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.
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.
Authors: J Tuomilehto; M Arstila; E Kaarsalo; J Kankaanpää; M Ketonen; K Kuulasmaa; S Lehto; H Miettinen; H Mustaniemi; P Palomäki Journal: Eur Heart J Date: 1992-05 Impact factor: 29.983