Imputed Rare-Variant Aggregate Analysis Is a Powerful Approach to Detect Genetic Associations

Imputation can cost-effectively generate genotypes for millions of variants. This study evaluated the power of performing rare-variant aggregate association tests using imputed genotypes. UK Biobank exome-sequence data and variants obtained via imputation using the HRC (r1.1) (N = 32,488 samples) or TOPMed (r3) (N = 133,597 samples) reference panels were analyzed. Simulations were used to compare the power of performing rare-variant aggregate association analysis using either sequence or imputed data. The number of genes available for analysis, that is, those with ≥ 2 rare variants, was approximately the same for exome-sequence and TOPMed imputed data, but HRC imputed data had ~10% fewer genes. For imputed data, using a less stringent R² threshold (i.e., > 0.3 vs. > 0.8) led to greater power to detect aggregate associations due to additional rare variants included in the analysis. Exome-sequence data provided the highest power to detect rare-variant aggregate associations. TOPMed imputed variants usually had < 20% reduction in power in comparison to exome-sequence data, whereas HRC imputed variants provided substantially less power. Rare-variant aggregate association analyses using exome-sequence data, and imputed variants for PCSK9 and low-density lipoprotein and APOC3 and triglyceride levels were also performed. For these analyses, even when only ultra-rare variants (minor allele frequency < 0.001) were analyzed, significant aggregate associations could be detected for all data types. Although there is a decrease in power for rare-variant aggregate association tests when analyzing imputed variants compared to sequence data due to missing variants and uncertainty in genotypes, analysis of imputed data is a viable approach to detect rare-variant aggregate associations when sequence data are unavailable.