Abstract
A new study highlights the biases and inaccuracies of polygenic risk scores (PRS) when predicting disease risk in individuals from populations other than those used in their derivation. The design bias of workhorse tools used for research, particularly genotyping arrays, contributes to these distortions. To avoid further inequities in health outcomes, the inclusion of diverse populations in research, unbiased genotyping, and methods of bias reduction in PRS are critical.
Highlights
A new study highlights the biases and inaccuracies of polygenic risk scores (PRS) when predicting disease risk in individuals from populations other than those used in their derivation
genome-wide association studies (GWAS) have been successful in identifying a subset of the genes and causal variants behind polygenic common diseases, such as coronary artery disease (CAD), cancers, and type 2 diabetes
PRS constructed from large-scale GWAS of five common diseases could identify individuals within the UK Biobank with high disease risk
Summary
A new study highlights the biases and inaccuracies of polygenic risk scores (PRS) when predicting disease risk in individuals from populations other than those used in their derivation. It was initially hoped that once the genetic architecture of a trait was identified, the observed effects of the risk-associated alleles could be used to construct a combined score and to predict individuals at the tail ends of the risk distribution. Defining the role of PRS in healthcare The causation of common human diseases is complex as it results from a combination of genetic and environmental factors.
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