title: A sample selection strategy for next-generation sequencing.
abstract: Next-generation sequencing technology provides us with vast amounts of sequence data. It is efficient and cheaper than previous sequencing technologies, but deep resequencing of entire samples is still expensive. Therefore, sensible strategies for choosing subsets of samples to sequence are required. Here we describe an algorithm for selection of a sub-sample of an existing sample if one has either of two possible goals in mind: maximizing the number of new polymorphic sites that are detected, or improving the efficiency with which the remaining unsequenced individuals can have their types imputed at newly discovered polymorphisms. We then describe a variation on our algorithm that is more focused on detecting rarer variants. We demonstrate the performance of our algorithm using simulated data and data from the 1000 Genomes Project.
authors.affiliation: Department of Preventive Medicine, Keck School of Medicine, USC, Los Angeles, California, USA.
authors.forename: Chul Joo
authors.initials: CJ
authors.lastname: Kang
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004171:Diploidy; D014644:Genetic Variation; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006239:Haplotypes; D059014:High-Throughput Nucleotide Sequencing; D016045:Human Genome Project; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*