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

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.