Imputation-Based Coverage Assessments of Current Human Genotyping Arrays

Microarray SNP genotyping, combined with imputation of untyped variants, has been widely adopted as an efficient means to interrogate variation across the human genome. “Genomic coverage” is the total proportion of genomic variation captured by an array, either by direct observation or through an indirect means such as linkage disequilibrium or imputation. We have performed imputation-based genomic coverage assessments of eight current genotyping arrays that assay from ~0.3 to ~5 million variants. Coverage was determined separately in each of the four continental ancestry groups in the 1000 Genomes Project phase 1 release. We used the subset of 1000 Genomes variants present on each array to impute the remaining variants and assessed coverage based on correlation between imputed and observed allelic dosages. Over 75% of common variants (minor allele frequency>0.05) are covered by all arrays in all groups except for African ancestry, and up to ~90% in all ancestries for the highest density arrays. In contrast, under 40% of less common variants (0.01<minor allele frequency<0.05) are covered by low density arrays in all ancestries and 50-80% in high density arrays, depending on ancestry. We also calculated genome-wide power to detect variant-trait association in a case-control design, across varying sample sizes, effect sizes, and minor allele frequency ranges, and compare these array-based power estimates with a hypothetical array that would type all variants in 1000 Genomes. These imputation- based genomic coverage and power analyses are intended as a practical guide to researchers planning genetic studies.
SNP microarrays, Power, Genomic coverage, Genome-wide association study
Nelson, Sarah C., Doheny, Kimberly F., Pugh, Elizabeth W., Romm, Jane M., Ling, Hua, Laurie, Cecelia A., Browning, Sharon R., Weir, Bruce S., & Laurie, Cathy C. (2013, in press). Imputation-Based Genomic Coverage Assessments of Current Human Genotyping Arrays. G3: Genes, Genomes, Genetics.