A new multipoint method for genome-wide association studies by imputation of genotypes.

Journal article

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.

Authors: Marchini, J; Howie, B; Myers, S; McVean, G; Donnelly, P

• Publication status: Published
• Journal: Nature genetics
• Volume: 39
• Issue: 7
• Pages: 906-913
• Publication date: 2007-07-01
• DOI: 10.1038/ng2088
• EISSN: 1546-1718
• ISSN: 1061-4036
• Language: English
• Keywords: Genetic Markers; Polymorphism, Single Nucleotide; Models, Genetic; Humans; Genomics; Genotype; Genetics, Population; Genome, Human; Case-Control Studies