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How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?
Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873880/ https://www.ncbi.nlm.nih.gov/pubmed/33584799 http://dx.doi.org/10.3389/fgene.2020.602526 |
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author | Fernandes, Samuel B. Zhang, Kevin S. Jamann, Tiffany M. Lipka, Alexander E. |
author_facet | Fernandes, Samuel B. Zhang, Kevin S. Jamann, Tiffany M. Lipka, Alexander E. |
author_sort | Fernandes, Samuel B. |
collection | PubMed |
description | Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments. |
format | Online Article Text |
id | pubmed-7873880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78738802021-02-11 How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? Fernandes, Samuel B. Zhang, Kevin S. Jamann, Tiffany M. Lipka, Alexander E. Front Genet Genetics Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments. Frontiers Media S.A. 2021-01-08 /pmc/articles/PMC7873880/ /pubmed/33584799 http://dx.doi.org/10.3389/fgene.2020.602526 Text en Copyright © 2021 Fernandes, Zhang, Jamann and Lipka. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Fernandes, Samuel B. Zhang, Kevin S. Jamann, Tiffany M. Lipka, Alexander E. How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title | How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title_full | How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title_fullStr | How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title_full_unstemmed | How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title_short | How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy? |
title_sort | how well can multivariate and univariate gwas distinguish between true and spurious pleiotropy? |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873880/ https://www.ncbi.nlm.nih.gov/pubmed/33584799 http://dx.doi.org/10.3389/fgene.2020.602526 |
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