2.9 | Population structure of B. schroederi in
Qinling and Sichuan
A total of 240 samples collected from individuals in captivity and 26
samples from individuals in wild were re-sequenced using the
DNBSEQ-T1&T5 platform. High-quality reads were aligned to the reference
genome using BWA-MEM (0.7.13-r1126) (H. Li & Durbin, 2009) with default
parameters. SAMtools (v0.1.19) (H. Li et al., 2009) and Genome Analysis
Toolkit (GATK v 4.0.3.0) (Depristo, Banks, Poplin, Garimella, & Daly,
2011) were used to obtain the SNP set within the population. Hard
filtering was applied to the raw variant set using ”QD < 2.0
|| FS > 60.0 || MQ
< 40.0 || MQRankSum < -12.5
|| ReadPosRankSum < -8.0” –filter-name
”snp_filter”. SNPs with >0.5% missing data or
<0.01 minor allele
frequency (MAF) were filtered out
using vcftools (v0.1.12a) (Danecek et al., 2011). PCA analysis of SNPs
was carried out using EIGENSOFT (Nick et al., 2006) software, and the
population clustering analysis was conducted in PLINK (Purcell et al.,
2007). We used the whole-genome SNPs to construct the ML phylogenetic
tree with 1000 bootstrap using iqtree (v1.6.12) (Lam-Tung, Schmidt,
Arndt, & Quang, 2015), and using an genome sequence information ofP. univalens as an outgroup. Population structure of all was
analyzed using the ADMIXTURE (v1.3.0) program with a block-relaxation
algorithm. To explore the convergence of individuals, we predefined the
number of genetic clusters K from 2 to 5.