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QC-ing and merging truth data Michael Eberle September 15, 2016

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2

Pedigree validation (alone)

●  Using the inheritance allows us to systematically incorporate “good” variants from different callers -  Systematic errors are unlikely to result in pedigree consistent calls

●  PG merges calls from six different workflows -  Small variant call set includes 4,862,204 SNVs and 758,540 indels

●  Compared to single sample or trios this call set will… -  Mostly fail calls co-segregating with germline or cell line CNVs -  Fail germline or cell line de novo mutations

●  Even with pedigree check problems can still arise -  Same variant may be included twice (merging problem) -  Systematic errors may create pedigree consistent calls (SV edges) -  Incorrect ploidy may still produce “platinum” variants

89 90 91 92

77 78

79 80 81 82 83 84 85 87 86 88 93

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3

Validating & filtering variants with k-mers

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG ALT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG REF

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATAT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGCCAGGAAATTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGC

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGCCATTTGAAAAGGTATAAGTTCTGGAAGGTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAGGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGCCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

GTTCTGGAAGCTTAACAACGCCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG CAACGGCCGCCGTCAAAAATGAAATCCTAATCTTTGGCAGGAACTTTG

AAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCTTAATCTTTGGCAGGAACTTTG TATAGGTTCTGGAGGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGGTCTGCAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGA

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAATTGATATCCTA

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGAAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAA

CGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG TGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

9 Reads do not fully span the 51-mer 4 Reads contain base errors in 51-mer

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4

Validating & filtering variants with k-mers

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG ALT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG REF

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATAT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTT

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGCCAGGAAATTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGC

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGCCATTTGAAAAGGTATAAGTTCTGGAAGGTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAGGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGCCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

GTTCTGGAAGCTTAACAACGCCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG CAACGGCCGCCGTCAAAAATGAAATCCTAATCTTTGGCAGGAACTTTG

AAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCTTAATCTTTGGCAGGAACTTTG TATAGGTTCTGGAGGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGGTCTGCAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGA

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAATTGATATCCTA

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

CCATTTGAAAAGGTATAGGTTCTGGAAGCTTAACAACGGCTGCCGTCAA

CGGCTGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG TGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTG

Validate the REF with 6 k-mers and ALT with 5 k-mers

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5

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAG

TAAAGGTATAGGGTCTGGAAGCTTAACAACGGCCGCCGTC AAAAAGATATC

TAAAAGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACCTTG

TAAAGGTATAGGTTCTGGAAGCTTAAAAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAAATTTGTCTTTCC

TAAAGGTATAGGTTCTGGAAGCCTAACAACGGCCGCCGTCAA

TAAAGGTATAGGTTCTGGAAACTTAACAACGGCCGCCGTAAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCCTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCCGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAAAGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCCTAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

AAGCTTAACAACGGCCGCCGTCAAAATTGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

ACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC CGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

AGGTTCTGGAAGCTTAACAAAGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC TCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAAC

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCT

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTT

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAAAT

CGTAAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC AAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC

Inconsistent variants (homopolymer example)

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC ALT 1TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAAATGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC REF

TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTCAAAA TGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC ALT 2TAAAGGTATAGGTTCTGGAAGCTTAACAACGGCCGCCGTC AAA TGATATCCTAATCTTTGGCAGGAACTTTGTCTTTCC ALT 1+2

Validated the REF allele with 6 k-mers but 0 ALT k-mers

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6

K-mer reporting on consistent variants

●  Number of GT errors within the 13 member pedigree -  GT Pass means that there is one k-mer supporting each allele/haplo

§  Homozygous calls require two supporting k-mers

●  Number of k-mer errors in four founders -  Pass means that the haplo-predicted k-mer is observed in the founder

●  Normalized count for each k-mer -  Number of times each k-mer is observed in 13 member pedigree

divided by number of predicted haplotypes §  e.g. if six family members have the haplotype and we count the k-mer 60 times

then normalized count is 60/6 = 10

●  Minimum normalized count for each variants -  K-mer with the lowest normalized count for each variant

89 90 91 92

77 78

79 80 81 82 83 84 85 87 86 88 93

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7

K-mer filtering removes ~195k “artefacts”

1Failed/Passing means that all pedigree GTs are supported by k-mers 2Failed/Passing means that all founder k-mers are observed in the proper founders

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8

Adding content with k-mers

●  Some k-mer failures due to conflicting representations

●  Nearby variants that are missed can cause k-mer failures

●  We only consider pedigree-consistent variants

●  Working on a modified k-mer application that can take in many variants for hypothesis testing -  Merge PG and GIAB-specific variants (or other putative variants) -  Resolve conflicting representations -  Recover pedigree-failed variants

●  Need more complete truth data & improved k-mer GT-ing & assessment algorithms

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9

Beyond “platinum” variants

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10

Recoverable SNVs

●  Identified 334,652 “high-quality” SNV calls that are not pedigree consistent -  High quality = SNV called by >1 pipeline with consistent GTs (when

called) and every sample contains a GT call

●  Broke these into four categories based on likely cause -  CAT1 (191,087) = het in every sample (dup. or paralogous sequence) -  CAT2 (3,861) = GTs consistent with deletion in the pedigree -  CAT3 (49,800) = variant called in only one sample (cell line de novo) -  CAT4 (25,501) = all others (duplications and cell line artefacts)

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11

●  See an excess of “all-het” failed SNVs (red) versus consistent SNVs (blue)

●  Depth confirms that these are likely true variants - Could incorporate these as 2

ref & 2 alt or 1 ref & 3 alt…

●  ~34% of CAT1 SNVs overlap population duplications from Sudmant et al [2015]

CAT1 failed SNVs: Duplications

NA12878 NA12877 Children

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12

●  Clusters of CAT2 failed SNVs (red) identifies deletions in this pedigree

●  Depth confirms that these are deletions in the pedigree -  This deletion is common

(~15%) in the population

CAT2 failed SNVs: Deletions

NA12878 NA12877 Children

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13

Incorporating CNVs into pedigree check

●  Many SNVs and indels that fail the pedigree check are true variants -  Many also show population-level HW deviations consistent with

predicted call

●  Can modify the pedigree check to include CNVs to create a more complete call set -  Straightforward for deletions -  Duplications will require additional “B-Allele frequency” consideration

●  Need to improve CNV characterization first -  Improved breakpoint resolution -  Tandem duplications will work but not translocations