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Wending towardgenomically personalized healthcare

Nathan Pearson Principal Genome ScientistIngenuity Systems/QIAGEN npearson@ingenuity.com

Platform Ingenuity’sViews mineTalk #tweetable @genomenathan

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• Conveying how bench and drylab methods work, fail, and change

• Setting standards for pipeline validation, reporting, other sharing, and payment

• Starting to teach med students

• Convening stakeholders

• Blunting hype

What we, as a community, are doing right

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• Plain English (see genomena.com/variants)

• Treatment response findings

• Reference genome(s)

• Genotypes vs. variants

• Sharing individuated genomes

What we, as a community, are neglecting

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• Read: accurately sequence person’s chromosomes

Recommend: Align & call against a genome most like this person’s

Where hard to guess (e.g., HLA), try many

• Write: compress person’s genome; compare to others’ via common coordinates

Recommend: Report against human ancestral reference

Report no-calls

• Interpret: understand person who carries this genome

Recommend: Focus on genotypes, not variants

For QC, start with heterozygosity

Reference genome: 3 distinct uses

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• Arbitrary, unrealistic, and ethnocentric

• Misaligns most real people’s genomes

• Hides informative summary patterns (QC, functionally relevant evolution)

• Includes rare and risky variants

• Mismatches gene-specific references used by clinical geneticists

Reference genome: Why today’s fails

Switch to common-variant-only reference?

Switch to healthy-variant-only reference?

Instead…

Switch to common-variant-only reference? What’s common varies.

Switch to healthy-variant-only reference? What’s healthy depends.

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• Read: accurately sequence person’s chromosomes

Recommend: Align & call against a genome most like this person’s.

Where hard to guess (e.g., HLA), try many.

• Write: compress person’s genome; compare to others’ via common coordinates

Recommend: Report against human ancestral reference.

Report no-calls.

• Interpret: understand person who carries this genome

Recommend: Focus on genotypes, not variants.

For QC, start with heterozygosity.

Reference genome: 3 distinct uses

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• At each site, base carried by last forebear of all people

• Like current reference, comprises mostly common and healthy variants

• Includes source DNA for nearly all alignment-relevant chunks of real genomes

• Roughly equidistant from everyone

• Clearly reveals summary patterns of variation and evolution

• Lesson long learned for mtDNA1

But gene-specific reference discrepancies will remain, so…

1See Behar et al. 2012 (PMID 22482806), as well as Balasubramanian et al. 2011 (PMID 21205862)

Report against human ancestral reference

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• Reference-independent

• More clearly convey what’s risky & what’s not, in given person

• Slightly bigger data

• Needed, to capture complexity that big studies are already revealing

Neatly convey: dominant/recessive/complex site-specific effects

sex-specific risk (sex chromosome epistasis)

Readily extrapolate to: classic compound het etiology

haplotypes (and diplotypes)

other compound etiology

Classify genotypes, not variants

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• What variants appear together, at what zygosities, in sick vs. healthy genomes?

• Needed, to capture complex etiology

Classic compound heterozygous etiology

Sex-specific risk (sex chromosome epistasis)

Other compound association (e.g., classic burden)

• Lets us refine sequence (phase, impute)

Query individuated genomes, not allele frequencies

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• Defined by multiple variants (rs429358C, rs7412C)

• One variant rare, one common (neither is a mutation)

• Harmful for Alzheimer disease & longevity…but helpful for cancer?

• Genotype matters…as does interaction (e.g., intronic BACE1 variant)

• Chronically hard to call…highlights need to report no-calls

• Other familiar examples: globinopathies, BRCA1 modifiers, &c.

Why this stuff is tricky: APOE4 example

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• Secure (HIPAA-, Safe Harbor-compliant) web platform for interpreting called human genomes

• Smart interface to flexibly annotate & compare genomes, to shortlist candidate variants, genes, & gene sets

• Leverages field’s deepest functional knowledge base, with rigorous clinical-depth curation of published findings, well structured ontology, and smart interaction modeling

• Statistically robust methods for interpreting single/multi-proband, matched tumor, (multi-)kindred, and big case/control cohort studies

• Suits central labs’ needs to manage clients’ human genome data, help interpret it, and broker sensible sharing

• Clinicians, stay tuned.

Ingenuity® Variant Analysis™ in a nutshell

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¡Gracias!