Volume 31 Issue 5
Sep.  2010
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LIN Fang-Jun, JIANG Ping-Ping, DING Ping. Genetic analysis of microsatellite polymorphism in the Elliot’s Pheasant (Syrmaticus ellioti) in China. Zoological Research, 2010, 31(5): 461-468. doi: 10.3724/SP.J.1141.2010.05461
Citation: LIN Fang-Jun, JIANG Ping-Ping, DING Ping. Genetic analysis of microsatellite polymorphism in the Elliot’s Pheasant (Syrmaticus ellioti) in China. Zoological Research, 2010, 31(5): 461-468. doi: 10.3724/SP.J.1141.2010.05461

Genetic analysis of microsatellite polymorphism in the Elliot’s Pheasant (Syrmaticus ellioti) in China

doi: 10.3724/SP.J.1141.2010.05461
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  • Author Bio:

    LIN Fang-Jun

  • Corresponding author: DING Ping
  • Received Date: 2010-01-25
  • Rev Recd Date: 2010-08-03
  • Publish Date: 2010-10-22
  • In this study, we reported the population genetic analyses in the Elliot’s Pheasant(Syrnaticus ellioti) using seven polymorphism microsatellite loci based on 105 individuals from 4 geographical populations. Departures from Hardy-Weinberg equilibrium were found in four geographical populations. The average number of alleles was 8.86, with a total of 62 alleles across 7 loci; observed heterozygosity (HO) was generally low and the average number was 0.504. For the seven microsatellite loci, the polymorphism information content ranged from 0.549 to 0.860, with an average number 0.712. Population bottlenecks of the four geographical populations were tested by infinite allele mutation model, step-wise mutation model and two-phase mutation model, which found that each population had experienced bottleneck effect during the recent period. Fst analysis across all geographical populations indicated that the genetic differentiaton between the Guizhou geographical population and the Hunan geographical population was highly significant (P<0.001), a finding supported by the far genetic relationship showed by the neighbor-joining tree of four geographical populations based on Nei’s unbiased genetic distances. Using hierarchical analysis of molecular variance (Guizhou geographical population relative to all others pooled), we found a low level of the genetic variation among geographical populations and that between groups. However, differences among populations relative to the total sample explained most of the genetic variance (92.84%), which was significant.
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