Transcriber: Sakina Gulamhusein Reviewer: Rhonda Jacobs
I'm a data scientist and a biomedical researcher
at Harvard Medical School.
And we use the tools of data science to help us figure out robust associations
between new biological phenomena and disease and risk.
A lot of words there. So what is data science?
Data science is the application of emerging new computational tools
to understand large data sets, to make them useful.
And I really got first exposed to this practice of data science
when I was actually not in academia or at school, actually, but in industry.
And it was during the dawn of the Human Genome Project.
And so during the 2000s, we needed data scientists to decode
what was thought to be one of the most critical questions of all of life:
That is, how can we sequence this molecule that's passed down from mom and dad
and understand the biological units of the sequence of the genome?
And that's exactly what the genome was.
And so I was deeply inspired when, as an engineer in the biotech industry,
where I saw colleagues of mine,
for example, Craig Venter of Salerno Genomics,
stand up there with Bill Clinton,
our President of the United States
at the time,
at the White House with Francis Collins to announce this ambitious project,
with great public fanfare and time,
and then of course, the scientific papers that came as a result.
So sequencing of the genome has launched a new, efficient paradigm
for biological discovery.
But is it the entire story?
So what happened after that?
So what we did - the community did -
was essentially learn how you and I
and all the populations around the world differ
using these new tools of sequencing.
And so we've sequenced individuals from around the globe
to figure out how you and I differ,
and then quickly thereafter developed tools and technologies
to measure these things at scale.
So grabbing thousands to now millions of individuals
to figure out how in our less than 1% of what makes us different,
the variants that are associated with these diseases and traits.
So we can measure on the order of millions of variants,
all simultaneously, on people.
And so we have created essentially these search engines
to discover new variants associated with diseases and traits,
and this has been, again, a paradigm shift.
So for example, in 2005, just right after the sequencing of the genome,
we had two variants that we could positively connect with disease.
And to the current day,
we've amassed almost 100,000, or more, actually,
of these millions or so variants associated with disease and outcome.
Incredibly, a useful story to understand the role of biology in medicine.
But is it the entire story?
So, I would argue in fact that decoding life hasn't been fulfilled,
even after the successful mission of the genome project.
And in fact, when we're trying to decode the genome,
we always like to talk in software terms -
when you have this software code to decode the genome
and what it means to health,
this function of health as a function of genome -
is actually not the entire story.
And in fact, this is an oversimplistic view,
and health is not a function of our genomes.
So how can we better illustrate this? So here's some data.
I'm a data scientist, so I hope to show you some data today.
On the x-axis here is the percent of the genomic variation we can explain
from family-based studies.
Okay, so this is a proxy for the genome.
And on the y-axis, it's a bit hard for you to read,
but these are different diseases and traits, okay?
We're asking the question:
How much of genetic variation can we explain
in these different diseases and traits?
So at the bottom here you'll see hair curliness and eye color.
So if I took a magic algorithm,
I put in you and your genomes in a sequencer and asked the question:
"Could I predict your eye color or hair curliness?"
I probably could with very high confidence using your genome.
But other things we care about today,
like type two diabetes, like heart disease, like longevity,
can't be really predicted
even with the most fanciest AI technologies with your genome.
And things like cancer even worse, okay?
So where are we? What is the rest of the story here?
Where is the rest of the variation in phenotype and disease?
And this is the central question I think we need to address
after this ambitious genome project.
And on top of that, actually, while there's a very few -
there's now emerging evidence to support the modification of the genome,
in large scale, we cannot change our genome.
So this is another issue.
Even if we did know what were the risk factors for prediction,
we still can't change those.
So my primary question to address to you today
is that our health is a function of our genome
and our exposome, if you will.
So these are the non-genetic factors that contribute to disease biology,
and it's time is now for an exposome project
at a larger scale than the genome project
to scalably decode human health and disease.
So, we've been decoding the role of environment,
or the non-genetic factors in disease for a very long time.
So one successful story is in fact from Richard Doll and A. Bradford Hill,
two folks I greatly admire, epidemiologists,
who looked at single exposures, okay?
So single exposures such as smoking,
and then physicians and looking at diseases such as lung cancer.
Highly successful, and no one is going to debate the fact
that smoking is in fact a causal factor for mortality or cancer today.
But in fact, we're still living in this 1958 model
of studying one thing against another thing.
And besides from smoking,
a single causative factor does not exist for most diseases.
So of course, I'll totally submit to you
that smoking is in fact a huge risk factor for disease.
But of course, we have thousands of other things we're exposed to,
this haystack, if you will,
and figuring out the needles in this haystack,
the needles among the needles, is a new challenge,
data analytically, for us in the exposomics field.
And so, what is the exposome?
Why am I talking about it? Another omics?
What is all this about?
So it's a complex list of non-genomic factors,
exposures that we encounter from birth to death.
So let's take, in fact, this 70-year-old female from the Midwest United States.
What is her exposome?
So it consists of the millions or billions or so bacteria living within her,
like bacterioides.
It could be the flu exposure she had a couple of years ago
that might increase her risk for certain diseases.
Of course, it's the exposures from consumable products,
like bisphenol A.
It's the drugs that she takes.
So she's a little bit older, so she's on quite a few drugs,
potentially is at risk for diabetes,
so metformin, for example, is part of her exposome.
The small particles in the air pollution that she's exposed to
from the walk to the park,
and of course, the diets that she chooses.
So we have tons of potential diets, like the Mediterranean diet,
the keto diet,
and these are all factors of our exposome.
But then, of course, her grandchild from the west of the United States
might have a more homogeneous exposome, if you will.
Maybe there are still the same factors, the bacteria, or the flu,
but other types of diets, for example, formula
and other exposure factors that might be transmitted from her mom.
So to get a sense of the complexity of tracing these different factors
from birth to death,
might be a little bit more than tackling the genome.
And of course, when you put into account the spatial factors, like where you live,
of course, the exposome is another complex factor
that is affected by location.
So for example, here's a plot of air pollution around the United States,
and you can see that in certain parts of the country
you have higher exposures than others just by looking at this simple plot here.
So what we do today, to repeat, is that we ignore the complexity of the exposome.
We study one thing at a time, for example, asking the question:
"Is smoking associated with cancer?"
But in reality, we have this battery of exposures
that we could possibly consider when it comes to our disease and health.
So why is it a problem if we don't study things
in this sort of a reductionist manner?
We do not ask this question:
What is the relationship between the entire exposome and disease?
And there's multiple problems with that.
So first of all, here's an example of why it's so complex.
So here's an example that we published
about exposures that we're all exposed to
from the Centers for Disease Control and Prevention data
that we published in JAMA.
And we ordered all these 317 different exposure factors in this globe here,
and then we plotted a simple line
between exposures that were highly correlated with one another.
If you're exposed to smoking, for example,
like cotinine, which is a metabolite of nicotine,
you're also exposed to 37 other things.
So you have to question,
okay, you correlate smoking with a disease; is it that?
Or is it 37 other things that's coming along for the ride?
And you start thinking about other factors of the exposome
like mercury, for example, founded in fish - in fatty tissue in fish,
or cadmium, another exposure factor
that's found in air pollution and smoking exposure,
or even your diet, like carotene's found in carrots and other vegetables.
A whole suite of other factors come along for the ride.
So it becomes a very difficult challenge of understanding what causes what.
So how does that affect our studies and us as consumers of this information?
So here's an example,
a couple of months ago I'd just opened my e-mail
for a journal that I had followed, JAMA Internal Medicine.
"Association Between Soft Drink Consumption
and Mortality in 10 Different Countries."
Very interesting, I thought. Let's take a look.
What are we recommending to our physicians to tell their patients?
These guys studied 500,000 Europeans.
Highly powered study; we like those in statistics.
Average years of the people in the study are 51,
and a higher risk of death for individuals are found
for 1.5 cans of sugar-sweetened soda per month.
Okay, I can kind of believe that. What about three cans or five cans?
Those are the questions that we should address and need to address,
but this result in itself seemed to be believable.
But then I was like: "What the heck?"
In the next segment of the paper when they told me
that artificially sweetened soda was also associated with death.
So you drink 1.5 cans of diet soda, you're also at risk for higher mortality.
So the issue then becomes, is it sugar or is it the artificial sweetener,
or is the similarities between the populations
that are drinking these beverages?
Okay, so it's very unclear to decipher when we study exposures in isolation.
And so we also have an issue of a higher chance of false findings.
So when you test one exposure in this haystack,
did you account for the other things in the exposome matrix?
Did you test others?
If you did test others, did you tell us that you did?
And so you have a high chance of false findings.
So here's what happens, okay?
You build a database, an observational database,
I'll ask you all what you drank yesterday,
and then maybe we'll talk again in 10 years
and ask whether you got a disease or not.
And then we build this database and what we do is say,
"Okay, is X associated with Y?"
So for example, is orange soda associated with Y?
I'll put it into my handy-dandy calculator,
a P-value significance comes out and it says: "Ah, no."
Okay, I'll move on to the next one: Transparent soda. No dice.
Move on to the next one: Green soda. No, but getting there.
The P-value is creeping up to significance.
Blue soda? Not really.
So let's move back to vanilla soda. Okay. Nah.
And then diet soda finally. We put it all together and say:
"Okay, yes, stop the presses. It's significant.
I can publish my Ph.D thesis."
But the thing is we're data dredging, okay?
We're P-value hacking
with all these different factors in our large databases,
and we're not accounting for that.
And so we have a higher chance of false findings.
So here's a great example to kind of illustrate this.
How many of you would believe that the letters in the winning word
of a Scripps National Spelling Bee
is actually causally related to people killed by venomous spiders?
Probably not any of you in this room, but, in fact, with these large databases,
we are increasing the rate of discovering these types of associations.
And in fact, these are very provocative results
that even the popular press has covered.
Of course, I'm not the only scientist to say:
"Oh, my gosh. What the heck is going on?"
Others are also up in angst.
So, we need to start embracing this complexity,
and we need to execute the human exposome project.
So how can we do this?
Like the genome project,
we nee massive data and compute power to do this.
So first of all, we need health on individuals.
We need their biosamples, like the saliva samples,
urine samples, blood, feces, to measure the exposome at scale.
We need to understand where people live,
where they're distributed around their country, around the world
to understand how the exposures are varied in different places,
along with understanding the role of lifespan, essentially,
from life to death of this variation.
So let me give you an example here.
So this is a great type of study from the National Nutrition Examination Survey
from the Centers for Disease Control and Prevention.
They'll send out these trucks to different places around the United States
and assess one's health conditions; they'll collect their blood.
They'll measure their environmental factors -
a few of them, not the entire list of the exposome,
but it gives you a sense of how we could potentially scale
by doing such a project.
And in fact, they are collecting things like nutrients and vitamins,
drugs, infectious agents, plastics and consumables, pesticides.
So we need a way to eventually follow these individuals over time
and correlate that with disease risk and do it of course across the globe,
like we did with Human Genome Project.
All right. So let me show you an example from my group
to show how might this help accelerate research.
So we asked a fundamental question:
Do the exposomes in twins actually affect their health and disease?
So as you know, twins provide a great example of a natural experiment
where you actually are fixing genetics
and testing for things that are different between the twins
that could be due to extrensic factors like the exposome.
So here is an example here of, like you could pause it,
but these are two identical twins; they share their entire genomes.
And one is a smoker; one's a non-smoker.
You can just see from essentially their facial expression,
their facial phenotype, if you will,
that they're, in fact, different in some way.
And so this is just an example of one exposure,
smoking and non-smoking,
but you could consider the entirety of the exposome
and how these individuals might be different.
That's what we did.
So for example, we collected health from a massive amount of individuals
by collaborating with a health insurance company
and understood and tried to assimilate where they are also lived.
This is a great study led by a postdoctoral fellow in my group
named Chirag Lakhani, no relation, great first name.
So we got the 45 million individuals from this insurance database,
and we've figured out where people lived.
And out of those 45 million, figured out who were twins in the dataset.
And from there we said:
"Okay, we had 60,000 twin pairs, 560 diseases, things like acne,
attention deficit disorder, cancer, obesity, etc.,
and figured out based on where they lived, their exposome.
So we linked this information to air pollution information,
weather information, geographical, socioeconomic information
with complex data to actually link to where individuals lived and work
and their health status.
By the way, because we had twin status,
we actually could figure out
the relationship between the genetics and disease as well.
And so here's a kind of 20,000-foot overview of our study here.
So on the x-axis here is the fraction of disease
that we could explain using these different modalities.
And the y-axis are the 560 diseases here.
Okay, so, you could view these lines here as different diseases
and their aggregate influence of these different data modalities,
like the genome, in blue,
and like the exposome, in red and green.
And briefly, just to give you a high-level view,
if you're looking on average across all these cross 560 diseases,
the genome as assessed by twin status explained about 30%
on average of these 560 diseases,
and the exposome explained anything from 10 to 50%.
So a large amount of variation that we could possibly explain
in disease and risk.
So to give you a couple examples here from the bottom to the top
to actually understand
how such a nuanced type of role the genetics and the exposome has,
on the bottom you have things more related to genomic risk in health,
so traits like height or developmental disorders.
But as you move to the top,
you see things that are little bit nuanced in their picture.
So obesity has a shrong genetic influence about 20 to 30 to 40%,
but the exposome has even a bigger one.
And as you move to the top there, low birth weights
and things that we really care about -
low birth weight, anemia, and factors such as the flu,
you could potentially have a huge exposomic relationship.
All right. so, in the end here, I hope I convince you
that we need to embrace this complexity of the exposome
to actually make actionable sense of human health and disease,
to help us actually disencode the role of genome with respect to the disease
as well as the exposome.
And the time is now, in the aftermath of the genomic project,
to actually execute something alongside the exposome project
to scalably decode human health and disease.
And if anything, I hope you agree with me
that health is a function of both for your genome and your exposome.
So thanks so much for your attention. I look forward to talking to you.
(Applause)