MoGUL: Detecting Common Insertions and Deletions in a Population

While the discovery of structural variants in the human population is ongoing, most methods for this task assume that the genome is sequenced to high coverage (e.g. 40x), and use the combined power of the many sequenced reads and mate pairs to identify the variants. In contrast, the 1000 Genomes Project hopes to sequence hundreds of human genotypes, but at low coverage (4-6x), and most of the current methods are unable to discover insertion/deletion and structural variants from this data. In order to identify indels from multiple low-coverage individuals we have developed the MoGUL (Mixture of Genotypes Variant Locator) framework, which identifies potential locations with indels by examining mate pairs generated from all sequenced individuals simultaneously, uses a Bayesian network with appropriate priors to explicitly model each individual as homozygous or heterozygous for each locus, and computes the expected Minor Allele Frequency (MAF) for all predicted variants. We have used MoGUL to identify variants in 1000 Genomes data, as well as in simulated genotypes, and show good accuracy at predicting indels, especially for MAF > 0.06 and indel size > 20 base pairs.