Abstract
Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1× coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1× genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8×, 0.6×, and 0.4× coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4× coverage sequencing) to 99.2% (for 1× coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 90%, which was comparable to the imputation r2 from 0.4× coverage sequencing, while the mean imputation r2 from 1× sequencing data was 96%. These results indicate that low-pass sequencing to a depth above 0.4× coverage attains higher power for trait mapping when compared to the PMRA.
