Ascertainment bias can create the illusion of genetic health disparities

Accurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genetic association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data, GWAS results, and computer simulations to examine how ascertainment bias causes disease risks to be mis-inferred in non-study populations. We find that genetic disease risks are substantially overestimated for individuals with African ancestry - risk allele frequencies at known disease loci are 1.15% higher on average in Africa. These patterns hold for multiple disease classes (e.g., cancer, gastrointestinal, morphological, and neurological diseases). A contributing factor to this bias is that existing genotyping arrays are enriched for SNPs that have higher frequencies of ancestral alleles in Africa. Computer simulations of GWAS that use samples from bottlenecked non-African populations recapitulate regional differences in allele frequencies at disease susceptibility loci. These differences cause genetic disease risks to be overestimated for individuals with African ancestry and underestimated for individuals with non-African ancestry. We find that the extent of ascertainment bias depends on the genotyping platform used, numbers of cases and controls, demographic history, the proportion of ancestral vs. derived risk alleles, and choice of study population (African GWAS are less biased). Importantly, biases are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Our results indicate that caution must be taken when using GWAS results from one population to predict disease risks in another population.