Geographic Variation and Bias in the Polygenic Scores of Complex Diseases and Traits in Finland

Abstract

Polygenic scores (PSs) are becoming a useful tool to identify individuals with high genetic risk for complex diseases, and several projects are currently testing their utility for translational applications. It is also tempting to use PSs to assess whether genetic variation can explain a part of the geographic distribution of a phenotype. However, it is not well known how the population genetic properties of the training and target samples affect the geographic distribution of PSs. Here, we evaluate geographic differences, and related biases, of PSs in Finland in a geographically well-defined sample of 2,376 individuals from the National FINRISK study. First, we detect geographic differences in PSs for coronary artery disease (CAD), rheumatoid arthritis, schizophrenia, waist-hip ratio (WHR), bodymass index (BMI), and height, but not for Crohn disease or ulcerative colitis. Second, we use height as a model trait to thoroughly assess the possible population genetic biases in PSs and apply similar approaches to the other phenotypes. Most importantly, we detect suspiciously large accumulations of geographic differences for CAD, WHR, BMI, and height, suggesting bias arising from the population's genetic structure rather than from a direct genotype-phenotype association. This work demonstrates how sensitive the geographic patterns of current PSs are for small biases even within relatively homogeneous populations and provides simple tools to identify such biases. A thorough understanding of the effects of population genetic structure on PSs is essential for translational applications of PSs.

Polygenic risk, susceptibility genes, and breast cancer over the life course

“…A population structure-related bias analysis was performed by following the approach described in detail in Kerminen et al 16 In brief, the method measures the accumulation of PRS differences between the Western and Eastern subpopulations of Finland using a "random PRS", made from a randomly chosen set of independent (r 2 <0.1) variants with minor allele frequency >0.05 that are not associated with breast cancer (breast cancer GWAS 6 p-value >0.5). If such random PRS accumulated differences between the subpopulations, that could indicate a population genetic bias in effect estimates of the GWAS, rather than a real difference in genetic susceptibility of breast cancer between the subpopulations.…”

Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals

“…Furthermore, as environment interactions, Linkage Disequilibrium patterns, allele frequencies and rare polymorphisms are often population specific, effect sizes might be in part population dependent 3,8,9 . This may lead PS to exhibit a directional bias and a lower predictivity in individuals from populations not closely related to the one where the GWAS study was performed [10][11][12][13][14] or even from the same population as it was shown for UK and Finnish cohorts [15][16][17] . This is particularly problematic with recently admixed individuals, where the various ancestries composing a given genome may be closely or distantly related to the population used to infer the adopted genetic effect sizes 18 .…”

How robust are cross-population signatures of polygenic adaptation in humans?

“…Furthermore, when we performed an artificial meta-analysis on the UKBB data, emulating the methodology of GIANT, we observed increased dispersion of polygenic scores among populations than when using single GWAS cohorts of more homogeneous ancestries, echoing findings by Kerminen et al (2019) at a more localized geographic scale. This increase in score dispersion in turn causes an inflation of the Q X statistic.…”