Machine learning
Python package for haplotype phasing and local ancestry inference (population genetics)

Thomas Dias-Alves

Julien Mairal

Michael Blum

Ghislain Durif


May 19, 2018

(contribution and maintenance)

Original work by Thomas Dias Alves (c.f. his PdD thesis and the corresponding paper

Collaboration with:

  • Michael Blum (CNRS - TIMC - Univ Grenoble Alpes)
  • Julien Mairal (Inria Grenoble)

Admixture between populations provides opportunity to study biological adaptation and phenotypic variation. Admixture studies rely on local ancestry inference for admixed individuals, which consists of computing at each locus the number of copies that originate from ancestral source populations. Existing software packages for local ancestry inference are tuned to provide accurate results on human data and recent admixture events. Here, we introduce Loter, an open-source software package that does not require any biological parameter besides haplotype data in order to make local ancestry inference available for a wide range of species. Using simulations, we compare the performance of Loter to HAPMIX, LAMP-LD, and RFMix. HAPMIX is the only software severely impacted by imperfect haplotype reconstruction. Loter is the less impacted software by increasing admixture time when considering simulated and admixed human genotypes. For simulations of admixed Populus genotypes, Loter and LAMP-LD are robust to increasing admixture times by contrast to RFMix. When comparing length of reconstructed and true ancestry tracts, Loter and LAMP-LD provide results whose accuracy is again more robust than RFMix to increasing admixture times. We apply Loter to individuals resulting from admixture between Populus trichocarpa and Populus balsamifera and lengths of ancestry tracts indicate that admixture took place ∼100 generations ago. We expect that providing a rapid and parameter-free software for local ancestry inference will make more accessible genomic studies about admixture processes.


  • Python
  • R (in development)


  • Optimization
  • Population genetics
  • Local ancestry inference


  • Python
  • Solaris