 SNPs in three different sheep breed populations
Gene SNP
|
Sheep breed
|
Genotype frequencies
|
Allele frequencies
|
Ratio
|
χ2
|
ABCA1
18100859
|
|
AA
|
AG
|
GG
|
A
|
G
|
A/G
|
|
Altay sheep (104)
|
0.327(34)
|
0.481(50)
|
0.192(20)
|
0.567(118)
|
0.433(90)
|
1.311
|
0.045
|
XFW sheep (104)
|
0.212(22)
|
0.423(44)
|
0.365(38)
|
0.423(88)
|
0.577(120)
|
0.733
|
1.849
|
Hu sheep (104)
|
0.135(14)
|
0.365(38)
|
0.500(52)
|
0.308(66)
|
0.683(142)
|
0.464
|
2.552
|
ABCA1
18167532
|
|
TT
|
TC
|
CC
|
T
|
C
|
T/C
|
|
Altay sheep (104)
|
0(0)
|
0.106(11)
|
0.894(93)
|
0.053(11)
|
0.947(197)
|
0.056
|
0.324
|
XFW sheep (104)
|
0.865(90)
|
0.096(10)
|
0.038(4)
|
0.913(190)
|
0.087(18)
|
10.556
|
15.97**
|
Hu sheep (104)
|
0(0)
|
0.962(100)
|
0.038(4)
|
0.481(100)
|
0.519(108)
|
0.926
|
89.16**
|
CPT1A
45468209
|
|
GG
|
GT
|
TT
|
G
|
T
|
G/T
|
|
Altay sheep (104)
|
0.346(36)
|
0.654(68)
|
0(0)
|
0.673(140)
|
0.327(68)
|
2.058
|
24.54**
|
XFW sheep (104)
|
0.385(40)
|
0.577(60)
|
0.038(4)
|
0.673(140)
|
0.327(68)
|
2.058
|
10.05**
|
Hu sheep (104)
|
0.173(18)
|
0.644(67)
|
0.183(19)
|
0.495(103)
|
0.505(105)
|
0.981
|
8.661*
|
FADS2
39768783
|
|
CC
|
CT
|
TT
|
C
|
T
|
C/T
|
|
Altay sheep (104)
|
0.106(11)
|
0.894(93)
|
0(0)
|
0.553(115)
|
0.447(93)
|
1.237
|
68.01**
|
XFW sheep (104)
|
0.385(40)
|
0.615(64)
|
0(0)
|
0.692(144)
|
0.308(64)
|
2.250
|
20.54**
|
Hu sheep (104)
|
0.683(71)
|
0.317(33)
|
0(0)
|
0.803(175)
|
0.139(33)
|
5.303
|
3.698
|
FBP2
31747535
|
|
AA
|
AG
|
GG
|
A
|
G
|
A/G
|
|
Altay sheep (104)
|
0.529(55)
|
0.385(40)
|
0.087(9)
|
0.721(150)
|
0.279(58)
|
2.584
|
0.198
|
XFW sheep (104)
|
0.644(67)
|
0.269(28)
|
0.087(9)
|
0.779(162)
|
0.221(46)
|
3.522
|
4.964
|
Hu sheep (104)
|
0.596(62)
|
0.337(35)
|
0.067(7)
|
0.764(159)
|
0.236(49)
|
3.244
|
0.447
|
PLIN1
20197576
|
|
CC
|
CG
|
GG
|
C
|
G
|
C/G
|
|
Altay sheep (78)
|
0(0)
|
0.231(24)
|
0.770(80)
|
0.115(24)
|
0.885(184)
|
0.130
|
1.327
|
XFW sheep (78)
|
0(0)
|
0.077(8)
|
0.923(96)
|
0.038(8)
|
0.962(200)
|
0.040
|
0.125
|
Hu sheep (78)
|
0(0)
|
0.295(30)
|
0.705(74)
|
0.147(30)
|
0.853(178)
|
0.168
|
2.333
|
SLC27A2
57036072
|
|
GG
|
GT
|
TT
|
G
|
T
|
G/T
|
|
Altay sheep (104)
|
0.337(35)
|
0.587(61)
|
0.077(8)
|
0.630(131)
|
0.370(77)
|
1.701
|
6.915*
|
XFW sheep (104)
|
0.038(4)
|
0.125(13)
|
0.837(87)
|
0.101(21)
|
0.899(187)
|
0.112
|
89.163**
|
Hu sheep (104)
|
0.385(40)
|
0.596(62)
|
0.019(2)
|
0.683(142)
|
0.317(66)
|
2.151
|
14.70**
|
* P < 0.05 (χ20.05 = 5.99), ** P < 0.01 (χ20.01 = 9.21). |
Based on data obtained from 104 individuals of each sheep breed, the distribution of g. 18167532 T/C mutation of ABCA1 and g. 57036072 G/T mutation of SLC27A2 in these three populations showed significant differences. For the g. 18167532 T/C mutation of ABCA1, 89.4% individuals in the fat-rumped Altay sheep population were CC genotype, 96.2% of fat-tailed Hu sheep (with fat-tailed phenotype between Altay and XFW sheep) were TC genotype, and 86.5% individuals in the long thin-tailed XFW group were TT genotype. The results of the Chi-square test showed that this SNP was not in Hardy-Weinberg equilibrium in XFW and Hu sheep populations (P < 0.01) while the Altay population was in Hardy-Weinberg equilibrium at this site (P > 0.05).
For the g. 57036072 G/T mutation of SLC27A2, G allele was main genotype in fat-rump Altay and short fat-tailed Hu sheep populations (63.0% and 68.3% of individuals had the G allele, respectively) while in the thin-tailed XFW sheep population, 89.9% of individuals had the T allele. The three sheep populations were not in Hardy-Weinberg equilibrium at this SNP.