Multiple-ancestor localization for recently admixed individuals

Yaron Margalit, Yael Baran, Eran Halperin*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Inference of ancestry from genetic data is a fundamental problem in computational genetics, with wide applications in human genetics and population genetics. The treatment of ancestry as a continuum instead of a categorical trait has been recently advocated in the literature. Particularly, it was shown that a European individual’s geographic coordinates of origin can be determined up to a few hundred kilometers of error using spatial ancestry inference methods. Current methods for the inference of spatial ancestry focus on individuals for whom all ancestors originated from the same geographic location. In this work we develop a spatial ancestry inference method that aims at inferring the geographic coordinates of ancestral origins of recently admixed individuals, i.e. individuals whose recent ancestors originated from multiple locations. Our model is based on multivariate normal distributions integrated into a two-layered Hidden Markov Model, designed to capture both long-range correlations between SNPs due to the recent mixing and short-range correlations due to linkage disequilibrium. We evaluate the method on both simulated and real European data, and demonstrate that it achieves accurate results for up to three generations of admixture. Finally, we discuss the challenges of spatial inference for older admixtures and suggest directions for future work.

Original languageEnglish
Title of host publicationAlgorithms in Bioinformatics - 15th International Workshop, WABI 2015, Proceedings
EditorsMihai Pop, Hélène Touzet
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783662482209
StatePublished - 2015
Event15th International Workshop on Algorithms in Bioinformatics, WABI 2015 - Atlanta, United States
Duration: 10 Sep 201512 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Workshop on Algorithms in Bioinformatics, WABI 2015
Country/TerritoryUnited States


  • Admixture
  • Ancestry inference
  • Hidden Markov Model
  • Multivariate-normal distribution
  • Spatial model


Dive into the research topics of 'Multiple-ancestor localization for recently admixed individuals'. Together they form a unique fingerprint.

Cite this