Effect of Genome-Wide Genotyping and Reference Panels on Rare Variants Imputation

Hou-Feng Zheng; Martin Ladouceur; Celia M.T. Greenwood; J. Brent Richards

doi:10.1016/j.jgg.2012.07.002

Volume 39 Issue 10

Oct. 2012

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2012 > 39(10): 545-550

PDF( 435 KB)

Effect of Genome-Wide Genotyping and Reference Panels on Rare Variants Imputation

doi: 10.1016/j.jgg.2012.07.002

a
Department of Medicine, Human Genetics, McGill University, Montreal, Quebec H3T 1E2, Canada
b
Department of Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec H3T 1E2, Canada
c
Research Center of Montreal Heart Institute, Montreal, Quebec H1T 1C8 Canada
d
Department of Oncology, McGill University, Montreal, Quebec H3T 1E2, Canada
e
Twin Research and Genetic Epidemiology, King's College London, London SE1 7EH, United Kingdom

More Information

Corresponding author: E-mail address: hou.zheng@mail.mcgill.ca (Hou-Feng Zheng); E-mail address: brent.richards@mcgill.ca (J. Brent Richards)
Received Date: 2012-06-16
Accepted Date: 2012-07-03

Available Online: 2012-07-24

Publish Date: 2012-10-20

Abstract

Abstract

Common variants explain little of the variance of most common disease, prompting large-scale sequencing studies to understand the contribution of rare variants to these diseases. Imputation of rare variants from genome-wide genotypic arrays offers a cost-efficient strategy to achieve necessary sample sizes required for adequate statistical power. To estimate the performance of imputation of rare variants, we imputed 153 individuals, each of whom was genotyped on 3 different genotype arrays including 317k, 610k and 1 million single nucleotide polymorphisms (SNPs), to two different reference panels: HapMap2 and 1000 Genomes pilot March 2010 release (1KGpilot) by using IMPUTE version 2. We found that more than 94% and 84% of all SNPs yield acceptable accuracy (info > 0.4) in HapMap2 and 1KGpilot-based imputation, respectively. For rare variants (minor allele frequency (MAF) ≤5%), the proportion of well-imputed SNPs increased as the MAF increased from 0.3% to 5% across all 3 genome-wide association study (GWAS) datasets. The proportion of well-imputed SNPs was 69%, 60% and 49% for SNPs with a MAF from 0.3% to 5% for 1M, 610k and 317k, respectively. None of the very rare variants (MAF ≤ 0.3%) were well imputed. We conclude that the imputation accuracy of rare variants increases with higher density of genome-wide genotyping arrays when the size of the reference panel is small. Variants with lower MAF are more difficult to impute. These findings have important implications in the design and replication of large-scale sequencing studies.
- Genotype imputation,
- Genome-wide association study,
- 1000 Genome Project,
- HapMap,
- Rare variant,
- Common disease

These authors contributed equally to this work.

FullText(HTML)

References(26)

References

[1]	Andrew, T., Hart, D.J., Snieder, H. et al. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women Twin Res., 4 (2001),pp. 464-477
[2]	Browning, B.L., Browning, S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals Am. J. Hum. Genet., 84 (2009),pp. 210-223
[3]	Browning, S.R., Browning, B.L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering Am. J. Hum. Genet., 81 (2007),pp. 1084-1097
[4]	Consortium, I.H. A haplotype map of the human genome Nature, 437 (2005),pp. 1299-1320
[5]	Consortium, T.G.P. A map of human genome variation from population-scale sequencing Nature, 467 (2010),pp. 1061-1073
[6]	Dickson, S.P., Wang, K., Krantz, I. et al. Rare variants create synthetic genome-wide associations PLoS Biol., 8 (2010),p. e1000294
[7]	Eichler, E.E., Flint, J., Gibson, G. et al. Missing heritability and strategies for finding the underlying causes of complex disease Nat. Rev. Genet., 11 (2010),pp. 446-450
[8]	Holm, H., Gudbjartsson, D.F., Sulem, P. et al. Nat. Genet., 43 (2011),pp. 316-320
[9]	Howie, B., Marchini, J., Stephens, M. Genotype imputation with thousands of genomes G3 (Bethesda), 1 (2011),pp. 457-470
[10]	Howie, B.N., Donnelly, P., Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies PLoS Genet., 5 (2009),p. e1000529
[11]	Howie, B., Fuchsberger, C., Stephens, M. et al. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing Nat. Genet., 44 (2012),pp. 955-959
[12]	Ladouceur, M., Dastani, Z., Aulchenko, Y.S. et al. The empirical power of rare variant association methods: results from Sanger sequencing in 1998 individuals PLoS Genet., 8 (2012),p. e1002496
[13]	Li, L., Li, Y., Browning, S.R. et al. Performance of genotype imputation for rare variants identified in exons and flanking regions of genes PLoS ONE, 6 (2011),p. e24945
[14]	Li, Y., Willer, C., Sanna, S. et al. Genotype imputation Annu. Rev. Genomics Hum. Genet., 10 (2009),pp. 387-406
[15]	Li, Y., Willer, C.J., Ding, J. et al. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes Genet. Epidemiol., 34 (2010),pp. 816-834
[16]	Maher, B. Personal genomes: the case of the missing heritability Nature, 456 (2008),pp. 18-21
[17]	Marchini, J., Howie, B. Genotype imputation for genome-wide association studies Nat. Rev. Genet., 11 (2010),pp. 499-511
[18]	Marchini, J., Howie, B., Myers, S. et al. A new multipoint method for genome-wide association studies by imputation of genotypes Nat. Genet., 39 (2007),pp. 906-913
[19]	Nothnagel, M., Ellinghaus, D., Schreiber, S. et al. A comprehensive evaluation of SNP genotype imputation Hum. Genet., 125 (2009),pp. 163-171
[20]	Pei, Y.F., Li, J., Zhang, L. et al. Analyses and comparison of accuracy of different genotype imputation methods PLoS ONE, 3 (2008),p. e3551
[21]	Purcell, S., Neale, B., Todd-Brown, K. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses Am. J. Hum. Genet., 81 (2007),pp. 559-575
[22]	Richards, J.B., Kavvoura, F.K., Rivadeneira, F. et al. Collaborative meta-analysis: associations of 150 candidate genes with osteoporosis and osteoporotic fracture Ann. Intern. Med., 151 (2009),pp. 528-537
[23]	Richards, J.B., Rivadeneira, F., Inouye, M. et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study Lancet, 371 (2008),pp. 1505-1512
[24]	Shea, J., Agarwala, V., Philippakis, A.A. et al. Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction Nat. Genet., 43 (2011),pp. 801-805
[25]	Teo, Y.Y., Inouye, M., Small, K.S. et al. A genotype calling algorithm for the Illumina BeadArray platform Bioinformatics, 23 (2007),pp. 2741-2746
[26]	Zeggini, E. Next-generation association studies for complex traits Nat. Genet., 43 (2011),pp. 287-288