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Scripts

This directory contains the source code used for producing the results and figures for the publication. It consists of a pipeline of 6 shell scripts, which make use of supporting files and scripts inside the lib directory. A citable permanent version of these scripts is archived in a Figshare repository.

Imputation Pipeline

The following are the functions of each script in the imputation pipeline developed.

1_setup.sh - Sets up the imputation reference panel and chunks. Downloads the 1000 Genomes genotypes and extracts variable positions from it as files that can read by bcftools and GLIMPSE1. Then calculates imputation chunks out of the variable positions extracted before.

2_gl_calling.sh - Calculates genotype likelihoods with bcftools mpileup for given CRAM files and saves them to VCF files. Uses the file lib/samples_sex.txt to determine chromosome X ploidy for each sample. Also uses the file lib/samples.txt to normalise the sample name VCF header field in the validation files with bcftools reheader, required to run GLIMPSE_concordance.

3_glimpse_impute_parallel.sh - Imputes calculated genotypes for each chunk with GLIMPSE1, using the 1000 Genomes data as the reference panel, and ligates chunks together to create separate files for each chromosome. Then, merges chromosomes together to create whole genome (chromosomes 1 to 22 and X) files.

4_vcf_filtering.sh - Filters variants in the imputed VCF files by allele frequency (INFO/RAF higher than 2% and lower than 95%) and genotype probabilities (FORMAT/GP higher than 80%) 1. After all the imputed VCF files are filtered, calculates the average number of SNVs and 95% confidence interval for the entire sample cohort.

5_glimpse_concordance.sh - Calculates squared Pearson correlation between high-coverage and imputed dosages across chromosomes 1 to 22 and X, and writes them to several text files. Then runs lib/concordance_plot.py to create the concordance plots used in the publication.

6_pca.sh - Using plink, determines population-specific genetic markers from the 1000 Genomes reference panel, then extracts those variants from the imputed files, and finally runs lib/pca.py to plot the samples against the 1000 Genomes superpopulations.

Footnotes

  1. Sousa da Mota, B., Rubinacci, S., Cruz Dávalos, D.I. et al. Imputation of ancient human genomes. Nat Commun 14, 3660 (2023). https://doi.org/10.1038/s41467-023-39202-0 ↩