A framework for variation discovery and genotyping using next-generation DNA sequencing data

• Published in: Nature genetics Vol. 43; no. 5; pp. 491 - 498
• Main Authors: DePristo, Mark A, Banks, Eric, Poplin, Ryan, Garimella, Kiran V, Maguire, Jared R, Hartl, Christopher, Philippakis, Anthony A, del Angel, Guillermo, Rivas, Manuel A, Hanna, Matt, McKenna, Aaron, Fennell, Tim J, Kernytsky, Andrew M, Sivachenko, Andrey Y, Cibulskis, Kristian, Gabriel, Stacey B, Altshuler, David, Daly, Mark J
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.05.2011; Nature Publishing Group
• Subjects: 631/208/2489/144; 631/208/514/2254; Agriculture; Animal Genetics and Genomics; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Cancer Research; Data Interpretation, Statistical; Data processing; Databases, Nucleic Acid; Design; DNA sequencing; Exons; Fundamental and applied biological sciences. Psychology; Gene Function; Genetic diversity; Genetic Variation; Genetics; Genetics of eukaryotes. Biological and molecular evolution; Genetics, Population - methods; Genetics, Population - statistics & numerical data; Genome, Human; Genomes; Genotype; Human Genetics; Humans; Integrated approach; Methods; Mutation; Nucleotide sequencing; Polymorphism, Single Nucleotide; Sequence Alignment - methods; Sequence Alignment - statistics & numerical data; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - statistics & numerical data; Single nucleotide polymorphisms; Software; Studies; technical-report; United States; Sequencing; DNA
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.806

Mark DePristo and colleagues report an analytical framework to discover and genotype variation using whole exome and genome resequencing data from next-generation sequencing technologies. They apply these methods to low-pass population sequencing data from the 1000 Genomes Project. Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.