A block-based imputation approach with adaptive LD blocks for fast genotype imputation


This paper addresses the issue of improving long imputation time usually required for a large volume of SNP genotype data which can be easily obtained by biological experiments with the genomewide SNP chip or the next-generation sequencing technology. For this purpose, we propose a block-based imputation approach that generates adaptive LD blocks with observed SNP genotype data and applies an imputation procedure for each block separately. Also, we implemented the block based imputation to maximize the use of computing resources. Specifically, each task of block imputation is allocated to individual processor and is executed on each processor independently. Thus, multiple tasks of block imputation can be executed on multiple processors in parallel where the parallelization can reach up to the maximum number of processors allowed by user’s computing environment. Our experiment was performed with Mao et al.’s prostate cancer dataset. The results show that our adaptive block approach can reduce the imputation time up to 60–70% of original imputation time given by MaCH without the loss of imputation accuracy.

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  1. 1.

    Ellinghaus, D., Schreiber, S., Franke, A. & Nothnagel, M. Current software for genotype imputation. Hum. Genomics 3, 371–380 (2009).

    CAS  Google Scholar 

  2. 2.

    Nothnagel, M., Ellinghaus, D., Schreiber, S., Krawczak, M. & Franke, A. A comprehensive evaluation of SNP genotype imputation. Human Genetics 125, 163–171 (2009).

    Article  CAS  Google Scholar 

  3. 3.

    Zhang, B. et al. Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate. Stat. Interface 4, 339–352 (2011).

    Google Scholar 

  4. 4.

    MACH 1.0, http://www.sph.umich.edu/csg/abecasis/MACH/index.html.

  5. 5.

    Li, Y. & Abecasis, G.R. Mach 1.0: Rapid haplotype reconstruction and missing genotype inference. Am. J. Hum. Genet. S79, 2290 (2006).

    Google Scholar 

  6. 6.

    IMPUTE version 2, http://mathgen.stats.ox.ac.uk/impute/impute_v2.html.

  7. 7.

    BEAGLE Genetic Analysis Software Package, http://faculty.washington.edu/browning/beagle/beagle.html.

  8. 8.

    Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  9. 9.

    Marchini, J. et al. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  10. 10.

    International HapMap Project, http://hapmap.ncbi.nlm.nih.gov/.

  11. 11.

    1000 Genomes, http://www.1000genomes.org.

  12. 12.

    Anderson, C.A. et al. Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms. Am. J. Hum. Genet. 83, 112–119 (2008).

    Article  CAS  Google Scholar 

  13. 13.

    Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644 (2006).

    Article  CAS  Google Scholar 

  14. 14.

    Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3: Genes, Genomics, Genetics 1, 457–470 (2011).

    Google Scholar 

  15. 15.

    Baum, L.E. & Petria, T. Statistical inference for probabilistic functions of finite state Morkov chains. Annals of Mathematical Statistics 37, 1554–1563 (1966).

    Article  Google Scholar 

  16. 16.

    Sanna, S. et al. Common variants in the GDF5-UQCC region are associated with variation in human height. Nat. Genet. 40, 198–203 (2008).

    Article  CAS  Google Scholar 

  17. 17.

    Gene Expression Omnibus (GEO). http://www.ncbi.nlm.nih.gov/geo/.

  18. 18.

    Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265 (2005).

    Article  CAS  Google Scholar 

  19. 19.

    Gabriel, S.B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2001).

    Article  Google Scholar 

  20. 20.

    Benusiglio, P.R. et al. Common ERBB2 polymorphisms and risk of breast cancer in a white British population: a case-control study. Breast Cancer Res. 7, 204–209 (2005).

    Article  Google Scholar 

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Correspondence to Miyoung Shin.

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Kim, J., Shin, M., Chung, M. et al. A block-based imputation approach with adaptive LD blocks for fast genotype imputation. BioChip J 7, 63–67 (2013). https://doi.org/10.1007/s13206-013-7110-2

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  • SNP chip
  • Next Generation Sequencing
  • Imputation
  • Linkage disequilibrium block
  • Haplotype reference panel