A key hurdle for research using GWAS results is that risk SNPs identified in GWAS may not cause adverse health outcomes, but may instead be proxies for (correlated with) unmeasured disease-causing variation in the genome 7,8. GWAS methods exploit LD across the genome to leverage measurement of 100,000 – 1 million SNPs to capture variation in the 10 million plus SNPs the genome is estimated to contain. The very large sample sizes in GWAS permit detection of risk associations even when proxy SNPs are in imperfect LD with disease-causing variation (correlation<1). GWAS findings are generally applied to smaller samples designed to elucidate etiological and clinical correlates of discovered genes. When GWAS SNPs are translated to research using smaller samples, the measurement error resulting from imperfect LD with disease causing variants can attenuate associations below levels these samples are powered to detect. Genetic risk scores (GRSs) summarize risk-associated variation across the genome 9 by aggregating information from multiple risk SNPs (the simplest GRSs count disease-associated alleles). Because GRSs pool information from multiple SNPs, each individual SNP is less important to the
