Designing optimally multiplexed SNP genotyping assays

Yonatan Aumann, Efrat Manisterski, Zohar Yakhini

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We consider the task of SNP (Single Nucleotide Polymorphism) genotyping. In many studies, genotyping of a large number of SNPs must be performed. Multiple SNPs can be genotyped together in the same assay (a process called multiplexed genotyping) provided they adhere to some constraints. We address the optimization problem of designing assays that maximize the number of genotyped SNPs, subject to the multiplexing constraints. We focus on the SNP genotyping method based on primer extension and mass-spectrometry (PEA/MS). We translate the optimization problem to a graph coloring problem, and provide essentially optimal heuristics for solving the corresponding coloring problem. In addition, we consider a method that enables a dramatic increase in the multiplexing rate by modifying primer masses. In this case, the multiplexing design problem can be modelled as a matching problem in hypergraphs. We analyze both theoretical and practical aspects of the problem, providing hardness results and practical heuristics. The heuristics are tested using simulation methods, and prove to be close to optimal in practice.

Original languageEnglish
Pages (from-to)399-417
Number of pages19
JournalJournal of Computer and System Sciences
Issue number3
StatePublished - May 2005


  • Approximation algorithms
  • Genotyping
  • Graph coloring
  • High throughput genotyping


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