Comprehensive variation discovery in single human genomes

• Main Authors: Weisenfeld, Neil I (Author), Yin, Shuangye (Author), Sharpe, Ted (Author), Lau, Bayo (Author), Hegarty, Ryan (Author), Holmes, Laurie (Author), Sogoloff, Brian (Author), Tabbaa, Diana (Author), Williams, Louise (Author), Russ, Carsten (Author), Nusbaum, Chad (Author), MacCallum, Iain (Author), Jaffe, David B. (Author), Lander, Eric Steven (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Biology (Contributor), Lander, Eric S. (Contributor)
• Format: Article
• Language: English
• Published: Nature Publishing Group, 2015-06-05T15:14:06Z.
• Subjects: Article
• Online Access: Get fulltext

 Complete knowledge of the genetic variation in individual human genomes is a crucial foundation for understanding the etiology of disease. Genetic variation is typically characterized by sequencing individual genomes and comparing reads to a reference. Existing methods do an excellent job of detecting variants in approximately 90% of the human genome; however, calling variants in the remaining 10% of the genome (largely low-complexity sequence and segmental duplications) is challenging. To improve variant calling, we developed a new algorithm, DISCOVAR, and examined its performance on improved, low-cost sequence data. Using a newly created reference set of variants from the finished sequence of 103 randomly chosen fosmids, we find that some standard variant call sets miss up to 25% of variants. We show that the combination of new methods and improved data increases sensitivity by several fold, with the greatest impact in challenging regions of the human genome.