AGBT13 – Thu 2/21 Morning session

Plenary Session: Clinical Genomics I
Heidi Rehm, Partners Healthcare Center for Personalized Genetic Medicine, Chair

— useful collection of links by @nextgenseekagbt-2013-first-day-link-roundup
AGBT13 stories (updated twice daily based on tweets)

9:00 a.m. – 9:30 a.m.
Russ Altman, Stanford University
“Pharmacogenomics (PG) in the Era of Genome Sequencing”

Founder of Personalis, and I really like his saying from elsewhere “We have to bring “genome-drug” interactions to (physicians’) attention just as we currently bring “drug-drug” interactions to their attention.”
– Current PG knowledge (PharmGKB) – from common variants with large effects on metabolism or action of drugs
– Key is understanding the clinical significance of less common variants on drug response
– Need for large repositories of empirical data about drug response & genome sequence
– VIP = Very Important Pharmacogene!
– Dosing guidelines manually curated in PharmGKB
– Impressive Pharmacogenomics ‘good news’ and ‘bad news’ summaries, along with novel non-synonymous damaging SNPs in VIP genes and relevant drugs
– Also presented exome seq of dose extremes in African-Americans – found a common variant (not in CEU) with strong effect on dose
– 900 genes that have 1 variant with very well characterized drug response. and 900 drugs with 1 variant

9:30 a.m. – 10:00 a.m.
Jon Seidman, Harvard Medical School
– Inherited cardioMyopathy
– understand their genetic bases of hypertrophy (thick wall) and dilatation (thin) as precursors of heart failures
– 2 models – common disease common variant or common disease lots of rare var
– most individuals in US with HCM (500k/yr) have novel de novo mutations rather than more rare founder mutations
– 80+ genes implicated in idiopathic DCM making this more complicated to evaluate
– 25% of DCM caused by truncating mutations in TTN (largest human gene)

10:00 a.m. – 10:30 a.m.
Christine Eng, Baylor College of Medicine
“Clinical Whole Exome Sequencing (WES): Results and Outcome of First 450 Reported Clinical Cases”

– Technical, bioinformatic and interpretive WES pipelines in CAP-CLIA lab to identify causative mutations underlying disease phenotypes (genetic disorders)
– Variable pickup rate of Sanger seq based on gene
– NIH undiagnosed disease study results: 39/160 diagnosed
– 900+ WES tests, 450 reported to physicians (medical geneticists/neurologists)
– Causative mutant allele identified in 121/450 (27% confirmed diagnostic rate)
– OMIM = 3500 genes & genetic disorders, mostly rare, phenotypically het – leads to undiagnosed individuals with various genetic syndromes – WES to the rescue

11:00 a.m. – 11:30 a.m.
Elizabeth Worthey, Medical College of Wisconsin
“Making a Definitive Diagnosis: Experiences from Our WGS Based Genomic Medicine Clinic”

Very similar to the earlier exome talk, but this is whole-genome! Way to go…
– Integrated informatics solutions supporting variant annotation, prioritization, analysis, interpretation and reporting – integration into clinical genetics lab – CAP-CLIA (= 1400+ pg docs, 220 SOPs …)
– ACMG classification of variants into reporting categories
– 100-120 variants (170x coverage) per genome flagged for in-depth review, most not clincally reportable
– trouble with poorly covered regions – options – WES, Sanger, PacBio or something else
– end-to-end time from sample-prep, sequencing, bioinformatics, interpretation to reduce from ~2 weeks (2012) to 2 days (2013)!!
– amazingly precise cost slides – $1500 WGS, $1050 whole-genome interpretation and so on..

11:30 a.m. – 12:00 p.m.
Kjersti Aagaard, Baylor College of Medicine
“The Emerging Era of Metagenomics Medicine”

– Human + their microbiome = co-evolved physiologic community
– metagenome = incredibly diverse & plastic
– Human Microbiome Project Consortium = ca. structure, function, diversity of healthy  – microbiome = “reference”
– Metagenomic medicine – needs robust body sampling & strong analytic approaches
– How pregnancy and mitochondrial genome structures the microbiome
– 16S-based metagenomics using Roche FLX and Ion; WGS using Illumina
– 16S metagenomics shows us “who is there” while WGS metagenomics shows us “what they may be doing”


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