TagSNP transferability and relative loss of variability prediction from HapMap to an admixed population

• Autor(a) principal: Lins, Tulio Cesar de Lima
• Data de Publicação: 2009
• Outros Autores: Abreu, Breno Silva de, Pereira, Rinaldo Wellerson
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UCB
• Texto Completo: http://twingo.ucb.br:8080/jspui/handle/10869/579; https://repositorio.ucb.br:9443/jspui/handle/123456789/7751

Background: The application of a subset of single nucleotide polymorphisms, the tagSNPs, can be useful in capturing untyped SNPs information in a genomic region. TagSNP transferability from the HapMap dataset to admixed populations is of uncertain value due population structure, admixture, drift and recombination effects. In this work an empirical dataset from a Brazilian admixed sample was evaluated against the HapMap population to measure tagSNP transferability and the relative loss of variability prediction. Methods: The transferability study was carried out using SNPs dispersed over four genomic regions: the PTPN22, HMGCR, VDR and CETP genes. Variability coverage and the prediction accuracy for tagSNPs in the selected genomic regions of HapMap phase II were computed using a prediction accuracy algorithm. Transferability of tagSNPs and relative loss of prediction were evaluated according to the difference between the Brazilian sample and the pooled and single HapMap population estimates. Results: Each population presented different levels of prediction per gene. On average, the Brazilian (BRA) sample displayed a lower power of prediction when compared to HapMap and the pooled sample. There was a relative loss of prediction for BRA when using single HapMap populations, but a pooled HapMap dataset generated minor loss of variability prediction and lower standard deviations, except at the VDR locus at which loss was minor using CEU tagSNPs. Conclusion: Studies that involve tagSNP selection for an admixed population should not be generally correlated with any specific HapMap population and can be better represented with a pooled dataset in most cases.