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A phenome-wide scan reveals convergence of common and rare variant associations

BACKGROUND: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the scien...

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Autores principales: Zhou, Dan, Zhou, Yuan, Xu, Yue, Meng, Ran, Gamazon, Eric R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683189/
https://www.ncbi.nlm.nih.gov/pubmed/38017547
http://dx.doi.org/10.1186/s13073-023-01253-9
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author Zhou, Dan
Zhou, Yuan
Xu, Yue
Meng, Ran
Gamazon, Eric R.
author_facet Zhou, Dan
Zhou, Yuan
Xu, Yue
Meng, Ran
Gamazon, Eric R.
author_sort Zhou, Dan
collection PubMed
description BACKGROUND: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. METHODS: Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. RESULTS: We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e − 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. CONCLUSIONS: The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01253-9.
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spelling pubmed-106831892023-11-30 A phenome-wide scan reveals convergence of common and rare variant associations Zhou, Dan Zhou, Yuan Xu, Yue Meng, Ran Gamazon, Eric R. Genome Med Research BACKGROUND: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. METHODS: Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. RESULTS: We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e − 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. CONCLUSIONS: The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-023-01253-9. BioMed Central 2023-11-28 /pmc/articles/PMC10683189/ /pubmed/38017547 http://dx.doi.org/10.1186/s13073-023-01253-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhou, Dan
Zhou, Yuan
Xu, Yue
Meng, Ran
Gamazon, Eric R.
A phenome-wide scan reveals convergence of common and rare variant associations
title A phenome-wide scan reveals convergence of common and rare variant associations
title_full A phenome-wide scan reveals convergence of common and rare variant associations
title_fullStr A phenome-wide scan reveals convergence of common and rare variant associations
title_full_unstemmed A phenome-wide scan reveals convergence of common and rare variant associations
title_short A phenome-wide scan reveals convergence of common and rare variant associations
title_sort phenome-wide scan reveals convergence of common and rare variant associations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683189/
https://www.ncbi.nlm.nih.gov/pubmed/38017547
http://dx.doi.org/10.1186/s13073-023-01253-9
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