Announcer: The following presentation,
is part of the Agronomy and Horticulture Seminar Series,
at the University of Nebraska Lincoln.
-: So welcome everyone to the UN department
of agronomy and article to a seminar series.
I'm Marc Libault, as a professor in the department.
So to that, I'm very happy to introduce
Doctor Ed Buckler, who is a receptionist at USDA-ARS.
But also an adjunct professor
in the Department of Plant Breeding and Genetics,
at Cornell University.
Ed has expertise in molecular evolution and archeology,
and his research group used genomics computational
and the field approaches to complex trends.
And with a goal to accelerate breeding,
of bios crop species,
including maize sorghum and cassava.
It's always those kinds of technologies.
And now which has been applied to over 2000 species
the class group focuses on exploring new ways to
re-engineer globally char production systems,
to maximize food production,
improve nutrition,
and also to enhance response of plants to climate change.
With the USDA-ARS,
is leading also informatics and genomic platform,
to help accelerating breeding,
for specialty crops and animals.
Its contribution to consent to genetics and genomics,
has been recognized,
with his election to the US National Academy of Sciences,
and also as a recipient of the inaugural NAS
Food and Agriculture Award.
So today Doctor Buckler,
is going to talk to us about his work,
to maximize food security,
and maximize plant response to climate change
is taught is on title.
How can transferable biology and breeding contribute
to improving food systems and climate change.
-: Well, thank you, Marc.
So I want to talk a little bit about
this whole area of ,
where can some of these areas
in transferable biology and breeding
really contribute to improving the food system,
and climate change.
And first mentioned that the basic science
we'll be talking about,
it's been supported by USDA,
and the National Science Foundation.
And then we've had larger collaborative projects,
funded by the Gates Foundation and USAID,
that have really crystallize some of the needs
on the breeding side.
So the way I think a breed is
really about the design,
and selection of organisms for human purposes.
And I think the next decade,
or actually next couple of decades,
this question about what we should be designing for,
is really key.
And I want to start out,
I talk with why I think we need to
think hard about what we're designing for.
And I think we have three really big drivers of change.
The first is I think there's a real revolution
in food technologies underway.
The second I think everybody realizes
there's climate change.
And then third,
we have a real nutrition problem.
and it's this three drivers that I think are likely
to fundamentally change
breeding in the next 20 years.
And I want to mention to folks about,
when we think about how fast these things can happen,
to remind people what happened with the uptake,
of hybrid maize.
And a couple of these other really big changes.
How fast has change happened?
Well, the change from
open pollinated varieties to hybrids
in Iowa occurred in about six years.
And the technology,
provided a 20% yield increase.
And so when change does happen,
it frequently happens really fast.
Now, sometimes in basic science,
we may have been out here in 1910,
what we're doing maybe out here in 1910
and it took 20 years before we could really come to market.
But once it comes to market,
if it's a really big advance,
the shifts can be fast.
Same thing happened with the horse,
the car replacing the horse.
Happened in about a 10 year shift.
Took a couple of decades
to invent a lot of the technology behind the car,
but that shift happened incredibly quickly.
And the same thing with the smartphone.
Essentially, it totally changed markets from you know
camera makers, taxis,
GPS, cell phone makers and everything,
with essential technology,
that went to market dominance,
in about nine years.
And so why do I raise these issues?
Because I think we could be seeing some really big changes.
In the next 20 years.
Here's one example,
of a group in terms called Rethink.
They're arguing that alternative protein,
and what they mean here are high quality proteins
that can you reduce by a fermentor,
they argue that,
essentially in a couple years from now,
that'll be down to $10 per kilogram.
And by 2035, there'll be $1 per kilogram.
And they see this as reducing the number of cows
in the US by 70%.
Is this realistic?
Are these things, potentially going to happen?
If you were to do a linear change right now,
we would be actually predicting,
an increased number in days.
So you know for example,
in this area,
I asked the question,
whether or not those basic numbers,
they were talking about.
In terms of the cost were realistic.
And I plotted out there essentially three numbers here
of where they think that,
these high quality alternative proteins
are going to go from about $40,
down to $1 over the next 15 years.
And I plotted where other proteins lie
and you can see the reason why alternative proteins
aren't displacing animal production,
is because they're more expensive.
But that's likely to change very quickly.
And as you look across these numbers,
I've been able to kind of check out that
$1 per kilogram number,
and ask the question.
Is that a realistic possibility?
And things like yeast extract,
and of course, soybean meal,
is already less expensive than this,
dried distillers grains are already less expensive,
and ammonia and starch,
the basic materials to make that precision protein,
are also already substantially less expensive.
And so I think it's totally plausible,
You could get to a precision protein,
costing $1 per kilogram.
If that happens, that's sevenfold cheaper
than the protein that's in milk,
and it's 30 fold cheaper
than the protein that's in ground beef.
So I don't expect at all,
a society to all change.
And I'm a meat eater
I like to have a steak,
but you can see in all sorts of other markets,
that there's a shift when technology allows
to is allowing a tech,
something that is this much cheaper
than the current technologies.
This could have massive changes on ecosystem.
What we're using are amazing soybean land uses,
where essentially there's potentially a free enough
of nearly a hundred million acres for other services.
And if you think I know there's some really
great wheat breeders in the community here
if you were to start putting your wheat
on the best may soybean land in the country,
those wheat varieties,
could essentially produce the wheat
for 45% of the globe.
And essentially it caused disruptions
around the rest of the globe.
The second big driver,
of course is climate change.
And a lot of dealing with climate change
has to do with of course changing
our energy production systems.
And that conventional abatement,
but almost every thing realizes that we're also
going to need to have ways to do
negative greenhouse gas emissions
and plants and those amount of about 20 gigatons
of CO2 per year.
And plants are pretty good candidate
for pulling out a lot of that CO2.
They're already 80% of the biomass of the planet.
And so when we put those two elements together,
you start asking, well, what's the future?
Current may future may soy system looks like
something like this?
We're, we're growing at 180 million acres.
We're generating about $160 billion
of grain off of those producing these systems.
But at the end of the day, then those
all that grain and all that CO2 is being respired away.
And it's about 1.6 gigatons of CO2 per year.
Right now they estimate the societal cost
of CO2 is $51 per ton
and it's slightly rising.
So this is our current system.
And if you think about what 1.6 gigaton to CO2
just going back into the atmosphere,
is that's essentially $79 billion lost
to the atmosphere that we could be capturing.
And if you were to ask the question,
where would we be with yields 25 years from now?
And what those future products are likely to be?
And let's imagine we can,
I think where we will see definitely a lot more use
of that land for energy production
hydrogen precursors for plastics and everything else
maybe less used for animals.
And, but then the question also is how do you
use capture as much as that CO2 as possible?
If we figure out clever ways,
to reduce our greenhouse gas emissions on this land,
and capture a substantial portion of that CO2
let's say we can capture two thirds of it.
You're talking about an additional $127 billion of value,
added to the system.
And the third area where I think we're going to have lots
of change is in this area of calories
and nutrition in the last 50 years was really focused
on case Norman Borlaug getting enough calories
and protein into the hands of people.
And the unfortunate people with insufficient calories
and protein has dramatically dropped over the last 50 years.
But if you look at what, where we are right now
and as we population grows to nine or 10 billion people,
you notice that most of the people
on the planet have poor nutrition or unbalanced diets.
And I think that that's the third real big driver
we'll be having is how do we shift that?
I think that first group on insufficient calories
and protein, a lot of this is about alternative proteins
and adapted productive row crops for different environments.
But I think the other big wedge there is how do we drive
down in balanced diets across the US and across the globe.
And I know you all do a lot of thinking
about this also, but I think it's breeding
and producing accessible quality fruits and vegetables
and microbial foods to really address a lot of those issues.
So I think we have those three big drivers.
And if you want some more information
on some of the logic behind some of the arguments here
I pointed to my YouTube,
YouTube video on this
that we have that provides some more information.
But the take home from the brilliant perspective is,
we've done a really good job of breeding on about 10 species
and are using advanced technologies on those 10 species.
But with those three big drivers out there
I think we're shifting
to a world where we certainly need to do well
and continue doing well on those,
but we're going to be doing much more fermentation,
breeding intensively,
using advanced technologies that are fruits and vegetables,
fuels and polymers
carbon capture and assisted migration.
And so if you add up all the species
that you're starting to talk about there
just now say, we need to really
have accelerated breeding for thousands of species.
And so what a lot of the basic research
in my group that we're focused on right now,
is how do we build an infrastructure,
to have accelerated breeding,
for thousands of species?
The heart of modern breeding is essentially this cycle
of selection, crossing and evaluation,
and it works well.
In case of maize
you always see the plot we've increased yield
at eight fold.
When some of that is breeding.
Some of it is agronomy,
but this basic cycle really works.
That problem is of course that time cycle
of five to 10 years.
And the key engine
I think we need to use to make these improvements
across hundreds, if not thousands of species,
is genomic selection.
That is powered by genomic prediction.
Where essentially as soon as you make that selection
or that cross, you evaluate the genome
and you predict which ones get advanced.
That doesn't mean you have a new product,
every four months or a year,
but you're able to have the central engine
of selection and improvement
making the population better on that type of scale.
And with new approaches
in speed breeding and other ones
I think bring a lot of species can even perennials
and long live perennials,
can have their period of selection
that cycle time driven down to a year or two.
And so our challenge challenges
genomics selection works well
for these species plus valid dozen more,
but how do we really make it work well,
for all of the species?
So I think part of that is about building just
the technologies and getting them in the hands
of breeders who don't have large groups, you know
who don't have 5,000 people,
running a dyno genomic selection effort,
across the globe at one of the major seed companies.
And this is what we
in ARS and started with breeding insight.
And this is about essentially developing the genomic
and digital ecosystem for specialty species.
And so she is the director of this project
but essentially we're getting the genomic tools
and essentially informatics tools
into the hands of specialty breeders.
And this is an area where I think, you know, it's a big
and useful value add that there's a lot of commonality
between all these systems that we can really
make some useful progress.
And so those genomic prediction requires accurate
mathematical models,
in order to make this work.
And we know we can do this really well already,
if we have lots of observations.
The issue is if we have few observations,
how our accuracy can start dropping
and the question then is can we really learn
across bringing all those other types of information
the learning across species,
so we get high accuracy.
And put this in more concrete terms,
and if you look at genome-wide prediction,
you know thousands of papers have been published
on this in the last two decades.
And if you're working at a major crop
or animal life stalker,
and so on,
if you're doing population
within population prediction,
accuracy it can be quite hard. 30 to 90%.
You try to predict outside the population,
accuracy frequently dropped by
you know, can drop to zero, or at least by 50%.
And if you're working with a smaller species
you just have less data to work with.
And those prediction accuracies are lower.
And essentially there's no information that has been derived
from going from one species
into another species.
Because what do you know why
prediction is modeling,
is really modeling inheritance,
at a very it's the mechanism it's modeling,
it's really about modeling heritage really accurately
across various genomic regions.
And so I think the question that we
and many others are asking is how do we bring
we know a lot of these mechanisms,
Ken Addy mechanism,
molecular mechanisms to do, you know
my prediction models really give us a useful bump
and the type of increases in accuracy.
I'm envisioning if we did really well
we'd build improve these models by 20%.
So essentially for the free computational effort,
you've improved your models by 20%.
So why don't we do better already with
do you know, my prediction, other ones?
Well, the problem is that if you look
at the number of genetic variants that are
in a genome or in a breeding population
it can be on the order of tens of millions.
This is the example for our maze,
one of our maze population
we have about 80 million segregating genetic variance
and we only have about 5,000 observations to map with.
And so you have this massive NP problem.
We can do a good job of dealing with prediction,
but dissecting each one of those
down to their individual new cause of nucleotides
we just have too many parameters
for a number of observations.
And so the question we're asking
in the group a lot is can we learn more
by using some tall data versus Y data where
we have more observations than parameters?
And actually there's a lot of different situations.
Were we have farmer observations, then we have parameters
for example, evolutionary conservation.
If you're asking about how did a single base pair evolve
across evolutionary time, you can compare
that amino acid residue
to a thousand other species.
Or you chromatin structure.
You can model how to nucleus zone region,
of sequence,
what happens to it in terms of Chromatin structure
or RNA expression, same thing with transcription factors.
A lot of these areas we are able to
generate more data than we have parameters.
And so NSF sometimes talks about this
his learning the rules of life.
But what I would highlight this is
I think we actually know many of the rules of life.
I think it's really
about parameterizing the functions of life.
You know, how do we create mathematical models
that allow us to go through this whole process?
And you know, what I want to highlight here is
that what we are doing is really breaking
down this into essentially,
a few different elements.
One is the central thing we're trying to aim
towards is estimating protein activity.
And I kind of mean this in a fairly abstract concept
but being able to rank the different illegals
in different varieties, into their relative activity level.
And you can imagine protein activity,
is essentially the product of protein sequence
the product of gene regulation.
And if you think about for this problem
you know, given protein, there's only about on average
about 330 amino acids, we need to concern ourselves with,
on the protein sequence side,
and in gene regulation
there's probably a couple thousand base pairs
per gene that are important there.
So we're now not talking
about having to worry about 80 million things simultaneously
or at a smaller scale.
And then of course, once we, if we can break it down
to 30,000 proteins,
then we can start working through relating it
from that protein activity
to through the various levels of physiology to whole plant.
I want to first talk
about our modeling that we've kind of done and this area
of going through the protein sequence all the way
through physiology.
And the hypothesis we had here is
that essentially angiosperm evolutionary conserved
site should predict segregating deleterious mutations.
And so if something has been conserved across a wide range
of evolutionary time across flowering plants
and he gets broken and maize or sorghum,
then that's likely to produce the deleterious.
And so a few years ago, Eli Rogers Melnick and the group,
he lined maze up against all the other at the time, well
sequence genomes and found that about 10%
of the mace genome was alignable and constraint.
And then he started asking, well
what happens when base pair changes
and amino acid changes are occurring
at that portion of the genome?
Well, it turned out Angiosperm constraint,
did a good job of predicting
segregating deleterious mutations.
Some of the predictions that we tested there were
that they should be enriched in low recombination regions
of the genome because you aren't able to purge them.
That was supported.
An extreme version of low recombination is essentially
vegetative propagation will increase deleterious mutations.
And we saw that cassava breeding clones
where they're substantially vegetatively, propagated
massive issues having to do with build off
of deleterious mutations.
And I find those
in both maize hybrids
and sorghum deleterious mutations do explain
about half of the variants in the field.
So
they are,
as a group, even though each
of these deleterious mutations are rare together as a group
they probably explain about half
of a variance for a yield,
but Guillaume Ramstein
in the group really wanted to ask the question.
Yeah, does deleterious mutations together
explain a big problem
but can I use them to start increasing prediction accuracy
to make it much more actionable item?
And so we have large
by parental cross instead of hybrids and diverse hybrids.
And he did training validation in different directions here
and he created a series of models to test this out.
And so for traits like height,
adding these group scores some miles,
a small
increase in the accuracy.
And we've looked at both cassava and sorghum
we generally see this effect that yes
we get an increase in case of maize and yams work
it was significant,
but it was anywhere
from zero to 10% increase in accuracy, not
a really big game that we were really kind of hoping for
and some of these accuracies.
So what Guillaume did is,
he, again
hypothesis assuming that fitness effects are consistent
across land plant, and he took a bunch of genomic sequence
and he essentially
when you start using different annotations
whether protein ontology
various ways to predict amino acid disruption, and some
of the bald ones were the ones that really matter the most
in these models.
And what he did with that was different is
instead of essentially using evolutionary constraint
directly in these models to say,
this is a deleterious mutation.
He uses a machine learning model to
predict evolutionary constraint.
So using aspects of just sequence analysis
and training that model against evolution and constraint.
And so he gets out to
we have a couple of predictions that come from this.
So let me walk through this plot here.
The observed evolutionary constraint is the level
of constraint we see.
We observe when we align a bunch
of genomes against one another across the
the predicted is the line that he's predicted based
on this model that was trained based
on some of that data, we expect when we
none of this data had ever seen any of the intro
the variation within maize, but when you applied it
and what you see here is
that we expect deleterious mutations to be very rare.
And you see the predicted evolutionary constraint
does a better job of predicting rare
and deleterious mutations and the observed one does.
So this suggests that we,
these machine learning models are actually cleaning up a lot
of the mess of reading out what's going on in evolution.
And so then he took these types of models
and he started same type of training testing type of set
starts looking at grain yield.
And he starts looking at the most highly selected low side
the ones that look most deleterious.
And what he sees is that if you look
at the 0.1%, 1%, most severe deleterious mutation.
So now we're talking about a few hundred
of deleterious mutations in this 0.1% or few thousand
and the 1% tail we're starting to see some really
substantial increases in prediction accuracy.
So I think this is a route where we can start going
and making this even better by starting to pick these out.
The next area that I want to mention
that we've been doing a lot of work on is gene regulation.
And here the hypothesis
that we've been frequently been testing,
it started out
with observations that Jim Bursar made
over the last several decades
about how important balanced gene expression is
produces a healthy plant.
And if you have gene expression going all over the board
you essentially have all sorts of dosage issues.
And this can come from all sorts
of stock you measure things having to do with
to put putting together protein complexes
and metabolism and so on.
But essentially the healthy plant will be
if it has consistent middle
of the road expression everywhere.
And so a couple of years ago
we tested this by joining expression profiling
in 300 maize lines across seven different tissues
at first asked whether or not extremes allelic expression
what were they associated with?
Well, it turned up
that they were associated with upstream, rare alleles
and these rare alleles were likely deleterious alleles
based on population genetics,
Carl though then tried to take that type of data
on just overall dysregulation and relate that to yield.
And if you don't have any way to wait
on that ahead of time, that correlation it exists
but it's not particularly strong
and not really an actionable thing we can work on.
So the second way we looked at it was we looked
at those deviations and expression and ask, can we fit them
in a mixed model to use it, to predict seed weight?
And again, here we found that essentially those deviations
and expression did just explain about 20 to 30%
of seed weight.
So that suggests that we can
at a quantitative genetics level really model these
and those, that dosage model is likely to be really key
for these yield like traits.
Bashing song in the group
has recently done an analysis using some
of the same data
but then combining it with really careful alignment
across a range of different interval Guyanese species.
And he's finding that the conserved non-coding sequences
essentially these his
regulatory regions are highly explainable.
Whether they're transcription factor
binding sites or chromatin loop
and so on they were highly explainable.
So what he did is he was able to then use this type
of these well-characterized highly conserved regions to
ask is that what's producing those extreme phenotype
differences. And that is what we saw essentially
a low expression was being driven by disruptions
in these conserved non-coding sequences.
So we now know what some of those moved out from the effect
of deleterious mutation, testing proteins
to seeing the effects of deleterious mutations
in these regulatory regions.
And so Anju Giri and the group then said, okay
I want to try to take these these expression deviation
and make them more actionable for breeding.
And so the way she approached the problem is thinking
about it in terms of, well, we have measured expression
what is that a product of, well, it's a product
of cis expression, trans or cis effects
Trans regulators affecting it.
And of course,
all of the error and environmental impacts on it
in this experiment, we have treated environment
as essentially is error.
So obviously there's a lot more you could do in this area.
And she then partitioned using haplotypes.
She essentially partitioned out the genetic variant
from the measured expression.
You use the genome-wide haplotype relationship matrix to
get the trans effects.
And then from that was able to estimate the Cis effect
for each at the haplotype type level.
And so when we're talking haplotype here
we're kind of, we're talking of gene scale haplotype.
And the first thing she saw was
that essentially the Cis component was explaining
about a third of the variants
and trans was explaining the other two thirds.
Most of the genes had some heritable variation
but it wasn't necessarily a large dit one
but she was able to partition out that cis.
And that was pretty reasonable that, of course
transcription factors and everything else are very
big players in regulating the expression
of the given gene in a given time.
But then she asked the question whether or not
the Cis was more transferable than measured expression.
So this is measured expression between tissues
and you see that there's actually a whole bunch
of them that are pretty near zero.
There's absolutely expression that is correlated
across tissues, but there's a big group
of it that is pretty close to zero.
However, when you start breaking it
down into the just the cis haplotype effect
and you've removed the trans effect, then you see
that a much larger set of the expression between,
that cis component between tissues is much more consistent.
And I think this gave us hope that, Oh, well
if this is consistent
then maybe we can use this for genome-wide prediction.
And so that's what she did is she started
she set up, I think, careful with testing approach to see
whether or not we were doing better over baseline
haplotype structure
because the haplotype structure overall already
give you decent prediction accuracies, even
with random data layered on random expression lies
there on top of it.
And so we do everything over this haplotype baseline.
And once she was seeing was when she trained on a small set
tested on large set, it was getting a
3, 4, 5% increases in accuracy.
And then when she trained on a large set
like the name population with less diversity
and tested there, she was getting all the way
up to over 10% improvements in accuracy.
And this was from a wide range of different traits.
I think this was about 25 different traits.
She was testing these in.
So we're starting to see that
if you can partition out these and break it down
you can get some substantial improvements.
So we're at this stage where we're really trying to model
from the bottom up to meet with where we are elsewhere.
And, let's talk,
finish up here on
on how we're doing some of this modeling from the bottom up.
When you get down to the gene level
and we do a lot of, we've done a lot of machine learning
at the level of proteins.
And so on, early on, we were certainly making lots of errors
and creating models that were not transferable at all.
Hai Wang and Jacob Washburn
and the group figured out why that was.
And that's because we weren't
we were essentially memorizing evolution.
And so they came up with a couple
of approaches to set up training and test sets.
So the don't just memorize evolution
and you can actually start learning mechanism.
And so how do we go about that?
Well, one project in the group has been looking
at protein sequence of trying to go there
and try to make some estimates of activity.
And Sara Jensen a graduate student
in the group and her basic thought was, well, the biophysics
of how a protein is stable should be consistent in bacteria
in archaea all the way up to eukaryotes,
can she determine whether
or not our protein will be stable essentially
across 3 billion years of evolutionary time?
And so the first thing she needed to build was
essentially a way to estimate the temperature
of which bacteria was adapted to
she and Emre Cimen developed a machine learning model
that worked out quite good across a wide range of bacteria
and archaea that essentially just looking at the tRNA
and some aspect of the tr likely tRNA stability
and modeling that they're able to essentially
predict what any archaea
or bacteria is adapted to in terms of temperature.
Once she has those numbers
of what a given bacteria was adapted to
in terms of temperature
she can then take all the all bacterias PFAM domains
line them up and ask residue by residue, is that correlated
and associated with temperature adaptation.
And what she was finding was about 15%
of residues were associated
with temperature in Butler K in bacteria.
And there's a lot
of significant emergency shared sites there.
And a lot of what the basic properties
of what was being shared, made a lot of sense.
And essentially for each PFAM domain
you have a whole series of sites that you now
know that are temperature adapted under various conditions.
And so the question she's been working
on right now is whether
or not these GMOs conclusions can be extended to plants.
She's done a little bit of work
with groups,
working on memories
of Reuben as group and founder,
some of the predictions she saw
lined up really well with Mays.
And she's continuing to see some
of the same types of facts right now, amazing rabid offices.
So I think we have at least some evidence
that we can start making some of those connections.
The next area we've been working
on working from the bottom up is,
can we predict gene regulation directly
from sequence and without having to look,
measure the expression directly.
And so with all the ATAC seek
and M and S seek data that's been available
out there a couple of years ago, Katherine Mejia
and the group developed some models about whether
or not she could predict open chromatin accessibility.
And it was essentially getting some models that were
over 95% accurate.
Then working with Silin Zhong's group,
we get, I got a dataset with
over a hundred transcription factor, binding gypsy data.
And again,
they were able to make some very accurate models
from 80 to 95% accurate of where
various T astring modeling were
where they were binding.
But I think the most interesting thing
for me in the longterm out
of this paper was this phylogeny you see here
and I want to spend a little bit
of time explaining what's going on here.
Each node on here is essentially one transcription factor.
The data from one transcription
in factor in is gypsy data
Katherine then trained a machine learning model for it.
And it essentially what she's done here
then clustered all the machine learning models
against one another.
So this is really a phylogeny of machine learning models
and how similar they are to one another.
And what you see, and then the way it's colored though
is the transcription factor families that would be
recognized in Arabidopsis
And I want to highlight
that these models were trained in mace,
and they essentially are recapitulating
the known transcription factor families that are known
from Arabidopsis.
So we've been able to go
from machine learning in maize all
and essentially recapitulate knowledge and Arabidopsis.
And so I think this has shows over the 150 million years
that these two species have diverge
much of the basic mechanics of this gene regulation.
Really a large scale of share it isn't
just one transcription factor here.
There really is them involved that they're very similar.
And so Travis Wrightsman is taking this type of work
and then started to ask the question, can he,
train models in one species and apply them to another
and in case of taking a model and Arabidopsis
and applying it to maze, doesn't do as well
as going from Arabidopsis to Arabidopsis
but he found that going,
training both species
and applying to either one works just fine.
So I think that's what we're starting to see is
that we can start training models of all those species
and then applying them to everything else.
Similarly, he took some predictions
of overall expression that were trained in maize
applied it to Arabidopsis,
the models dropped by an accuracy by about 50%
but this is an area where we are pretty sure
that we now train these multiple species.
We can substantially increase that
and restore a lot of those that accuracy.
So I think when it comes
to chromatin expression translation and so on
we're pretty sure we can transfer a lot
of information across all angiosperms, animals and fungi.
I'm not sure yet we've tried a little bit so far.
It hasn't been very promising.
So what are our hypotheses now and transferability
so in terms of leveraging transferability
when it comes to protein sequence and protein,
I think alpha fall two
and the Google deep mind group have demonstrated,
the rules are pretty much the same over 3 billion years.
And Sarah Jensen's work has also shown that, yeah
we're seeing a lot of transferability of about protein just
over all of life.
When it comes to gene regulation.
I think we've got lots
of good evidence that we have transferability
over about 150 million year range.
When we think about deleterious mutations going from changes
in the amino acids all the way through physiology
because here we're predicting whole plant phenotype.
We're seeing some useful information and ranking
and transferability across about a 70 million year period.
When we go from the expression levels and some estimates
of protein activity to whole plant phenotype
right now we're seeing evidence
that it works across essentially within species
or which means really about thousands of years.
But I think the big question is
that I want to know is how much can we leverage to say
across all the grasses?
Is there
a lot of useful,
constraint of these networks that we can move
across all of the grasses to make useful prediction?
So where are we right now?
Well, I think we're at a stage in genomics.
We're going to be able to sequence a genome
for about $2,500 a genome and earth bio genome
wants to you actually do this
for nearly a million species, but functionally
prioritizing a single species costs anywhere
from five to a hundred billion dollars.
And so I think this whole question of transferability
and building models that are transferrable is really key
for all of plant breeding and breeding in general.
So when people start thinking about these questions
I talked the second half of this talk a lot
about advanced ways we could do this a lot better
but I want to highlight and just remind people if you had
or driving a breeding program today
just do genomic selection.
It's relatively you can get it off the ground.
And as long as you have high quality phenotypic data
and a few months, or six or six or nine months,
you can make progress.
And then we can think about adding in these other components
to really leverage a lot more information, to hopefully
get some improvements on those prediction accuracies.
So I think we, we went through the 20th century where
success in breeding looked a lot like this careful
note-taking high quality experimental design
and thoughtful breeding approaches.
But I think of course, our 21st century success where
we have even more incredible challenges or really
require the integration of lots of different communities
working together for this all off.
But I think we can do it.
So I'll end there and just highlight, I've mentioned a lot
of the postdocs and graduate students who did the work
I'm discussing today, but I want to mention my group leader
Sarah Miller, Cinta Romay and Peter Bradbury
who are really critical for managing the group leading
and collaborating with all the great collaborators we have.
So I'd be happy to take some questions
-: I have already one paused question
so from our own Alison, how would you address
to the production of food crops such as bananas
and or tomatos extremely dependent
on vegetative groaning, especially if they are incapable
of producing sexually?
Second question are best included
within these fitness models.
-: So, I mean, I think
if you can't do can't go through my OC's, then yes.
I think you have to do it
through editing and other technologies.
I think obviously I think there's a lot
we can editing technologies
can allow us to deal
with things like disease and some
some specialty traits fairly quickly
for getting rid of deleterious mutations.
When a lot of these genomes have hundreds
if not thousands of deleterious mutations that build
up through vegetative propagation over time
and maybe editing gets good as in
editing will get a lot better
If it gets more high-throughput
then you can start doing that
but I think you can also,
in some of these species have, may be worthwhile going back
and making it, where you had a good diploid
and essentially just breeding very quickly
on that diploid and essentially making a new crop.
So I'm not, I don't know enough
about banana to make real good recommendations on that
but I definitely seen in cassava,
genomic selection, we're dealing with issues.
It's, it's not, it doesn't produce seed very well
so vegetary propagate, but we're making progress on
it's just something you slept for
and you can make progress on that too.
So I think,
hasn't been a system I haven't seen where
we can't use some genomic selection well.
-: Other questions from Suzanne,
what are your trans thoughts
on the derivation of actual houses given the work
on military use mutation identification?
-: Yeah, so, you know, I, I think, you know, everything
we kind of see,
the vast majority
of heterosis is coming from deleterious mutations.
And now that doesn't mean it's necessarily
exactly a dominance or pseudo over dominance model.
I think there's a wide range of dosage
natures of dosage that are really critical.
And so I think the models we see
on these there's each protein, each protein complex
require and metabolite requires a different balance there.
And so some of those,
from a high level look like dominance
some of them look like other types
of dosage relationships and so I think
but most of the deleterious mutation, or,
I think heterosis is pretty much all, well, not all
let's say 85% if I had to guess that doctors' mutations.
-: So we have a quick question from, from Vicus with
with some introduction, but biggest question is
can you please share your thoughts on having initiatives
like reading insight within each university, is this needed
and is it possible given all the practical constraints?
-: So I don't know that we
I think it's really helpful to have
so the way Brittany insight is kind of structured
there's a, a software development team in the
but there are collaborating also with,
the cassava base team and other open source platform
teams to create those resources.
And, and then I do think then on the genomics
and science side, we essentially are hiring one coordinator
for about every four species.
And, and that type of thing, I think,
is something that we don't certainly don't
need to replicate the bioinformatics everywhere.
And I think it is probably useful to have one
person kind of helping out a team all
across the country for each university or so on.
And then it probably is something like
one coordinator for every four or five specialty species
is probably about the right type level of support
is necessary to get a lot of the smaller programs
are running and supported
because especially crop readers are doing just
a tremendous range of things
from the figuring out the market,
the economics
and having to know all the biology
of their species and everything else.
And I think if they can help
off help them offload a little bit of the genomics
and the mechanics of doing genomic selection
they can focus their efforts, what they want do best
because I think it's, I'm always
in awe of what these bridgers are doing, specialty crops
just an amazing range of things.
And so how do we,
do what can be common across a lot of species
get that into kind of like a core facility type of thing
and let them focus on it.
They don't need to go and to have sequence DNA.
-: We have a question from Phil McLean.
Do you have any thoughts
on competition for sips trait among, among enzymes
in a password and how has that will affect phenotype?
How long might the transferability be stable?
-: Right. So
So Phil I'll say one thing.
So the answer is I have not thought a lot about this.
One thing we're trying to think about these problems
at a higher level at is rather than just thinking
about proteins or dominance or dosage myth.
We think about all of those really now
at the level of the other group.
And I think
one of the key things
that we really important is once things
like alpha fold to come out, we'll be able to tell whether
or not to orthologs in paralogs are likely to
have similar substrate specificity.
And I think that will then allow us to start dividing
up proteins and those proteins that are pretty
much just differentiation.
Don't the biochemistry they do is the same
but it's mostly just differential expression.
And versus those that are actually
catalyzed different biochemistry.
But I think this is where things like alpha fall too
just, you know, and that general area
whether or not that's the right tool, I don't,
eventually will be the winner tool.
I don't know.
But I think the fact that that's possible is, yeah
I think that's the single most important science other
than coming in with COVID vaccine last year, that happens
whereas the alpha fall too.
-: Lots of questions.
So when your examine the interface
of cell biochemistry and plant anatomic
over evolutionary timeframe, do you find the seminar
of genomic prediction alignment
-: Cell biochemistry.
-: And plant anatomy.
And physiology.
-: So I
I think there's going to be if we break these things
-: Down correctly,
I think we can find lots of transferability
but I think we do
we need to get specialists who really understand that level
of biology to help us create the models.
Okay. So I think when I look at,
some of the crops growth models
The, the basic model for maize and sorghum
isn't that is pretty much the same.
It's just a different set of parameters on it.
And I think, you know, how do we figure
out the key elements of these areas and,
so they can be generalized
I think that's, and, and as we, I
I just don't know
at that level of scale
I'm not as good a physiologist
as I am some of these other things.
So I think that's where other people really need to
take the lead in it and have much more insight than I do.
-: Well, did you mentioned to me that we would like to focus
on the C4 versus C3 photosynthesis processes when referring
to his question.
-: Okay.
Yeah, no, I think, well, I think as we also learn more
about C4 plants, we see that there a lot more variation
even within a single individual between being C3 or C4.
So I think we should try to generalize some
of our models so that they do fit together.
And one of the things I've been really impressed
with in the last couple of years is,
these deep learning networks, but combining reasoning
with them and reasoning is really
about adding mechanism to deep learning models.
And I think we really need to start thinking
about what's in some of these neat crop growth models
and others types of models
and adding reasoning to the deep learning frameworks.
They're much more efficient.
You know, deep learning is much more efficient
if you've added reasoning to it,
like a hundred a thousand fold, more efficient
-: A few more questions, couple more.
Do you think that the right expression by adults
in trans are also digital use to crop fitness?
-: I mean
essentially everything that I think the reason why
this is working at the level of is right now is
that all heritable trans what cis somewhere.
Okay.
So you follow the trans network back at some point
it was either caused by a different amino acid change
or difference in regulations.
-: And then one more question is all we'll use
stricture models to help define it.
The erotic pools
-: That was, I mean
essentially the a maize had erotic pools were
done in such a way that their deleterious mutations
got distributed in a
in a way that they fully complemented.
So what would happen there was that you would,
frequently get one set of deleterious mutations fixing
in one head erotic group, and then you had a range
of other appetites on the other ones
of deleterious mutations, as long as they weren't the same.
You essentially had general combining ability.
And
I think we can now start thinking about these models
in terms of really, I've got a set of deleterious mutations.
Can I set up a brilliant structure that will put more
of those on two opposite sides?
And I think you can probably make some progress on that,
but again,
you can also just drive this pretty quickly
but generally just randomly choose two groups
make them and move fast with genomic selection.
So in certain regards
depending on the resources that you have in your group,
I would go GS, if it's small, I would go GS first
if you're a Cartiva then yeah.
Where you should worry
about individual deleterious mutations
and how to purge them off.
-: I would like to ask you one more, maybe general questions.
So you provide information
on gene expression and chromatin accessibility
protein sequence and function, but
and how do you integrate all this information to
recreate a model which can allow
for an unseen genomic prediction?
-: So essentially when we do like expression levels
we have for each haplotype, we have a value
of what know relative value against all of the other alleles
and that matrix is then used for general life prediction.
So we have 30,000 genes.
We may have modeled.
We essentially have 30,000 values
at a one value at each gene.
And it for each for each individual
and we use that to connect
to whole plant phenotype
and deleterious mutations
It's kind of the same type of thing.
You have a a hundred or a thousand or ten thousand
of these deleterious mutations across the genome
that you're keeping track of and each of the individuals
and you're adding those together.
What we have not done is of course
fused those deleterious mutations
at a given protein necessarily with the expression.
Okay. We certainly can, we can put them
in the same matrix that we want, but I think,
we'd like to be a little bit more
sophisticated than that.
Although being unsophisticated I think is fine to do also.
So, it gets you that works for right now and do it.
-: Thank you very much.
Ed, I think it's time to two hands is necessary.
So thanks a lot for this, for your talk.
It was excellent.