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Gene-based burden scores identify rare variant associations for 28 blood biomarkers
BACKGROUND: A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successful...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476296/ https://www.ncbi.nlm.nih.gov/pubmed/37667186 http://dx.doi.org/10.1186/s12863-023-01155-0 |
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author | Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo |
author_facet | Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo |
author_sort | Aldisi, Rana |
collection | PubMed |
description | BACKGROUND: A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. METHODS: We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). RESULTS: Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. CONCLUSION: This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-023-01155-0. |
format | Online Article Text |
id | pubmed-10476296 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104762962023-09-05 Gene-based burden scores identify rare variant associations for 28 blood biomarkers Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo BMC Genom Data Research BACKGROUND: A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. METHODS: We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). RESULTS: Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. CONCLUSION: This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12863-023-01155-0. BioMed Central 2023-09-04 /pmc/articles/PMC10476296/ /pubmed/37667186 http://dx.doi.org/10.1186/s12863-023-01155-0 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 Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title_full | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title_fullStr | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title_full_unstemmed | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title_short | Gene-based burden scores identify rare variant associations for 28 blood biomarkers |
title_sort | gene-based burden scores identify rare variant associations for 28 blood biomarkers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476296/ https://www.ncbi.nlm.nih.gov/pubmed/37667186 http://dx.doi.org/10.1186/s12863-023-01155-0 |
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