Anatomy of Q1
- recode_and_sex_impute:
- plink --recode vcf: The user specified genotype file (VCF or PLINK format) is read in and coverted into VCF by PLINK.
- plink --impute-sex: To infer the genetically determined sex
- sample_qc:
- plink –missing: To calculate missing rate
- chr_and_vep:
- plink --make-just-bim: To generate a bim file counting the number of SNPs by chromosome.
- python packages pandas & matplotlib: The variants are read in and processed by pandas, and then displayed in bar plot by matplotlib
- get_maf:
- plink --freq: To generate frequency statistics.
- python packages pandas & matplotlib: The frequency statistics is read in by pandas, and then displayed in histogram by matplotlib
2a. PCA:
- plink --maf 0.01 --misng 0.01--hwe 1E-50: To retain high quality SNPs
- plink --indep-pairwise --kb 50kb--r2 0.2: To keep independent SNPs
- plink --pca –freq: To calculate principal components and SNP frequency
- plink --read-freq --score no-mean-imputation variance-standardize: To generate PC for the “personal genome”
- python packages pandas & matplotlib: The principal components result is read in by pandas, and then displayed by matplotlib
2b. UMAP：
- python package UMAP: To reduce the genetics data into two dimensions
- python packages pandas & matplotlib: The 2-dimensional result is read in by pandas, and then displayed by matplotlib
- check_concordance:
- python package matplotlib_venn: To displayed the venn diagram for variants of two genotypes files.
- ref_qc, to show the QC result of the reference population.
- plink --freq --missing --test-missing --hardy --het --check-sex --genome: To get the information of minor allele frequency, missing genotype rates, Hardy-Weinberg equilibrium, heterozygosity rate, sex discrepancy, and cryptic relatedness in reference population.
- python packages pandas & matplotlib: The information obtained above is read in by pandas, and then displayed in histogram by matplotlib
