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Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.

Original publication

DOI

10.1038/ng2088

Type

Journal article

Journal

Nature genetics

Publication Date

07/2007

Volume

39

Pages

906 - 913

Addresses

Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.

Keywords

Humans, Genetic Markers, Case-Control Studies, Genetics, Population, Genomics, Genotype, Polymorphism, Single Nucleotide, Genome, Human, Models, Genetic