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Genotype-Imputation Accuracy across Worldwide Human Populations
 

Summary: ARTICLE
Genotype-Imputation Accuracy
across Worldwide Human Populations
Lucy Huang,1,2,* Yun Li,1 Andrew B. Singleton,3 John A. Hardy,4 Gonc¸alo Abecasis,1
Noah A. Rosenberg,1,2,5 and Paul Scheet1,6
A current approach to mapping complex-disease-susceptibility loci in genome-wide association (GWA) studies involves leveraging the
information in a reference database of dense genotype data. By modeling the patterns of linkage disequilibrium in a reference panel,
genotypes not directly measured in the study samples can be imputed and tested for disease association. This imputation strategy
has been successful for GWA studies in populations well represented by existing reference panels. We used genotypes at 513,008 auto-
somal single-nucleotide polymorphism (SNP) loci in 443 unrelated individuals from 29 worldwide populations to evaluate the ``porta-
bility'' of the HapMap reference panels for imputation in studies of diverse populations. When a single HapMap panel was leveraged for
imputation of randomly masked genotypes, European populations had the highest imputation accuracy, followed by populations from
East Asia, Central and South Asia, the Americas, Oceania, the Middle East, and Africa. For each population, we identified ``optimal''
mixtures of reference panels that maximized imputation accuracy, and we found that in most populations, mixtures including individ-
uals from at least two HapMap panels produced the highest imputation accuracy. From a separate survey of additional SNPs typed in the
same samples, we evaluated imputation accuracy in the scenario in which all genotypes at a given SNP position were unobserved and
were imputed on the basis of data from a commercial ``SNP chip,'' again finding that most populations benefited from the use of combi-
nations of two or more HapMap reference panels. Our results can serve as a guide for selecting appropriate reference panels for impu-
tation-based GWA analysis in diverse populations.
Introduction

  

Source: Abecasis, Goncalo - Department of Biostatistics, University of Michigan
Rosenberg, Noah - Departments of Human Genetics & Biostatistics, University of Michigan

 

Collections: Biology and Medicine; Biotechnology; Mathematics