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.
Marchini
Jonathan
J
Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
Howie
Bryan
B
Myers
Simon
S
McVean
Gil
G
Donnelly
Peter
P
D016022:Case-Control Studies; D005819:Genetic Markers; D005828:Genetics, Population; D015894:Genome, Human; D023281:Genomics / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide
D005819:Genetic Markers