A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples

Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.

Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with tsinfer version 0.2.1 and tsdate version 0.1.4, as described in Wohns et al. (2021). The files were compressed using tszip. All data is in GRCh38.

The full data pipeline used to generate these tree sequences and associated metadata is available on GitHub. A description can be found in the Supplementary Material of Wohns et al. (2021).

Tree sequences can be decompressed as follows:

$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz

Once decompressed, trees files can be loaded and processed in Python using tskit. 

import tskit
ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees")
# ts is an instance of tskit.TreeSequence
print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))

Accessing variant sites in the tree sequence provides the position and id of variants:

import json
site = ts.site(1000)
site_metadata = json.loads(site.metadata)
print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))

Metadata associated with individuals and populations was derived from the original sources (TGP, HGDP, and SGDP) and converted to JSON form. For example, to access individual metadata we can use:

ind = ts.individual(0)
metadata_dict = json.loads(ind.metadata)

The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,

pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()]
ind_node = ts.node(ind.nodes[0])
ind_pop_metadata = pop_metadata[ind_node.population]

After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.