Missing data imputation and haplotype phase inference for genome-wide association studies
- 11 October 2008
- journal article
- research article
- Published by Springer Nature in Human Genetics
- Vol. 124 (5), 439-450
- https://doi.org/10.1007/s00439-008-0568-7
Abstract
Imputation of missing data and the use of haplotype-based association tests can improve the power of genome-wide association studies (GWAS). In this article, I review methods for haplotype inference and missing data imputation, and discuss their application to GWAS. I discuss common features of the best algorithms for haplotype phase inference and missing data imputation in large-scale data sets, as well as some important differences between classes of methods, and highlight the methods that provide the highest accuracy and fastest computational performance.Keywords
This publication has 54 references indexed in Scilit:
- Evaluating the Effects of Imputation on the Power, Coverage, and Cost Efficiency of Genome-wide SNP PlatformsAmerican Journal of Human Genetics, 2008
- Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetesNature Genetics, 2008
- Simple and Efficient Analysis of Disease Association with Missing Genotype DataAmerican Journal of Human Genetics, 2008
- Haplotypic analysis of Wellcome Trust Case Control Consortium dataHuman Genetics, 2008
- A Statistical Method for Predicting Classical HLA Alleles from SNP DataAmerican Journal of Human Genetics, 2008
- A second generation human haplotype map of over 3.1 million SNPsNature, 2007
- A new multipoint method for genome-wide association studies by imputation of genotypesNature Genetics, 2007
- Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controlsNature, 2007
- A haplotype map of the human genomeNature, 2005
- Evaluating associations of haplotypes with traitsGenetic Epidemiology, 2004