Genetic Structure Of Lohpura Nycthemera Populations Based On Markers Of SSR And D-loop

Based on the molecular markers of 5 pairs of SSR primers and partial Mitochondrial D-loop sequence, the genetic structures of Lohpura nycthemera subspecie and population were discussed by comparing 82 individuals from 20 different locations of China. All 82 individuals were grouped into 11 unique geographical populations and classified into 3 subspecies (L. n. nycthemera, L. n. omeiensis and L. n. fokiensis). The Gannan population owned the highest heterozygosity (HE=0.4500, Ho=0.6000), but the Chuannan had the lowest (HE=0.2000, Ho=0.2000). Microsatellite data showed no proof of genetic drift existence either in respectively 11 geographical populations or 3 subspecies based on Bottleneck analysis (P>0.05). Twenty haplotypes defined by 32 variable sites were found in Lohpura nycthemera D-loop data. Seven of them are shared by difference populations, but 13 of them belong to unique population. Haplotype 2 was found in 7 populations. Meanwhile, no unique haplotypes was found in Ganzhong, Gannan, Chuannan, Wanbei, Ebei populations. D-loop data analysis also indicated the high quality of haplotype gene diversity but low quality of nucleotide diversity. All 20 haplotypes could be categorized in 3 branches based on median-joining network, Bayesian tree, maximum likelihood tree and maximum parsimony tree analyses. Apparently, Minbei population was almost all categorized in one branch and Jinggangshan population was all categorized in another one. Genetic differences showed among the Lohpura nycthemera subspecies, populations and haplotypes, but no relationship to geographical isolation. For example, distance between Minbei population and Chuannan population was the farthest (1777km), but their genetic difference was not significant (SSR:Fst=0.09704, P=0.99099; D-loop:Φst=0.52703, P=0.99099), which maybe coursed by the low amount of individuals in Chuannan population. While distance between Ganzhong population and Jinggangshan population was the nearest (170km), but their genetic difference showed significance (SSR:Fst=0.12509, P=0.00000; D-loop:Φt=0.41326, P=0.00901), which could be explained by that Jinggangshan population was surrounded by the mountains and lack of gene communication with other populations. AMOVA analysis illustrated that the most genetic variation was discovered within populations (SSR:Rst=0.12319, P= 0.00020+-0.00014,87.68%; D-loop:Fst= 0.22502, P=0.00000+-0.00000,77.50%), while little among subspecies (SSR:Rct= 0.04014, P= 0.20554+-0.00412,4.01%; D-loop:Fct=-0.00772, P=0.50653+-0.00506,-0.77%). Totally, the genetic differences among populations were not quite relevant with the geographical distances (SSR:r= 0.126, P> 0.05; D-loop:r=-0.045, P> 0.05). So, geographical factors don’t have the key impact to influent on different populations.