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SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries

Nature Methods volume 5, pages 247–252 (2008)Cite this article

Abstract

High-density single-nucleotide polymorphism (SNP) arrays have revolutionized the ability of genome-wide association studies to detect genomic regions harboring sequence variants that affect complex traits. Extensive numbers of validated SNPs with known allele frequencies are essential to construct genotyping assays with broad utility. We describe an economical, efficient, single-step method for SNP discovery, validation and characterization that uses deep sequencing of reduced representation libraries (RRLs) from specified target populations. Using nearly 50 million sequences generated on an Illumina Genome Analyzer from DNA of 66 cattle representing three populations, we identified 62,042 putative SNPs and predicted their allele frequencies. Genotype data for these 66 individuals validated 92% of 23,357 selected genome-wide SNPs, with a genotypic and sequence allele frequency correlation of r = 0.67. This approach for simultaneous de novo discovery of high-quality SNPs and population characterization of allele frequencies may be applied to any species with at least a partially sequenced genome.

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Figure 1: Distribution of sequence and genotype derived allele frequencies (r = 0.67) in the SNP discovery populations.

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Acknowledgements

J.F.T. and R.D.S. were supported by National Research Initiative grants 2005-35205-15448, 2005-35604-15615, 2006-35205-16701 and 2006-35616-16697 from the US Department of Agriculture Cooperative State Research, Education and Extension Service. C.P.V.T., T.S.S., and L.K.M. were supported by National Research Initiative grant 2006-35205-16888 from the US Department of Agriculture Cooperative State Research, Education, and Extension Service and by Projects 1265-31000-081D and 1265-31000-090-00D from the United States Department of Agriculture Agricultural Research Service. T.P.L.S. was supported by Project 5438-31000-073D from the US Department of Agriculture Agricultural Research Service. L.K.M. was also supported by National Research Initiative grant 2006-35205-17878 from the US Department of Agriculture Cooperative State Research, Education and Extension Service. We gratefully acknowledge the early prepublication access under the Fort Lauderdale conventions to the draft bovine genome sequence provided by the Baylor College of Medicine Human Genome Sequencing Center and the Bovine Genome Sequencing Project Consortium.

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Authors and Affiliations

  1. United States Department of Agriculture, Bovine Functional Genomics Laboratory, Agricultural Research Service, 10300 Baltimore Avenue, Beltsville, 20705, Maryland, USA

    Curtis P Van Tassell, Lakshmi K Matukumalli & Tad S Sonstegard

  2. United States Department of Agriculture, US Meat Animal Research Center, Agricultural Research Service, P.O. Box 166, Clay Center, 68933, Nebraska, USA

    Timothy P L Smith

  3. Bioinformatics and Computational Biology, George Mason University, 10900 University Blvd., Manassas, 20110, Virginia, USA

    Lakshmi K Matukumalli

  4. Division of Animal Sciences, 920 East Campus Drive

    Jeremy F Taylor & Robert D Schnabel

  5. University of Missouri, Columbia, 65211, Missouri, USA

    Jeremy F Taylor & Robert D Schnabel

  6. Illumina, Inc., 25861 Industrial Blvd., Hayward, 94545, California, USA

    Cynthia Taylor Lawley & Christian D Haudenschild

  7. Department of Agricultural, Food and Nutritional Science, University of Alberta, 410 AgFor Centre, Edmonton, T6G 2P5, Alberta, Canada

    Stephen S Moore

  8. Genome Sequencing Center, Washington University School of Medicine, Campus Box 8501, 4444 Forest Park Avenue, St. Louis, 63108, Missouri, USA

    Wesley C Warren

Authors
  1. Curtis P Van Tassell
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  2. Timothy P L Smith
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  3. Lakshmi K Matukumalli
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  4. Jeremy F Taylor
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  5. Robert D Schnabel
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  6. Cynthia Taylor Lawley
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  7. Christian D Haudenschild
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  8. Stephen S Moore
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  9. Wesley C Warren
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  10. Tad S Sonstegard
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Contributions

C.P.V.T. and L.K.M. developed and implemented the SNP discovery algorithm; J.F.T. and C.P.V.T. performed SNP discovery modeling; C.P.V.T., T.S.S. and L.K.M. performed in silico genome analysis; W.C.W. suggested the reduced representation strategy; T.P.L.S. constructed the RRLs; J.F.T., R.D.S., T.P.L.S. and T.S.S. identified cows for DNA pools; T.S.S. managed the DNA collection; C.T.L. genotyped the discovery animals and managed the assay synthesis; C.D.H. sequenced the RRLs; L.K.M., T.S.S., R.D.S., S.S.M. and T.P.L.S. conducted pilot validations; and C.P.V.T., J.F.T., T.S.S. and T.P.L.S. coordinated manuscript writing and editing.

Corresponding author

Correspondence to Curtis P Van Tassell.

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Competing interests

C.T.L. and C.D.H. are employees of Illumina, Inc.

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Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Table 1, Supplementary Methods (PDF 1163 kb)

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Van Tassell, C., Smith, T., Matukumalli, L. et al. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5, 247–252 (2008). https://doi.org/10.1038/nmeth.1185

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