Welcome to the Cannabis Data Science Meetup Group.
Happy to have you all here.
So long story short, my name is Keegan.
I started a company, Cantlydx, spent a little bit of time in the cannabis space, preaching
data.
My background is in statistics.
And so I was thinking, well, this is something that I know a bit about.
So why not get together with some other knowledgeable people, data scientists, cannabis operators,
regulators, you name it, and see if we can use statistics to help advance the cannabis
industry.
So that's where I'm coming from.
This is basically a roundtable for us all to get together, talk about what's on our
minds, what's pressing, what we want to accomplish, how we think data science can help us.
Any pressing research questions we may have.
Then I'm always happy to share my research and ideas with you.
Always open to your feedback, because your feedback is how improvement happens.
And then if you have any cool ideas or research endeavors, and I'm always happy to hear about
those too, just so we can keep bouncing ideas off of each other.
So that's enough about me.
I'll share with you some cool statistics here today, cannabis related.
Before we get to that, just want to give everybody a chance just to say, hey, a bunch of new
faces today.
So you don't have to, but if you want, spend anywhere from 30 seconds to a couple minutes
talking about maybe your background or what you hope to get out of applying data science
to cannabis.
So Nick, good to meet you.
What are you interested in?
What do you hope to learn here in 2023?
Well, I was involved in cannabis growing for about four years.
And basically I'm interested, how the business will go further in 2023, because we all know
what is now happening and people are struggling and how data analytics can help people achieve
their goals, I guess.
I love it.
And I love your optimism, right?
And then just mute me while I talk, just there was a little bit of feedback there.
So long story short is I love your optimism because yes, you pointed out there's a problem
at hand, but there's ways to fix it.
And how do we start?
Let's just start by any solution.
Let's just start thinking of ideas.
Cannabis doesn't have to be the best idea in the world, but it can be a starting point.
So as you said, there's some companies that are struggling.
We looked at the survival rate of cannabis companies and it varies by the type of business,
say if you're a retailer, a processor, a cultivator, but on average, cannabis companies weren't
having a lifespan much longer than a year on average, which is not an encouraging sight.
It's not comparable to say the restaurant industry, but people are out there trying
to start long-term companies.
They want to have successful cultivations.
They want to have successful retail establishments, successful processing.
So exactly.
So how can we alleviate some of the burdens?
Well, maybe statistics can help.
So we'll talk about how we can get back to the basics today.
Real cool things coming up.
So just once again, give everybody a chance to say, hey, Noah, good to meet you.
We'd love to hear about your area that you're interested in.
Yeah.
Hi.
Good to meet everyone.
Yeah.
I just found this meetup a few days ago.
I thought it'd be really interesting.
I just graduated from a degree in computer science with a data science emphasis.
Still was kind of going around looking for work.
I just relocated to Portland area.
And I don't know.
I'm just I'm really passionate about cannabis.
And I've spent some time growing my own cannabis.
And I'm really interested in some of the applications of using reinforcement learning to improve
growth and that kind of thing.
And I'm also interested in using large language models to kind of work out some of the effects
of cannabis on different people.
I think there's some really interesting application there and some holes that I think can see
consumers could benefit from kind of understanding other people's experience with like certain
strains and that kind of thing.
But yeah, I'm mainly just here to just kind of hear what I didn't even know there was
like a field of analytics in cannabis.
So it's exciting to see that there are people working here and I'm just here to learn.
So I'll give you a little taste of everything.
So of course, I've got a lot of enthusiasm for this area.
So I think there's a lot of value to be added by applying data science to cannabis.
And so I started can let it just to show the other side.
Data science is essentially a bleeding edge field.
So it's really new.
And so it can be difficult to convince people of the value.
That's part of the one of the difficulties in a lot of what analytics companies do is
talk to people and try to say try to essentially convince them that the analytics are in fact
valuable.
I guess don't want to to, you know, miss sell anything.
But I think they are there.
I think it's just asymmetric information.
So to any who there is is a little bit of an uphill battle.
But in my opinion, there's a lot of value to be added.
The subjects you're talking about are exactly what people are interested in.
Of course, on the retail side, especially since the medical industry is with how do
you say they're they can't wait to dip their toes in the to dip their toes in.
And of course, why?
Because they're interested in potential medical effects.
This is a lot of the talk on the town on the retail side.
Another thing you raised is, well, what about the the home growers?
And this was something that was neglected for a long time.
The reason being in a lot of states, for example, Washington State, Washington State is still
hesitant to permit home grow as Massachusetts has because they're worried about diversion.
I don't know how legitimate of a claim that is, but I was thinking that analytics could
be something that home growers could find quite useful.
And I've encouraged meetup members in the past that this is something that I think people
have dabbled in.
But once again, you can always have a fresh take on it.
Especially home grow analytics.
And I don't know if it's just the material that I've searched for.
But I see a lot of people doing, you know, hydroponic setups.
And I wouldn't be surprised if at home hydroponic farming could be a thing in the future.
But once again, bleeding edge, but you're well positioned.
So I'm kind of rambling.
If you have any more thoughts, feel free to chime in.
And then as I continue to think about all your cool ideas, Noah.
Michelle, welcome to the group.
Happy to have you.
We'd love to hear about what you're particularly interested in.
That way we can make sure to touch on your interests.
Sure.
Hi.
Keeping my camera turned off because I tend to get up and move around a lot.
But like Noah, I kind of just stumbled upon the meetup a few days ago.
I'm a math teacher who's actually, I'm not a math teacher anymore, but I'm transitioning
into data analysis.
And as I've been looking for projects to do and things like that, one of the things on
my list was the cannabis industry.
And I have a gajillion questions from not only the business aspect, but also how it's
affecting society, you know, benefiting, I think probably benefiting most.
But I would like to see the data on that for, you know, just other, you know, social ailments
and mental struggles and that sort of thing.
Depression.
I deal with depression.
But most of all, cannabis has been a part of my life for as long as I can remember.
I grew up in the 70s with a hippie dad.
He grew it on the roof of our apartment in New York.
And yeah, so I just thought it was a really interesting way that I could maybe develop
a project for my portfolio as well.
I love it, Michelle.
And you attended on the perfect day.
Everything that you raised are essentially exactly what we're touching on.
So first off, math.
Coincidentally, we actually have a fun math or statistical exercise today.
Going back to the 70s, we're actually going to hit on some real cool history today, which
is tangentially related to growing on rooftops.
And we'll get more into that next week.
And then the final thing is, and I'll get to this more, relates to the insight of the
day.
And I'll sort of tease that.
There's sort of a big takeaway at the end of today.
Statistical insight.
So I'll tease that.
But that's all coming up.
So you attended on the perfect day.
Now, Isaac, happy to see you today.
I love that you made it to the morning.
So hopefully this works out well for you.
We're doing, and the final thing is we're going to be working on a lot of projects.
So this is essentially the, we're going into the third year of the Cannabis Data Science
Meetup Group.
The first year was a lot of data wrangling.
So we were just trying to find what are all the cannabis data sources?
What are all the data points?
How can we get them?
That was a lot of what the first year was.
The second year, we laid out all of the statistical tools that we'll be using.
So we started to lay out model after model.
We have about 30 solid models that we can pull from.
And then we began to see how we could apply those in the cannabis space.
Now this year, we've got the data.
We've got our statistical tools, and now it's time to finally embark on interesting projects.
And so that's why it's all hands on deck.
So if any of you, Michelle, Noah, Nag, Isaac, are looking for projects, feel free to get
in touch because I'm actually starting to work with this group, cannabis data, and we're
just wrangling data in statistics.
And there's a lot of issues, open source project.
So feel free to see if there's any way that that could shoot your fancy and you could
get some value out of that.
So anywho, before I continue rambling on, Isaac, any cool projects that you're interested
in tackling in this coming year?
Well, one thing that I'm interested in is lab fraud.
So last year, well, end of last year, I tried to use Benford to do some detection.
And also, last week, I plotted out the microbiome results from Washington and I noticed a strange
decrease of detections around the state regulatory limit.
So that seems pretty fishy to me and I perhaps dig into that a little bit more.
And also, I found a pretty good test.
It's called McCray density test.
And it's like, well, so one method for detection or for just in general, measurement around
one intervention is called discontinuity design.
And it's that there is a, you are basically working around a ball of data around one single
point and you're trying to see if the means are different.
And this method called McCray density test allows you to do that on a histogram, say
of a normal distribution.
And you can use that to measure if there is a gap of difference of whatever mean or standard
deviation of a normal distribution to detect if it's really off.
So that seems pretty promising method.
So yeah.
I love it, Isaac, and I love that you bring this topic up today because once again, it
is the exact topic at hand, lab results and statistical distributions.
And if you've got time, I'll be attending.
There is a quality control meeting that's open to the public in Washington state tomorrow.
And these meetings, there's often a lot of people in the industry there, right?
There'll be representatives from the labs and the licensees, but very few times do you
see people from the public.
So if you were able to attend, that would be a whole new perspective.
And as you mentioned, and this was sort of the topic last week, there's all the, of course,
there's a lot of talk about, you know, THC and CBD.
There's a lot of natural variation there too.
And like you said, of course we do want to identify any fraud.
However do we only want to look at those analytes?
And as we were pointing out last week, you know, moisture and water activity were looking
a little odd.
And I kind of messed up my Benford's analysis, I realized.
So I'm happy that you repeated it.
And so the moisture and water activity could use another look.
And then as you said, the microbes and the mycotoxins, especially the mycotoxins could
potentially use another look.
Because they may have changed the method in Washington state.
I need to follow it a bit more closely, but as you, I'm sure you know, there's multiple
ways that you can screen for mycotoxins.
From my understanding, you can use what's called plating, which I think takes maybe
24 to 48 hours and maybe isn't as precise as one may hope.
And then I think people may be moving more towards testing for mycotoxins on a mass spec,
which is faster because I think you can run maybe a mass spec run maybe in under an hour.
But it's more expensive because you have to have a mass spec.
You have to have somebody that can run a mass spec and then you can have somebody that can
analyze the results.
So it's a bit more complicated.
So it's interesting that you noted that.
And so it's worth investigating a bit more because the labs and the licensees, they have
their priorities about what they want to talk about, which will probably be lab testing
is expensive.
However, consumers have their concerns.
And so if we see, say, mycotoxins is being measured oddly, that's a good point to bring
up.
So I like it.
So just to go ahead and dive in to the topic at hand.
Well I'll go ahead and show you.
I got in a hold of this book.
I think you can get it for fairly cheap.
This is Sense Amelia Tips compiled by Tom Alexander.
I got a used copy, I think, for under 10 bucks or so.
So you may be able to find a copy.
This is a surprisingly good find.
So I was expecting just your typical news journal.
But there's actually a good bit of substance to this.
This was published in 1988.
So that's, I don't know when in 1988, but that's going on 35 years ago.
And that was when it was compiled.
So those could have been written before then.
So this could be over 35 years old.
And so let's see if there's any nuggets of wisdom in there.
Sorry, could you repeat the title again?
I couldn't see the title clearly.
Sure.
I'll make all these links available, but it's SINSEMILATIP.
And so Sense Amelia is, I think it may even be a Spanish name.
That's when they, originally, when people cultivated cannabis, you would just have one
field, where you would just have female and male plants.
Well, the male plants would pollinate the female plants, and you'd get flowers filled
with seeds.
And so that's why the whole sublime song, pick out the seeds from stands.
So people used to have to pick out the seeds because they just grew it all together.
And then in the 70s or so, definitely by the 80s, people would realize if you are solely
interested in the flower bud, then if you pick out the male flowers, then it's more
efficient.
You're using water and nutrients and light and space only for the productive female plants,
and they don't get pollinated by the males.
So cannabis is an odd plant, so there still may be an odd seed or two, but there's fewer
seeds.
So that's Sense Amelia, a technique that became more and more popular.
So anywho, that's the name of the article.
And this journal was put together by this interesting character, Tom Alexander, who
was growing cannabis in Oregon.
And I think they confiscated his crop, and I think he was facing some criminal penalties.
I think the case ended up getting dropped for one reason or the other, I do believe.
But then after that, he just went into publishing.
And so he just talked with a bunch of cultivators, growers, and compiled a lot of techniques.
So it's a real interesting look into the past here.
But enough of me rambling on.
Let me actually show you what's valuable about this.
Okay, too cool.
So this is a question that's come up multiple times.
And Isaac, you may have brought this up at one point.
And I think it's pretty well known, but I wanted to go back to the source and really
try to figure out some of the early research that's been done on this.
So okay, so we see a lot of variation in THC and CBD.
And so the question is, well, is that because of environmental factors?
And so that would be, are some cultivators better than others?
Do some people have brighter lights?
Do some people have more nutrients?
Do some people have less disease?
So on and so forth.
So those would be environmental factors.
And then, of course, people are interested in about breeding.
And so here we get to the math.
So I'll blast you with some of the math and statistics, and then we'll get to the takeaways.
So sigma, this is a typo.
This should be a sigma squared.
This is a measure of variance.
So it's a measure of central tendency.
So we know about the mean.
So if you've got a bunch of observations, you can calculate the average.
But then you can also calculate how much do each of these vary from each other.
So that's your variance.
And so if we're interested in about, say, THC, well, presumably, there's some variance
in THC that comes from genetics.
So if you have a good seed, maybe that leads a certain amount to your variance in THC,
as well as the environment.
So if you have dim lights, one would expect your THC maybe a little lower.
Likewise, if you've got poor quality soil, poor nutrients, poor airflow, odd temperatures,
all of these things are your environment and could lead to different THC concentrations.
And as we've seen, we've observed people selecting different breeds of cannabis to go into quite
different directions.
You see people breeding for high THC.
And then you also see people breeding for low THC, people trying to grow the CBD hemp,
who are trying to stay under the 0.3% THC limit.
OK, so you've got genetic variance and environmental variance.
And so we're going to define heterotability of a trait as the amount of total variance.
So the total variance we see is the genetic variance plus the environmental variance.
So we want to know what percentage of all the variance that we see comes from genetics.
Is it a small amount or is it a large amount?
This is really the crux.
Because if genetics doesn't matter, then you just want to get your environment as good
as possible.
Likewise, if genetic variance, if that represents a large proportion of heritability, then you'd
be more interested in focusing on breeding and less focused on environment.
So can we disentangle these two?
Also, just pointing this out, this was written by Chief 7 Turtles.
And the turtle is a symbol for cantlytics because we're sort of the turtle over the
hare.
And so I just thought this was just a funny coincidence.
Anywho, what are some of the key points?
And then I'll get to the actual data.
So some populations have more genetic variation than others.
Likewise, certain traits are more susceptible to genetic variation than others.
Of course, environmental variation depends on your conditions.
The example given in Sinsemilia tips was your environmental variation will be different
outdoors than it will be in a greenhouse.
I need to think a little bit harder on that.
But the conditions matter, and also the trait matters as well.
So those two are pretty straightforward and logical.
Those make sense.
Okay, well, why am I droning on about this?
Well, heritability is actually a difficult topic to study.
And it's normally infeasible.
What's interesting about cannabis is it's often grown with clones.
So people will clone cannabis.
It's pretty typical.
Well, when you make a clone, there's going to be no genetic variation.
All you'll have in the clone is environmental variation.
So the idea is you can see the total variation in a crop of plants, and you can subtract
away the variation from the clones.
And what you're left with is the genetic variation.
And then once you're left with the genetic variation and you know the environmental variation,
you can calculate the heritability.
So the long story short is heritability can range from zero, something doesn't inherit,
to one.
It's going to...
The genetics is going to explain 100% of the variability in that trait.
So we're interested in is the heritability of THC, is that closer to zero or is it closer
to one?
So that is sort of the question at hand.
And if this is getting a little abstract, then don't worry because we're going to get
our hands on the data.
And I should have committed this to the repository beforehand, but I'll commit this right afterwards.
So in this book, Since Amelia Tips, there's a table where they have two different plots,
plot one and plot two, and in each plot, they have some plants that were grown from seed
and some plants that were grown from clone, and they were measuring the THC concentration
of these plants.
And so this is, I was saying last week that I think cultivators have been keeping track
of THC concentration for a long time.
Well, it turns out they've been keeping track of THC concentration perhaps for more than
35 years, which is wild to think about.
So let's actually...
Oops.
Let's not give everything away.
So first things first, just going to read this data in, and just to show you, this is
all the data, right?
We were just looking at in Excel, just going to print it out to the console, and then we'll
essentially repeat the analysis done in Since Amelia Tips.
I wasn't sure about if I could just take a picture of their table or not.
I decided not to, but maybe it would have been a helpful benchmark.
So basically the idea, well, an idea of the scientific process is reproducibility.
And for statistics, this becomes really interesting.
It's actually non-trivial to repeat someone's statistics, and it's often a good exercise.
So if somebody publishes a paper, to go back through, get a hold of the data, and try to
follow the steps they took to reproduce the same statistics, because you'll have to follow
through all the assumptions, and it can be difficult.
So anywho, we've got this data here.
Well, let's start looking at it.
So we've got seeds and clones.
So if we just look at the seeds, so these were cannabis grown in two different plots
by seed.
And so as you can see, back in 1988, you've got an average THC of around 8%.
So the average THC has more than doubled in the past 35 years.
So that's an observation.
Next we can look at the distribution of clones.
And so these are clones in two different plots.
And this would be environmental variance between these two different plots.
There's no genetic variation between clones.
So any variation in the distribution, so any variation in variation, will be explainable
from environment.
So if you did the same study that Chief Seven Turtles conducted, then you would see, oh,
plot one is the better plot for growing clones than plot two.
And that's purely environmental.
Cool.
So now if you plot the seeds versus the clones, it becomes quite interesting.
So here you see the distribution of plants grown from seeds, distribution grown by clones.
What's readily apparent, the variance is much smaller for plants grown from clone than from
plants grown from seed.
The mean is lower, but that may just be the clone that was selected.
So for example, if you just selected a random plant from this distribution as your clone,
maybe they selected a plant from a seed from this end of the distribution.
Cool.
So now we can actually calculate heritability the same way that Tom Alexander did.
So the way they did, they actually use standard deviation.
And so I'll show you this with standard deviation, and then I'll go back to variance.
But basically, if you look at the, they also calculated variance grouped by plot.
And so this is what's so interesting about reproducing statistics, because all they have
is a table with the means and variances in their calculations.
And so it takes reproducing it to realize that, OK, when they calculated the standard
deviation, they did this by grouping it by plot, and then they took the average.
So basically, they took the standard deviation of these two plots, which is odd that it's
the same, but it is.
It could be just the nature of numbers.
Anywho, you've got the standard deviation of clones.
And as you can see, the standard deviation of seeds is higher.
So basically, the argument is all of the variation from clones is environmental variation.
So that's how you get the environmental variation of 0.79% THC.
And then they say, OK, well, what's the phenotypic variation?
And so the phenotypic variation, well, that's environment plus genetics.
And so they say, oh, we'll just get that by taking the standard deviation of the seeds.
And we'll call that our phenotypic variation, because that's going to be all of the environment
variation that the seeds experience plus the genetic variation of the seeds.
Cool.
And so then they say, oh, well, we can just calculate the genetic variation by subtracting
the environmental variation from the phenotypic variation.
Cool.
So now they're saying the genetic variation is around 1.27% THC.
And so you can now get a measure of heritability.
Drum roll.
So you're interested in, on a scale of 0 to 1, how heritable is THC?
Dun-dun-dun-dun.
Chief, seven turtles calculated the heritability of cannabis, or THC in cannabis,
to be approximately 0.62, or 62%, which is quite high.
So this would indicate that THC is, I guess you could argue what strongly is,
but in my opinion, is strongly heritable.
And so this would explain, okay, people figured out in 1988 that you can control
variation in THC through genetics.
So, for example, going back to our chart here,
this now explains how people have been able to grow both CBD low THC cannabis
and high THC cannabis because it's strongly heritable.
So, for example, this clone would actually be a good selection if you wanted to move
in the low THC direction.
And so the idea would be you'd grow a bunch of seeds and you would pick one from the low end
of the distribution and use that one for breeding and just repeat the process over
and over again and try to push this distribution towards zero.
Conversely, if you're interested in high THC, you would pick, you would breed the plants
that tested at high concentrations and just keep doing selective breeding.
Quick note, technically the formula for heritability is variance.
And for I don't know why, for whatever reason, they chose to use standard deviation.
I don't know if this is just because statistics has kind of evolved or people's understanding
of statistics have kind of evolved in the past 35 years.
I mean, statistics as a whole is a relatively new study in the grand scheme of things,
rigorously, maybe a past 150 years or so, or not rigorously, that's maybe when it began
and then maybe more rigorously, I don't know where, I'll quit conjecturing.
The long story short is if we did say use variance, then we would get a higher measure
of heritability.
So if we used variance, we would measure heritability at almost 85%, which seems too high to me.
But I also want to point out a couple shortcomings real quick.
One, this is a statistic.
So our estimation of heritability will become better as sample size increases.
So here we have a sample size of 20 plants grown by seed and 20 plants grown by clone.
So we could potentially use a bigger sample size.
Also, what the author points out, measurement matters.
And they say that you can view measurement error as an environmental variation, essentially,
but they stress that you really want to measure as accurately and as consistently as possible.
And then also, if you really do want to try to disentangle environmental variation from genetic variation,
then you want to try to have as uniform an environment as possible.
This was just a short, quick exercise just demonstrating how you can measure heritability of traits in cannabis.
And remember, this will vary by population.
So that means it may vary by strain population.
I mean, it's not out of the realm. You'd have to do some investigation.
But potentially, different strains may have different rates of heritability.
Different traits may have different rates of heritability.
So, for example, CBD may have a different rate of heritability than THC.
And there's other factors people select for.
So right out of the gate, they mentioned people are selecting for yield, for height, how many days it takes the flower.
There's many factors that people are selecting for.
So you'll have to look at heritability for each one.
But this lays the framework for how you can apply statistics to be a better breeder.
So this could apply to home growers or large scale cultivating.
So I've been rambling long enough. I'll let you all ask any questions.
And a teaser for next week. This is what I was going to do this week.
But once again, lots of material.
And so we wanted to take this quick tangent on heritability.
For next week, I'll see if you can't find the author of this quote or the person who this can be attributed.
But in relation to statistics, the idea is you don't want to try too hard.
If you do, you may get spurious causation.
And so in fact, obviously, I started a company, Candelitics.
So we're interested in the cannabis space.
As you're researching cannabis, it would be easy and you would have to be careful not to let your biases take over.
So, for example, if you really, really did want to see a medicinal effect from CBD, that may bias your research.
And so if there is just a readily apparent effect from CBD, then there may be something there that's worth looking at more.
But the idea is if there's not just a real readily available signal or noise, you may want to be cautious.
It doesn't mean don't proceed.
You just may want to be cautious about biases, spurious causation, overfitting your model.
And so, for example, basically, the lesson from today, how could we apply that lesson today?
Well, if you really wanted to disentangle the environmental variation,
there may be something going on between plot one and plot two, but it may be a stretch.
However, it does seem readily apparent that there's differences in variance between seeds and clones.
You've got a small sample size, 40 total observations, and already you can start to see a difference between the two.
So that may warrant further investigation.
So that's just sort of a word of caution as you're using statistics.
And so if you're interested in this, then try to find the author behind this, because we'll continue to, or I'll at least continue to talk about this.
And if you're not interested, then please let me know what sparks your interest as we continue to search for the genetic histories behind some of these cannabis treatments.
Anywho, that's what I had to share with you all today, my latest journey.
So my main project is compiling cannabis data from Washington state.
And so, Michelle, this may be of direct relevance to you because you could start to do real interesting statistics as far as population level statistics goes there.
So lots of lots more cool data coming down the pipelines.
But real quick, does anyone have any thoughts, comments, questions from the material on heritability?
Yeah, I can go first. I mean, I'm just wondering if there's anything we can do with more modern data because that and also the environmental variable, I mean, itself could be different.
So I'm just thinking how the experiment can be improved with modern data to get a better measurement.
Maybe we can even get a constant that's applied to most cannabis plants as one number, say 0.7.
And then that number as an anchor point will allow us to do more analysis.
But yeah, I agree 100 percent, Isaac.
In fact, I would encourage you to and I'm going to be setting up a lot more on heritability now that we've figured this out.
I think there are more sophisticated ways to estimate heritability.
I think what you're talking about is more of a regression based approach where you'll want to control for more factors and, of course, increase your sample size.
So I think the regression based approach, maybe the modern day approach.
Remember, this was this was research that was done 35 years ago.
So I think there is more sophisticated readily.
And I mean, for all we know, they may have been calculating these means and variances with pen and paper.
So we can do a lot better job today.
So, for example, just loading the data into Python, right, I'm able to to do all these groupings and calculate means and variances and standard deviations really quickly.
So it was really quick for me to to figure out, oh, they use standard deviation and not variance.
So we can work quicker, work smarter.
I think it's just just a starting point.
And then the other part you raised about the environment, I think that does need to be taken into consideration because the way they went about identifying the environmental variation and then subtracting it from the variation of the seeds,
it seems a little ad hoc.
So this may be an ad hoc method for for calculating or estimating heritability.
So this may be a starting point just to just get an idea of, OK, it is heritability closer to zero or maybe closer to one.
So this got us an idea that, OK, there may be a significant amount of heritability there.
And so, as you said, I think a more sophisticated analysis is needed.
People are going to want to see a lot more controls of environment.
That's a big criticism that people give to cannabis research.
It's inevitable. We've talked about this before.
You can't control for every factor under the sun.
Right. If you controlled for everything and you did it in Massachusetts, someone will say, well, why didn't you do that same study in Washington state?
You know, the air pressure is different or so, you know, so on and so forth.
So there's always going to be something. But as you said, just the more things you control for the better.
That gets us to sort of the lesson of the day.
The more things you control for should get you an accurate, more accurate and consistent measure.
But hopefully it's still relatively apparent, even if you say make a mistake or two or don't control for everything.
I love it, Isaac. And so I'll continue thinking about this.
The main thing is just working on the Washington state data was going to almost had a hold of some Oregon data that's still in the coming down the pipeline.
And that may come for you as well as Massachusetts and Florida data.
So hopefully those things come.
OK, what about now? Awesome. Awesome. I can hear you. Love it.
I'm talking about from a grover's perspective about these levels. So basically what I know that I was growing the same train outdoor greenhouse and indoor.
And it was giving very different levels of THC.
So the environment variance is very important in growing. Also, the seeds were growing faster than the clones.
I know why they develop faster. They just have looks like they have the better genetics than the clones.
And that's what I observed. OK, so there's a lot going on here.
One thing I want to hit on that I don't know if a lot of people mention, but this has come across my research.
From my understanding, seeds have tap roots, whereas clones do not.
And to me, that makes it seem like that would have a big impact.
It seems to me like the tap root would be important.
So that maybe that may explain why it would grow faster.
Technically, the clones are genetically identical to where they were cut from.
So if they did grow differently, one could maybe chalk that up to maybe structural.
They don't have a tap root. Also, there may be environment there.
Also, it depends on a mother plant where you cut the clones.
If you cut more than 30 percent of the plant, they say, and I also noticed that the clones can become hemophrodites.
So they can just change. But also, we didn't talk about the nutrients that you put into the soil.
So that's also very important for the plant to develop THC.
Also, timing when you put certain things, is it like phosphorus or calcium or things like that?
That's also very important.
A hundred percent. This is the tricky part.
Because there's variation in THC and obviously there's some genetics.
That's how people have been able to breed over time. But even if, say, the genetics is 60 percent,
that's still 40 percent or give or take more or less of variation that's explained from your environment.
And that will be things like whether it was indoor or outdoor, because those are two different environments.
How much nutrients the plant gets, that's an environmental factor.
So those will all affect the environmental variation.
And this matters when you're cultivating, right, because you want to grow top notch flower.
The idea is, and this may be just a simple fact, but essentially, it may be obvious,
you can't just select by clone to get better and better cannabis, because it's going to be genetically identical.
Basically, the point was being in order to basically increase the THC levels over time,
you would have to approach that genetically.
It wouldn't be possible to just select clones that test higher and higher percentages.
And then this is sort of the interesting thing is the seed may have genetics to produce high THC,
but say, because of the environment, it may just have a lot of environmental variation.
So it doesn't look like a high THC plant.
And this is my argument about how California may have such prized genetics,
because I think they're growing outside by seed.
So these seeds in the plants, they may not grow into the best plants ever, right?
They may be getting eaten by bugs and they're subject to all the elements.
But people are able to then select the better and better variety.
So if you then say, yeah, you took that clone from those plants and you went and grew it in a warehouse,
you may be able to get higher THC on average.
But over time, the people outdoor selecting and selecting and selecting will be able to increase the average.
And then Noah, you had a question?
Yeah, it was kind of related to that. Have there been any studies yet on like epigenetic factors?
I'm curious if that's something that is even in effect here or I don't know.
So this is bleeding edge.
I can only report back hearsay.
So as you may have known, LinkedIn is a good place for cannabis, people in the cannabis industry to socialize.
And I've actually heard talk on the town that, yes, potentially cannabis may be especially susceptible to epigenetic change.
But this is this is really new frontier.
And I don't know how much I want to stray into it because I'm a super novice.
I wouldn't even consider myself a biologist or a chemist.
So actual biologists and chemists would probably frown upon upon me. So I kind of want to stay as humble as possible.
But Michelle, you had a thought or question?
Yeah, I was just wondering whether or not given that so many states are legalizing cannabis now, whether or not commercial growers,
I know that they're tracking their THC levels and their CBD levels and levels.
Are they required to report that data so that there's some sort of database that can be accessed?
Yes. We have that data for you, the entire population in Washington state.
And so that's what's real cool. We may even be able to parse out if things came from clone or seed.
I don't know how well that's recorded in the traceability system, but we do have plant data.
And all they were keeping track of were THC levels. And we have those.
So we've got THC levels, CBD levels, and we may even have the propagation.
We just may not have the genetic lineage.
So there is maybe work we can do with data that we have at this time in Washington state.
So let's keep talking about this because I think there is work we can do, Michelle.
No, no, what further thoughts?
Yeah, just following up on what Michelle said, I've heard anecdotal information about some of those THC testing,
at least the ones reported on the label, and that they can be dubious.
How do we know that these numbers that are being reported by the growers or the cultivars are accurate
and aren't just kind of being, you know, torturing the numbers to kind of get high THC or low THC or whatever?
Perfectly reasonable question. And the short answer is that's measurement error.
So this is specifically, you know, even way back, I keep pointing this out,
but it just shocks my mind how these topics were on mind 35 years ago.
And exactly, if you have inaccurate measures of lab results,
you're not going to be able to parse out the genetic variation as well.
So disentangling environmental variation and genetic variation requires accurate and consistent testing.
So that's why it is important for Isaac. I love that you're working on this.
And that's why, you know, it is important for labs to measure accurately and consistently.
It helps the breeders in the long run.
Unfortunately, we may just have to anticipate that there is some measurement error.
What I would say is, so for example, Isaac or anyone else studying this,
that can be a condition that you try to control for.
So say you were going to replicate their study at scale.
Well, maybe one of the conditions you could control for would be laboratory.
So maybe you would just grow all these plants from seed and clone,
and then you'd send them to three or more different laboratories.
And then you would just use that as one of your conditions
and try to disentangle any measurement error coming from labs.
Try to disentangle that from your genetic variation.
So from a cultivator point of view, the measurement error is surmountable.
I think it's not great, but I think you can overcome it.
And you can still do, I think you can still do heritability studies.
It just throws a monkey wrench into things, but you can just account for that.
But Isaac, the question at hand?
Yeah, just to chime in my two cents on the question.
I mean, there are many sources of this uncertainty in measurement.
On one hand, it could definitely, it's the most standard spread of measurement.
You measure something exactly the same thing, and you'll get a spread.
That's one type. And another type is that there might be lab issues
that are just their control samples or their spikes was called is having issues.
So it's like their quality control issues.
And that might skew their test results, maybe 10% plus or minus,
and also their habits of actually measuring it.
So there is just natural variation.
And second is variation based on their reference standards.
And the third one, it could be based on their method for calculating the area
or the amount of say THC.
And it's only the fourth one is that they might knowingly fudge the number.
So there are a lot of sources and the variations can be attributed to a combination.
But in terms of how to get a data set that you can trust,
it will take a small leap of faith.
That is, you just look at the data of, for example, the Washington data set.
It consists of data from 12 labs, I believe.
And just by looking at the distribution of their THC measurements
and also their micro measurements, some labs does have a better or more normal,
like naturally occurring distribution than other ones.
So if it's a concern for you that you want to go for reliable data,
then perhaps you can filter for the labs that have the best looking distributions.
Good points. And this is the beauty in the art of statistics.
And why stress the, don't read into things too hard because there is a lot of noise.
So you don't want to lean too much into the noise.
But I love that this is the way you're thinking, Noah,
because this is something that's often overlooked.
And a good statistician like yourself will hammer this home.
What about measurement error?
Because things like this compound, right?
Because your statistical analysis assumes maybe some variation.
But then if all of a sudden there's other variation that you're not taking into consideration,
like measurement error or other imperfections in the data, missing data, imputations,
it just adds to the uncertainty.
And I'll just leave it there that I don't know if you all know,
but from my understanding, statistics is the study of uncertainty.
And so that's what we're doing.
This is an uncertain topic.
And we're just trying to study it and see if we can't glean any knowledge out of it.
Let's continue on this.
I may want to go ahead and conclude this here.
I just want to be respectful of everybody's time.
And we've covered a lot of ground today.
And for next week, I'll continue teasing some real fun things out of cannabis history
and try to tie them back to the modern day with modern data sets,
for example, this data set out of Washington State.
So lots of cool things coming.
And then also, if any of you have projects that you want to embark on,
feel free to get in touch and let's keep the conversation going throughout the week.
Because as I said, we're at the cutting edge, the bleeding edge of the cannabis area of research.
And there's a lot to uncover.
So I think there's exciting things to come.
Thank you all for coming.
Thank you for bringing your eyes, your ears, your brilliant minds.
Your attention helps advance cannabis research, cannabis science.
And so I think if we can help move things forward, even if it's only one molecule at a time,
then let's keep at it.