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Estimating and interpreting F(ST): The impact of rare variants
In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F(ST) as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in met...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3759727/ https://www.ncbi.nlm.nih.gov/pubmed/23861382 http://dx.doi.org/10.1101/gr.154831.113 |
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author | Bhatia, Gaurav Patterson, Nick Sankararaman, Sriram Price, Alkes L. |
author_facet | Bhatia, Gaurav Patterson, Nick Sankararaman, Sriram Price, Alkes L. |
author_sort | Bhatia, Gaurav |
collection | PubMed |
description | In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F(ST) as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F(ST) from available data. Considering this confusion, wide variation in published estimates of F(ST) for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F(ST) from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F(ST) that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F(ST) for a single SNP, (2) combining estimates of F(ST) across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F(ST) estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions. |
format | Online Article Text |
id | pubmed-3759727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-37597272014-03-01 Estimating and interpreting F(ST): The impact of rare variants Bhatia, Gaurav Patterson, Nick Sankararaman, Sriram Price, Alkes L. Genome Res Method In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F(ST) as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F(ST) from available data. Considering this confusion, wide variation in published estimates of F(ST) for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F(ST) from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F(ST) that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F(ST) for a single SNP, (2) combining estimates of F(ST) across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F(ST) estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions. Cold Spring Harbor Laboratory Press 2013-09 /pmc/articles/PMC3759727/ /pubmed/23861382 http://dx.doi.org/10.1101/gr.154831.113 Text en © 2013, Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/3.0/ This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/. |
spellingShingle | Method Bhatia, Gaurav Patterson, Nick Sankararaman, Sriram Price, Alkes L. Estimating and interpreting F(ST): The impact of rare variants |
title | Estimating and interpreting F(ST): The impact of rare variants |
title_full | Estimating and interpreting F(ST): The impact of rare variants |
title_fullStr | Estimating and interpreting F(ST): The impact of rare variants |
title_full_unstemmed | Estimating and interpreting F(ST): The impact of rare variants |
title_short | Estimating and interpreting F(ST): The impact of rare variants |
title_sort | estimating and interpreting f(st): the impact of rare variants |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3759727/ https://www.ncbi.nlm.nih.gov/pubmed/23861382 http://dx.doi.org/10.1101/gr.154831.113 |
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