ArticleHeredity2018
Inference of multiple-wave admixtures by length distribution of ancestral tracks.
Article in Heredity, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Adaptive admixture at ACKR1, the Duffy blood group locus, may have shaped Plasmodium vivax prevalence in Oman.American journal of human genetics · 2026Article
- Reconstructing complex admixture history using a hierarchical model.Briefings in bioinformatics · 2024Article
- Estimating the timing of multiple admixture events using 3-locus linkage disequilibrium.PLoS genetics · 2022Article
- Human genetic admixture through the lens of population genomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
- Admixture mapping of anthropometric traits in the Black Women's Health Study: evidence of a shared African ancestry component with birth weight and type 2 diabetes.Journal of human genetics · 2022Article
- Refining models of archaic admixture in Eurasia with ArchaicSeeker 2.0.Nature communications · 2021Article
- AdmixSim 2: a forward-time simulator for modeling complex population admixture.BMC bioinformatics · 2021Article
- Dating admixture events is unsolved problem in multi-way admixed populations.Briefings in bioinformatics · 2020Article
- AdmixSim: A Forward-Time Simulator for Various Complex Scenarios of Population Admixture.Frontiers in genetics · 2020Article
- MultiWaver 2.0: modeling discrete and continuous gene flow to reconstruct complex population admixtures.European journal of human genetics : EJHG · 2019Article
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Authors and funding
7 authors.
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Abstract
The ancestral tracks in admixed genomes are valuable for population history inference. While a few methods have been developed to infer admixture history based on ancestral tracks, these methods suffer the same flaw: only population admixture history under some specific models can be inferred. In addition, the inference of history might be biased or even unreliable if the specific model deviates from the real situation. To address this problem, we firstly proposed a general discrete admixture model to describe the admixture history with multiple ancestral populations and multiple-wave admixtures. We next deduced the length distribution of ancestral tracks under the general discrete admixture model. We further developed a new method, MultiWaver, to explore multiple-wave admixture histories. Our method could automatically determine an optimal admixture model based on the length distribution of ancestral tracks, and estimate the corresponding parameters under this optimal model. Specifically, we used a likelihood ratio test (LRT) to determine the number of admixture waves, and implemented an expectation-maximization (EM) algorithm to estimate parameters. We used simulation studies to validate the reliability and effectiveness of our method. Finally, good performance was observed when our method was applied to real data sets of African Americans and Mexicans, and new insights were gained into the admixture history of Uyghurs and Hazaras.
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