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

Aldi T. Kraja; E. Warwick Daw; Petra Lenzini; Lihua Wang; Shiow J. Lin; Christine A. Williams; Alan B. Wells; Kathryn L. Lunetta; Joanne M. Murabito; Paola Sebastiani; Giuseppe Tosto; Sandra Barral; Ryan L. Minster; Anatoly Yashin; Thomas Perls; Michael A. Province

doi:10.1504/IJBRA.2020.104855

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

Aldi T. Kraja, E. Warwick Daw, Petra Lenzini, Lihua Wang, Shiow J. Lin, Christine A. Williams, Alan B. Wells, Kathryn L. Lunetta, Joanne M. Murabito, Paola Sebastiani, Giuseppe Tosto, Sandra Barral, Ryan L. Minster, Anatoly Yashin, Thomas Perls, Michael A. Province

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	59-84
Number of pages	26
Journal	International Journal of Bioinformatics Research and Applications
Volume	16
Issue number	1
DOIs	https://doi.org/10.1504/IJBRA.2020.104855
State	Published - 2020

Keywords

1000 Genomes reference
FCGENE software
Genetic imputation
IMPUTE2 software
LLFS
Long life family study
MACH software
MINIMACH software
SHAPEIT2 software
Sequence reference

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Kraja, A. T., Warwick Daw, E., Lenzini, P., Wang, L., Lin, S. J., Williams, C. A., Wells, A. B., Lunetta, K. L., Murabito, J. M., Sebastiani, P., Tosto, G., Barral, S., Minster, R. L., Yashin, A., Perls, T., & 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

@article{d1599558615e47c497e747685213e4a3,

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

abstract = "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.",

keywords = "1000 Genomes reference, FCGENE software, Genetic imputation, IMPUTE2 software, LLFS, Long life family study, MACH software, MINIMACH software, SHAPEIT2 software, Sequence reference",

author = "Kraja, {Aldi T.} and {Warwick Daw}, E. and Petra Lenzini and Lihua Wang and Lin, {Shiow J.} and Williams, {Christine A.} and Wells, {Alan B.} and Lunetta, {Kathryn L.} and Murabito, {Joanne M.} and Paola Sebastiani and Giuseppe Tosto and Sandra Barral and Minster, {Ryan L.} and Anatoly Yashin and Thomas Perls and Province, {Michael A.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2020 Inderscience Enterprises Ltd.",

year = "2020",

doi = "10.1504/IJBRA.2020.104855",

language = "English",

volume = "16",

pages = "59--84",

journal = "International Journal of Bioinformatics Research and Applications",

issn = "1744-5485",

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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 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, vol. 16, no. 1, pp. 59-84. https://doi.org/10.1504/IJBRA.2020.104855

TY - JOUR

T1 - A comparison of genetic imputation methods using Long Life Family Study genotypes and sequence data with the 1000 Genome reference panel

AU - Kraja, Aldi T.

AU - Warwick Daw, E.

AU - Lenzini, Petra

AU - Wang, Lihua

AU - Lin, Shiow J.

AU - Williams, Christine A.

AU - Wells, Alan B.

AU - Lunetta, Kathryn L.

AU - Murabito, Joanne M.

AU - Sebastiani, Paola

AU - Tosto, Giuseppe

AU - Barral, Sandra

AU - Minster, Ryan L.

AU - Yashin, Anatoly

AU - Perls, Thomas

AU - Province, Michael A.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - 1000 Genomes reference

KW - FCGENE software

KW - Genetic imputation

KW - IMPUTE2 software

KW - LLFS

KW - Long life family study

KW - MACH software

KW - MINIMACH software

KW - SHAPEIT2 software

KW - Sequence reference

UR - http://www.scopus.com/inward/record.url?scp=85079535147&partnerID=8YFLogxK

U2 - 10.1504/IJBRA.2020.104855

DO - 10.1504/IJBRA.2020.104855

M3 - Article

AN - SCOPUS:85079535147

SN - 1744-5485

VL - 16

SP - 59

EP - 84

JO - International Journal of Bioinformatics Research and Applications

JF - International Journal of Bioinformatics Research and Applications

IS - 1

ER -

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

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Keywords

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