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Nature Genetics | Technical Report

日本語要約

A framework for variation discovery and genotyping using next-generation DNA sequencing data

Journal name:
Nature Genetics
Volume:
43,
Pages:
491–498
Year published:
DOI:
doi:10.1038/ng.806
Received
Accepted
Published online

Abstract

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

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  1. Framework for variation discovery and genotyping from next-generation DNA sequencing.
    Figure 1: Framework for variation discovery and genotyping from next-generation DNA sequencing.

    See text for a detailed description.

  2. Integrative genomics viewer (IGV) visualization of alignments in region chr.1: 1,510,530-1,510,589 from the Trio NA12878 Illumina reads from the 1000 Genomes Project (a) and NA12878 HiSeq reads before (left) and after (right) multiple sequence realignment (b).
    Figure 2: Integrative genomics viewer (IGV) visualization of alignments in region chr.1: 1,510,530–1,510,589 from the Trio NA12878 Illumina reads from the 1000 Genomes Project (a) and NA12878 HiSeq reads before (left) and after (right) multiple sequence realignment (b).

    Reads are depicted as arrows oriented by increasing machine cycle; highlighted bases indicate mismatches to the reference: green, A; orange, G; red, T dashes, deleted bases a coverage histogram per base is shown above the reads. Both the 4-bp indel (rs34877486) and the C/T polymorphism (rs2878874) are present in dbSNP, as are the artifactual A/G polymorphisms (rs28782535 and rs28783181) resulting from the mis-modeled indel, indicating that these sites are common misalignment errors.

  3. Raw (pink) and recalibrated (blue) base quality scores for NGS paired-end read sets of NA12878 of Illumina/GA (a), Roche/454 (b) and Life/SOLiD (c) lanes from the 1000 Genomes Project and Illumina/HiSeq (d).
    Figure 3: Raw (pink) and recalibrated (blue) base quality scores for NGS paired-end read sets of NA12878 of Illumina/GA (a), Roche/454 (b) and Life/SOLiD (c) lanes from the 1000 Genomes Project and Illumina/HiSeq (d).

    For each technology, the top panel shows reported base quality scores compared to the empirical estimates (Online Methods); the middle panel shows the difference between the average reported and empirical quality score for each machine cycle, with positive and negative cycle values given for the first and second read in the pair, respectively; and the bottom panel shows the difference between reported and empirical quality scores for each of the 16 genomic dinucleotide contexts. For example, the AG context occurs at all sites in a read where G is the current nucleotide and A is the preceding one in the read. Root-mean-square errors (RMSE) are given for the pre- and post-recalibration curves.

  4. Results of variant quality recalibration on HiSeq, exome and low-pass data sets.
    Figure 4: Results of variant quality recalibration on HiSeq, exome and low-pass data sets.

    (a) Relationship in the HiSeq call set between strand bias and quality by depth for genomic locations in HapMap3 (red) and dbSNP (orange) used for training the variant quality score recalibrator (left), (b) and the same annotations applied to differentiate likely true positive (green) from false positive (purple) new SNPs. (c–e) Quality tranches in the recalibrated HiSeq (c), exome (d) and low-pass CEU (e) calls beginning with (top) the highest quality but smallest call set with an estimated false positive rate among new SNP calls of <1/1000 to a more comprehensive call set (bottom) that includes effectively all true positives in the raw call set along with more false positive calls for a cumulative false positive rate of 10%. Each successive call set contains within it the previous tranche's true- and false-positive calls (shaded bars) as well as tranche-specific calls of both classes (solid bars). The tranche selected for further analyses here is indicated.

  5. Variation discovered among 60 individuals from the CEPH population from the 1000 Genomes Project pilot phase plus low-pass NA12878.
    Figure 5: Variation discovered among 60 individuals from the CEPH population from the 1000 Genomes Project pilot phase plus low-pass NA12878.

    (a) Discovered SNPs by non-reference allele count in the 61 CEPH cohort, colored by known (light blue) and new (dark blue) variation, along with non-reference sensitivity to CEU HapMap3 and 1000 Genomes Project low-pass variants. (b) Quality and certainty of discovered SNPs by non-reference allele count. The histogram depicts the certainty of called variation broken out into 0.1%, 1% and 10% new FDR tranches. The Ti/Tv ratio is shown for known and new variation for each allele count, aggregating the new calls with allele count >74 because of their limited numbers. (c,d) Genotyping accuracy for NA12878 from reads alone (blue squares) and following genotype-likelihood based imputation (pink circles) called in the 61 sample call set as assessed by the NRD rate to HiSeq genotypes as a function of allele count (c) and sequencing depth (d).

  6. Sensitivity and specificity of multi-sample discovery of variation in NA12878 with increasing cohort size for low-pass NA12878 read sets processed with N additional CEPH samples.
    Figure 6: Sensitivity and specificity of multi-sample discovery of variation in NA12878 with increasing cohort size for low-pass NA12878 read sets processed with N additional CEPH samples.

    (a) Receiver operating characteristic (ROC) curves for SNP calls relating specificity and sensitivity to discover non-reference sites from the NA12878 HiSeq call set. The maximum callable sensitivity, 66%, is the percent of sites from the HiSeq call set where at least one read carries the alternate allele in the low-pass data for NA12878; it reflects both differences in the sequencing technologies (36–76-bp GAII for the low-pass NA12878 sample compared to 101-bp HiSeq) as well as the vagaries of sampling at 4× coverage. Because most of these missed sites are common and are consequently called in the other samples, imputation recovers ~50% of these sites. (b,c) Increasing power to identify strand-biased, likely false positive SNP calls with additional samples. Histograms of the strand bias annotation at raw variant calls discovered in the low-pass CEU data using NA12878 at 4× combined with one other CEU individual (b) and with 60 other individuals (c) stratified into sites present (green) and not (purple) in the 1000 Genomes Project CEU trio.

References

  1. The 1000 Genomes Project Consortium. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
  2. Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).
  3. Ng, S.B. et al. Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. 42, 30–35 (2009).
  4. Lee, W. et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 465, 473–477 (2010).
  5. Pleasance, E.D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2009).
  6. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
  7. Roach, J.C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).
  8. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009).
  9. Li, H., Ruan, J. & Durbin, R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 18, 1851–1858 (2008).
  10. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
  11. Ning, Z., Cox, A.J. & Mullikin, J.C. SSAHA: a fast search method for large DNA databases. Genome Res. 11, 1725–1729 (2001).
  12. Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998).
  13. Brockman, W. et al. Quality scores and SNP detection in sequencing-by-synthesis systems. Genome Res. 18, 763–770 (2008).
  14. Li, M., Nordborg, M. & Li, L.M. Adjust quality scores from alignment and improve sequencing accuracy. Nucleic Acids Res. 32, 5183–5191 (2004).
  15. Li, R. et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 19, 1124–1132 (2009).
  16. Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010).
  17. Bentley, D.R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).
  18. Koboldt, D., Chen, K., Wylie, T. & Larson, D. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009).
  19. Wheeler, D.A. et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 452, 872–876 (2008).
  20. Mokry, M. et al. Accurate SNP and mutation detection by targeted custom microarray-based genomic enrichment of short-fragment sequencing libraries. Nucleic Acids Res. 38, e116 (2010).
  21. Shen, Y. et al. A SNP discovery method to assess variant allele probability from next-generation resequencing data. Genome Res. 20, 273–280 (2010).
  22. Hoberman, R. et al. A probabilistic approach for SNP discovery in high-throughput human resequencing data. Genome Res. 19, 1542–1552 (2009).
  23. Malhis, N. & Jones, S. High quality SNP calling using Illumina data at shallow coverage. Bioinformatics 26, 1029 (2010).
  24. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
  25. Handsaker, R.E., Korn, J.M., Nemesh, J. & McCarroll, S.A. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat. Genet. 43, 269–276 (2011).
  26. McKenna, A.H. et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
  27. Browning, B.L. & Yu, Z. Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false-positive associations for genome-wide association studies. Am. J. Hum. Genet. 85, 847–861 (2009).
  28. Langmead, B., Schatz, M.C., Lin, J., Pop, M. & Salzberg, S.L. Searching for SNPs with cloud computing. Genome Biol. 10, R134 (2009).
  29. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
  30. Green, R.E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).
  31. Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat. Biotechnol. 27, 182–189 (2009).
  32. Ng, S., Turner, E., Robertson, P. & Flygare, S. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).
  33. Mckernan, K.J. et al. Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding. Genome Res. 19, 1527–1541 (2009).
  34. Ebersberger, I., Metzler, D., Schwarz, C. & Pääbo, S. Genomewide comparison of DNA sequences between humans and chimpanzees. Am. J. Hum. Genet. 70, 1490–1497 (2002).
  35. Freudenberg-Hua, Y. et al. Single nucleotide variation analysis in 65 candidate genes for CNS disorders in a representative sample of the European population. Genome Res. 13, 2271–2276 (2003).
  36. Durbin, R., Eddy, S., Krogh, A. & Mitchison, G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. (Cambridge University Press, Cambridge, UK, 1998).
  37. Dohm, J.C., Lottaz, C., Borodina, T. & Himmelbauer, H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36, e105 (2008).
  38. HUGO Consortium. et al. Mapping human genetic diversity in Asia. Science 326, 1541–1545 (2009).
  39. Bishop, C. Pattern Recognition and Machine Learning (Springer, New York, New York, USA, 2006).

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Author information

Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Mark A DePristo,
    • Eric Banks,
    • Ryan Poplin,
    • Kiran V Garimella,
    • Jared R Maguire,
    • Christopher Hartl,
    • Anthony A Philippakis,
    • Guillermo del Angel,
    • Manuel A Rivas,
    • Matt Hanna,
    • Aaron McKenna,
    • Tim J Fennell,
    • Andrew M Kernytsky,
    • Andrey Y Sivachenko,
    • Kristian Cibulskis,
    • Stacey B Gabriel,
    • David Altshuler &
    • Mark J Daly

  2. Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Anthony A Philippakis

  3. Harvard Medical School, Boston, Massachusetts, USA.

    • Anthony A Philippakis,
    • David Altshuler &
    • Mark J Daly

  4. Center for Human Genetic Research, Massachusetts General Hospital, Richard B. Simches Research Center, Boston, Massachusetts, USA.

    • Manuel A Rivas,
    • David Altshuler &
    • Mark J Daly

Contributions

M.A.D., E.B., R.P., K.V.G., J.R.M., C.H., A.A.P., G.d.A., M.A.R., T.J.F., A.Y.S. and K.C. conceived of, implemented and performed analytic approaches. M.A.D., E.B., R.P., K.V.G., G.d.A., A.M.K. and M.J.D. wrote the manuscript. M.A.D., M.H. and A.M. developed Picard and GATK infrastructure underlying the tools implemented here. M.A.D., S.B.G., D.A. and M.J.D. lead the team.

Competing financial interests

The authors declare no competing financial interests.

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