**3.5. Genetic differentiation and altitude**

populations [29]. The estimated pairwise *FST* values are from Liu et al. [29]. The *FST* values ranged from −0.05 to 0.24. DM and LKZ had the closest pairwise *FST* value among the 15 Tibetan sheep populations and AW were more distantly related to JZ, compared with other

**Table 1.** Altitude and Pearson correlation coefficients between absolute differences in altitude and each genetic distance

and genetic differentiation between 15 Tibetan sheep populations.

**Figure 1.** Genetic distance and absolute difference between altitudes for population LZ.

140 Mitochondrial DNA - New Insights

**Population Altitude, m Genetic dist, r Genetic diff, r**

GD 3100 0.07 0.43 QL 3540 −0.13 0.53 TJ 3217 0.06 0.54 QH 3630 −0.21 0.58 MX 3180 0.08 0.56 GJ 3022 0.06 0.42 QK 3410 0.01 0.53 GN 3616 −0.13 0.60 LKZ 4459 −0.29 0.09 JZ 4398 −0.41 −0.17 GB 4403 −0.36 −0.51 HB 4614 −0.12 −0.46 DM 4780 −0.11 0.34 AW 4643 −0.05 −0.01 LZ 4292 −0.42 −0.11

We test whether genetic differentiation between populations can be explained by altitude. Graphically, for the focal population of GD, **Figure 2** plots genetic differentiation between GZ and each of the remaining populations as a function of the absolute value of the difference between their altitudes. Genetic differentiation tends to increase with altitude (r = 0.4315, one-tailed P = 0.062) (square root of 0.1862 indicated in **Figure 2**). Analyses similar to those in **Figure 2** showed that genetic differentiation increases with absolute differences in altitude, specifically for nine populations ((significance at P < 0.05 indicated by \*) QL\*, TJ\*, QH\*, MX\*, GJ, QK\*, GN\*, DM, and LKZ) and decreases for the remaining five populations (GB,HB, JZ, LZ, and AW), mainly GB\* and HB\* (**Table 1**).

This association between genetic differentiation and absolute difference between altitudes is most positive for populations at high altitudes and most negative for those at low altitudes. This association between altitude and Pearson correlation coefficients obtained between genetic differentiations and absolute differences in altitudes (**Table 1**) has itself r = −0.75, one-tailed P = 0.0011.

the populations significantly (*P* < 0.05). The *FST* was 0.05, which indicated that 4.50% of the total genetic variation was due to differences between populations, and the remaining 95.50%

Phylogenetic Evolution and Phylogeography of Tibetan Sheep Based on mtDNA D-Loop Sequences

http://dx.doi.org/10.5772/intechopen.76583

143

The sample size for most of the populations was more than 30, so the detection of population expansion was performed at the individual population level (data not shown) and in all haplotype sequences. Liu et al. showed the mismatch distribution analysis of the complete dataset (lineages A, B, C, D and 15 Tibetan sheep populations of mtDNA D-loop) [29]. Neutrality tests (Ewens-Watterson test, Chakraborty's test, Tajima's D test, Fu's FS test) were used to detect population expansion [29]. The charts of the mismatch distribution for the samples of the 15 Tibetan sheep populations and the total samples were multimodal. However, the mismatch distribution for LZ was a unimodal function. The mismatch distribution of the complete dataset showed that there were two major peaks, with maximum values at 4 and 27 pairwise differences and two smaller peaks at 45 and 51 pairwise differences [29]. These results suggest that at least two expansion events occurred during the population demographic history of the Tibetan sheep population. The mismatch distribution analysis revealed a unimodal bell-shaped distribution of pairwise sequence differences in lineages A, B and C, but that of the lineage D was a sampling function duo to small sample effects. The complete dataset of 15 Tibetan sheep populations did not produce a significantly negative Ewens-Watterson test, whereas Chakraborty's neutrality test of JZ was significant (12.63, *p* = 0.03), and Tajima's D neutrality of TJ test was

which GN, QK, HB, GB, GJ, QH, QL and TJ were highly significant (*p* < 0.01 or *p* < 0.001). This finding suggests the occurrence of two expansion events in the demographic history of the 15 Tibetan sheep populations. This result is consistent with a demographic model showing two

The 15 Tibetan sheep populations in our study showed a high level of genetic diversity. This finding is consistent with archeological data and other genetic diversity studies [21, 40–43], while in this study, the haplotype diversity was higher than that found in a previous study [44], and the nucleotide diversity was lower compared with the data in a previous study [7]. The genetic diversity among the 15 Tibetan sheep populations was relatively higher compared with other sheep populations [1, 44]. For instance, the haplotype diversity values of Turkish sheep breeds distributed in a Turkish population were 0.95 ± 0.01 [44]. However, according to Walsh's work, based on the required sample size for the diagnosis of conservation units [45], a sample of 59 individuals fails necessary to support the hypothesis that individuals with unstamped ("hidden") character states exist in the population size. Thus, the sample size necessary to reject a hidden state frequency of 0.05 is 56 when sampling from a finite

large and sudden expansions, as inferred from the mismatch distribution.

**4.1. High mtDNA D-loop diversity of Tibetan sheep populations**

value was −7.48 for the 15 Tibetan sheep populations, of

came from differences among individuals within each population.

**3.7. Population expansions**

also significant (−0.47, *p* = 0.02). Fu's *FS*

**4. Discussion**

**Figure 2.** Genetic differentiation and absolute difference between altitudes for population GD.

#### **3.6. Phylogenetic relationships**

To extend our knowledge of the phylogenetic relationship of the 15 Tibetan sheep populations, a phylogenetic tree was constructed using ME based on the complete mtDNA D-loop sequences of 642 individuals and 350 haplotypes from 15 Tibetan sheep populations and six reference breeds [29]. We determined four distinct cluster haplogroups: A, B, C and D. Of the 350 haplotypes, there was no common haplotype identified in all of the Tibetan sheep populations; 98 haplotypes were shared, and 252 haplotypes were singletons, which including 38 in GB, 33 in GJ, 28 in TJ, and 24 in QH. The leading haplotype (Hap 39) was found in 39 individuals. The next most common haplotype was Hap 42, composed of 19 individuals, and the remaining nine haplotypes were composed of 7–10 individuals. Haplotype 42 was composed of JZ, MX, QL and TJ. Haplotype 4 was composed of 14 of the Tibetan sheep populations, but excluding LKZ, indicating close clustering. The majorities of the 490 individuals were grouped in haplogroup A, followed by haplogroups B and C; however, only one animal from the LZ belonged to haplogroup D. The DM was composed of two haplogroups, the AW was composed of one haplogroup and the remaining 13 Tibetan sheep populations were composed of three haplogroups [29]. Moreover, the maximum composite likelihood method was used to analyze the genetic distance between populations, which were in the units of the number of base substitutions per site. More specifically, the neighbor-joining phylogenetic tree of the 642 sequences of the mtDNA D-loop, based on units of the number of base substitutions per site divided the 15 Tibetan sheep populations and six reference breeds into four groups effectively. *O. ammon* and *O. vignei* were genetically distinct and separated initially. The 15 Tibetan sheep populations and four reference breeds were then divided into three sub-clusters. The first cluster included JZ, QL, QH, GN, QK, MX and GD. The second cluster included *OasiaA*, AW, TJ, GJ, LKZ, DM, GB, HB and LZ. The third cluster included *Omexic*, *Oeuro*reB and *Omusimon*. The AMOVA was conducted, and the results are shown in Liu et al. [29]. The AMOVA revealed a variation of 4.46% among the populations and of 95.54% within the populations significantly (*P* < 0.05). The *FST* was 0.05, which indicated that 4.50% of the total genetic variation was due to differences between populations, and the remaining 95.50% came from differences among individuals within each population.
