ARG_tools.md

ARG inference tools

tsinfer

• Publications: Kelleher et al. (2019), Wohns et al. (2022)
• Repository:
  • code: https://github.com/tskit-dev/tsinfer
  • publication: https://github.com/mcveanlab/treeseq-inference
• Inputs:
  • for WGS genotypes
  • phased genotypes (VCF --> samples file)
  • genetic map
  • ancestral alleles
  • contemporary samples only
• Scalability: High
• Accuracy: Intermediate, but hard to quantify!

tsinfer-sparse

• Publication: Kelleher et al. (2019)
• Repository: Zhang et al. (2023) used https://github.com/mcveanlab/treeseq-inference
• Inputs:
  • for SNP array genotypes
  • phased genotypes (VCF --> samples file)
  • genetic map
  • ancestral alleles
  • contemporary samples only
• Scalability: High
• Accuracy: Intermediate, but hard to quantify!

tsdate

• Publication: Wohns et al. (2022)
• Repository: https://github.com/tskit-dev/tsdate
• Inputs:
  • .trees file

Relate

• Publication: Speidel et al. (2019)
• Repository: https://myersgroup.github.io/relate, https://github.com/MyersGroup/relate
• Conversion: https://github.com/leospeidel/relate_lib
• Inputs:
  • for WGS genotypes
  • phased genotypes (VCF/HAP --> samples file)
  • genetic map
  • ancestral alleles
  • contemporary and ancient samples
• Scalability: Intermediate
• Accuracy: High, but hard to quantify!

SINGER

• Publication: Deng et al. (2024)
• Repository: https://github.com/popgenmethods/SINGER
• Inputs:
  • for WGS genotypes
  • phased genotypes (VCF/HAP --> samples file)
  • genetic map
  • contemporary samples only?
• Scalability: Intermediate
• Accuracy: High, but hard to quantify!

ARG-Needle

• Publication: Zhang et al. (2023)
• Repository:
  • https://palamaralab.github.io/software/argneedle/
  • https://github.com/palamaraLab/arg-needle-lib
  • https://github.com/PalamaraLab/arg-needle-scripts
  • ASMC & FastSMC https://github.com/PalamaraLab/ASMC
• Inputs:
  • for SNP array genotypes
  • phased genotypes (HAP file)
  • genetic map
  • demography file
  • contemporary samples only?
• Scalability: High
• Accuracy: High, but hard to quantify!

Threads

• Publication: Gunnarsson et al. (2024)
• Repository:
  • https://palamaralab.github.io/software/threads
• Inputs:
  • for WGS array genotypes
  • phased? genotypes (PGEN [Plink binary] file)
  • genetic map
  • demography file
  • contemporary samples only?
• Scalability: High
• Accuracy: High, but hard to quantify!

References

Deng et al. (2024) Robust and Accurate Bayesian Inference of Genome-Wide Genealogies for Large Samples. bioRxiv. https://doi.org/10.1101/2024.03.16.585351

Gunnarsson et al. (2024) A scalable approach for genome-wide inference of ancestral recombination graphs. bioRxiv. https://doi.org/10.1101/2024.08.31.610248

Kelleher et al. (2019) Inferring whole-genome histories in large population datasets. Nat Genet. https://doi.org/10.1038/s41588-019-0483-y

Speidel et al. (2019) A method for genome-wide genealogy estimation for thousands of samples. Nat. Genet. https://doi.org/10.1038/s41588-019-0484-x

Speidel et al. (2021) Inferring population histories for ancient genomes using genome-wide genealogies. Molecular Biology and Evolution. https://doi.org/10.1093/molbev/msab174

Wohns et al. (2022) A unified genealogy of modern and ancient genomes. Science https://doi.org/10.1126/science.abi8264

Zhang et al. (2023) Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nat Genet. https://doi.org/10.1038/s41588-023-01379-x