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Improved imputation of common and uncommon SNPs with a new reference set

Nature Genetics volume 44, pages 6–7 (2012)Cite this article

Statistical imputation of genotype data is an important statistical technique that uses patterns of linkage disequilibrium observed in a reference set of haplotypes to computationally predict genetic variants in silico1. Currently, the most popular reference sets are the publicly available International HapMap2 and 1000 Genomes data sets3. Although these resources are valuable for imputing a sizeable fraction of common SNPs, they may not be optimal for imputing data for the next generation of genome-wide association studies (GWAS) and SNP arrays, which explore a fraction of uncommon variants.

We have built a new resource for the imputation of SNPs for existing and future GWAS, known as the Division of Cancer Epidemiology and Genetics (DCEG) Reference Set. The data set has genotypes for cancer-free individuals, including 728 of European ancestry from three large prospectively sampled studies4,5,6, 98 African-American individuals from the Prostate, Lung, Colon and Ovary Cancer Screening Trial (PLCO), 74 Chinese individuals from a clinical trial in Shanxi, China (SHNX)7 and 349 individuals from the HapMap Project (Table 1). The final harmonized data set includes 2.8 million autosomal polymorphic SNPs for 1,249 individuals after rigorous quality control metrics were applied (see Supplementary Methods and Supplementary Tables 1 and 2).

Table 1 Samples included in the DCEG Reference Set
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Figure 1: Imputation accuracy for individuals of European ancestry with the DCEG Reference Set and publicly available reference sets.

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Acknowledgements

The genotyping in the Multiethnic Cohort Study (MEC) was supported by a Department of Defense Breast Cancer Research Program Era of Hope Scholar Award (W81XWH-08-1-0383 to C.A.H.) and an NIH grant (CA132839).

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Authors and Affiliations

  1. Core Genotyping Facility, SAIC-Frederick, National Cancer Institute (NCI)-Frederick, Frederick, Maryland, USA

    Zhaoming Wang, Kevin B Jacobs, Meredith Yeager, Amy Hutchinson & Xiang Deng

  2. Division of Cancer Epidemiology and Genetics, NCI, US National Institutes of Health (NIH), Bethesda, Maryland, USA

    Zhaoming Wang, Kevin B Jacobs, Meredith Yeager, Amy Hutchinson, Joshua Sampson, Nilanjan Chatterjee, Demetrius Albanes, Sonja I Berndt, Charles C Chung, Xiang Deng, Ann W Hsing, Mark P Purdue, Phil Taylor, Margaret Tucker & Stephen J Chanock

  3. Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA

    W Ryan Diver, Susan M Gapstur & Lauren R Teras

  4. Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California, USA

    Christopher A Haiman, Brian E Henderson & Daniel Stram

  5. Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland

    Jarmo Virtamo

  6. Illumina, San Diego, California, USA

    Michael A Eberle & Jennifer L Stone

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Correspondence to Stephen J Chanock.

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Supplementary Methods, Supplementary Figures 1–3 and Supplementary Tables 1 and 2. (PDF 779 kb)

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Wang, Z., Jacobs, K., Yeager, M. et al. Improved imputation of common and uncommon SNPs with a new reference set. Nat Genet 44, 6–7 (2012). https://doi.org/10.1038/ng.1044

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