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Using Population Mixtures to Optimize the Utility of Genomic Databases: Linkage Disequilibrium and Association Study Design in India
Author(s) -
Pemberton T. J.,
Jakobsson M.,
Conrad D. F.,
Coop G.,
Wall J. D.,
Pritchard J. K.,
Patel P. I.,
Rosenberg N. A.
Publication year - 2008
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2008.00457.x
Subject(s) - international hapmap project , linkage disequilibrium , imputation (statistics) , tag snp , genetic association , single nucleotide polymorphism , haplotype , biology , population , linkage (software) , association mapping , selection (genetic algorithm) , genetics , genotype , missing data , computer science , statistics , artificial intelligence , gene , demography , mathematics , sociology
Summary When performing association studies in populations that have not been the focus of large‐scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis – such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.