10.5061/DRYAD.HDR7SQVK8
Adebiyi, Ezekiel
0000-0002-1390-2359
Covenant University
Adam, Yagoub
Covenant University
Sadeeq, Suraju
Covenant University
Kumuthini, Judit
Centre for Genomic Regulation
Ajayi, Olabode
Centre for Genomic Regulation
Wells, Gordon
Centre for Genomic Regulation
Solomon, Rotimi
Covenant University
Ogunlana, Olubanke
Covenant University
Adetiba, Emmanuel
Covenant University
Iweala, Emeka
Covenant University
Brors, Benedikt
German Cancer Research Center
Polygenic risk score in Africa populations: progress and challenges
Dryad
dataset
2022
FOS: Biological sciences
National Human Genome Research Institute
https://ror.org/00baak391
U24HG006941
World Bank
https://ror.org/00ae7jd04
National Human Genome Research Institute
https://ror.org/00baak391
U2RTW010679
2023-02-08T00:00:00Z
2023-02-08T00:00:00Z
en
https://arxiv.org/abs/2102.08468
https://doi.org/10.12688/f1000research.76218.1
76155 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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
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 application which investigateĀ 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
reviewed the conventional PRS methods and their application to Sub-Saharan
African communities. We concluded 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 Africa population data for clinical
evaluation of PRSs of interest and predicting rare diseases.