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GenRisk: a tool for comprehensive genetic risk modeling
SUMMARY: The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here,...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048672/ https://www.ncbi.nlm.nih.gov/pubmed/35266528 http://dx.doi.org/10.1093/bioinformatics/btac152 |
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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 | SUMMARY: The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. AVAILABILITY AND IMPLEMENTATION: GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9048672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-90486722022-04-29 GenRisk: a tool for comprehensive genetic risk modeling Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo Bioinformatics Applications Notes SUMMARY: The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. AVAILABILITY AND IMPLEMENTATION: GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-03-10 /pmc/articles/PMC9048672/ /pubmed/35266528 http://dx.doi.org/10.1093/bioinformatics/btac152 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Aldisi, Rana Hassanin, Emadeldin Sivalingam, Sugirthan Buness, Andreas Klinkhammer, Hannah Mayr, Andreas Fröhlich, Holger Krawitz, Peter Maj, Carlo GenRisk: a tool for comprehensive genetic risk modeling |
title | GenRisk: a tool for comprehensive genetic risk modeling |
title_full | GenRisk: a tool for comprehensive genetic risk modeling |
title_fullStr | GenRisk: a tool for comprehensive genetic risk modeling |
title_full_unstemmed | GenRisk: a tool for comprehensive genetic risk modeling |
title_short | GenRisk: a tool for comprehensive genetic risk modeling |
title_sort | genrisk: a tool for comprehensive genetic risk modeling |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048672/ https://www.ncbi.nlm.nih.gov/pubmed/35266528 http://dx.doi.org/10.1093/bioinformatics/btac152 |
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