1.
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Wending toward
genomically personalized healthcare
Nathan Pearson
Principal Genome Scientist
Ingenuity Systems/QIAGEN
npearson@ingenuity.com
Platform Ingenuity’s
Views mine
Talk #tweetable @genomenathan

2.
• 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
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What we, as a community, are doing right

3.
• Plain English (see genomena.com/variants)
• Treatment response findings
• Reference genome(s)
• Genotypes vs. variants
• Sharing individuated genomes
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What we, as a community, are neglecting

4.
• 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
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Reference genome: 3 distinct uses

5.
• 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
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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.

6.
• 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.
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Reference genome: 3 distinct uses

7.
• 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)
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Report against human ancestral reference

8.
• 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
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Classify genotypes, not variants

9.
• 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)
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Query individuated genomes, not allele frequencies

10.
• 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.
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Why this stuff is tricky: APOE4 example

11.
• 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.
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Ingenuity® Variant Analysis™ in a nutshell

12.
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¡Gracias!