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The 1000 Genomes Project Lessons From Variant Calling and Genotyping

The 1000 Genomes Project Lessons From Variant Calling and Genotyping. October 13 th , 2011 Hyun Min Kang University of Michigan, Ann Arbor. Overview of Phase 1 CALL SET . 1000 Genomes integrated genotypes. Low-pass Genomes. Low-pass Genomes. Low-pass Genomes. Low-pass Genomes.

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The 1000 Genomes Project Lessons From Variant Calling and Genotyping

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  1. The 1000 Genomes ProjectLessons From Variant Calling and Genotyping October 13th, 2011 Hyun Min Kang University of Michigan, Ann Arbor

  2. Overview of Phase 1 CALL SET

  3. 1000 Genomes integrated genotypes Low-pass Genomes Low-pass Genomes Low-pass Genomes Low-pass Genomes Low-pass Genomes Low-pass Genomes Low-pass Genomes Low-pass Genomes Deep Exomes Low-pass Genomes SVs 15k SNPs 38M INDELs 4.0M Integrated Genotypes ~42M

  4. Methods for integrated genotypes

  5. From PILOT to PHASE1 • PHASE1 • 36.8M SNPs • Ts/Tv 2.17 • Includes 98.9% HapMap3 • PILOT • 14.8M SNPs • Ts/Tv 2.01 • Includes 97.8% HapMap3 Autosomal chromosomes only

  6. From PILOT to PHASE1 • PHASE1-only • 23.8M SNPs • Ts/Tv 2.16 • Includes 1.2% of HapMap3 • PILOT ∧ PHASE1 • 13.1M SNPs • Ts/Tv 2.18 • Includes 97.7% of HapMap3 • PILOT-only • 1.7M SNPs • Ts/Tv 1.11 • Includes 0.15% HapMap3

  7. From PILOT to PHASE1 100k monomorphic SNPs in 2.5M OMNI Array (>1,000 individuals) • PHASE1-only • 23.8M SNPs • Ts/Tv 2.16 • Includes 1.2% of HapMap3 • PILOT ∧ PHASE1 • 13.1M SNPs • Ts/Tv 2.18 • Includes 97.7% of HapMap3 • PILOT-only • 1.7M SNPs • Ts/Tv 1.11 • Includes 0.15% HapMap3

  8. From PILOT to PHASE1 : Improved SNP calls 100k monomorphic SNPs in 2.5M OMNI Array (>1,000 individuals) • PHASE1-only • 23.8M SNPs • Ts/Tv 2.16 • Includes 1.2% of HapMap3 • PILOT ∧ PHASE1 • 13.1M SNPs • Ts/Tv 2.18 • Includes 97.7% of HapMap3 0.3% of OMNI-MONO • PILOT-only • 1.7M SNPs • Ts/Tv 1.11 • Includes 0.15% HapMap3 1.2% of OMNI-MONO 59.6% of OMNI-MONO OMNI-MONO information was not used in making phase1 variant calls

  9. improvement in methodssince pilot

  10. 1000 Genomes’ engines for improved variant calls and genotypes • INDEL realignment • Per Base Alignment Quality (BAQ) adjustment • Robust consensus SNP selection strategy • Variant Quality Score Recalibration (VQSR) • Support Vector Machine (SVM) • improved Genotype Likelihood Calculation • BAM-specific Binomial Mixture Model (BBMM) • Leveraging off-target exome reads

  11. INDEL Realignment : How it works… • Given a list of potential indels… • Check if reads consistent with SNP or indel • Adjust alignment as needed • Greatly reduces false-positive SNP calls Ref: AAGCGTCGG AAGCGT AAGCGTC AAGCGTCG AAGCGC AAGCGCG AAGCG-CGG AAGCGTCG AAGCGT AAGCGTC AAGCGTCG AAGCG-C AAGCG-CG AAGCG-CGG Read pile consistent with the reference sequence With one read, hard to choose between alternatives Read pile consistent with a 1bp deletion Neighboring SNPs? Short Indel? Eric Banks and Mark DePristo

  12. Per Base Alignment Qualities Short Read GATAGCTAGCTAGCTGATGA GCCG 5’-AGCTGATAGCTAGCTAGCTGATGAGCCCGATC-3’ Reference Genome Heng Li

  13. Per Base Alignment Qualities Should we insert a gap? Short Read GATAGCTAGCTAGCTGATGAGCC-G 5’-AGCTGATAGCTAGCTAGCTGATGAGCCCGATC-3’ Reference Genome Heng Li

  14. Per Base Alignment Qualities Compensate for Alignment Uncertainty With Lower Base Quality Short Read GATAGCTAGCTAGCTGATGAGCCG 5’-AGCTGATAGCTAGCTAGCTGATGAGCCCGATC-3’ Reference Genome Heng Li

  15. Per Base Alignment Qualities Compensate for Alignment Uncertainty With Lower Base Quality Short Read GATAGCTAGCTAGCTGATGAGCCG 5’-AGCTGATAGCTAGCTAGCTGATGAGCCCGATC-3’ Reference Genome Improves quality near new indels and sequencing artifacts Heng Li

  16. Producing high-quality consensus call sets Ryan Poplin

  17. Consensus SNP site selection under multidimensional feature space Feature 3 Feature 2 • Filter PASS • Filter FAIL Feature 1 Goo Jun – Joint variant calling and … - Platform 192, Friday 5:30 (Room 517A)

  18. Improved likelihood estimationproduces more accurate genotypes Evaluation in April 2011 KEY IDEA in BBMM: Re-estimate the genotype likelihood by clustering the variants based on the read distribution HET-discordance(MAQ model) HET-discordance(BBMM) Fuli Yu, James Lu – An integrative variant analysis… - Poster 842W

  19. Off-target exome reads improves genotype quality Integrated on-target coding genotypes are also more accurate than low-coverage-only or exome-only platforms

  20. Genotype Qualities in SVs and INDELs Bob Handsaker

  21. More in-depth view ofphase 1 integrated genotypes

  22. Sensitivity at low-frequency SNPs 0.1% 0.5% 1.0%

  23. >96% SNPs are detected compared to deep genomes

  24. Genotype discordance by frequency Genotype Discordance

  25. Impact of sequencing depth on genotype accuracy(interim integrated panel, chr20)

  26. Highlights • The quality of phase 1 call set is much more improved compared to pilot call set • 1000G engines for phase1 variant calls produced high-sensitivity, high-specificity variant calls • >99% of genotypes are concordant with array-based genotypes • Likelihood-based integrated improves off-target & on-target genotyping qualities

  27. Acknowledgements The 1000 Genomes Project 1000 Genomes Analysis Group

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