The genomic footprint of social stratification in admixing American populations

Abstract
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Abstract

Cultural and socioeconomic differences stratify human societies and shape their genetic structure beyond the sole effect of geography. Despite mating being limited by sociocultural stratification, most demographic models in population genetics often assume random mating. Taking advantage of the correlation between sociocultural stratification and the proportion of genetic ancestry in admixed populations, we sought to infer the former process in the Americas. To this aim, we define a mating model where the individual proportions of the genome inherited from Native American, European and sub-Saharan African ancestral populations constrain the mating probabilities through ancestry-related assortative mating and sex bias parameters. We simulate a wide range of admixture scenarios under this model. Then, we train a deep neural network and retrieve good performance in predicting mating parameters from genomic data. Our results show how population stratification shaped by socially constructed racial and gender hierarchies have constrained the admixture processes in the Americas since the European colonisation and the subsequent Atlantic slave trade.

Data availability

The current manuscript uses already published data, so no data have been generated for this manuscript. The code used for the computational analyses is made available at the address stated in the methods.

The following previously published data sets were used

Article and author information

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Alex Mas Sandoval

Department of Life Sciences, Imperial College London, Ascot, United Kingdom

For correspondence
alex.massandoval@unibo.it

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-1712-9404
Sara Mathieson

Department of Computer Science, Haverford College, Haverford, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0484-0838
Matteo Fumagalli

Department of Life Sciences, Imperial College London, Ascot, United Kingdom

For correspondence
m.fumagalli@qmul.ac.uk

Competing interests
The authors declare that no competing interests exist.

Funding

Leverhulme Trust (RPG-2018-208)

Alex Mas Sandoval
Matteo Fumagalli

National Institutes of Health (R15HG011528)

Sara Mathieson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.