Efficient compression and analysis of large genetic variation datasets

Efficient compression and analysis of large genetic variation datasets
Ryan M Layer , Neil Kindlon , Konrad J Karczewski , Exome Aggregation Consortium ExAC , Aaron R Quinlan
doi: http://dx.doi.org/10.1101/018259

The economy of human genome sequencing has catalyzed ambitious efforts to interrogate the genomes of large cohorts in search of deeper insight into the genetic basis of disease. This manuscript introduces Genotype Query Tools (GQT) as a new indexing strategy and powerful toolset that enables interactive analyses based on genotypes, phenotypes and sample relationships. Speed improvements are achieved by operating directly on a compressed index without decompression. GQT’s data compression ratios increase favorably with cohort size and therefore, by avoiding data inflation, relative analysis performance improves in kind. We demonstrate substantial query performance improvements over state-of-the-art tools using datasets from the 1000 Genomes Project (46 fold), the Exome Aggregation Consortium (443 fold), and simulated datasets of up to 100,000 genomes (218 fold). Moreover, our genotype indexing strategy complements existing formats and toolsets to provide a powerful framework for current and future analyses of massive genome datasets.

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