Polygenic risk, population structure and ongoing difficulties with race in human genetics

Abstract

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Celebrating 50 years since Lewontin's apportionment of human diversity

“…Kaplan & Fullerton [28] consider contemporary efforts to predict disease risk using polygenic scores in light of Lewontin's 1972 paper. As in forensic genetics, human genetic structure, despite generally low levels of differentiation, causes complications for polygenic scores.…”

“…The genetic differences that do exist can be relevant in biomedical settings, in part because they can affect population-level differences in Mendelian disease allele frequencies, genetic risk factors for complex disease, transplantation matching probabilities and treatment responses (e.g. [28,29]). As discussed by Novembre [2], Lewontin's paper [1] has been referenced beyond the field of human evolutionary genetics as supporting an oversimplified view of human variation, in which population structure is completely ignored even in situations in which it might be relevant, such as matching problems in forensics or transplantation, where population variation in match probabilities affects the societal use of population-genetic computations [27,29].…”

AI-driven risk scores: should social scoring and polygenic scores based on ethnicity be equally prohibited?

“…However, adding parameters of ethnicity to the calculation of polygenic risk scores may reveal statistical correlations and thus interest researchers. It is now widely accepted that most of the genetic diversity in the human species exists between individuals within populations and that only a small fraction of the total genetic diversity is related to variation between ethnic populations ( Kaplan & Fullerton, 2022 ). As geneticist Richard Lewontin (1972) famously asserted, these features of human genetic variation mean that racial classification is of “virtually no genetic or taxonomic significance” and hence should be abandoned.…”

Building causal knowledge in behavior genetics

“…In behavior genetics, the associations between particular loci and behavioral traits are almost absurdly small, and the only reason that they rise to statistical significance at all is that genome-wide association studies (GWASs) can leverage sample sizes in the hundreds of thousands or millions. Even when accumulated across thousands of loci in polygenic scores, the application of polygenic scores is plagued by problems with portability because of effects of both real biological complexity and methodological artifacts (Kaplan & Fullerton, 2022;Matthews, 2022). These issues, in addition to the generally low predictive power, render polygenic scores of little to no use at the individual level (Fusar-Poli, Rutten, van Os, Aguglia, & Guloksuz, 2022; Morris, Davies, & Smith, 2020) and a large amount of uncertainty of estimates appears to hinder the ability to even accurately and consistently stratify individuals into high-risk groups (Ding et al, 2022;Muslimova et al, 2023;Schultz et al, 2022).…”