Reduced signal for polygenic adaptation of height in UK Biobank

By Jeremy J. Berg, A. Harpak, Nicholas A. Sinnott-Armstrong, A. M. Jørgensen, H. Mostafavi, Yair Field, Evan A. Boyle, Xinjun Zhang, Fernando Racimo, J. K. Pritchard and 1 more. 2018

326 citations recorded by the source corpus.

There is considerable variation in average height across European populations, with individuals in the northwest being taller, on average, than those in the southeast. During the past six years, a series of papers reported that polygenic scores for height also show a north to south gradient, and that this cline results from natural selection. These polygenic analyses relied on external estimates of SNP effects on height, taken from the GIANT consortium and from smaller replication studies. Here, we describe a new analysis based on SNP effect estimates from a large independent data set, the UK Biobank (UKB). We find that the signals of selection using UKB effect-size estimates for height are strongly attenuated, though not entirely absent. Because multiple prior lines of evidence provided independent support for directional selection on height, there is no single simple explanation for all the discrepancies. Nonetheless, our current view is that previous analyses were likely confounded by population stratification and so the conclusion of strong polygenic adaptation in Europe now lacks clear support. Moreover, these discrepancies highlight (1) that current methods for correcting for population structure in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of polygenic differences between populations should be treated with caution until these issues are better understood.

Subfields: Genetics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “, 2018), but face difficulties when applied to new patient populations (Martin et al., 2017; Duncan et al., 2019; Berg et al., 2019).”
Geographic variation and bias in polygenic scores of complex diseases and traits in Finland (2018, background): “A few possible reasons for this bias accumulation could be inadequate adjustment for population structure in GWAS (Sohail et al. 2018, Berg et al. 2018) or partially overlapping or related samples between GWAS samples and test data.”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, background): “Comparison to within-family GWAS of siblings, which are more robust to population stratification, may also be informative (Berg et al., 2019).”