However, efforts to use polygenic scores face substantial obstacles. Image credit: Panel A – top ( Stux, CC0), middle ( Figure 1, Hu et al., 2016, CC BY 4.0), bottom (Jennifer Blanc) Panel B (Adapted from Figure 4, Zaidi and Mathieson, 2020). ( C) However, when differences in the environmental factors were localized to a single square in the grid (shown in yellow), not even the rare PCA model could eliminate the correlation between genetic and environmental effects (indicated by asterix). Correcting the scores with the 'common PCA' approach (middle right) does not solve this problem, but correction with the 'rare PCA' approach (bottom right) does. The uncorrected mean polygenic scores (top right) have a structure that clearly mirrors the structure in the environment. ( B) Mathieson and Zaidi simulated genetic data for a population that separated into subpopulations in the recent past the environment was simulated as a six-by-six grid (left) in which environmental factors associated with the trait of interest vary smoothly from top to bottom. to be taller or to have a greater risk of disease), while those with a lower polygenic score are predicted to have a lower trait value (bottom graph). Individuals with a higher polygenic score (orange) are predicted to have a higher trait value (e.g. This information is used to compute the polygenic score of individuals not in the original sample. ( A) A genome-wide associate study (GWAS) measures the trait of interest (phenotype) and the genotype of a sample of individuals and uses this data (middle graph) to see which genetic variants (represented by individual dots) are associated with the trait of interest (shown in red). These scores have been used to predict a person’s risk of developing a disease ( Torkamani et al., 2018), to study our evolutionary past ( Rosenberg et al., 2019), and to help understand complex social outcomes ( Harden and Koellinger, 2020). This information makes it possible to take the genome of someone who was not involved in the original GWAS and add up the effects of multiple genetic variants to calculate a polygenic score for that trait ( Figure 1A).
For each participant, researchers measure numerous genetic variants across their genome, together with a trait of interest, and use this data to determine the extent to which different variants are associated with the trait. To build a polygenic score, geneticists first enroll a large number of people in a genome-wide association study (GWAS). To cut through some of this complexity, human geneticists use a tool called a polygenic score, which attempts to predict a person’s traits solely from their genes ( Rosenberg et al., 2019). doi: 10.7554/eLife.61548Ī person’s traits – such as their height or risk of disease – result from a complex interplay between the genes they inherit and the environments they experience over their lifetime.
Demographic history mediates the effect of stratification on polygenic scores. Related research article Zaidi AA, Mathieson I.
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