ClinSeq: Piloting Large-Scale Medical Sequencing for Translational Genomics Research Leslie Biesecker, M.D. National Human Genome Research Institute, NIH ™

Translational genomics research space

Genetic architecture of disease Common variants Rare variants

Genetic architecture of disease Common variants Rare variants The only way to assess both is by sequencing

Building a clinical genomics research program 1.Develop a robust infrastructure for the generation and use of genomic data in clinical research 2.Genomically dissect a phenotype with complex genetic architecture 3.To understand how to interact with subjects re genomic data

Initial approach 1,000 subjects recruited from general population Initial phenotype for atherosclerosis – Consented for follow-up sequencing – Consented for re-contact for phenotyping Sequence ~400 candidate genes – Selected for many reasons – Consented for WGS Associate variants with phenotypes Return results – A goal of the study is to learn from the subjects which results we should return

Progress – the numbers Enrollment began January, patients enrolled Feb DNAs sequenced – PCR/3730 – 326 ClinSeq subject samples – 28 HapMap samples 219 genes –3,500 genomic target sequences > 1.7 M sequence reads to date ~825,000,000 bp of bidirectional genomic sequence

Quality measures Sample ID spike – Non-human genomic clone spike HapMap control DNAs – 1/30 ClinSeq DNAs Manual review of traces CLIA sample confirmation

Visual quality % coverage Q20

Yield snapshot Coverage – Sample 140 genes Targeted 402,907 bp ROI Yielded 357,912 bp – 88.8% design coverage Variants – Total 3,353 – Adjust sample coverage to exactly 500 chromosomes Variants 2,271 – Extrapolate false positive rate 1,984 variants

Uncommon alleles common

Data flow

Sub-projects underway Positive controls Validating recent assoc. of rare variants & phenotypes Sequencing genes under GWAS peaks for rare, high penetrance variants Testing associations of candidate genes with phenotype Control cohort for other sample sets Search for miRNA variants cDNA sequencing pilot to measure expression & isoforms Capture method refinement Patient motivations and preferences for results of medical sequencing Testing automated vs. manual pedigree acquisition

Positive result example 65 yo female High cholesterol diagnosed at 25 years RX: atorvastatin, ezetimibe, hctz, lisinopril, niacin Coro Ca ++ 1,726 Chol 172, Trig 50, HDL 75, LDLd 88 LDLR p.Y188X Family members diagnosed & treatment started

Replications ANGPTL4 – (decr trigs) – 30 NS variants, one novel, p.E40K x 3 – Not quite significant, await full sample SLC12A3, SLC12A1, & KCNJ1 (decr BP) – 15 NS variants – Not assoc with decr BP

Penetrance & frequency Frequency Penetrance

Penetrance & frequency Frequency Penetrance We understand this work

Penetrance & frequency Frequency Penetrance We understand this work Explore this

Penetrance & frequency Frequency Penetrance We understand this work Explore this This is probably not clinical genetics!

Classic translational paradigm Sort Phenotype Generate Hypothesis Apply Assay Correlate

Novel translational paradigm Sort Phenotype Generate Hypothesis Apply Assay Correlate Sort Genotype Generate Hypothesis Apply Assay Correlate (Sort) Phenotype

Implications Large numbers of patients are interested Possible to consent subjects to WGS Clinically relevant results can be sifted Subjects can receive and act upon results Going forward: – Next-Gen: Exome -> WGS – More diverse population – Discovering associations of variants and phenotype – Learning subject’s view in real setting

Collaborators NISC – Jim Mullikin, Bob Blakesly, Gerry Bouffard, Praveen Cherukuri, Pedro Cruz, Nancy Hanson, Morgan Park, Alice Young NHGRI – Eric Green, Flavia Facio, Stephanie Brooks, Amy Linn, Paul Gobourne, Jennifer Johnston, Teri Manolio, Jamie Teer, Clesson Turner, Alec Wilson NHLBI – Richard Cannon, Andrew Arai, Paul Hwang, Toren Finkel, Vandana Sachdev, Bob Shamburek NIDDK – Monical Skarulis, Kristina Rother NIHCC – Alan Remaley ™