Les Biesecker, NHGRI/NIG - “ClinSeq: Piloting Large-Scale Medical Sequencing for Translational Genomics Research”
Clinicians are more conservative than biologists. Change will be hard, and will take time to see which of them work, and how to use them. [Interesting observation.. but not new]
First figure. Three main traits: Genome Bredth, # of subjects, Clinical data. Each displayed on a different axis. [Uh oh, I can already tell this talk is going to be way outside of the realm of my interest, but I'll try to take decent notes.]
We basically want all of these: lots of genomes, lots of people, and lots of clinical data – that gives you the ideal study.
Genetic architecture of disease: it's a spectrum, with rare to common and alleles, and with high and low penatrance, with lots of admixture of diseases and phenotypes. (Using a Yin-Yang variant diagram to explain it.)
What we need to do is develop the clinical infrastructure to allow this type of data to be produced. We need to get to the point where we have a clinician with a patient in one room with full access to the patient's genome. [odd, that.. do you really think that's the way to go?]
Initial approach to one study (athrosclerosis):
Told people not to sign up to the project unless you're willing to have your whole genome sequenced and used for the study. Apparently, sequencing genomes for clinical purposes is a “radical” idea. However, they have really been overwhelmed by applicants. Currently seqeunced 300 patients, have ~600 people recruited. (Using current PCR pipeline) The idea is to use an older technology, and then once the pipeline is in place, do the substitution, so that everything is in place. The key bottle neck is the bioinformatics, not the generation of data.
Something about “CLIA”, the process a sample can be flowed through and return results to patients.. I've never heard of it, but is apparently a part of clinical studies.
Coverage: 140 genes, 402k bp,
Variants: Oops too slow. ~3000?
985 Hapmaps overlapped.
Uncommon alleles chart – seems to have a lot of very uncommon SNPs, so you're still finding lots of snps.
Back to CLIA. They have a data flow pipeline, which brings the patient back to the clinic, so that they can review the results.
List of subprojects: positive controls, validating recent assoc. of rare variants & phenotypes, sequencing genes under GWAS peaks for rare, high pentrance variants, testing associations, control cohort for other sample sets, search for miRNA variants, cDNA sequencing to measure expression, capture method refinement, patient motivations and preferences for results of medical sequencing, testing automated vs manual pedigree acquisition.
One positive example: By doing genomic sequencing (and several other tests), they identified a patient who had a mutation in LDL, which changed the way that that whole family is being treated. [Neat.]
Of interest: compared their results to another study and showed that they had a completely different result. [I missed what that other study was.... different genotyping variants found, I think.]
Pentrance: we currently know how to work with high penetrance variance, and so maybe that's where we should start, and then wander down the penetrance curve till we get to the low end. The ones at the VERY low end, Dr. Beisecker claims they're not clinical tests... they are just “noise”, if I understand him correctly.
Classic paradigm: hypothesis, phenotype, apply assay, Correlate.
New paradigm: apply assay, sort genotype, generate hypothesys, sort phenotype, Correlate
There are no conclusions to the talk or the study, because they're just getting underway. Many patients are interested in this research, and don't shy away from genome sequencing. We can use this pipeline to look for variants.. and it will accept new sequencing technolgies as they are developed. When exon seqeucning is ready, they'll do it.. and one day they'll move to whole genome.
My comments: Actually, not a bad talk, but really so far outside of the realm of what I'm used to working on that I'm not sure what to make of it. Doing whole genome association is never easy, and the assertion that we need to get there eventually is good. He acknowledged that we don't know how to get there – and that's not really a surprise for the clinical setting.
First figure. Three main traits: Genome Bredth, # of subjects, Clinical data. Each displayed on a different axis. [Uh oh, I can already tell this talk is going to be way outside of the realm of my interest, but I'll try to take decent notes.]
We basically want all of these: lots of genomes, lots of people, and lots of clinical data – that gives you the ideal study.
Genetic architecture of disease: it's a spectrum, with rare to common and alleles, and with high and low penatrance, with lots of admixture of diseases and phenotypes. (Using a Yin-Yang variant diagram to explain it.)
What we need to do is develop the clinical infrastructure to allow this type of data to be produced. We need to get to the point where we have a clinician with a patient in one room with full access to the patient's genome. [odd, that.. do you really think that's the way to go?]
Initial approach to one study (athrosclerosis):
- 1000 subjects
- Initial phenotype
- Sequence 400 candidate genes (“Completely wigs [clinicians] out.”)
- Associate variants with photypes
- Return results.
Told people not to sign up to the project unless you're willing to have your whole genome sequenced and used for the study. Apparently, sequencing genomes for clinical purposes is a “radical” idea. However, they have really been overwhelmed by applicants. Currently seqeunced 300 patients, have ~600 people recruited. (Using current PCR pipeline) The idea is to use an older technology, and then once the pipeline is in place, do the substitution, so that everything is in place. The key bottle neck is the bioinformatics, not the generation of data.
Something about “CLIA”, the process a sample can be flowed through and return results to patients.. I've never heard of it, but is apparently a part of clinical studies.
Coverage: 140 genes, 402k bp,
Variants: Oops too slow. ~3000?
985 Hapmaps overlapped.
Uncommon alleles chart – seems to have a lot of very uncommon SNPs, so you're still finding lots of snps.
Back to CLIA. They have a data flow pipeline, which brings the patient back to the clinic, so that they can review the results.
List of subprojects: positive controls, validating recent assoc. of rare variants & phenotypes, sequencing genes under GWAS peaks for rare, high pentrance variants, testing associations, control cohort for other sample sets, search for miRNA variants, cDNA sequencing to measure expression, capture method refinement, patient motivations and preferences for results of medical sequencing, testing automated vs manual pedigree acquisition.
One positive example: By doing genomic sequencing (and several other tests), they identified a patient who had a mutation in LDL, which changed the way that that whole family is being treated. [Neat.]
Of interest: compared their results to another study and showed that they had a completely different result. [I missed what that other study was.... different genotyping variants found, I think.]
Pentrance: we currently know how to work with high penetrance variance, and so maybe that's where we should start, and then wander down the penetrance curve till we get to the low end. The ones at the VERY low end, Dr. Beisecker claims they're not clinical tests... they are just “noise”, if I understand him correctly.
Classic paradigm: hypothesis, phenotype, apply assay, Correlate.
New paradigm: apply assay, sort genotype, generate hypothesys, sort phenotype, Correlate
There are no conclusions to the talk or the study, because they're just getting underway. Many patients are interested in this research, and don't shy away from genome sequencing. We can use this pipeline to look for variants.. and it will accept new sequencing technolgies as they are developed. When exon seqeucning is ready, they'll do it.. and one day they'll move to whole genome.
My comments: Actually, not a bad talk, but really so far outside of the realm of what I'm used to working on that I'm not sure what to make of it. Doing whole genome association is never easy, and the assertion that we need to get there eventually is good. He acknowledged that we don't know how to get there – and that's not really a surprise for the clinical setting.
Labels: AGBT 2009
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