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Analysis of rare coding variants reveals novel genetic risk factors for type 2 diabetes

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David Curtis explores findings from a previous study, utilizing an expanded sample of 470,000 exome-sequenced individuals. The aim was to identify genes linked to type 2 diabetes (T2D) and examine the distribution of rare variants within these genes.

Image courtesy of interviewee. November 30, 2023

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This video video discusses research involving the UK Biobank, which comprises half a million volunteer participants in the UK who have provided extensive health data, tissue samples, and undergone various assessments, including DNA sampling. This video video study focuses on exome sequencing, allowing researchers to read every DNA base in about 20,000 genes, a method that extends beyond traditional genome-wide association studies, which often examine common genetic variants predominantly outside of coding regions.

The author explains the significance of identifying rare genetic variants that could lead to diseases such as type 2 diabetes. The analysis involved comparing exome sequences of individuals with type 2 diabetes against those without it to find damaging genetic variants. Out of 20,000 genes examined, three genes were highlighted—two were previously known to affect diabetes risk (GCK and HNF4A), while one new gene (GIGYF1) was identified as a potential risk factor.

However, the author emphasizes that very few individuals with type 2 diabetes carry these rare variants, suggesting that less than 1% possess a genetic mutation that directly causes their condition. As such, the expectation that personalized medicine can reliably identify specific genetic causes for diseases and inform treatment is challenged. The author concludes that while these genetic studies may help advance the understanding of disease mechanisms, they do not yet translate to effective personalized treatment approaches for the vast majority of patients with type 2 diabetes.

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Here are some articles that are similar to the video you , focusing on similar themes of genetic research, the UK Biobank, exome sequencing, and the implications for personalized medicine in relation to conditions like type 2 diabetes:

1. **"Understanding the Genetic Basis of Type 2 Diabetes: Insights from the UK Biobank"**
- This article discusses findings from the UK Biobank regarding genetic factors linked to type 2 diabetes and how entire genome sequencing provides insights into both common and rare variants contributing to the disease.

2. **"Exome Sequencing and Its Role in Uncovering Rare Genetic Variants Associated with Metabolic Disorders"**
- The focus of this article is on how exome sequencing is used to identify rare genetic variants and how these findings influence our understanding of metabolic disorders, including diabetes.

3. **"The Impact of Rare Genetic Variants on Type 2 Diabetes: Lessons from Exome Studies"**
- This article delves into rare genetic variants discovered through exome sequencing and their implications for type 2 diabetes risk, along with challenges in translating genetic data into clinical practice.

4. **"Personalized Medicine in Diabetes: Are We There Yet?"**
- The author examines the current state of personalized medicine in diabetes treatment, emphasizing the difficulties of linking genetic findings to practical treatment options.

5. **"The Role of the UK Biobank in Genetic Research: A Treasure Trove of Data"**
- An overview of the UK Biobank's contributions to our understanding of genetics and health, including discussions on the methodologies used such as DNA sampling, exome sequencing, and the importance of studying both common and rare variants.

6. **"Rare Variants in Diabetes: Understanding Their Role Through Exome and Genome Sequencing"**
- This article discusses research aimed at understanding how rare genetic variations contribute to the risk of type 2 diabetes, showcasing new findings and ongoing challenges in personalized treatment.

7. **"Exome Sequencing in Clinical Research: Implications for Public Health"**
- Explores the broader implications of exome sequencing in public health, particularly regarding chronic diseases like diabetes, and discusses the potential pitfalls of overinterpreting genetic data.

These articles can help deepen your understanding of the research landscape related to genetic variations in diseases like type 2 diabetes and the role of large-scale biobanks like the UK Biobank in advancing this research.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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There's this sample called the UK Biobank,

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which is half a million volunteers,

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in Britain who've given their health information

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and tissue samples and undergone various assessments.

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And one of the things they've done was to give a, a sample

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for the DNA

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and that had been the subjects of many,

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many studies called genome-wide association studies using,

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a million markers spread across all 23 chromosomes.

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Um, of, and these are markers

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where there's common variation in people's genomes.

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Sometimes they have 1D

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NA based, sometimes they have another.

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And there've been lots and lots of these studies, done

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before these genetic studies.

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What was new was that rather than just,

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have these common markers,

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the UK bank bank had organized for these, this DNA

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to be what's called exome sequence,

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which means you read every single DNA base

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in every single gene.

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So you've got 20,000 genes.

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You read every single base in every one of those genes.

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And that really has, that differs from the previous Genome

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Wide association studies in, in two ways really.

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One is you are looking at variants that are actually

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inside genes that are the kind of important bits

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of the genome, the one

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or 2% which is actually coding for proteins

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and kind of really doing all the work.

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So you're looking in the important places,

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whereas genome-wide association studies,

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their markers are spread across all the chromosomes.

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A lot of 'em are not in genes at all, nowhere near genes.

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We dunno what they do. So you're looking at the genes.

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And the other thing is, is that

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because you are reading every single base in

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how many people, thousands

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and thousands of people, instead of just looking

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for common variation, you can see if somebody has a very,

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very rare variance that maybe only they have

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or mainly only half a dozen people have.

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So you find rare variance in genes.

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And the difference between rare variants

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and common variants is common variants.

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We all have lots of variation in code

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and it has almost no effect on, on us.

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It has tiny effects.

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And if you think about it, it's almost sort

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of common sense really

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with hindsight, that that would be the case.

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Because if anything had a very, if there's a change

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that had a very big effect, made us much taller,

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made us much smarter, whatever it was then,

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or made us much more sick, any big effect,

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you get evolution happening

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and it gets either, if it's advantageous, everybody gets it.

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And if it's disadvantageous, then nobody gets it.

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So the common variation that all

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around doesn't really do very much,

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but occasionally you can have a, a rare variant will occur

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and it will survive in the population for a while,

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even if it's quite harmful, even if it kind

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of really increases your risk

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of getting a disease such as diabetes.

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And so what had happened was you get back, biobank as said,

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organized for, it was at that time 200,000 exome sequences

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to be released from their volunteers.

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And this gave the opportunity to look at these

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200,000 exomes

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and see if there are any very rare variants which might have

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very big effects in terms of increasing risk of, of diabetes

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or one of a number of other diseases.

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So this was what I was going into to find,

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and the i the point being that if you can then find

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that if there's a rare variant,

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which has a big effect in terms of damaging a gene

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and that gives you a disease like diabetes,

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then you can say, well, that's, you know,

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potentially you'll say, that's interesting.

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If that gene doesn't work properly, then you get diabetes.

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Does that tell us something helpful

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about what causes diabetes?

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And this approach has been applied to a lot of disease

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and genes and and you don't know whether you're gonna find

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out something helpful or not

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unless you do that to do those analysis.

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So the Biobank data, what, what people go in

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as they volunteer by main, they go

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through all sorts of assessments.

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They are, they go, one

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of the things they do is they get asked about their health,

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what diseases they have.

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They get asked what medication they're taking.

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Also, they, they consent for UK Biobank to examine the,

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health records that the National Health Service holds on

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people every time they go into hospital, if there's,

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there's discharge, there's diagnosis, they get assigned

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or, if, if, I mean people joined

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many years ago now.

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So if somebody's died in the meantime, then you get access

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to the health, code of what their course of death was.

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Um, and then people will do blood tests.

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They get all sorts of things measured.

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And some of 'em have been scanned an X-ray,

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an MRI scan and what have you.

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So the, the, the information that I used for this was,

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I wanted to say, I want

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to find out the people who've got type two diabetes

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and you've got 200,000 people there.

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Some of 'em have diabetes and some of 'em don't.

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And so I looked at a number of different things.

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I looked at in their original thing had they said,

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I've got type two diabetes as one of their health things.

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I looked at, have they got a ICD 10 code from, from a,

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a hospital appointment or commission

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or whatever saying they've got type two diabetes.

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And the other thing I did was I looked at the list

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of medications that they were taking

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and I said, have, are they taking a medication?

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And that medication is generally used

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for people with type two diabetes.

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And I took, so I took all

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these bits of information together.

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Now of course, each one

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of them itself might not be a hundred percent accurate, but,

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and you can certainly argue about that.

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There'll be some false negatives, some false positives.

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Some people there don't have diabetes,

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some people will be missed.

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But by and large, when you've got thousands, thousands

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of people, then you are getting to,

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you're getting a rough idea.

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You've got many, many thousand people who've probably got

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type two diabetes and you've got many other thousands

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of people who don't have type two diabetes.

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Uh, and so I simply took those as cases and controls.

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I looked at the exome sequence data.

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I looked to the, the sequence data for one gene at a time.

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And then I looked for each gene.

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I said, are there DNA variants which will damage that gene,

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will stop it working completely or,

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or will produce a different protein,

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an amino acid change that might be harmful.

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But those kind of things brought all that to, and,

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and I said, are the people for any given gene,

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are the people with type two diabetes more likely

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to have DNA variants which damage that gene than the people

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who don't have type two diabetes?

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I made a if for each gene, for each person, I made a, a sort

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of damage score, a burden score of the, the

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whatever DNA variants they carried, weighted by how damaging

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that variant would be to the gene, how,

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what the size effect it might be on a protein.

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So it might be that you didn't expect

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to make no protein at all,

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or it might be that you'd expect to make a protein,

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but it would be different and it wouldn't work very well.

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Added up that sort of damage score, the cost of variance

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for every gene for every person.

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And I said, have the people for every gene I said,

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have the people with type two diabetes got a higher weighted

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burden score than the people

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that don't have type two diabetes.

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And, and then you, you do that for 20,000 genes

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and you see which genes,

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have the highest scores for that test.

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There were three genes.

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But once you, you, you know, you're testing 20,000 genes.

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So you need a lot of evidence, very small p value,

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to say that this is statistically significant.

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There were three, three genes that passed that test.

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Two were already known, GCK

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and H NF four A.

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Um, and we say,

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I'll say something about those later, I expect.

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And then there was one gene which hadn't been identified

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as a risk factor for diabetes

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before, which was G giggy giggy, F1,

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GIGY F1.

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It's got its name is no more helpful than that.

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So I'll just call it Giggy F1 for now.

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Um, and,

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and my, I just picked, I just got in first

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as the first study to identify GG F1

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as a diabetes risk gene.

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And then very soon

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after another study was published,

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which said the same thing, and you know, pretty much my,

08:02.165 --> 08:03.325
I think mom was just in first,

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but they, I wouldn't wanna argue about it.

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And and that study actually had access

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to a bigger data set.

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I only had 200,000. They had three 50,000 I think.

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Uh, 'cause it was, that they were sort of internal,

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to, to develop doing the X sequencing.

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So, so those are the findings really.

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Three genes, two, three genes for type two diabetes.

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I, I guess again, I'll come back to this later,

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I think it's probably the most useful thing to come out is

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that the thing to I would want to emphasize is that very,

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very, very few people with type two diabetes have got one

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of these variants in one of these things.

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Fewer than 1%. I said.

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So only one in a hundred people with D 99%

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or more of people with type two diabetes.

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If you look at the whole exome, you will not find

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a genetic variant in a gene.

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Say, that's why they've got type two diabetes.

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And I think that probably

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Has the most important implications of, of anything

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that's sort of come from this study.

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My takeaway conclusion from this study,

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and I mean it's a big, big statement I think from this study

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and others that I have done on or that done myself

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and have been involved with these XM sequencing studies.

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My i is that, is that when somebody turns up at their GP

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practice with a disease, then essentially

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getting their DNA sequence and studying it

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and saying, ha you know, we are gonna

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do personalized medicine on you.

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We're gonna find out why you've got this disease.

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We're gonna get you, once we know that your genetic profile,

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we're gonna give you the treatment that will best suit you

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'cause of the genetic profile you've

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got now of that's gonna work.

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That's my, that's my conclusion, for disease

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after disease after disease that we've got had,

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including this one is one example of it.

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Almost nobody with the disease has got a genetic variant,

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which, you can say, they've got the disease

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because of that variant.

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What, you know, one in 200 people might,

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but the rest of them you've got no idea.

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And actually I'd even add that even if you do see

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that somebody's got a particular variant, you say,

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you've got type two diabetes

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because you've got a GCK mutation, you know you've got it

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'cause you've got a GigE F1 mutation.

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Even if you see that, does that mean you then know

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how to treat the disease?

10:19.855 --> 10:22.495
Does that helped at all? Is treatment any different answer?

10:22.995 --> 10:26.215
No, it's not. So, so what I think the conclusion is, is

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that this whole notion of personalized medicine

10:28.095 --> 10:30.775
that was sold to us, I mean, no, don't use

10:30.775 --> 10:33.695
that language sold to us, but was promoted, you know, 10,

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20 years ago with this, technology's gonna turn medicine

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around, transform medicine.

10:37.465 --> 10:39.015
It'll be the, the right treatment,

10:39.015 --> 10:40.295
the right patient at the right time.

10:40.755 --> 10:43.335
You go in, you have a blood test, you get thing,

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you get the right prescription based

10:44.935 --> 10:46.135
on this, that's not gonna happen.

10:46.795 --> 10:51.375
It do, doesn't work. Um, and that's kind of interesting.

10:51.515 --> 10:53.695
Now that's not the tool to say these studies don't have

10:53.695 --> 10:57.015
value, but they, that they don't have that value.

10:57.555 --> 10:59.895
The value that they have is what I said

10:59.895 --> 11:04.545
before is if you find a particular gene is abnormal

11:04.605 --> 11:06.465
and that kind of gives you the disease

11:06.465 --> 11:08.025
or massively increases the risk of disease,

11:08.325 --> 11:11.705
or conversely, if it's protected against the disease, then

11:11.705 --> 11:15.585
that can be helpful in terms of understanding the,

11:15.645 --> 11:17.545
the proteins which involve in molecules involved

11:17.685 --> 11:19.305
by chemical processes that are involved.

11:19.645 --> 11:21.865
And that might be helpful, you know, in the disease.

11:22.525 --> 11:24.225
And then that might be helpful in terms

11:24.225 --> 11:26.545
of developing new treatments and so on.

11:27.185 --> 11:29.325
Now, I, I would add that I don't think it has been helpful

11:29.545 --> 11:32.205
for type two diabetes and I can talk about

11:32.205 --> 11:33.645
what these genes do a little bit

11:33.645 --> 11:34.565
and it's kind of, it's

11:34.725 --> 11:35.885
interesting, but I'm not sure it's helpful.

11:36.505 --> 11:40.165
Um, but one doesn't doesn't know until one asks

11:40.815 --> 11:42.325
until one does that investigation.

11:42.985 --> 11:44.725
So, you know, it might've been

11:44.725 --> 11:47.685
that we found some completely unexpected gene,

11:47.735 --> 11:49.245
which did something that we never

11:49.245 --> 11:50.365
thought was anything through with diabetes.

11:50.385 --> 11:52.005
And we go, my goodness me, look at this.

11:52.425 --> 11:53.885
Uh, that's, that can give you diabetes.

11:53.965 --> 11:55.365
I wonder why that is. Let's develop this.

11:55.745 --> 11:57.845
You know, we've got new insight from that and so on.

11:58.225 --> 12:00.645
Uh, and I think that in this study that isn't really

12:01.315 --> 12:02.165
what has happened.

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