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A general linear model-based approach for inferring selection to climate

BACKGROUND: Many efforts have been made to detect signatures of positive selection in the human genome, especially those associated with expansion from Africa and subsequent colonization of all other continents. However, most approaches have not directly probed the relationship between the environme...

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Autores principales: Raj, Srilakshmi M, Pagani, Luca, Gallego Romero, Irene, Kivisild, Toomas, Amos, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853933/
https://www.ncbi.nlm.nih.gov/pubmed/24053227
http://dx.doi.org/10.1186/1471-2156-14-87
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author Raj, Srilakshmi M
Pagani, Luca
Gallego Romero, Irene
Kivisild, Toomas
Amos, William
author_facet Raj, Srilakshmi M
Pagani, Luca
Gallego Romero, Irene
Kivisild, Toomas
Amos, William
author_sort Raj, Srilakshmi M
collection PubMed
description BACKGROUND: Many efforts have been made to detect signatures of positive selection in the human genome, especially those associated with expansion from Africa and subsequent colonization of all other continents. However, most approaches have not directly probed the relationship between the environment and patterns of variation among humans. We have designed a method to identify regions of the genome under selection based on Mantel tests conducted within a general linear model framework, which we call MAntel-GLM to Infer Clinal Selection (MAGICS). MAGICS explicitly incorporates population-specific and genome-wide patterns of background variation as well as information from environmental values to provide an improved picture of selection and its underlying causes in human populations. RESULTS: Our results significantly overlap with those obtained by other published methodologies, but MAGICS has several advantages. These include improvements that: limit false positives by reducing the number of independent tests conducted and by correcting for geographic distance, which we found to be a major contributor to selection signals; yield absolute rather than relative estimates of significance; identify specific geographic regions linked most strongly to particular signals of selection; and detect recent balancing as well as directional selection. CONCLUSIONS: We find evidence of selection associated with climate (P < 10(-5)) in 354 genes, and among these observe a highly significant enrichment for directional positive selection. Two of our strongest 'hits’, however, ADRA2A and ADRA2C, implicated in vasoconstriction in response to cold and pain stimuli, show evidence of balancing selection. Our results clearly demonstrate evidence of climate-related signals of directional and balancing selection.
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spelling pubmed-38539332013-12-16 A general linear model-based approach for inferring selection to climate Raj, Srilakshmi M Pagani, Luca Gallego Romero, Irene Kivisild, Toomas Amos, William BMC Genet Methodology Article BACKGROUND: Many efforts have been made to detect signatures of positive selection in the human genome, especially those associated with expansion from Africa and subsequent colonization of all other continents. However, most approaches have not directly probed the relationship between the environment and patterns of variation among humans. We have designed a method to identify regions of the genome under selection based on Mantel tests conducted within a general linear model framework, which we call MAntel-GLM to Infer Clinal Selection (MAGICS). MAGICS explicitly incorporates population-specific and genome-wide patterns of background variation as well as information from environmental values to provide an improved picture of selection and its underlying causes in human populations. RESULTS: Our results significantly overlap with those obtained by other published methodologies, but MAGICS has several advantages. These include improvements that: limit false positives by reducing the number of independent tests conducted and by correcting for geographic distance, which we found to be a major contributor to selection signals; yield absolute rather than relative estimates of significance; identify specific geographic regions linked most strongly to particular signals of selection; and detect recent balancing as well as directional selection. CONCLUSIONS: We find evidence of selection associated with climate (P < 10(-5)) in 354 genes, and among these observe a highly significant enrichment for directional positive selection. Two of our strongest 'hits’, however, ADRA2A and ADRA2C, implicated in vasoconstriction in response to cold and pain stimuli, show evidence of balancing selection. Our results clearly demonstrate evidence of climate-related signals of directional and balancing selection. BioMed Central 2013-09-22 /pmc/articles/PMC3853933/ /pubmed/24053227 http://dx.doi.org/10.1186/1471-2156-14-87 Text en Copyright © 2013 Raj et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Raj, Srilakshmi M
Pagani, Luca
Gallego Romero, Irene
Kivisild, Toomas
Amos, William
A general linear model-based approach for inferring selection to climate
title A general linear model-based approach for inferring selection to climate
title_full A general linear model-based approach for inferring selection to climate
title_fullStr A general linear model-based approach for inferring selection to climate
title_full_unstemmed A general linear model-based approach for inferring selection to climate
title_short A general linear model-based approach for inferring selection to climate
title_sort general linear model-based approach for inferring selection to climate
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3853933/
https://www.ncbi.nlm.nih.gov/pubmed/24053227
http://dx.doi.org/10.1186/1471-2156-14-87
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