The pros and cons of GWAS
Summary
TLDRThis video introduces genome-wide association studies (GWAS), which identify gene loci associated with traits or phenotypes. The host explains the pros and cons of GWAS, emphasizing its hypothesis-free nature and its potential therapeutic benefits. Limitations such as missing heritability and a lack of diversity in the studies are discussed, along with the need for further experiments to enhance accuracy. The video also touches on polygenic risk scores, which predict the likelihood of traits like obesity, and stresses the importance of making these scores actionable and independent of existing information.
Takeaways
- 🧬 Genome-wide Association Studies (GWAS) are used to identify gene loci associated with a trait or phenotype of interest.
- 🔍 GWAS involves scanning the entire genome to find single nucleotide polymorphisms (SNPs) that may be associated with a particular trait.
- 🌟 One of the main benefits of GWAS is that it is hypothesis-free, allowing researchers to identify potential gene associations without prior knowledge.
- 💊 GWAS can have therapeutic potential, particularly when combined with other data like polygenic risk scores, which can predict the likelihood of certain conditions.
- 📈 The data for GWAS is becoming more accessible due to the prevalence of sequencing data, making these studies technically easier to conduct.
- 🔄 Combining GWAS with other studies, like single-cell RNA sequencing, can help overcome some of the limitations and provide a fuller understanding of gene expression.
- ⚠️ A key limitation of GWAS is the issue of 'missing heritability', where identified genetic variants only account for a small percentage of the genetic influence on a phenotype.
- 🔎 GWAS often identifies loci rather than specific genes, which can be challenging as it's the gene function that researchers are typically more interested in understanding.
- 🌐 There is a lack of diversity in many GWAS, which can limit the applicability of findings to different populations and may contribute to 'missing heritability'.
- 🏥 Polygenic risk scores generated from GWAS can be used to predict an individual's risk for certain diseases or conditions, potentially enabling early intervention.
Q & A
What are genome-wide association studies (GWAS)?
-Genome-wide association studies (GWAS) are used to identify gene loci associated with a specific trait or phenotype by analyzing genetic variations across the entire genome. These studies look for single nucleotide polymorphisms (SNPs) that may be linked to certain traits.
What is the main advantage of genome-wide association studies?
-One major advantage of GWAS is that they are hypothesis-free. Researchers do not need prior knowledge about the genome, making it possible to identify genetic variants that are likely associated with a particular trait.
What are some limitations of genome-wide association studies?
-Key limitations include the inability to establish causation, the issue of missing heritability, the challenge of identifying rare variants, and the potential for identifying non-coding regions or loci without clearly linked genes.
What does 'missing heritability' mean in the context of GWAS?
-Missing heritability refers to the gap between the genetic variants identified through GWAS and the full genetic explanation for a trait. The variants typically explain only a small percentage of the heritability, leaving most of the genetic factors unaccounted for.
How can combining GWAS with other studies improve results?
-Combining GWAS with studies such as single-cell RNA sequencing can help researchers better understand which genes are expressed in specific tissues and confirm the association between genetic variants and phenotypes, enhancing the reliability of findings.
Why is the lack of diversity in GWAS a problem?
-Most GWAS have used sequencing data primarily from European populations, which may limit the generalizability of results. Including more diverse ethnic groups could help address missing heritability and provide more comprehensive insights into genetic associations.
What is a polygenic risk score, and how is it related to GWAS?
-A polygenic risk score is a measure that combines the effects of multiple genetic variants identified through GWAS to estimate an individual's likelihood of developing a specific trait or disease. It can be used for early disease prediction and prevention.
What are some potential applications of polygenic risk scores?
-Polygenic risk scores can help predict the likelihood of developing conditions such as obesity, cancer, or other complex diseases. By identifying individuals at higher risk, early intervention and preventive measures can be implemented.
What is the main challenge with using polygenic risk scores for actionable insights?
-The challenge lies in ensuring that polygenic risk scores provide independent and actionable information beyond what is already known. For example, if someone is already obese, a risk score confirming their likelihood of obesity offers little new information.
How can rare genetic variants influence the effectiveness of GWAS?
-GWAS typically focus on common genetic variants, but rare variants may have a greater effect size on traits. These rare variants might be missed in standard GWAS but could play a significant role in explaining missing heritability.
Outlines
🧬 Introduction to Genome-Wide Association Studies
This paragraph introduces genome-wide association studies (GWAS), explaining their purpose and methodology. GWAS are used to identify gene loci associated with a specific trait or phenotype by scanning the entire genome for single nucleotide polymorphisms (SNPs) that may correlate with the trait. The paragraph provides an example of how a SNP could be associated with tallness. It also mentions previous GWAS examples, such as those related to chronotype and obesity, and a recent study on same-sex sexual behavior. The pros of GWAS include being hypothesis-free, having therapeutic potential, and being technically easy to perform due to the prevalence of sequencing data. The paragraph also touches on the importance of combining GWAS data with other studies, like single-cell RNA sequencing, to gain a fuller understanding of the genetic associations.
🔎 Limitations and Potential of Genome-Wide Association Studies
The second paragraph delves into the limitations of GWAS, such as the challenge of associating genetic variants with causation rather than just correlation. It discusses the issue of 'missing heritability,' where the sum of identified genetic variants only accounts for a small percentage of the likelihood of a phenotype. The paragraph also addresses the problem of identifying common variants over rare ones, which might have a greater effect size. It suggests that combining GWAS with other experiments, like RNA sequencing, can help overcome some of these limitations. The paragraph also highlights the importance of including diverse populations in GWAS to improve the understanding of genetic associations and heritability. It concludes by discussing the potential of GWAS in generating polygenic risk scores, which can predict the likelihood of developing certain conditions or phenotypes, and the importance of these scores being actionable and independent of other available information.
📝 Conclusion on Genome-Wide Association Studies
The final paragraph summarizes the discussion on GWAS, emphasizing their benefits and current limitations. It reiterates the potential of GWAS in conjunction with other studies to advance genetic research and the importance of addressing the limitations to fully harness their capabilities. The paragraph ends on a note of thanks to the audience for their attention, indicating the conclusion of the video script.
Mindmap
Keywords
💡Genome-wide Association Studies (GWAS)
💡Single Nucleotide Polymorphisms (SNPs)
💡Phenotype
💡Polygenic Risk Scores
💡Loci
💡Missing Heritability
💡Hypotheses-Free
💡Therapeutic Potential
💡Linkage Disequilibrium
💡RNA Sequencing
Highlights
Introduction to genome-wide association studies (GWAS) and their role in identifying gene loci associated with traits or phenotypes.
GWAS are hypothesis-free, meaning they don't require prior knowledge to identify gene loci associated with a phenotype.
GWAS can have therapeutic potential, particularly when combined with polygenic risk scores to predict disease likelihood.
Advancements in sequencing technology have made it easier to access the data required for GWAS, increasing their prevalence.
Combining GWAS with other experiments, such as single-cell RNA sequencing, provides a more comprehensive understanding of gene expression and its relationship with traits.
One major limitation of GWAS is that identifying an association between a gene and a trait does not imply causation.
The concept of 'missing heritability' refers to the fact that GWAS often explain only a small percentage of the genetic variance for complex traits.
GWAS tend to miss rare variants, which may have a greater effect on the trait of interest compared to common variants.
Another limitation of GWAS is that they often identify loci without identifying the gene itself, making it harder to understand the function and role of the gene.
There is a lack of diversity in GWAS, with many studies relying predominantly on European populations, which can limit the generalizability of findings.
Polygenic risk scores, derived from GWAS, can be used to estimate the likelihood of an individual developing a certain disease or phenotype.
Polygenic risk scores could enable earlier detection of diseases and more personalized treatments based on genetic risk.
The actionable potential of polygenic risk scores is still being evaluated, as they must offer independent predictive value beyond existing information.
An example of actionable polygenic risk scores is the use of these scores to assess the risk of obesity, which could lead to targeted interventions.
The future of GWAS and polygenic risk scores depends on addressing limitations such as rare variant identification, heritability gaps, and study diversity.
Transcripts
hello and welcome back to the cheeky
sign jus so in today's video we're going
to look at the pros and cons of
genome-wide Association studies so
before I jump straight in and tell you
about the pros and cons I'll first
introduce you to what are genome-wide
Association studies and then after
looking at the pros and cons we'll look
at what polygenic risk scores are and
how genome-wide Association studies are
used to look at putting at risk scores
so firstly what are genome-wide
Association studies so genome-wide
Association studies are used to identify
gene loci associated with a trait or
phenotype of interest so the idea is
that you look across the entire genome
and try to identify single nucleotide
polymorphisms as you can see here with T
a and G and whether or not the T the a
witha G is associated with a phenotype
of interest so let's say people who had
a T instead of an a were also had the
phenotype of being really tall and so
that could be a low save associated with
the traits of being tool that make sense
and so if you do this across the entire
genome you'll find that certain leucite
are more likely to be associated with
that phenotype than others and ones that
surpass the threshold are of interest
because it suggests that that is a low
site why there was a correlation between
having that genetic variant and the
traits so in previous videos I've
already given examples of where
genome-wide Association studies have
been done for example I gave an example
of where it's been done with chronotype
and also with obesity but this paper
here is a recent paper that's forces on
a genome-wide Association study with
Lisa associated with same-sex sexual
behavior and so my point is is that they
are happening all the time and can be
done with many different phenotypes but
why do them what are the benefits of
genome-wide Association studies so
firstly the beauty of genome-wide
Association studies are that they are
hypotheses free
you don't need to know anything
beforehand and so starting from nothing
you can identify a site that have genes
that are likely associated with the
phenotype that you're studying and then
these genes can be further characterized
and understood to understand what causes
the phenotype of interest
wow that was a long-winded explanation
but genome-wide Association studies may
also have therapeutic potential as we'll
see later when we look at polygenic
Briscoes and lastly the data for
genome-wide Association studies requires
sequencing data which is becoming ever
more prevalent and so is technically
quite easy to get now or at least easier
than it was previously
and these datasets can be even more
informative when they're combined with
other studies as well and actually
combining genome-wide Association
studies with other studies helps they
become some of the limitations that I've
seen with genome-wide Association
studies so one experiment that I mean is
single-cell RNA sequencing data because
with that information you can see which
genes are being expressed in a cell and
which shells from assassin tissue and so
if you can see the same subset of genes
being expressed as the same subset of
genes identified from a genome-wide
Association study you have a fuller
understanding and better confidence that
the genes that you're looking at are
possibly associated with the phenotype
that you're studying it would make more
sense probably if you had an example but
bear with me for now but there are
downsides to genome-wide Association
studies the first thing the keywords
Association Association of valais site
with the traits doesn't necessarily mean
causation and so the genes that are
being identified may have new world
value in terms of understanding the
cause of a phenotype so the next
limitation with genome-wide Association
studies is the missing heritability and
so what I mean by this is that if you
took all the low-side that surpassed the
thresholds of being associated with that
phenotype and you added them all
together the total sum of all those
those I in terms of the likelihood of
you getting that phenotype condition
that sees whatever you're looking at is
only around like two to three percent
maximum for most of these genome-wide
Association studies so what about you
know that the other 97% where is this
missing heritability and so part of this
issue comes from the fact that the low
site with like a strong link with the
phenotype I'm being identified and that
comes down to a kind of a flaw we're not
really a floor-by-floor of the
genome-wide Association studies that you
have your population of different whole
genome sequences that you're looking at
and you're trying to find lo site in all
of them all of the people with the
condition that have that trains that
have you know what I mean and the
problem is is if you're looking for
common variants you're going to miss the
rare variants that let's say one or two
people who have the condition also have
that variance and it seems possible that
these rare variants are the ones that
have a greater effect size as you can
see in the graph here but there is still
hope that these rare variants can be
identified given that now we have the
tools to easily from whole genome
sequencing and with this knowledge that
we could be missing them we're more
likely to look for the reference so
another limitation with genome-wide
Association studies is that it often
identifies loose I where you have a
genetic variant but not a gene why are
it's the genes that were more interested
in because then we can understand the
function of the gene where the genes
expressed I understand how that links it
to the phenotype and so certain reasons
could be that the low size and a
non-coding region in which case it could
be affecting the expression also not
just one gene but multiple genes that
altogether could be influenced in the
phenotype um there's also a linkage
disequilibrium whereby there might be
more than one gene
in that same region and/or there's so is
more to do the fact that there could be
multiple variants within a certain low
sign is determining which of those
variants is responsible and then lastly
is looking at which tissues important in
terms of the gene so as I said there
might be a gene that you've identified
but in might only be expressed and
Sasson tissues and so to get answers to
these different points you need to
combine genome-wide Association studies
with other experiments such as the RNA
sequencing data that I spoke about
earlier and another important issue that
is now being better addressed is the
lack of diversity in the studies at the
moment so a lot of the genome-wide
Association studies have used sequencing
data from mainly European populations
and say by including this including more
ethnicities into these studies could
also explain why there seems to be a
lack of heritability in the results and
so it would be valuable to include them
and so if some of these limitations can
be addressed there's great excitement
for genome-wide Association studies for
being used to generate polygenic riscos
whereby they can take your genetic
information and look at which less
variants you've got and therefore your
risk or likelihood of getting a certain
disease or phenotype and so this could
be the likelihood that you could get a
certain type of cancer and this
predictive information kids enable
better identification and early
identification of certain diseases which
will leads a better time available for
treatment and so by taking body mass
index as an example the greater your
polygenic risk or the greater the
prediction of your BMI is and so if you
did this across a population you would
see a bell curve for the distribution or
Fresco's and so the risk score could
have some
kind of predictive value for assessing
people who are high risk of having a
high BMI and getting obesity and
therefore could be targeted through
effective treatment but that's the key
thing that I need to emphasize it's
about whether or not a risk score can be
actionable and so this was actually
already done in a recent paper whereby
they used a genome-wide polygenic risk
score to quantify the inherited
susceptibility to obesity and they did
this to see if they could identify
adults at risk of severe obesity and the
idea is that if you can then
you can try and help to prevent that
from happening but the other key thing
is that these polygenic risk scores have
to be independent of other information
that you can already get so if you were
going to help somebody and that person
was already obese and getting a probably
done at risk score that tells you that
they're going to be obese is already
very much help then is it because it's
already gonna be obvious so it's all
about whether or not play gentle risk
scores are going to be actionable and
can be independent of other sources of
information that we can already attained
so I hope this video has given you a
good introduction to what genome-wide
Association studies are and their
benefits and the current limitations
with them but also what we can to you
with them so thanks for listening
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