2.11 | The change of effective population size on the function of time
We inferred the demographic history of the B. schroederi by use of WGS data generated by DNBSEQ-T1 from one individual. We simultaneously perform the same analysis on a giant panda by using resequencing short reads of an individual download from SRA database (accession SRA053353). For this analysis, we used BWA (v0.7.13-r1126)(H. Li & Durbin, 2009) to map the clean reads to each genome with the default parameters. Next, the PSMC method(H. Li & Durbin, 2011) was used to evaluate dynamic change of effective population size (Ne ) of B. schroederi and giant panda. Following Li’s procedure(H. Li & Durbin, 2011), we applied a bootstrapping approach, repeat sampling 100 times to estimate the variance of simulated results for bothB. schroederi and giant panda. We used 0.17 and 12 years per generation (g) and mutation rate (μ ) of 9×10-9and 1.29×10-8 for c and giant panda, respectively(Cutter, 2008). Since fluctuations in the effective population size of giant pandas have been reported to closely reflect changes in climate and atmospheric dust(S. Zhao et al., 2013), we added the mass accumulation rate (MAR) of Chinese loess over the past 250,000 years for comparison. In addition, we implemented the MSMC2(Schiffels & Durbin, 2014) which can infer the recent effective population size history. We phased all SNPs of each individual by using beagle (v5.0)(Browning & Browning, 2007),then it calculated used the following parameters: -i 20 -t 6 -p ’10*1+15*2’. The mutation rate (μ ) of B. schroederi for MSMC2 were used the same values as for PSMC.