ArticleGenetic epidemiology2012
A sample selection strategy for next-generation sequencing.
Article in Genetic epidemiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Article
- Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.Genetic epidemiology · 2020Article
- Imputation of missing genotypes within LD-blocks relying on the basic coalescent and beyond: consideration of population growth and structure.BMC genomics · 2017Article
- Choosing Subsamples for Sequencing Studies by Minimizing the Average Distance to the Closest Leaf.Genetics · 2015Article
- Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.