Mining genetic variation in any species with GEMINI

Plant and Animal Genomics
January 9, 2016

Transcript

1. Aaron Quinlan
   University of Utah
   quinlanlab.org
   @aaronquinlan
   
   Plant and Animal Genome Conference
   January 9, 2016
   
   
   Mining genetic variation in any species
   with GEMINI
   

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2. Origins of GEMINI:
   Genetics of hypersensitivity to ionizing radiation
   Impact of standard radiation
   therapy in an undiagnosed
   ataxia-telangiectasia (A-T) patient
   •  140 such patients screened for dysfunction
   in known radiosensitivity genes (e.g., ATM
   and NBN). None found.
   •  Thus, opportunity to discover new genes
   underlying response to DNA damage.
   •  Hypothesis: each patient has a single gene
   disorder, yet the phenotype is only
   observed when they receive radiation.
   

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3. Interpreting genetic variation: context is crucial
   ...CCTCATGCATGGAAA...
   Genetic variation
   ...CCTCATGTATGGAAA...
   ...CCTCATGCATGGAAA...
   ...CCTCATGCATGGAAA...
   ...CCTCATGTATGGAAA...
   ...CCTCATGCATGGAAA...
   ...CCTCATGTATGGAAA...
   Chromatin marks
   DNA methylation
   RNA expression
   TF binding
   

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4. GEMINI: a flexible framework for exploring genome variation
   Uma Paila Brad Chapman
   github.com/arq5x/gemini
   gemini.rtfd.org
   Brent Pedersen
   

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5. How does GEMINI work?
   

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6. The GEMINI database model.
   

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7. Ad hoc variant exploration: genotype/phenotype filters
   gemini -q "SELECT *
   FROM variants
   WHERE impact_severity == ‘HIGH’
   AND max_aaf_all <= 0.001”
   --gt-filter “(gt_types).(LDL > 300).(!=HOM_REF).(count > 100)
   and
   (gt_types).(LDL < 100).(!=HOM_REF).(count < 10)"
   Which rare, deleterious variants are enriched in people with
   high LDL (>300 mg/dL) levels?
   gemini -q "SELECT *
   FROM variants
   WHERE impact_severity == ‘HIGH’
   AND max_aaf_all <= 0.001”
   --gt-filter “(gt_types).(breed=“angus”).(!=HOM_REF).(count > 100)
   and
   (gt_types).(breed=“belgian”).(!=HOM_REF).(count < 10)"
   Which rare, deleterious variants are enriched in Angus cattle but not
   Belgian Blue? (theoretical at the moment)
   

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8. Automated tools for disease inheritance models
   A/A
   A/G
   A/G
   Dominant
   A/G
   G/G
   A/G
   Recessive (consang.)
   C/C
   A/G
   A/A
   A/G
   C/T
   C/T
   Recessive (compound heterozygous)
   A/A
   A/G
   A/A
   De novo
   

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9. GEMINI is popular for rare disease research.
   UW Center
   for Mendelian Genomics
   

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10. Two key drawbacks of GEMINI
    • 
    Currently best for exome studies. Scales poorly for WGS.
    genome >> exome
    data size
    complexity (non-coding)
    • 
    Anthropocentic. Currently human (build 37) only.
    ! " " " " " "
    

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11. Improve speed for WGS datasets: use GQT
    Genotype
    Query Tools
    github.com/ryanlayer/gqt
    Nature Methods, 2015
    

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12. Improve speed for WGS datasets: RDBMS flexibility
        
    SQLite
    (current)
    PostgreSQL MySQL CloudSQL BigQuery
    SQLAlchemy: database abstraction layer
    
    

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13. Improve variant annotation speed and flexibility
    #CHROM POS ID REF ALT QUAL FILTER
    2 41647 . A G 4495.41 PASS
    2 45895 . A G 463.75 PASS
    2 224970 . C T 4241.64 PASS
    2 229934 . A G 5037.95 PASS
    2 234130 . T G 3958 PASS
    2 242732 . T TAAC 3193.19 PASS
    2 242800 . T C 3929.77 PASS
    2 243504 . C T 6628.06 PASS
    2 243567 . T TA 3398.03 HRunFilter
    2 262553 . T C 3503.49 PASS
    2 264895 . G C 3774.13 PASS
    2 269352 . G A 9802.28 PASS
    2 276942 . A G 5878.58 PASS
    2 277250 . G A 7051.35 PASS
    2 279705 . C T 7139.54 PASS
    2 283231 . A AT 6976.81 HRunFilter
    2 675831 . G T 865.05 PASS
    2 676177 . C G 4961.19 PASS
    2 905368 . C G 101.98 ABFilter;
    2 905369 . C G 28.97 ABFilter
    2 905393 . C G 930.81 QDFilter
    2 905427 . C G 140.17 QDFilter
    2 905442 . A T 131.51 ABFilter
    2 905492 . T G 550.3 QDFilter
    2 905494 . C G 48.5 ABFilter
    2 905533 . C T 320.33 ABFilter
    2 905576 . T G 72.09 QDFilter
    2 905581 . C T 1276.63 QDFilter
    2 905595 . G C 390.15 ABFilter
    2 905634 . A C 393.91 QDFilter
    2 905687 . C G 3233.06 ABFilter
    2 905736 . A T 1324.63 QDFilter
    2 905763 . G C 15.12 ABFilter
    Tabix’ed
    .
    .
    .
    

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14. vcfanno: flexible and fast VCF annotation
    Naked
    VCF
    vcfanno VCF
    w/ annotations
    in INFO field
    Brent Pedersen
    https://github.com/brentp/vcfanno
    VCF, BED, GFF, BAM, (soon BW)
    Manuscript in prep.
    

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15. [[annotation]]
    file=“ExAC.v3.vcf”
    fields=[“AF”, “AC_Het”]
    names=[“exac_aaf”, “exac_num_het”]
    ops=[“first”, “first”]
    [[annotation]]
    file="dbsnp.b141.vcf.gz"
    fields=["ID"]
    names=["rs_ids"]
    ops=[“concat"]
    [[annotation]]
    file="gerp.elements.bed.gz"
    columns=[4,4]
    names=[“gerp_mean”,”gerp_var”]
    ops=[“mean”, "lua:variance(vals)"]
    vcfanno configuration file.
    Allows multiple annotations
    from each file
    Can rename the annotations
    in the resulting VCF
    Multiple operations to
    summarize the results
    of multiple hits in annot. file:
    mean, max, min
    concat, count, uniq
    first, flag
    Match on POS+REF+ALT
    for VCF annotations.
    Lua for
    custom computations.
    variance() defined in
    custom.js
    

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16. before and after vcfanno
    AC=11;AF=0.017
    AC=11;AF=0.017;
    exac_aaf=0.0012;
    exac_num_het=8;
    rs_ids=1234;
    gerp_mean=7.25e-07
    gerp_var=1.39e-08
    Naked
    VCF
    Dressed
    VCF
    [[annotation]]
    file=“ExAC.v3.vcf”
    fields=[“AF”, “AC_Het”]
    names=[“exac_aaf”, “exac_num_het”]
    ops=[“first”, “first”]
    [[annotation]]
    file="dbsnp.b141.vcf.gz"
    fields=["ID"]
    names=["rs_ids"]
    ops=[“concat"]
    [[annotation]]
    file="gerp.elements.bed.gz"
    columns=[4,4]
    names=[“gerp_mean”,”gerp_var”]
    ops=[“mean”, "js:variance(vals)"]
    vcfanno
    configuration file
    

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17. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    []
    cache
    result
    q.1 q.2 q.3
    chromsweep is the fundamental algorithm
    underlying our bedtools software
    

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18. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    [3.1
    ]
    cache
    result
    q.1 q.2 q.3
    

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19. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.1
    }
    [3.1
    ]
    cache
    result
    q.1 q.2 q.3
    q.1 =
    

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20. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.1, 1.1
    }
    [3.1, 1.1
    ]
    cache
    result
    q.1 q.2 q.3
    q.1 =
    

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21. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    [3.1, 1.1
    ]
    cache
    result
    q.1 q.2 q.3
    3.1, 1.1
    q.1 =
    

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22. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    [3.1
    ]
    cache
    result
    q.1 q.2 q.3
    3.1, 1.1
    q.1 =
    

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23. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    []
    cache
    result
    q.1 q.2 q.3
    3.1, 1.1
    q.1 =
    

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24. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    []
    cache
    result
    q.1 q.2 q.3
    q.2 =
    3.1, 1.1
    q.1 =
    

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25. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.2
    }
    [3.2
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 =
    3.1, 1.1
    q.1 =
    

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26. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.2,2.1
    }
    [3.2,2.1
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 =
    3.1, 1.1
    q.1 =
    

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27. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.2,2.1,1.2
    }
    [3.2,2.1,1.2
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 =
    3.1, 1.1
    q.1 =
    

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28. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {3.2,2.1,1.2
    }
    [3.2,2.1,1.2
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 =
    *2.1 stays in the cache
    3.1, 1.1
    q.1 =
    

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29. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    [3.2,1.2
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 =
    Now 2.1 is removed
    3.2,2.1,1.2
    3.1, 1.1
    q.1 =
    

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30. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    [3.2
    ]
    cache
    result
    q.1 q.2 q.3
    q.2 = 3.2,2.1,1.2
    3.1, 1.1
    q.1 =
    

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31. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    []
    cache
    result
    q.1 q.2 q.3
    q.2 = 3.2,2.1,1.2
    3.1, 1.1
    q.1 =
    

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32. “chromsweep”: a sweeping algorithm for pre-sorted data
    VCF
    anno1
    anno2
    anno3
    1.1 1.2 1.3
    2.1 2.2
    3.1 3.2
    {}
    []
    cache
    result
    q.1 q.2 q.3
    q.3 = 1.3,2.2
    q.2 = 3.2,2.1,1.2
    3.1, 1.1
    q.1 =
    

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33. vcfanno implements the first parallel chromsweep
    VCF
    anno1
    anno2
    anno3
    Step 1: partition the query set at “breaks” in the data or when N (e.g. 10) intervals are found
    Step 2: Use Tabix to extract the records germane to a chunk from each annotation file
    Step 3: Chromsweep each chunk independently.
    

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34. vcfanno is speedy.
    18 annotations:
    29K variants / sec @ 12 cores
    

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35. vcfanno
    VCF
    hg38…
    VCF from any species
    and any genome build
    Vcfanno configuration file
    points to appropriate
    annotations
    GEMINI database
    is created based
    on vcfanno
    configuration file
    GEMINI database creation
    should be ~60X faster
    How do we support other species?
    

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36. [[annotation]]
    file=“cpg.hg38.bed.gz"
    fields=[4]
    names=[“cpg_density"]
    ops=[“mean"]
    [[annotation]]
    file=“rmsk.hg38.bed.gz"
    fields=[4]
    names=[“rmsk"]
    ops=[“concat”]
    [[annotation]]
    file="cytoband.hg38.bed.gz"
    fields=[4]
    names=[“cytoband”]
    ops=[“distinct"]
    How? Simply point vcfanno to the relevant annotations
    Human (hg38)
    [[annotation]]
    file=“cpg.bosTau8.bed.gz"
    fields=[4]
    names=[“cpg_density"]
    ops=[“mean"]
    [[annotation]]
    file=“rmsk.bosTau8.bed.gz"
    fields=[4]
    names=[“rmsk"]
    ops=[“concat”]
    [[annotation]]
    file="cytoband.bosTau8.bed.gz"
    fields=[4]
    names=[“cytoband”]
    ops=[“distinct"]
    Cow (bosTau8)
    

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37. Allows the use of the same query, regardless of species
    gemini -q "SELECT *
    FROM variants
    WHERE cpg_density >= 0.9
    Which variants overlap CpG islands whose CpG density is greater
    than or equal to 0.9?
    Human (hg38)
    
    Cow (bosTau8)
    
    

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38. Summary
    •  GEMINI is a flexible framework for exploring
    genetic variation from WES and WGS studies.
    •  Integrates variants, genotypes, phenotypes and
    annotations into a simple database.
    
    •  Current focus:
    •  Improving scalability for WGS
    •  Support for any (diploid) species
    
    •  Expected release: April 2016 github.com/arq5x/gemini
    gemini.rtfd.org
    

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39. Challenges
    •  Multi-allelic variants are a bugger.
    •  Even harder with polyploidy. The
    VCF format is ill-suited to this.
    •  Versioning & distributing annos.
    See GGD: https://github.com/arq5x/ggd
    

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40. Thank you.
    Funding:
    Brent
    Pedersen
    Ryan
    Layer
    Jim
    Havrilla
    

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41. First discovery with GEMINI:
    Defects in mitochondrial mRNA maturation cause radiosensitivity
    Sample A21: chr10, MTPAP, exon9, N478D homozygote
    

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