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Sept2016 smallvar nist intro

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Sept2016 smallvar nist intro

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Sept2016 smallvar nist intro

  1. 1. Genome in a Bottle Workshop Small Variant Data Jamboree Justin Zook and Marc Salit NIST Genome-Scale Measurements Group September 15, 2016
  2. 2. Integration Methods to Establish Reference Variant Calls Candidate variants Concordant variants Find characteristics of bias Arbitrate using evidence of bias Confidence Level Zook et al., Nature Biotechnology, 2014.
  3. 3. Integration Methods to Establish Reference Variant Calls Candidate variants Concordant variants Find characteristics of bias Arbitrate using evidence of bias Confidence Level Zook et al., Nature Biotechnology, 2014.
  4. 4. New calls (v3.3) vs. old calls (v2.19) V3.3 • 3441361 match PG • 550982 PG calls outside high conf • 124715 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 40 calls not in PG – 60 extra PG calls V2.19 • 3030717 match PG • 1018795 PG calls outside high conf • 122359 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 87 calls not in PG – 404 extra PG calls
  5. 5. New calls (v3.3) vs. old calls (v2.19) V3.3 • 3441361 match PG • 550982 PG calls outside high conf • 124715 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 40 calls not in PG – 60 extra PG calls V2.19 • 3030717 match PG • 1018795 PG calls outside high conf • 122359 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 87 calls not in PG – 404 extra PG calls More high-confidence calls match Platinum Genomes
  6. 6. New calls (v3.3) vs. old calls (v2.19) V3.3 • 3441361 match PG • 550982 PG calls outside high conf • 124715 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 40 calls not in PG – 60 extra PG calls V2.19 • 3030717 match PG • 1018795 PG calls outside high conf • 122359 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 87 calls not in PG – 404 extra PG calls Similar extra calls not in Platinum Genomes
  7. 7. New calls (v3.3) vs. old calls (v2.19) V3.3 • 3441361 match PG • 550982 PG calls outside high conf • 124715 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 40 calls not in PG – 60 extra PG calls V2.19 • 3030717 match PG • 1018795 PG calls outside high conf • 122359 calls not in PG • After excluding low confidence regions and regions around filtered PG calls: – 87 calls not in PG – 404 extra PG calls ~80% fewer differences from PG in high confidence regions
  8. 8. New calls (v3.3) vs. old calls (v2.19) Example vcf (verily) Stratified V3.3 • 17% of SNPs not assessed – 23% of SNPs in RefSeq coding – 53% of SNPs in “bad promoters” • 78% of indels not assessed – 0.7% difference rate • 17% FP in regions homologous to decoy V2.19 • 27% of SNPs not assessed – 36% of SNPs in RefSeq coding – 82% of SNPs in “bad promoters” • 78% of indels not assessed – 1.2% difference rate • 0.2% FP in regions homologous to decoy
  9. 9. Principles of Integration Process • Form sensitive variant calls from each dataset • Define “callable regions” for each callset • Filter calls from each method with annotations unlike concordant calls • Compare high-confidence calls to other callsets and manually inspect subset of differences – vs. pedigree-based calls – vs. common pipelines – Trio analysis • When benchmarking a new callset against ours, most putative FPs/FNs should actually be FPs/FNs
  10. 10. Criteria for including new callsets • Form sensitive variant calls from each dataset • Define “callable regions” for each callset • Good coverage and MapQ • Use knowledge about technology and manual inspection to exclude repetitive regions difficult for each dataset • For new callsets, ensure most FNs in callable regions relative to current high-confidence calls are questionable in the current calls • Filter calls from each method with annotations unlike concordant calls – Annotations for which outliers are expected to indicate bias should be selected for each callset
  11. 11. Ongoing work: With sufficient coverage, 10X phasing seems to specifically identify most SNP errors identified by pedigree phasing Collaboration with Nathan Edwards and Zhezhen Wang at Georgetown Univ
  12. 12. Ongoing work: How can we add more complex events that are not normalized? • Current integration only breaks into primitives – Some complex calls end up uncertain – If part of a complex variant is uncertain, we exclude the whole region • 3 approaches – Kevin Jacobs vgraph • Merge all callsets into a single graph • Still need to work on partial complex calls – Chen Sun and Paul Medvedev – varmatch • Start with one callset and match otther callers one at a time, adding in new variants from each – Sean Irvine and Len Trigg, RTG – vcfeval • Presentation today
  13. 13. Ongoing work: GRCh38 • Draft calls for chr20 on GRCh38 • Make calls on mapped reads for Illumina and 10X • Lift over calls for CG, Ion, and SOLiD • Preliminary comparisons to PG seem similar to those for GRCh37
  14. 14. Ongoing/Future Work and Questions • Integrate with pedigree calls for NA12878 – Mike Eberle, Illumina • Integrating phasing information from family, linked reads, etc. – Sean Irvine/Len Trigg, RTG • Integrate complex variants – Sean Irvine/Len Trigg, RTG – Chen Sun/Paul Medvedev, PSU • Incorporate more calls in difficult-to-map regions – 10X – Dovetail – PacBio • How to integrate indels 15-50bp? • Using ALT loci
  15. 15. Acknowledgements • NIST – Marc Salit – Jenny McDaniel – Lindsay Vang – David Catoe • Genome in a Bottle Consortium • GA4GH Benchmarking Team • FDA – Liz Mansfield – Zivana Tevak – David Litwack

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