Cargando…

Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Saitou, Marie, Masuda, Naoki, Gokcumen, Omer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896759/
https://www.ncbi.nlm.nih.gov/pubmed/34718708
http://dx.doi.org/10.1093/molbev/msab313
_version_ 1784663236412440576
author Saitou, Marie
Masuda, Naoki
Gokcumen, Omer
author_facet Saitou, Marie
Masuda, Naoki
Gokcumen, Omer
author_sort Saitou, Marie
collection PubMed
description Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.
format Online
Article
Text
id pubmed-8896759
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-88967592022-03-07 Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants Saitou, Marie Masuda, Naoki Gokcumen, Omer Mol Biol Evol Method Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a similarity-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 576 structural variants that show unusual population differentiation. Of these putatively adaptive structural variants, we further showed that 24 variants are multiallelic and overlap with coding sequences, and 20 variants are significantly associated with GWAS traits. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to: 1) population differentiation of rapidly evolving multiallelic variants, 2) incomplete sweeps, and 3) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants. Oxford University Press 2021-10-28 /pmc/articles/PMC8896759/ /pubmed/34718708 http://dx.doi.org/10.1093/molbev/msab313 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Method
Saitou, Marie
Masuda, Naoki
Gokcumen, Omer
Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title_full Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title_fullStr Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title_full_unstemmed Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title_short Similarity-Based Analysis of Allele Frequency Distribution among Multiple Populations Identifies Adaptive Genomic Structural Variants
title_sort similarity-based analysis of allele frequency distribution among multiple populations identifies adaptive genomic structural variants
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8896759/
https://www.ncbi.nlm.nih.gov/pubmed/34718708
http://dx.doi.org/10.1093/molbev/msab313
work_keys_str_mv AT saitoumarie similaritybasedanalysisofallelefrequencydistributionamongmultiplepopulationsidentifiesadaptivegenomicstructuralvariants
AT masudanaoki similaritybasedanalysisofallelefrequencydistributionamongmultiplepopulationsidentifiesadaptivegenomicstructuralvariants
AT gokcumenomer similaritybasedanalysisofallelefrequencydistributionamongmultiplepopulationsidentifiesadaptivegenomicstructuralvariants