Inferring human phenotypes using ancient DNA: from molecules to populations

Manuel Ferrando-Bernal; Colin M Brand; John A Capra

doi:10.1016/j.gde.2024.102283

Inferring human phenotypes using ancient DNA: from molecules to populations

Curr Opin Genet Dev. 2024 Nov 28:90:102283. doi: 10.1016/j.gde.2024.102283. Online ahead of print.

Authors

Manuel Ferrando-Bernal¹, Colin M Brand², John A Capra³

Affiliations

¹ Bakar Computational Health Science Institute, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. Electronic address: manuel.ferrandobernal@ucsf.edu.
² Bakar Computational Health Science Institute, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA. Electronic address: colin.brand@ucsf.edu.
³ Bakar Computational Health Science Institute, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA. Electronic address: tony@capralab.org.

PMID: 39612613
DOI: 10.1016/j.gde.2024.102283

Abstract

The increasing availability of ancient DNA (aDNA) from human groups across space and time has yielded deep insights into the movements of our species. However, given the challenges of mapping from genotype to phenotype, aDNA has revealed less about the phenotypes of ancient individuals. In this review, we highlight recent advances in inferring ancient phenotypes - from the molecular to population scale - with a focus on applications enabled by new machine learning approaches. The genetic architecture of complex traits across human groups suggests that the prediction of individual-level complex traits, like behavior or disease risk, is often challenging across the relevant evolutionary distances. Thus, we propose an approach that integrates predictions of molecular phenotypes, whose mechanisms are more conserved, with nongenetic data.

Publication types

Review