Interpreting patient genomes

• Sequencing of patient genomes increasingly common
• Can identify relevant variants
• … amongst a large number of unrelated variants
• … can be difficult to interpret
• Computational strategies critical to obtaining good set of candidates

Finding rare disease related variants

Rare variants are of particular interest

• May have large effects

• … but can be hard to find.

• Need large sample sizes

• … but may still struggle to identify causal variants.

Motivation

• Several DNA repair genes implicated in breast and ovarian cancer susceptibility.
• Strong evidence that rare loss-of-function variants confer increased risk.
• Sequencing large number of patients not carrying known risk variants should lead to discovery of new ones.

Study design

Analysis strategy

• Sequence pools with GAIIx HiSeq2000.
• Call variants in pools with Syzygy.
• Annotate variants to identify loss of function.
• Validate variants of interest.
• Sequence relevant genes in control panel.

Variant calling

• Syzygy called 34,564 variants in target region.

A simple plan

• Use EnsEMBL annotations (via Perl API)
• Identify protein truncating variants
• Group variants by gene to identify candidates for follow-up

Beware of transcript annotations

Beware of edge effects

Beware of misaligned indels

Selecting candidate genes

Investigating PPM1D

• PPM1D is a phosphatase
• Phosphatase domain encoded by first 5 exons

Investigating PPM1D

All identified truncating mutations validated with Sanger sequencing.

Phase 2: Case-control study

• Sequenced PPM1D an additional 2456 cases and 1347 controls.
• Identified 10 additional PTVs (none in controls)

Phase 2: Case-control study

• Sequenced final exon only in 5325 cases and 4514 controls.
• Identified 15 additional PTVs in cases (1 in controls)

Case-control summary

How does it work?

A complication

• Read counts for variant alleles appear low.
• Difficult to assess in pools but also visible in trace data.
• Consistently low frequency of PTVs.

Somatic variation?

• Could indicate that these are somatic mutations
• If these are germ line variants we should see them in children of carriers.

Further complications

• PPM1D truncating mutations appear to be mosaic in lymphocytes.
• What does PPM1D look like in the tumour tissue?
  • Deep sequencing of DNA from tumour, stromal tissue and blood in four cases.
  • Found expected mutations in blood but not in tumour or stroma

Possible interpretations

• Are these mutations present in cell of cancer origin but lost later?
• Is oncogenesis driven by lymphocytes?
• Are the PPM1D mutations only symptoms of an underlying problem that leads to cancer development in other tissues?
• Are PPM1D mutations and cancer unrelated?

Loss during cancer development

• Evidence for loss of heterozygosity at PPMID locus.
• The lost haplotype is the one carrying the PTV in lymphocytes.
• Unclear whether the mutation was present prior to LOH event.
• Loss of heterozygosity in this region is common in breast and ovarian cancers.

Symptom of a bigger problem

• Could be a sign of general genome instability.
• This might lead to clonal expansion of cells with PPM1D PTVs as well as cancers.
• Unclear what the driver of this would be.

Simply unrelated

• Evidence supporting some relationship between PPM1D PTVs and cancer seems strong.
• Observation has been replicated by Akbari et al., 2013
• Somatic PPM1D PTVs have been found in cancers (Kleiblova et al., 2013, Zhang et al., 2014)

Finding rare variants

• Strategy to sequence as many cases as possible paid off.
• Would not have found PPM1D PTVs if we had split initial sequencing between cases and controls.
• A lot, but very focused, follow-up required.
• Focus on candidate gene panel paid off for similar reasons
• … but means we have no easy way to check for other shared genomic variation amongst PPM1D PTV carriers.

Somatic variation

• Were lucky that study design was suited to discovery of somatic variation.
• Can find somatic variants through deep sequencing
• but proving that a variant is somatic can be difficult in absence of control tissue.
• Variant frequency in gDNA and RNA may differ markedly.

Variant annotation

• Be careful with automated annotations.
• Have improved a lot over the last few years
• … but can still be misleading or incomplete.
• Consider PPM1D PTVs

Variant annotation

• Be careful with automated annotations.
• Have improved a lot over the last few years
• … but can still be misleading or incomplete.
• Consider PPM1D PTVs
  • Truncation of final exon.
  • (Correctly) predicted to escape nonsense mediated decay.
  • So not loss of function.
  • Doesn’t mean we should ignore it!

Acknowledgements