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A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel

Publication ,  Journal Article
Kraja, AT; Warwick Daw, E; Lenzini, P; Wang, L; Lin, SJ; Williams, CA; Wells, AB; Lunetta, KL; Murabito, JM; Sebastiani, P; Tosto, G; Perls, T ...
Published in: International Journal of Bioinformatics Research and Applications
January 1, 2020

This study compares methods of imputing genetic markers, given a typed GWAS scaffold from the Long Life Family Study (LLFS) and latest reference panel of 1000-Genomes. We examined two programs for pre-phasing haplotypes MACH/SHAPEIT2 and MINIMAC/IMPUTE2 for imputation. SHAPEIT2 is advantageous for haplotype pre-phasing. MINIMAC and IMPUTE2 produced similar imputation quality. We used a 4MB region on chromosome 2 of LLFS and in the Supplement, we compared methods using chromosome 19 data from the Genetic Analysis Workshop-19. IMPUTE2 had the advantage of using two references 1000G and a sequence for a subset of subjects. SHAPEIT2 and IMPUTE2 were used to finalise the full LLFS autosome imputation. In LLFS, 44% of ~80M autosomal imputed variants showed good imputation quality (info ≥ 0.30). Low imputation quality was associated with a predominantly low allele frequency in 1000-Genomes. New emerging large-scale sequences and enhanced imputation methodologies will further improve imputation quality.

Duke Scholars

Published In

International Journal of Bioinformatics Research and Applications

DOI

EISSN

1744-5493

ISSN

1744-5485

Publication Date

January 1, 2020

Volume

16

Issue

1

Start / End Page

59 / 84

Related Subject Headings

  • Bioinformatics
  • 49 Mathematical sciences
  • 46 Information and computing sciences
  • 31 Biological sciences
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

APA
Chicago
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Kraja, A. T., Warwick Daw, E., Lenzini, P., Wang, L., Lin, S. J., Williams, C. A., … Province, M. A. (2020). A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel. International Journal of Bioinformatics Research and Applications, 16(1), 59–84. https://doi.org/10.1504/IJBRA.2020.104855
Kraja, A. T., E. Warwick Daw, P. Lenzini, L. Wang, S. J. Lin, C. A. Williams, A. B. Wells, et al. “A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel.” International Journal of Bioinformatics Research and Applications 16, no. 1 (January 1, 2020): 59–84. https://doi.org/10.1504/IJBRA.2020.104855.
Kraja AT, Warwick Daw E, Lenzini P, Wang L, Lin SJ, Williams CA, et al. A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel. International Journal of Bioinformatics Research and Applications. 2020 Jan 1;16(1):59–84.
Kraja, A. T., et al. “A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel.” International Journal of Bioinformatics Research and Applications, vol. 16, no. 1, Jan. 2020, pp. 59–84. Scopus, doi:10.1504/IJBRA.2020.104855.
Kraja AT, Warwick Daw E, Lenzini P, Wang L, Lin SJ, Williams CA, Wells AB, Lunetta KL, Murabito JM, Sebastiani P, Tosto G, Barral S, Minster RL, Yashin A, Perls T, Province MA. A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel. International Journal of Bioinformatics Research and Applications. 2020 Jan 1;16(1):59–84.

Published In

International Journal of Bioinformatics Research and Applications

DOI

EISSN

1744-5493

ISSN

1744-5485

Publication Date

January 1, 2020

Volume

16

Issue

1

Start / End Page

59 / 84

Related Subject Headings

  • Bioinformatics
  • 49 Mathematical sciences
  • 46 Information and computing sciences
  • 31 Biological sciences
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences