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Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations
With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149792/ https://www.ncbi.nlm.nih.gov/pubmed/35634920 http://dx.doi.org/10.1098/rstb.2020.0514 |
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author | Peter, Jackson Friedrich, Anne Liti, Gianni Schacherer, Joseph |
author_facet | Peter, Jackson Friedrich, Anne Liti, Gianni Schacherer, Joseph |
author_sort | Peter, Jackson |
collection | PubMed |
description | With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history. This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’. |
format | Online Article Text |
id | pubmed-9149792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-91497922022-06-09 Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations Peter, Jackson Friedrich, Anne Liti, Gianni Schacherer, Joseph Philos Trans R Soc Lond B Biol Sci Articles With the advent of high throughput sequencing technologies, genome-wide association studies (GWAS) have become a powerful paradigm for dissecting the genetic origins of the observed phenotypic variation. We recently completely sequenced the genome of 1011 Saccharomyces cerevisiae isolates, laying a strong foundation for GWAS. To assess the feasibility and the limits of this approach, we performed extensive simulations using five selected subpopulations as well as the total set of 1011 genomes. We measured the ability to detect the causal genetic variants involved in Mendelian and more complex traits using a linear mixed model approach. The results showed that population structure is well accounted for and is not the main problem when the sample size is high enough. While the genetic determinant of a Mendelian trait is easily mapped in all studied subpopulations, discrepancies are seen between datasets when performing GWAS on a complex trait in terms of detection, false positive and false negative rate. Finally, we performed GWAS on the different defined subpopulations using a real quantitative trait (resistance to copper sulfate) and showed the feasibility of this approach. The performance of each dataset depends simultaneously on several factors such as sample size, relatedness and population evolutionary history. This article is part of the theme issue ‘Genetic basis of adaptation and speciation: from loci to causative mutations’. The Royal Society 2022-07-18 2022-05-30 /pmc/articles/PMC9149792/ /pubmed/35634920 http://dx.doi.org/10.1098/rstb.2020.0514 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Peter, Jackson Friedrich, Anne Liti, Gianni Schacherer, Joseph Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title | Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title_full | Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title_fullStr | Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title_full_unstemmed | Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title_short | Extensive simulations assess the performance of genome-wide association mapping in various Saccharomyces cerevisiae subpopulations |
title_sort | extensive simulations assess the performance of genome-wide association mapping in various saccharomyces cerevisiae subpopulations |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149792/ https://www.ncbi.nlm.nih.gov/pubmed/35634920 http://dx.doi.org/10.1098/rstb.2020.0514 |
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