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.
Browning
Sharon R
SR
Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand. s.browning@auckland.ac.nz
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