Jan 2, 2019 · 25m · a16z
a16z Podcast | When Will Genomics Live Up to the Hype?
Gabriel Ott · 7m spoken Jeff Kaditz · 6m spoken Carlos Araya · 5m spoken Malinka Walaliyadde · 3m spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
At the a16z Summit, biotech entrepreneurs discuss why genomic data has yet to fully transform personal healthcare, addressing key technical, economic, and regulatory obstacles. They highlight how combining phenotypic context, innovative direct-to-consumer business models, and artificial intelligence can unlock the power of preventative precision medicine.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
The host as informed peer 3.2 Guest teaching 5.0 Guest disagreement 2.0 The host pushing back 1.8
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 11:21 Gabe attacks traditional diagnostic test inaccuracy
Gabe forcefully dismisses standard cancer diagnostic tools like mammograms and PSA tests, pointing out their 50-75% false positive rates mean patients are literally better off flipping a coin.
Hardest push from the host ▶ 17:46 Host counters payer skepticism with global self-pay marketsThe host pushes back against Jeff's skepticism about legacy US payers by pointing out expanding opportunities in direct-to-consumer and international markets such as India.
Biggest teaching moment ▶ 4:09 Jeff reframes genome as dynamic 500 TB/s streamJeff re-educates the panel and audience on genomic biology by calculating that somatic DNA replicates at 500 terabytes per second, framing cancer as an information corruption problem.
The host holds their own ▶ 10:37 Host breaks down the payer-prescriber incentive bottleneckThe host demonstrates deep domain expertise by detailing how traditional reimbursement requires convincing both insurance payers and prescribing doctors, leaving the patient excluded.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| The Human Genome Project and Genomic Dynamics | 2 | 6 | 1 | 1 | The host facilitates by asking baseline questions about the Human Genome Project and current use cases. The guests educate the room on how the genome is dynamic rather than static, highlighting a somatic DNA replication rate of 500 terabytes per second. | |
| Technical Bottlenecks and the Need for Phenotypic Context | 2 | 5 | 1 | 1 | Carlos and Gabe explain technical bottlenecks in genomic interpretation, focusing on functional gene maps and phenotypic data. The host primarily prompts the guests to clarify technical terms like phenotypic information. | |
| Commercialization, Regulatory Obstacles, and Reimbursement | 5 | 5 | 4 | 2 | The host outlines the complex triad of payers, doctors, and patients in healthcare commercialization. Guests aggressively critique regulatory roadblocks and the high false-positive rates of legacy screening tests. | |
| Business Models for Early Detection and Preventative Care | 4 | 4 | 2 | 3 | When guests question whether traditional payers will ever fund preventative tools, the host offers counterexamples of self-pay and international markets like India. Jeff uses the dental care system as an example of successful preventative health economics. | |
| Artificial Intelligence and Machine Learning in Genomics | 3 | 5 | 2 | 2 | The host steers the panel toward AI application in genomics and asks for clarification between Mendelian and complex traits. Guests explain how machine learning handles multi-variable genomic data far better than single-variable clinical assumptions. |
Statements from this episode (16)
Walaliate: Genomics has few true healthcare applications 20 years after HGP
“We certainly haven't lived up to that, and in fact, it's been a little difficult to see exactly where genomics has had a true application today.”
Otte: Expecting one genome snapshot to answer disease questions is ludicrous
“The fact that we thought taking one person's genome at one snapshot was going to answer every, you know, question about diseases and things like that was just ludicrous in retrospect.”
Kaditz: Somatic DNA replication transfers 500 terabytes of data per second
“I think the data transfer rate of somatic DNA in your body is about 500 terabytes per second.”
Otte: 23andMe analyzes less than one percent of the human genome
“You can do things like 23 and me, which looks at less than one percent of the entire genome, looking at specific mutations that you were born with, and what that can tell you about who you're going to be.”
Araya: DNA sequencing is the second-fastest advancing technology in history
“We've done a pretty good job at being able to acquire sequence information. That's, you know, some of the fastest advances in technology in the history of mankind. I'm told it's actually only beat by one other technology, which is the sort of the clarity of gl…”
Araya: Existing genomic maps lack the functional data needed for applications
“Unfortunately the maps that we have today are really maps of function that just say where things that are, things like genes, biomolecules, where they are encoded in the genome, but it says really nothing about how they function and which parts of the genes do…”
Otte: Genomic research is severely bottlenecked by lack of phenotypic data
“What's been severely lacking is a deeper understanding of the phenotypic information that we can associate back to genomic information, something that almost everyone that's doing research in genomics would agree it's really hard information to get, and It's r…”
Kaditz: Pharmacogenomics is the lowest-hanging fruit in genomic applications
“Using genetics to determine which drugs you're most likely to respond to. I think that that is, like, the lowest hanging fruit.”
Kaditz: Genomic disease prediction requires time-series biomarker data
“I think in order for genomics to be used in diagnostics or predictive models of are you going to get sick, I think it has to be combined with actual time series biomarker data”
Otte: PSA tests and mammograms have false positive rates up to 75%
“In the field of cancer screening, cancer diagnostics, we're used to, like, really, really bad tests. So like PSA for prostate cancer detection, mammography for breast cancer detection, these things have false positive rates of anywhere from, ah, 50 to 75%.”
Otte: Insurance payers and clinicians are blocking diagnostic innovation
“It's really the payers and some of the clinicians that are being the inhibitors to this progress.”
Otte: 80% of US cancer spending goes toward end-of-life care
“80% of all the money that we spend on treating and dealing with cancer in the healthcare system in the United States, something about between 75 and a hundred billion dollars a year, is to help people die of cancer. That's what we spend 80% of the money on rig…”
Kaditz: Dental care costs fell in real terms while healthcare skyrocketed
“If you look at the cost of dental care over time, it's flat or down in inflation-adjusted dollars, and the quality of the care has gone up. If you look at over the exact same period of time in healthcare, it's the exact opposite trend.”
Araya: Cancer gene tests find 95 unknown mutations per known mutation
“Those tests will basically find 95 mutations that they have absolutely no clue of what the effects of those mutations are in these important cancer-associated genes per each mutation that is known to cause disease.”
Kaditz: Building disease predictive models using single variables is asinine
“Human body is an extremely complicated system to build predictive models based on a single variable, which is exactly what clinical studies do. Because that's, you know, what they're designed to do, really, is, is pretty asinine.”