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Predicting Archaic Hominin Phenotypes from Genomic Data

Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans’ closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understan...

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Autores principales: Brand, Colin M., Colbran, Laura L., Capra, John A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250142/
https://www.ncbi.nlm.nih.gov/pubmed/35440148
http://dx.doi.org/10.1146/annurev-genom-111521-121903
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author Brand, Colin M.
Colbran, Laura L.
Capra, John A.
author_facet Brand, Colin M.
Colbran, Laura L.
Capra, John A.
author_sort Brand, Colin M.
collection PubMed
description Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans’ closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes—such as gene expression and protein function—is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes.
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spelling pubmed-102501422023-06-09 Predicting Archaic Hominin Phenotypes from Genomic Data Brand, Colin M. Colbran, Laura L. Capra, John A. Annu Rev Genomics Hum Genet Article Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans’ closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes—such as gene expression and protein function—is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes. 2022-08-31 2022-04-19 /pmc/articles/PMC10250142/ /pubmed/35440148 http://dx.doi.org/10.1146/annurev-genom-111521-121903 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information
spellingShingle Article
Brand, Colin M.
Colbran, Laura L.
Capra, John A.
Predicting Archaic Hominin Phenotypes from Genomic Data
title Predicting Archaic Hominin Phenotypes from Genomic Data
title_full Predicting Archaic Hominin Phenotypes from Genomic Data
title_fullStr Predicting Archaic Hominin Phenotypes from Genomic Data
title_full_unstemmed Predicting Archaic Hominin Phenotypes from Genomic Data
title_short Predicting Archaic Hominin Phenotypes from Genomic Data
title_sort predicting archaic hominin phenotypes from genomic data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10250142/
https://www.ncbi.nlm.nih.gov/pubmed/35440148
http://dx.doi.org/10.1146/annurev-genom-111521-121903
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