PRSet: Pathway-based polygenic risk score analyses and software.
Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accur...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-02-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1010624 |
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| author | Shing Wan Choi Judit García-González Yunfeng Ruan Hei Man Wu Christian Porras Jessica Johnson Bipolar Disorder Working group of the Psychiatric Genomics Consortium Clive J Hoggart Paul F O'Reilly |
| author_facet | Shing Wan Choi Judit García-González Yunfeng Ruan Hei Man Wu Christian Porras Jessica Johnson Bipolar Disorder Working group of the Psychiatric Genomics Consortium Clive J Hoggart Paul F O'Reilly |
| author_sort | Shing Wan Choi |
| collection | DOAJ |
| description | Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine. |
| format | Article |
| id | doaj-art-fa6cb295ad9744b995c23552d54898cf |
| institution | OA Journals |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-fa6cb295ad9744b995c23552d54898cf2025-08-20T02:31:41ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042023-02-01192e101062410.1371/journal.pgen.1010624PRSet: Pathway-based polygenic risk score analyses and software.Shing Wan ChoiJudit García-GonzálezYunfeng RuanHei Man WuChristian PorrasJessica JohnsonBipolar Disorder Working group of the Psychiatric Genomics ConsortiumClive J HoggartPaul F O'ReillyPolygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway -opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine.https://doi.org/10.1371/journal.pgen.1010624 |
| spellingShingle | Shing Wan Choi Judit García-González Yunfeng Ruan Hei Man Wu Christian Porras Jessica Johnson Bipolar Disorder Working group of the Psychiatric Genomics Consortium Clive J Hoggart Paul F O'Reilly PRSet: Pathway-based polygenic risk score analyses and software. PLoS Genetics |
| title | PRSet: Pathway-based polygenic risk score analyses and software. |
| title_full | PRSet: Pathway-based polygenic risk score analyses and software. |
| title_fullStr | PRSet: Pathway-based polygenic risk score analyses and software. |
| title_full_unstemmed | PRSet: Pathway-based polygenic risk score analyses and software. |
| title_short | PRSet: Pathway-based polygenic risk score analyses and software. |
| title_sort | prset pathway based polygenic risk score analyses and software |
| url | https://doi.org/10.1371/journal.pgen.1010624 |
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