Category Archives: Genomics

Gastric bypass surgery and the ever expanding world of GxE interactions

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All opinions are my own and do not necessarily reflect those of Novo Nordisk.

An early publication article from the Proceedings of the National Academy of Sciences reports the fascinating finding that children born to mothers before and after gastric bypass surgery show differences in the expression of  genes involved in, among other things, glucose metabolism and immune function.  The study is small, with only 50 children evenly split between cohorts born to moms before and after gastric bypass, but if it replicates, it’s another piece of evidence  showing how the environment influences the way our genes function.

Epigenetics has been a hot topic in genetics research for a while now.  It’s clear that DNA methylation changes over time and within an individual and can affect gene expression.  Studies in a number of institutions such as Washington State University in the lab of Michael Skinner have shown that changes can even persist through multiple generations (in rats, at least).  The PNAS report adds another twist in that the gene by environment interaction arose due to a change in maternal health induced by surgery.

There are a lot of implications to this, including the rather theoretical one of whether this knowledge would induce more potential mothers to undergo gastric bypass surgery, and also practical ones of whether weight loss alone without surgery or via, for example, a lap band, would have the same effect.  But the one I wonder about is what this might imply for drug development.

While many genetic variations are known to affect disease risk and progression, and drug metabolism, there has been considerable debate on how to use such data.  In many cases, such as with the majority of Genome Wide Association Study hits, the relative risk of discovered variants have been statistically significant but small.  However, as we have seen with Amgen’s purchase of DeCode, drug development companies are keen to use genetic information to help inform their drug development efforts, to find an edge.

In this PNAS report, however, I see a flag of caution.  I applaud the efforts of Amgen and other companies taking these risks, but this report of possible epigenetic effects following maternal surgery also points out how much we’re still discovering about basic human biology, how much we still don’t know about the diseases we study.  Understanding Gene by Environment interactions is, I think, one of the key factors we deal with in developing drugs, and not one to ignore.  And yet, it feels currently like one of those “unknown knowns,” the things we willfully decide not to think about, even though we know it’s there.

A Genomics Researcher’s Take on the Global Health Metrics Conference 2013

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

Over the past three days I had the opportunity to attend the Global Health Metrics Conference here in Seattle.  This is not my field; I’m a genomics researcher working in biomedical research and drug development, but I’ve also been curious about what’s going on in the area of public and global health.  This seemed like a good place to get a crash course.  The Lancet has kindly published all the abstracts and I wanted to give my impressions of what I heard.

First takeaway:  I was surprised and intrigued by how many parallels I saw between the work I do (primarily transcriptomics and genomics) and the work I saw reported.  Sure, global health researchers use surveys rather than high throughput sequencing, and gather data on nations rather than patients, and deal with the complexities of culture and government instead of human biology, and work in the public sphere as opposed to the private, and use a completely different vocabulary than I do, but other than that it was really similar.  So similar I put together this table:

Biomedical Genomics Research Global Health  Metrics
Increasing amount and types of data Yup
Biomarkers Indicators
Growing emphasis on efficacy measurements ditto
Lots of Acronyms, NIH, AMA, EULAR, ADME GBD, DALYs, CDVS, USAID
Struggle to understand what tissue, cell, analyte to measure Struggle to characterize the right metric to demonstrate effects/efficacy
Gene X Environment interactions poorly understood Local environment effects beginning to be captured
Personalized medicine Nation specific solutions
Noisy data, lots of unknowns Maybe even noisier data and, yeah, unknowns
More focus on longitudinal studies Already there

And so on.  I’ll elaborate on a few more below.  Another immediate takeaway:  I wasn’t even aware of the Institute for Health Metrics and Evaluation (sorry guys).  Now that I am, it’s a place I’d like to visit.

One thing that really impressed me was the work that IHME has put into making the Global Burden of Disease survey lucid, simple and accessible.  The data presentation by Kyle Foreman and Peter Speyer (@Peterspeyer) was terrific.  Not so much for any specific piece of data (although the trends and findings are all pretty fascinating), but rather for their demonstration of the power of dynamic presentation and facile web-based tools.  Static powerpoint charts are clearly so last decade.  Anyone wanting to check out their presentation can go here, or even better just go directly to the site.  As a scientist who also works with large, multifactorial datasets, I know the struggle to condense that data into a usable, comprehensible form.  I think Peter and Kyle have done a great job, and I also like the potential crowdsourcing aspect of it.  As I’ve commented on before, crowdsourcing methods, whether via games or other techniques, have a real potential to fully utilize large datasets and also to solve big problems.

Of the many talks I heard, a few I’ll highlight, just for the specific points I took away.  On the first day, Tanya Marchant showed interesting and cautionary data about making sure that what you’re measuring really measures what you think you’re measuring.  In this case, measuring the presence of skilled birthing assistants as a proxy for maternal care during childbirth turns out to be incomplete because of other factors such as availability of basic medical supplies.  Reminds me of debates over things like how best to measure drug efficacy in clinical trials–for example, response versus progression free survival in oncology.

Joseph Dieleman presented his work on looking on the effects of external aid to developing nations for health.  In a perfect world, external aid would just be added to pre-existing health expenditures, and after aid expired, local governments would maintain spending at pre-aid levels, or even higher.  Well, turns out this isn’t always the way this happens.  Aid comes in, local health budget gets shifted “temporarily,” but temporarily turns to permanently when the external aid leaves.  One of the thoughts that went through my head during this conference was to remember the law of unintended consequences.

I enjoyed Michael Wolfson‘s talk on functional health status.  Coming from an industry that really likes it’s tried and true measures like HDL/LDL levels, the concept of looking holistically at factors relating to actually feeling good was a nice contrast, and food for thought.

Bruce Hollingsworth had a great quote in his part, “People need incentives to provide accurate data.”  Yeah.  Tell me about it.  In transcriptomics it’s been a mantra for years that “Garbage in, garbage out,” in terms of incoming biological sample integrity and resulting data quality.  From what I saw, the data you can get trying to measure Global Health is maybe even noisier than the kinds of data that I normally deal with.  My main conjecture for why all hope is not lost due to data quality in Global Health is that GH researchers are able to bias the indicators they sample towards things with (hopefully) real meaning, else they would be adrift in a sea of not very useful data.  Maybe they feel that way anyway?  Bruce also made the point that there are external factors, again, which influence health.  Even people who know where to go for the best treatment may not because the facility is too far away.  Location, location, location.

Speaking of garbage (but not in a bad way), David Phillip‘s talk later that day referred to the problem of trying to extract useful data out of vital health records full of things like garbage codes.  That is, causes of death that are supremely unhelpful from a public health perspective, such as (I’m exaggerating here) death by lack of life.  His work on extracting useful proportions from this data based on the overall data distribution reminded me of imputation techniques that are used in genomics.

There were many more engaging talks, and I also had great conversations at lunch with different people. I suppose I shouldn’t be surprised by the similarities.  I think many research fields these days are converging on a similar emphasis on big data, analytics, efficacy, and finding the right metrics.  I also appreciated the long view shown by so many of these programs.  One of the drawbacks of private industry is the prevalence, often, of the short term view.  I could wish we had the decades-long commitment shown by various Global Health initiatives.

The aspect I find daunting in Global Health is how much uncertainty that community is dealing with, which greatly affects efficacy and efficiency.  An intervention might be exactly the right one when viewed in isolation, but can be so easily derailed by external factors.  Like biology, like baseball, it seems the key thing is to find the metrics that at least tell you that you made the change you hoped for, with the understanding that what happens at the end is so often, unfortunately, out of our control.

The Global Alliance for responsible sharing of genomic and clinical data: an Asilomar conference for today?

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All opinions are my own and do not necessarily reflect those of Novo Nordisk

I’ve been mulling over the recent announcement by several prominent genomics organizations of a set of standards for the generation and sharing of genomic data.  This is a fantastic development in the field, and one with great implications for the future of genomics data research.  It’s also particularly apropos given the recent news that the US government has been collecting internet-based metadata broadly, in a previously secret program to detect signs of terrorist activity.

People are waking up to the idea that we all produce data of all kinds, and that advances in technology now make gathering that data as easy as getting wet in the Seattle winter.  Whether that data is created digitally and automatically throughout our day-to-day life, or via an action as simple as sending a cheek swab to a company, there are some deep questions that are just beginning to be asked, by the government and other organizations, as to how the use of these data best serves the public good.

As a UC Berkeley graduate student in the late 80s and early 90s, I heard the lore about the Asilomar Conference on Recombinant DNA.  I think it was part of our standard indoctrination package, along with instructions on how to navigate Telegraph Avenue and reasons why Berkeley was superior to Stanford and UCSF.  That conference, in the early days of the age of gene splicing technologies, was an amazing example of self-regulation by a group of scientists who had the forbearance to actually stop and think about the possible implications of what they were doing,  The Asilomar conference led to a set of standards for the practice of recombinant DNA technology, and an overall greater mindfulness of what we were actually doing when we moved genes around among organisms.

The announcement of the Global Alliance has a similar flavor to me.  Again, technological advances have opened a huge window of potential research opportunities that were not possible before.  The implications, however, will be unclear for quite some time, and the effect that cheap genome (and other kinds of) sequencing will have on research, public health, ethics, medicine, and many other fields is unknown, other than that it will be huge.  We were already seeing the beginnings of chaos in terms of data repositories, standards, and practices, but this Alliance suggests that, at least at the level of key players like the Broad Institute and the Wellcome Trust, there is a commitment to taking  a step back and trying to find a good answer to the question of how to do this well, to best serve the public good, and to try to minimize harm.

I wish them luck.

Sea turtles: Nature’s smartphone

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The recent publishing of the green sea turtle genome brought back memories of my first encounter with baby sea turtles, and one of the most fun and astonishing examples of hardwired behavior I know of.  I had the good fortune to travel to Heron Island in the Great Barrier Reef during my undergraduate days.  We were there in March and April, the Down-Under Fall, when the sea turtles hatch and begin their journey to–well, to be eaten, frankly, although a few percent do make it to adulthood.

The first nest that I saw hatch emerged in the afternoon on a rainy day.  Normally sea turtles come out of the nest and make their way to the water at night, and use cooling temperatures as a cue.  Rain fools them into thinking it’s dark, and this hatch was making their break in broad daylight.  We watched the baby turtles–each no bigger than a big kiwi fruit–flop awkwardly towards the water, alternating their flippers and dragging themselves forward.  We shooed away the seagulls who watched us with petulant expressions, since the normal fate of a baby sea turtle hatching during the day is a short trip down a seagull’s throat.

But none of this is the behavior I was thinking of.  Rather, the amazing thing is what happens when you pick up a baby sea turtle.  Once the pressure of the sand beneath its belly is gone, the motion of its flippers magically switches from alternating to synchronized, both flippers flapping in unison like the oars of a sculler on Lake Union.   Even more remarkable is what happens when you tilt the turtle from side to side, or front downward or upward.  The rear fins, useless on land, become rudders, turning in just such a way as would correct the turtle’s posture in the sea so as to keep it level and moving straight ahead.

Tilt the turtle head down, both rear fins bend up.  Tilt it to the left, the left rear fin bends down and the right fin bends up, while one front flipper stops moving and the other churns frantically.  The newly hatched sea turtle needs to get out to sea fast, to avoid the predators in the reef along the way, and its coloring (dark on top, light on bottom) are optimized to make it difficult to see as long as it remains level.

This is what evolution does.  It takes random variation and selects for those combinations that lead to reproductive success.  If those variations have their root in genes, those genes get passed along.  I’m curious to see if anyone tackles the question of how the turtle keeps steady, now that we have the tool of its genome to help.  I’d love to learn what goes into hardwiring this kind of behavior and who knows?  Maybe nature’s figured out some tricks that the cellphone makers don’t know.

Sequencing Seattle: An Idea for the Future of the Northwest

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This post originally appeared on April 3, 2013 in Xconomy.  The views presented are solely those of the author and do not necessarily reflect those of Novo Nordisk.

 

What?

Let me make a modest proposal:  Seattle should commit to sequencing and interpreting the genomes of every willing member of its population and should do it within the next five years.  This program would elevate Seattle to the forefront of personalized genomic medicine, leverage many advantages unique to our area, and create a vibrant and sustainable economic engine that will drive our region for decades.

Why?

I can hear the howls of protest now, especially from the halls of the City Council and the mayor’s office. Seattle faces a lot of challenges.  Like most cities, Seattle is staring at revenue shortfalls, relatively flat growth projections, and a host of problems in environment, public health, education, social services, and infrastructure.  These are all pressing, immediate needs.

But at the same time, focusing too narrowly on the short term and not investing in infrastructure leaves a city’s continued relevance and growth at risk.  And while the planned waterfront tunnel is an example of a commitment to physical infrastructure, I’m talking about a similar commitment to infrastructure that supports, enhances and grows human capital, which I think will be at least as important for Seattle’s future.  Committing to genome sequencing is that kind of infrastructure, and when combined with the business, research and health synergies that would come out of such a commitment, this path makes a lot of sense.

Next generation DNA sequencing is a paradigm shifting technology.  It’s analogous to how the creation of the integrated circuit in 1958 led to the personal computer revolution via exponential growth in processing power.  Biomedical research today is undergoing radical change due to Next-generation sequencing platforms that started with Roche’s 454 in 2005 and continue today with platforms like Illumina’s HiSeq, Life Technologies’ IonProton, and others.

Over the past five years the cost of DNA sequencing has dropped at a rate that betters Moore’s law.  The original human genome draft sequence cost ~$2.8 billion dollars and was announced in 2001 after a decade of work.  Today, a human genome sequence can be had for about $5,000 and will arrive in as few as a couple of weeks.  Most experts in the field expect the cost of a genome sequence will drop below $1,000 in the next 3-5 years, and there’s no reason for the drop in cost to end there.  The $100 genome will happen.

By committing to sequencing everyone, Seattle will realize several key benefits.  On an immediate level, a large part of the cost can go right back into our community.  I’ll admit, $100 per genome, is a bit disingenuous.  The real cost of genome sequencing is not the outlay for the sequences but rather the costs of storing, analyzing, annotating and communicating the results of the raw data.  A commitment to sequence everyone in Seattle would require an additional, greater outlay of funds for analysis, and that money could funnel into our local institutions.

Few places in this country have comparable skill in genome analysis as the UW Genome Sciences Department, and none is better.  The UW Genome Sciences Department would be the logical and (I expect) willing partner for building and maintaining the analysis pipeline.   Likewise, the Seattle area is home to several companies, including Microsoft and Amazon, that would make natural and highly interested parties for storing data, assisting in the annotation, and providing platforms to help standardize and democratize giving data back to Seattle’s residents in easy to use, easy to understand, and portable online tools.  Imagine the cocktail party chatter when you whip out your genome app.

A commitment to sequencing Seattle would also provide an irresistible draw to all kinds of smart, innovative, ambitious professionals, such as bioinformaticists, genetic counselors, database and internet security specialists, clinicians, project managers, biomedical researchers, and public health administrators, as well as people who will create the occupations we don’t even know of yet, that will be needed to deal with this kind of data and infrastructure.  Economies aren’t driven solely by commodities and resources.  Intellectual and human capital will only grow in importance.

Seattle is already one of the more popular destinations for people looking nationally for where to live and have a career, and it’s not just because of our great weather.  They come because.  Seattle has a reputation as a forward looking, progressive city with a keen technological edge and an investment in the future.  Sequencing Seattle will enhance our reputation and strengthen our draw for the kinds of innovative, risk-taking, technologically-savvy people that keep a city and a culture from becoming as stale as a week-old  baguette.

And let’s not overlook the financial implications of a sequenced population. The incredible resource of a large population of sequenced individuals will draw in public and private funding from the government, non-profit agencies, and pharmaceutical companies.  ‘Applications by our researchers to the NIH will have an incredible advantage because the groundwork will already have been done for cutting edge genomic research.  Given the push for evidence based medical treatments following healthcare reform, pharmaceutical companies are under increasing pressure to tailor their new drugs towards defined responding subpopulations.  They could pay to have every person in their clinical trials sequenced.  Or they can choose to hold their clinical trials in a place like Seattle where that hurdle will have already been cleared.

Here’s another benefit:  cost savings for diagnostic and preventive medicine.  No one needs to be told how health care costs are rising.  Seattle can be on the forefront of taking genomic information and doing prospective studies on how that information can help guide medication, treatment, diagnosis, prognosis. We have one of the strongest concentrations in public health knowledge in the nation and can leverage that expertise to learn how genome information will make people healthier in the coming century.  In addition, in just one area, oncology, we are already seeing the benefits of rapid tumor genome sequencing to identify cancer-promoting mutations.  In this form of treatment, the baseline genome is compared to the tumor genome to look for changes.  Having the baseline genome already in the database speeds analysis and amortizes the cost.

Seattle can also be on the forefront of figuring out how can genome data be best used ethically and effectively.  This year the Presidential Commission for the Study of Bioethical Issues delivered their report on “Privacy and Progress in Whole Genome Sequencing.”  We can play a key role in leading and shaping the ethics of genome sequencing.

Last, I want to touch on what could possibly be the most amazing outcome:  citizen science, in which people are able to create their own research programs on the fly to answer questions.  Imagine that all participants are in a database with their own preferences on what kinds of queries (health, phenotype, behavior, etc.) that they’d be willing to participate in.  Someone comes up with a query and broadcasts it to the group:  “Is there a genetic element to coffee preferences?”  Anyone interested gets a text, chooses yes or no to participate, provides their feeling about vanilla lattes, and immediately the cohort is assembled, curated and QC’d by hard-coded heuristics developed by UW Genome Sciences and hosted on Amazon’s Web Services.

Within minutes, specific findings are reported back in whatever way you’ve selected.  At the same time, ghostwriting software automatically generates an academic paper that is immediately submitted to an open-source journal and posted online.  Maybe everyone contributes a dollar when they join in, to defray processing costs.  And that’s how you empower people to use this infrastructure to do novel science.

How?

These might seem like great benefits, but I’ve already pointed out the real and immediate demands on Seattle’s budget.  How can a multimillion dollar project like this get off the ground? Let’s break it down.

As I mentioned, I fully believe the sequencing of a human genome will drop to between $1,000 and $100 over the next few years.  For purposes of making some back of the envelope calculations, let’s go for the middle point and say the average genome over the next five years will cost $500 per individual.  Seattle has a population of approximately 616,000 people.  But, we’re sequencing only those who want to have their genomes sequenced, so let’s say initial uptake is 25 percent. That means about150,000 people, multiplied by $500, for a total cost of $75 million, just for the sequencing.  The entire city budget for 2013 is $951 million.  Seems like a non-starter.

But.  This is where creative thinking comes in.  Today globally we have a few major providers of genome sequencing services.  Ask them:  what can you do for us?  Remember, prices are going down faster than Moore’s law.  BGI or  Illumina might be willing to cut a low deal, not just for the business, but for the privilege of being involved.  Or maybe a new player gets involved. The promise of having hundreds of thousands of genomes to process might spur a Covance or other outfit to make a huge investment in these technologies.

What if the genome sequencing is backloaded like a bad ARod contract, so the bulk happens in years 3-5, with the first couple of years devoted to pilots and infrastructure?  Maybe the budget for the first year is just a few million to assess the possibilities, find vendors, build a business case and line up stakeholders.  Maybe the second year is just $5-10M, which by then will buy 10,000 genomes to start.

Go to Amazon and Microsoft and Google and pitch them on being involved.  They know how to handle and analyze data, and might be willing to cut a deal in order to work with this kind of resource.  Prioritize diversity and outreach in the initial cohorts and use that as a lever to attract the Bill & Melinda Gates Foundation and PATH and Seattle Biomed and other public health institutions that are trying to help people in underdeveloped countries.  Information we learn about the impact of different ethnic backgrounds on health can be applied back to the populations from which our local groups originated.  Indeed, one of the main drawbacks of current genomics research is the overwhelming emphasis on European-derived (*cough* white *cough*) populations.

To Sum It Up

Think of this as laying fiberoptic cables in India.  As Thomas Friedman described in The World is Flat, the vast investment in infrastructure in developing countries during the dotcom boom is why you can now outsource reading your X-rays or putting together that patent application to professionals in India for a fraction of the cost of doing it locally.  Sequencing in and of itself is not the point; building a vast reservoir of data and encouraging a culture of innovation to do something with it is.  And doing it now.  This idea is already being talked up with larger organizations, such as the United Kingdom’s NICE (even if they may not be going about it the right way).  Seattle has the opportunity to do it the right way, and if we wait until everyone else is also doing it, there’s not much advantage to that.

Think of this also as a call to arms.  I see sequencing as the logical tool to keep our area vibrant and growing, but that’s because I’m a genome scientist.  Ask someone in 3D printing and she might say Seattle should buy everyone a 3D printer (hey!…)  The point is, the future of a city, more than ever, depends not on its natural resources and history, but rather on the quality and creativity of the thinking that happens there.  What gets quality and creativity going are visions of the future that inspire.