Coancestry superposed on admixed populations yields measures of relatedness at individual-level resolution
Danfeng Chen, John D. Storey
TL;DR
This paper introduces a new genetic model called 'super admixture' that improves understanding of population structure and individual-level relatedness by allowing admixed populations to be related.
Contribution
The novel 'super admixture' model extends traditional admixture models by incorporating relatedness between admixed populations.
Findings
The super admixture model reveals finer population structure and individual-level relatedness in human genetic data.
The proposed method is computationally efficient, likelihood-free, and scalable to large datasets.
The model provides relatedness estimates comparable to the kinship model but with a probabilistic framework.
Abstract
The admixture model is widely applied to estimate and interpret population structure among individuals. Here we consider a “standard admixture” model that assumes the admixed populations are unrelated and also a generalized model, where the admixed populations themselves are related via coancestry (or covariance) of allele frequencies. The generalized model yields a potentially more realistic and substantially more flexible model that we call “super admixture”. This super admixture model provides a one-to-one mapping in terms of probability moments with the kinship model, the latter of which is a general model of genome-wide relatedness and structure based on identity-by-descent. We introduce a method to estimate the super admixture model that is based on method of moments, does not rely on likelihoods, is computationally efficient, and scales to massive sample sizes. We apply the…
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Taxonomy
TopicsGenetic Associations and Epidemiology · Genetic Mapping and Diversity in Plants and Animals · Cancer Genomics and Diagnostics