You are here

PLoS computational biology DOI:10.1371/journal.pcbi.1002604

Efficiency and power as a function of sequence coverage, SNP array density, and imputation.

Publication TypeJournal Article
Year of Publication2012
AuthorsFlannick, J, Korn, JM, Fontanillas, P, Grant, GB, Banks, E, DePristo, MA, Altshuler, D
JournalPLoS computational biology
Date Published2012/07/01

High coverage whole genome sequencing provides near complete information about genetic variation. However, other technologies can be more efficient in some settings by (a) reducing redundant coverage within samples and (b) exploiting patterns of genetic variation across samples. To characterize as many samples as possible, many genetic studies therefore employ lower coverage sequencing or SNP array genotyping coupled to statistical imputation. To compare these approaches individually and in conjunction, we developed a statistical framework to estimate genotypes jointly from sequence reads, array intensities, and imputation. In European samples, we find similar sensitivity (89%) and specificity (99.6%) from imputation with either 1× sequencing or 1 M SNP arrays. Sensitivity is increased, particularly for low-frequency polymorphisms ([Formula: see text]), when low coverage sequence reads are added to dense genome-wide SNP arrays - the converse, however, is not true. At sites where sequence reads and array intensities produce different sample genotypes, joint analysis reduces genotype errors and identifies novel error modes. Our joint framework informs the use of next-generation sequencing in genome wide association studies and supports development of improved methods for genotype calling.