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Subpopulation difference scanning: a strategy for exclusion mapping of susceptibility genes
  1. E Salmela1,
  2. O Taskinen2,
  3. J K Seppänen3,
  4. P Sistonen4,
  5. M J Daly5,
  6. P Lahermo1,
  7. M-L Savontaus6,7,
  8. J Kere8,9
  1. 1Finnish Genome Center, University of Helsinki, Finland
  2. 2Department of Epidemiology and Health Promotion, Diabetes and Genetic Epidemiology Unit, National Public Health Institute, Helsinki, Finland
  3. 3HIIT Basic Research Unit and Laboratory of Computer and Information Science, Helsinki University of Technology, Helsinki, Finland
  4. 4Red Cross Finland Blood Service, Helsinki, Finland
  5. 5Broad Institute, Cambridge, MA
  6. 6Department of Genetics, University of Turku, Turku, Finland
  7. 7Department of Medical Genetics, University of Turku, Turku, Finland
  8. 8Department of Biosciences at Novum and Clinical Research Centre, Karolinska Institutet, Stockholm, Sweden
  9. 9Department of Medical Genetics, University of Helsinki, Helsinki, Finland
  1. Correspondence to:
 Dr J Kere
 Karolinska Institutet, Department of Biosciences at Novum, CBT, Seventh Floor, 14157 Huddinge, Sweden; juha.kere{at}


Background: Association mapping is a common strategy for finding disease-related genes in complex disorders. Different association study designs exist, such as case-control studies or admixture mapping.

Methods: We propose a strategy, subpopulation difference scanning (SDS), to exclude large fractions of the genome as locations of genes for complex disorders. This strategy is applicable to genes explaining disease incidence differences within founder populations, for example, in cardiovascular diseases in Finland.

Results: The strategy consists of genotyping a set of markers from unrelated individuals sampled from subpopulations with differing disease incidence but otherwise as similar as possible. When comparing allele or haplotype frequencies between the subpopulations, the genomic areas with little difference can be excluded as possible locations for genes causing the difference in incidence, and other areas therefore targeted with case-control studies. As tests of this strategy, we use real and simulated data to show that under realistic assumptions of population history and disease risk parameters, the strategy saves efforts of sampling and genotyping and most efficiently detects genes of low risk—that is, those most difficult to find with other strategies.

Conclusion: In contrast to admixture mapping that uses the mixing of two different populations, the SDS strategy takes advantage of drift within highly related subpopulations.

  • AMI, acute myocardial infarction
  • MCMC, Markov chain Monte Carlo
  • SDS, subpopulation difference scanning
  • exclusion mapping
  • incidence difference
  • subpopulation
  • susceptibility gene

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  • Published Online First 27 January 2006

  • Competing interests: there are no competing interests