that is statistically associated with the phenotypic trait.
And given the variability of the phenotype,
there may be an environmental component to it.
So there may be population, in this case, just for
illustration purposes, there's multiple leaves in each sleeve.
Leaf has a different phenotype.
But there is a huge variation
in the phenotype in all the individuals of the population.
And if we want genotypes, this population and
identifies one allele that appears to be informative using statistical methods.
You can start to see that the alleles
can correlate with the variability that is found in the trait that is interested.
So we have a quantitative trait that we're interested in.
Once we genotype, we identify these alleles, and
then we associate these SNPs with the variation in the phenotype.
But, there is a caveat to these approaches.
Both QTL mapping, or GWAS,
are great in the sense that they allow us to identify loci genomic locations
that are associated with these complex diseases or traits.
But the issue is that these loci may contain
many genes, so a great number of genes.
So it is difficult to prioritize which of these genes are associated with a disease.
More recently, there's been a variation
of a QTL approaches called EQTL mapping,
where instead of just looking at a phenotype,
and associating the phenotype with DNA variation or
SNP, you're actually looking at mRNA
expression as an intermediate phenotype.
So in this type of approach, you're combining genotyping
with high throughput methods to monitor
the expression that results from this genetic variation.
And there's two types of eQTL.
One, the eQTL that basically associates
a region of DNA with mRNA expressions changes.
And then there is reQTL, which is a response
expression quantitative trait loci,
which involves a type of reQTL, where you're
associating with a mRNA expression changes after a stimulation.
There are two types of eQTLs.
There are those that occur in cis and those that occur in trans.
And what that means is that depending of the position of the eQTL,
with respect to the gene, will determine whether this is a cis or a trans.
So a cis eQTL featuring a SNP that is located
very close to the gene under regulation.
So, it's usually within 1 million base pairs window of the geno.
They're the start of the geno.
The stop quote end of the gene.
And it most likely will directly affect the expression of the target gene.
To illustrate this, we have three different genes, Gene A, B, and C,
and Gene B has a SNP very close to a starting position.
And so we can assume that
given how B is actually expressed less when the SNP is present,
that B is regulated by a cis eQTL.
So the SNP in the promoter region of gene B can affect transcription binding.
Now for example, that's a possible mechanism.