Integrating sequencing datasets to form highly confident SNP and indel genotype calls for a whole human genome
Justin M. Zook, Brad Chapman, Jason Wang, David Mittelman, Oliver Hofmann, Winston Hide, Marc Salit
(Submitted on 17 Jul 2013)
Clinical adoption of human genome sequencing requires methods with known accuracy of genotype calls at millions or billions of positions across a genome. Previous work showing discordance amongst sequencing methods and algorithms has made clear the need for a highly accurate set of genotypes across a whole genome that could be used as a benchmark. We present methods we used to make highly confident SNP, indel, and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. To minimize bias towards any sequencing method, we integrate 9 whole genome and 3 exome datasets from 5 different sequencing platforms (Illumina, Complete Genomics, SOLiD, 454, and Ion Torrent), 7 mappers, and 3 variant callers. The resulting genotype calls are highly sensitive and specific, and allow performance assessment of more difficult variants than typically investigated using microarrays as a benchmark. Regions for which no confident genotype call could be made are identified as uncertain, and classified into different reasons for uncertainty (e.g. low coverage, mapping/alignment bias, etc.). As a community resource, we have integrated our highly confident genotype calls into the GCAT website for interactive assessment of false positive and negative rates of different datasets and bioinformatics methods using our highly confident calls. Application of the concepts of our integration process may be interesting beyond whole genome sequencing, for other measurement problems with large datasets from multiple methods, where none of the methods is a Reference Method that can be relied upon as highly sensitive and specific.