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

@article{citeulike:1406134, abstract = {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.}, address = {[1] Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. [2] These authors contributed equally to this work.}, author = {Marchini, Jonathan and Howie, Bryan and Myers, Simon and McVean, Gil and Donnelly, Peter }, citeulike-article-id = {1406134}, doi = {10.1038/ng2088}, issn = {1061-4036}, journal = {Nat Genet}, keywords = {gwa, imputation, r, software}, month = {June}, posted-at = {2007-06-23 08:44:51}, priority = {2}, title = {A new multipoint method for genome-wide association studies by imputation of genotypes.}, url = {http://dx.doi.org/10.1038/ng2088}, year = {2007} }

TY - JOUR ID - citeulike:1406134 L3 - citeulike-article-id:1406134 TI - A new multipoint method for genome-wide association studies by imputation of genotypes. JF - Nat Genet AD - [1] Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. [2] These authors contributed equally to this work. SN - 1061-4036 N2 - 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. KW - gwa KW - imputation KW - r KW - software AU - Marchini, Jonathan AU - Howie, Bryan AU - Myers, Simon AU - McVean, Gil AU - Donnelly, Peter PY - 2007/06/17/ UR - http://dx.doi.org/10.1038/ng2088 ER -