Missing data imputation and haplotype phase inference for genome-wide association studies.
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.
Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. s.browning@auckland.ac.nz
Sharon R
SR
Browning
D000465:Algorithms; D001699:Biometry; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005819:Genetic Markers; D014644:Genetic Variation; D005826:Genetics, Medical / Q000706:statistics & numerical data*; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide
D005819:Genetic Markers