ArticleBMC bioinformatics2011
Selective phenotyping, entropy reduction, and the mastermind game.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle.Animal genetics · 2020Article
- Article
- A new method to infer causal phenotype networks using QTL and phenotypic information.PloS one · 2014Article
- Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2013Article
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3 authors.
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Abstract
backgroundWith the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately.
resultsHere we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of previously phenotyped individuals, thereby enabling an efficient incremental strategy. The effective performance of SPARE is demonstrated on simulated data as well as on an experimental yeast dataset.
conclusionsUsing entropy reduction as an objective criterion gives a natural way to tackle both issues of detection and localization simultaneously and to integrate intermediate phenotypic data. We foresee entropy-based strategies as a fruitful research direction for selective phenotyping.
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