Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved]
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleo...
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2023-04-01
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| Online Access: | https://f1000research.com/articles/11-175/v2 |
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| author | Rotimi Solomon Gordon Wells Benedikt Brors Emmanuel Adetiba Yagoub Adam Ezekiel Adebiyi Olabode Ajayi Suraju Sadeeq Olubanke Ogunlana Judit Kumuthini Emeka Iweala |
| author_facet | Rotimi Solomon Gordon Wells Benedikt Brors Emmanuel Adetiba Yagoub Adam Ezekiel Adebiyi Olabode Ajayi Suraju Sadeeq Olubanke Ogunlana Judit Kumuthini Emeka Iweala |
| author_sort | Rotimi Solomon |
| collection | DOAJ |
| description | Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases. |
| first_indexed | 2024-03-13T08:09:04Z |
| format | Article |
| id | doaj.art-14757a763eb148efb4ae84ba53808582 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-03-13T08:09:04Z |
| publishDate | 2023-04-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-14757a763eb148efb4ae84ba538085822023-06-01T00:00:00ZengF1000 Research LtdF1000Research2046-14022023-04-0111143350Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved]Rotimi Solomon0Gordon Wells1Benedikt Brors2Emmanuel Adetiba3Yagoub Adam4https://orcid.org/0000-0001-6874-2543Ezekiel Adebiyi5https://orcid.org/0000-0002-1390-2359Olabode Ajayi6Suraju Sadeeq7Olubanke Ogunlana8https://orcid.org/0000-0001-5781-592XJudit Kumuthini9Emeka Iweala10Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, NigeriaSouth African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South AfricaApplied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, GermanyCovenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, NigeriaCovenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, NigeriaCovenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, NigeriaSouth African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South AfricaCovenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, NigeriaCovenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, NigeriaSouth African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South AfricaCovenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, NigeriaPolygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.https://f1000research.com/articles/11-175/v2Prediction medicine GWAS post-GWAS PRS analysis Africa populationeng |
| spellingShingle | Rotimi Solomon Gordon Wells Benedikt Brors Emmanuel Adetiba Yagoub Adam Ezekiel Adebiyi Olabode Ajayi Suraju Sadeeq Olubanke Ogunlana Judit Kumuthini Emeka Iweala Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] F1000Research Prediction medicine GWAS post-GWAS PRS analysis Africa population eng |
| title | Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] |
| title_full | Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] |
| title_fullStr | Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] |
| title_full_unstemmed | Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] |
| title_short | Polygenic Risk Score in African populations: progress and challenges [version 2; peer review: 2 approved] |
| title_sort | polygenic risk score in african populations progress and challenges version 2 peer review 2 approved |
| topic | Prediction medicine GWAS post-GWAS PRS analysis Africa population eng |
| url | https://f1000research.com/articles/11-175/v2 |
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