Rxivist logo

Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications.

By Simon Haworth, Ruth Mitchell, Laura Corbin, Kaitlin H Wade, Tom Dudding, Ashley Budu-Aggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, George Davey Smith, Neil Davies, Dan Lawson, Nicholas Timpson

Posted 11 Apr 2018
bioRxiv DOI: 10.1101/294876 (published DOI: 10.1038/s41467-018-08219-1)

The inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis. We found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

Download data

  • Downloaded 1,914 times
  • Download rankings, all-time:
    • Site-wide: 10,103
    • In genetics: 424
  • Year to date:
    • Site-wide: 87,293
  • Since beginning of last month:
    • Site-wide: 126,092

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide