Basic Science and Pathogenesis

The DAWN Alzheimer's Research Study is a multi-site international project to recruit African-American, Hispanic/Latino, and African participants for genomic studies of Alzheimer's Disease (AD). In addition to clinical evaluations, cognitive assessments and biomarker data collection, array-based representative genotyping is being performed for all participants. To increase the value of these genotypic data a vastly richer dataset can be created using imputation, a process that requires a whole genome sequenced reference dataset. High quality imputation depends on having large reference datasets representative of the ancestries of the target dataset. Using inadequate reference datasets results in low imputation quality, fewer usable imputed variants and hinders downstream analysis. Given the inclusion of African-ancestry participants (whose reference datasets are small) in the DAWN study, we examined the impact of using different strategies on the accuracy of genotype imputation.

Using DAWN study data generated by the Illumina Global Screening Array, we performed genotype imputation using the TopMED R3 dataset and compared these results to a meta-imputation workflow using TopMED R3 supplemented by the Africa 6K dataset. This comparison explicitly tests the impact of increasing African ancestry in the imputation reference panel. Imputation results were assessed for chromosomes 1, 10, and 20 for total count of imputed variants, and by comparing variant counts across a range of imputation quality (R2) and variant rarity (MAF) filter criteria to identify apparent trends.

An additional 190,784 (0.3%) variants are captured from the meta-imputed (64,370,296) vs the single-imputed (64,179,512) dataset. Variant quality also improves, with an increase of ∼80,000 (5%) filter-passing variants (R2 > 0.8) in the meta-imputation compared to the TopMED-only imputation results (Figure 1).

The use of meta-imputation to better match the genetic background of the DAWN dataset through the use of multiple imputation references significantly increases the density and quality of the resulting genotypic dataset, enabling more powerful studies of AD genetics. This demonstrates the utility of meta-imputation for better matching the genetic background of samples when performing imputation.