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Author (up) Zhou, W.; Fritsche, L.G.; Das, S.; Zhang, H.; Nielsen, J.B.; Holmen, O.L.; Chen, J.; Lin, M.; Elvestad, M.B.; Hveem, K.; Abecasis, G.R.; Kang, H.M.; Willer, C.J. url  doi
  Title Improving power of association tests using multiple sets of imputed genotypes from distributed reference panels Type Journal Article
  Year 2017 Publication Genetic Epidemiology Abbreviated Journal Genet Epidemiol  
  Volume 41 Issue 8 Pages 744-755  
  Keywords Gwas; genotype imputation; multiple reference panels; population-specific; study power  
  Abstract The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trondelag Health Study (HUNT) from low-pass genome sequencing. We observed that the population-matched reference panel allowed for imputation of more population-specific variants with lower frequency (minor allele frequency (MAF) between 0.05% and 0.5%). The overall imputation accuracy from the population-specific panel was substantially higher than 1000G and was comparable with HRC, despite HRC being 15-fold larger. These results recapitulate the value of population-specific reference panels for genotype imputation. We also evaluated different strategies to utilize multiple sets of imputed genotypes to increase the power of association studies. We observed that testing association for all variants imputed from any panel results in higher power to detect association than the alternative strategy of including only one version of each genetic variant, selected for having the highest imputation quality metric. This was particularly true for lower frequency variants (MAF < 1%), even after adjusting for the additional multiple testing burden.  
  Address Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan, United States of America  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0741-0395 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:28861891 Approved no  
  Call Number HUNT @ maria.stuifbergen @ Serial 2026  
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