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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5515_Библиотеки_им_академика_М_И_Перельмана.pdf
region to genomic region. It is difficult to sort through these variables as a human, but a
computer can do it in a snap.
Once you have identified all the ORFs, you try to match the amino acid sequence of
the putative proteins to a reference database of known protein sequences. Most of the
proteins in these databases have been studied in great detail, and their functions are
well understood. If you find that your putative protein is highly similar to a reference
protein, you can use that information to infer the function of your newly discovered
protein. This process is called functional annotation, and it works because proteins with
highly similar amino acid sequences tend to have very similar functions.
This reference-based functional annotation process has been used to identify many
of the genes involved in the production of cannabinoids, lignins, flavonoids, and
terpenes in the cannabis genome. Epigeneticist Igor Kovalchuk and colleagues
generated a map (fig. 13.1) that summarizes what is known about the locations of these
genes. Beyond these simple principles, however, things can easily get more
complicated. For instance, what if one putative protein sequence is highly similar to not
one but eight different reference proteins? Those eight reference proteins must have
highly similar sequences, or else you would not have had matches to all of them in the
first place. Based on the principle above—that similar proteins have similar functions
—you can conclude that all eight of the reference proteins are in the same gene family
and have similar functions. This may occur because proteins evolve through common
ancestral proteins; often it occurs by duplication of the gene for the protein. As a result,
you might find several proteins in a row (linked or in close physical proximity on the
same chromosome) that have extremely similar sequences. This is indeed what happens
in the cannabis genome, where several cannabinoid synthase genes (CBDAS, THCAS,
and CBCAS) are found in close proximity to each other on cannabis chromosome 6.
It should be noted that while knowing the location of these genes is important, this
information is essentially only an ingredients list (and a crude one at that) for use in the
production of a cannabis plant. Variation in how these genes act to produce and
sequester the compounds they code for is what gives different marijuana strains their
particular characteristics, and this information can’t be understood simply from
knowing the genes’ location. Staying with the cooking analogy, the ingredients are
relatively meaningless until the chef decides how to prepare them. Likewise, the genes
involved in producing cannabis compounds can be combined in myriad ways through
changes in when, where, and what quantity they are expressed. The combined
knowledge of which genes are involved and how they interact to produce cannabinoids
(or any other important trait) is called the genetic architecture of the trait, which
requires research methods beyond those we have so far discussed.
Figure 13.1. The Cannabis genome, adapted from Kovalchuk et al. (2020). Abbreviations: AAE1, hexanoylCoA synthase 1; CBCAS, cannabichromenic acid synthase; CBDAS, cannabidiolic acid synthase; CBGAS,
cannabigerolic acid synthase; CMK, CDP-ME kinase; DXR, deoxyxylulose phosphate reductoisomerase;
DXS, deoxyxylulose phosphate synthase; FPPS, farnesyl pyrophosphate synthase; GPPS, geranylpyrophosphate synthase; HDR, hydroxymethylbutenyl diphosphate reductase; HDS, hydroxymethylbutenyl
diphosphate synthase; HMGR, hydroxymethylglutaryl-CoA reductase; HMGS, hydroxymethylglutaryl-CoA
synthase; MCT, methylerythritol phosphate cytidylyltransferase; MDS, MECDP-synthase; MEP,
methylerythritol 4-phosphate pathway; MVA, mevalonate pathway; MK, mevalonate kinase; MPDC,
mevalonate diphosphate decarboxylase; OAC, olivetolic acid cyclase; OLS, olivetol synthase; PMK,
phosphomevalonate kinase; THCAS, tetrahydrocannabinolic acid synthase; TPS, terpene synthase.
Before molecular biology started to become relevant to genetic analysis, even
determining the location of the genes responsible for a trait was cumbersome.
Researchers usually performed crosses between individuals with different versions of
the trait (for example, high versus low sugar content in corn) and analyzed hundreds of
individuals in each generation of a study. They needed to use organisms that bred
quickly, to get as many generations as possible crammed within a reasonable amount of
time. Researchers also needed markers that were dispersed on the chromosomes of the
organism’s genome. The crosses took advantage of a cellular process called
recombination: chromosomes line up with each other in a cell, and while they are in
close contact with each other, information can be swapped between them. This creates
new combinations of genes on a chromosome, hence the term recombination.
Recombination between any two genes mostly depends on how close together they
are on the chromosome. Next-door-neighbor genes are unlikely to recombine, because
there is only a tiny region where a recombination event would separate them, while for
genes located at opposite ends of a chromosome, any recombination event will separate
them. By using visible markers to track recombination events, early geneticists could
determine whether the gene for a specific trait was “close” to one of the markers. For
instance, for a chromosome with three markers, if crosses show that the trait co-occurs
with marker 1 we can conclude that the gene of interest is physically closer to marker 1
than to marker 2 or 3. We won’t know the exact distance between the gene and marker 1,
but with a more dense set of markers we can derive increasingly precise estimates of
where the gene is located. The underlying theory is simple, but this approach only
works if the number of individuals in the study is large. Unfortunately, undertaking
crosses that produce hundreds or thousands of progeny is not possible with most
organisms. But in cases where it is feasible (most famously in fruit flies, nematodes,
and yeast), this approach has enabled geneticists to map the genes responsible for many
traits with high precision. Barbara McClintock used these techniques to discover the
transposable elements in maize. But even when successful, the physical mapping
procedure is quite time- and labor-intensive; imagine studying hundreds of thousands of
flies to determine the location of a single gene.
One obvious example of an organism where controlled crosses and huge progeny
populations are not possible is human beings. The logistical and ethical issues are
insurmountable in humans, so other methods had to be developed to map human genes
by examining how traits are distributed within families. For instance, if over four
generations of a family’s history the only people who died from heart attacks were Aunt
Bertha, Grandma, Great-Grandma, and Great-Great-Aunt Sally, and they all had red hair
while all other relatives had brown hair, we can hypothesize that there is a gene for
heart disease located near the gene for red hair (and that it is only expressed in
females).
As modern genomics methods emerged, researchers interested in finding the genes
that cause human disease began to envision a broader molecular approach to mapping
genes. Their first approach was to try to replace visible markers (such as red eyes in
fruit flies) with molecular markers. Molecular markers are “visualized” by genetic
sequencing, rather than by inventorying an individual’s appearance. It is generally much
easier to find small genetic differences between organisms than to find visible
differences, so molecular markers provide a much denser set of values—instead of
three markers per chromosome, one can easily generate tens or even hundreds of
molecular markers. This approach is called quantitative trait loci mapping, because it
enables one to consider the contribution of many genes to a single trait. Traits are
quantitative if they differ in degree, like height or weight. Qualitative traits are either/or,
like having a widow’s peak. As a general rule, quantitative traits involve the actions of
many genes, while qualitative traits are controlled by one or a few genes. Quantitative
trait loci (QTL) studies have two basic requirements: a variable, well-characterized
trait, and a large set of variable molecular markers. Researchers begin by identifying
strains that vary with respect to a particular trait; if one is interested in understanding
why some plants express high levels of THCA, one would start with a single high
THCA strain and a single low (or no) THCA strain. To determine if the available
molecular markers are sufficient, researchers have to test whether there are enough
genetic markers that differ between the high and low strain. For example, marker 1 can
be identified as coming from the high versus the low strain. Ideally, the researcher will
also have solid information about the genomic context of marker 1 (what chromosome it
occurs on, what genes are located nearby, and so on). In other words, a relatively good
genome map of the organism is required.
Once a researcher concludes that the traits are solid and that there is sufficient
variation to carry out the QTL analysis, crosses are performed between the two strains.
Imagine a simplified case where strain 1 has a THCA concentration that is high, and
strain 2 has a THCA value that is low or zero. The first-generation hybrids are called
F1s, and they will each inherit half of their genome from strain 1 and half from strain 2.
They will thus be heterozygous for both the trait (all will have one high gene and one
low gene, which will produce an intermediate trait score of THCA = 5) and for the
QTL markers (all have one copy of each strain 1 marker and one copy of each strain 2
marker). The F1s are then crossed in various ways, and the second-generation offspring
(F2s) are examined and scored for the trait. In this simplified example, the laws of
inheritance dictate that the F2 trait scores will range from low to high. If we find a QTL
marker that occurs in all of the THCA = high F2s and none of the THCA = low F2s, we
can conclude that the marker is linked to the trait (that is, the gene for high THCA is
located in close proximity to the marker). This approach only works with organisms that
can be crossed to generate many different recombination events, because the goal is to
get the gene and the QTL marker very close to each other on a chromosome that has no
other genes from the high THCA strain. The QTL mapping approach has enabled
researchers to extend their work beyond standard model organisms such as fruit flies
and to start to answer questions about the genetic architecture of traits of economic and
medical importance.
In cannabis, QTL studies have been used to determine the genetic architecture of
several traits. In hemp-type cannabis, plant breeding specialist Jordi Petit and
colleagues used QTL mapping to characterize the genetic architecture of vegetative
time, flowering time, and sex determination, traits that are strongly associated with
hemp fiber quality and seed production. They identified a set of QTL markers
associated with these traits, and by examining the genes located near the quantitative
trait loci they concluded sex determination was affected by genes involved in light
perception and genes that regulate flowering interact to control flowering time, as well
as genes regulating the balance of two classes of plant hormones (auxins and gibberellic
acid). Also in hemp-like cannabis, crop geneticist Patrick Woods and colleagues used a
QTL approach to review variation in eight agronomic traits (such as seed yield and
stem biomass) and seventeen biochemical traits (such as terpene and cannabinoid
concentrations). They found thirty-four QTL markers associated with the agroeconomic
traits, but these clustered into just four regions; similarly, thirty-five QTL markers were
associated with variation in the biochemical traits, and these mostly mapped to two
regions. Their results suggest surprisingly simple genetic architecture for the traits they
examined. In particular, a single candidate gene encoding olivetol synthase was found to
be important in determining both agronomic and biochemical traits, indicating that this
gene may play a key role in the genetic architecture of many traits of interest to cannabis
breeders. However, another QTL study published in the same year had different
conclusions. Bioinformatics researcher Christopher Grassa and colleagues performed a
hemp-type × marijuana-type cross (as compared with hemp × hemp in the two previous
studies) to determine the genetic architecture of THC : CBD ratios and total
cannabinoid content. They found sixteen QTL markers associated with cannabinoid
traits, but these had little overlap with the QTL markers identified by Woods et al.
These inconsistent QTL results probably reflect the fact that many of the traits
cannabis breeders are most interested in are complex, meaning that the exact state of the
trait depends on the interaction of many genes in the genome. Even exactly the same
genes can have different effects depending on the genetic background in which a trait
occurs, and as a result QTL studies can find effects that are valid in the cross that was
performed for a particular study, but nonexistent in a different cross. This problem,
along with the technical challenges of obtaining sufficiently large sample sizes, has led
researchers to look for alternative approaches.
One of the most commonly used alternatives is the genome-wide association study
(GWAS). In GWAS one simply surveys a large population for trait variation, sequences
as many genomes as possible in the population, and examines the data to determine
whether, for example, all the high TCHA plants share some genetic variants that are
absent in the low THCA plants. The principle is similar to QTL mapping, but no
crosses are required. In a species (like humans) where controlled crosses can’t be
performed, or in one where generating large sample sizes from controlled crosses is
difficult, GWAS can enable researchers to begin to understand the genetic architecture
of a trait. This approach underlies many of the recent discoveries of genes associated
with an increased risk of heart disease, cancer, and other human ailments. Just as with
QTL mapping, a GWAS is convincing only if the traits of interest have been
meticulously defined and measured. This requirement for well-studied, carefully
characterized traits is essential, no matter what technological approach is employed.
The need for highly reliable trait data for cannabis to investigate the genetic basis
of the traits has both an upside and downside. The downside is that precise data on
cannabis phenotypes have only recently become publicly available, in contrast to other
crops such as corn (see chapter 12), where almost a hundred years of research have
produced many strains of maize with precisely defined phenotypes, making the genetics
of complex traits in corn a fairly tractable question. In cannabis such research has been
held back by the long-standing criminalization of the growing, selling, and consumption
of cannabis—no researchers in their right minds would risk incarceration for a
significant biological study of cannabis. As a result, only a small group of people have
studied cannabis traits—mostly those who grow, sell, or consume it. Much of this
information on trait variation in cannabis is therefore anecdotal, subjective, and
potentially unreliable.
There are numerous websites where the phenotypes of different cannabis cultivars
are described in great detail. Evolutionary biologist Matthew Aardema and I wondered
whether this anecdotal information could be successfully used as trait data for GWAS
studies. We therefore collected trait data from a variety of marijuana-centric websites
(including I Love Growing Marijuana [ILGM], CannaSOS, Leafly, Grow Marijuana,
NCSM, Allbud, Wikileaf, and Cannabisinfo.com; see also For Further Reading for this
chapter). The traits we examined included the CBDA and THCA levels in different
strains, which account for some of cannabis’s medicinal and psychoactive effects. As
you might expect, the cannabis websites are heavily focused on this biochemical aspect
of the plant, but not exclusively so—they also report on a strain’s growth habits, its
taste, its effect on the user’s mood, and its usefulness in treating medical issues. For
example, table 13.1 lists the information CannaSOS offers about the strain called
Charlotte’s Web.
By scouring the web for trait data such as these we were able to obtain 201 values
for almost 300 individual strains of high THCA cannabis that also had genome sequence
data available. Our question was: are these anecdotal trait data of high enough quality to
be used in a GWAS study of the genetic architecture of trait variation across these 300
cannabis strains? As mentioned above, GWAS studies begin by surveying trait variation
in a population; they then attempt to associate trait variation with genetic variation. If
either type of variation is absent or unreliable, the study’s conclusions will not be valid.
For example, suppose we are interested in the genetic architecture of lemony taste
in cannabis. We will need trait data for as many strains as possible (perhaps lemoniness
scored on a scale of 0 to 5), and the trait must be variable in the population. We will
also need genetic data for these same strains, and not all genetic data are equally
accurate or thorough, so this is another instance where avoiding “garbage in” is critical.
Having collected these data for many different strains, we next divide the strains into
“case” and “control” groups, just as in a human study where cases might be people with
heart disease and controls would be people without heart disease. In our hypothetical
study of lemoniness, the case group = strains with a lemony taste; control group =
nonlemony strains. Having grouped our trait data in this way, we next look for genomic
variation that distinguishes cases from controls.
Table 13.1. TRAITS FROM THE CANNASOS WEBSITE FOR CHARLOTTE’S WEB STRAIN OF
MARIJUANA
THC 3.72
CBD 11.72
Positive effects Euphoria, uplift, focus, creative, energetic
Negative effects Dry mouth, dry eyes, anxious, dizzy, headache
Flavors Pine, earth, lemon
Symptom alleviation Stress, pain, fatigue, muscle spasms
Flowering time 60–70 days
Height 72 inches
Yield 1.48 oz/ft squared
Growth diiffcult Moderate
As illustrated in figure 13.2, the genomic variants that are most commonly assessed
are single nucleotide polymorphisms (SNPs), which are single base pair differences at
a specific genomic location (e.g., having an A versus a T at position 500 on
chromosome 6). Although it may seem incredible that such a small difference would
have an effect, this has proven to be the case in many studies, including the one that
identified olivetol synthase as a candidate gene for several cannabis traits. Once we
find SNPs that distinguish our lemony versus nonlemony strains, how can we know
whether they have any effect on lemoniness? After all, we expect to find a lot of
variation between strains, and not all of it relates to lemoniness.
Deciding whether or not a SNP is “significant” in a GWAS study is not as simple
as one might think. A SNP’s significance score is basically a statement about its
frequency in the case versus control groups. If all the cases have an A at position 500 on
chromosome 6 and all of the controls have a T, then the SNP is clearly significant. But
anything less straightforward requires statistics: is the observed frequency different than
what we would expect by chance alone? This is what P-values try to quantify: if P =
0.9, the observed distribution matches what we would expect if the SNP state is random
with respect to the trait; if P = 0.00000001 (10–8), the SNP state is unlikely to be
random with respect to the trait.
Figure 13.2. Example of a GWAS for cannabis. SNP1 is an AT polymorphism in the first position of the
sequence. In this example 1,000 cannabis strains were examined; 400 of the strains have a lemony taste,
and 600 do not. The statistic calculated in this case is a chi-square P-value. In a typical study there are
thousands of SNPs that need to be examined. The larger the number of cases and controls, the more
precise the study will be.
In the example shown in fig. 13.2, SNP1 has a P-value of 1.537459e–12, and SNP2
has a P-value of 0.4795. While only two SNPs are shown here, most GWAS studies
involve thousands of SNPs, and a separate P-value must be calculated for each of the
SNPs. Depending on the study, as many as 130,000 known SNPs can be tested for
whether they are associated with a particular cannabis trait. Once P-values have been
calculated for all the SNPs, the SNPs can be visualized in a Manhattan plot (see fig.
13.3), which shows the genome locations and P-values for association with the trait.
Each point in the graph represents the P-value of a single SNP, and the significance
level cutoff is shown by a dotted line. In this made-up example there are eight regions of
the genome where significant associations of the trait (“lemony”) with SNPs in those
regions can be inferred. Because the positions in the genome of all the SNPs used in the
study are known, a researcher can now go back to the genome map and discover which
genes or genomic regions are close to the significant SNPs. These genes or genome
regions then become candidates for further study.
Figure 13.3. Made-up “Manhattan plot.” Ten chromosomes and thousands of SNPs are represented in the
diagram. The dotted line is the e10–8 significance cutoff. The points above the dotted line are SNPs that
show significant association with the trait in question. The eight arrows indicate potential genomic regions
that could be associated with the trait.
Several studies have used GWAS for exploring the cannabis genome in hopes of
identifying candidate genes for the genetic architecture of important traits. The most
critical aspect of these studies is the careful selection (or generation) of trait data.
There are several possible routes. First, one can use trait data from the literature or on
the web, which is what Matthew Aardema and I tried in our study using web-based trait
data. Alternatively, one could insist on lab-based data from specialized bioassays
designed to precisely measure each trait. Obtaining SNP data for marijuana strains is
fairly easy, because several commercial outfits have invested considerable money and
time into sequencing the various strains that they produce or sell. These SNP data are
simple to download and organize. The biggest issue is not in finding the genomic data of
a strain, but rather determining exactly what strain the DNA sequences represent. The
same strain may be called by many different names from one company or one grower to
another, and if it isn’t clear what strain the genetic data are from, the data are somewhat
useless.
Our study on web-based trait data was sobering. Although there is a lot of data on
these web sites, very few of the 200 or so traits we examined showed significant
associations with SNPs in the genomes of the nearly 300 strains we checked. Most of
the significant traits were for concentration of THCA and CBDA in the various strains.
Part of the problem with traits other than the chemical ones is sample size (there are
only a few strains typed as lemony), but most of the problem lies in the subjectivity or
inaccuracy of the web-based trait scores that we used.
This result suggests that much more attention needs to be focused on characterizing
interesting traits accurately and reproducibly. On the other hand, the study gave us hope
that at least for biochemical traits, GWAS studies might be able to determine the
underlying genetic architecture for those traits. In other words, if even the wobbly webbased biochemical trait data were able to reveal some SNP associations, once better
trait data are generated the results are likely to be even stronger.
Two kinds of GWAS studies have been the focus of cannabis researchers who use
the technique. Some researchers use the SNPs as markers to identify strains and then
examine how those strains may have evolved over thousands of years. I call this
“genomics as history.” The second kind of study, just discussed above, can be called
“genomics as architecture.” Until we understand the genetic architecture of cannabis’s
most useful traits, we will be unable to precisely control what effect cannabis has on
users. If one wants to use cannabis to control nausea in chemotherapy patients, for
example, it is important to be able to provide that effect safely and predictably. Once
we know the genetic architecture of such a trait, we are closer to being able to control
and manipulate it through breeding or engineering.
Genomics as Architecture