Genes and lifespan

27 February 2019

Interview with

Peter Joshi, University of Edinburgh

YOUNG-OLD-HANDS

Young and old hands touching

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Inside each of us are 3 billion letters of DNA that control how our bodies put themselves together in the first place, and how our systems operate day to day. And, as he explains to Chris Smith, what Edinburgh University’s Peter Joshi wanted to understand is how those genetic letters also affect how long we’ll live...

Peter - Each of us has got three billion letters of DNA. And we differ from each other, typically, at about 3 million different places on a genome, which is our collection of DNA. And we were interested in how those differences might affect how long you live relative to how long I might live. 

Chris - But that sort of study takes a lifetime to do which you don't have. None of us do so have you approached it?

Peter - That's a really good question. So the problem as you highlight is that if we were to recruit a whole lot of people in their say 40s, take their blood, read their DNA, we would perhaps have to wait 50 or more years until they died to understand how their DNA had affected how long they lived. What we realised was, if we take your DNA, say, Chris, we could ask you how long your parents or your grandparents had lived and because your DNA is of course their DNA we're able to understand how their DNA actually affected how long they lived.

Chris - So the obvious approach here is that because I'm the product of the mixing of the genes of both parents, I therefore am a proxy for what's going on in my parents, so if we find out what happens to them we can make a reasonable prediction about what genetically is driving that?

Peter - That's right. Genetically, you're half your mum and half your dad and we can use that - and a little bit of clever statistics - to make fairly precise probabilistic inferences about what your mum and dad's DNA are.

Chris - So talk us through then how you actually did this. What was the resource you used in order to get access to the sort of data you need to make those deductions?

Peter - In essence we took half a million people who had their DNA read, asked them "is your mum alive? If so, what age is she? If not what age did she die at?" And the same for your dad. We didn't actually gather all of that data. We've been really lucky. There's a fantastic resource UK Bio Bank where the UK Medical Research Council gathered together half a million people and asked these sort of questions; and then we top it up that with 24 other cohorts from around the UK Europe and the United States to give us an even larger resource.

Chris - And basically if you look at enough people enough times you're going to start to see hotspots in the genome regions which appear to be associated with the answers to those questions about how old you your parents were when you died. And so you can begin to say well that region must have some kind of role or association in how long we live.

Peter - That's right. By having enough people we can pinpoint specific DNA letters, amongst your three billion, that are associated with how long your mum lived or your dad lived.

Chris - Now we know that some regions of the genome that have already been quite powerfully linked to longevity. So did your study pull out those ones we already know about?

Peter - That's right. I mean, in fact, remarkably few regions were known to associate with longevity directly. As recently as 2014 there was only one robust association at a genomic region called up APO-E, which associates with Alzheimer's disease. Over the last two or three years, myself and other research groups around the world had shown three or four regions robustly associated with lifespan; and then our study increased that to 12 robust associations. As you ask, we did verify the previously-known loci in many cases. In some cases we actually refuted some tentative findings that people had made before as well.

Chris - And what are the 12 regions that you've now been known to highlight through this. And what do they do?

Peter - The 12 regions - essentially, we see mainly influenced cardio-metabolic health. So basically heart disease. Aongside those, we're seeing regions that interestingly are affecting propensity to smoke. You might not have thought of whether you're a smoker or not is a genetic trait but in fact it turns out the way your brain responds to nicotine is influenced by your genetics. That in turn influences how much you smoke and that in turn affects how long you live.

Chris - So you've got these 12 regions. If I take a person who has all the risk factors and a person who has none of them how many added years do I get through having none of them compared to someone who's got all of them?

Peter - So I don't have that precise number to hand; it must be about half a dozen. Typically the effects that we see - if you carry the good variant and I carry the less beneficial variant - it would be around half a year. So I would estimate it's about six years difference in the way that you describe, but the chances of you having all the good ones and me having all the bad ones are pretty small!

Chris - Are you surprised that you found these associations for so few regions? Twelve doesn't seem like a very big number when you've got such an extensive genome that we have...

Peter - Well that's right. We've studied height for example and with about quarter of a million people we found hundreds of genomic regions that affect how tall you are. Essentially there's two reasons for that. Firstly we've had to ask questions about parents' lifespan so the statistical signal is diluted. But really the main reason is that actually signals of lifespan effects from the genome have these relatively small effect sizes and those require a much bigger sample sizes. So what we would expect is if we were to increase the sample from a million to say three million then we would begin to see many more tens of signals. It's just that we want to be really confident when we report an association. 

Chris - Where we going with this then? Do you foresee a situation where we will take a healthy person - in inverted commas - and we throw a genetic analysis at them like this and this highlights a potential set of risk factors that may truncate or extend their life. Is that where you're sort of going with this?

Peter - I think that may well be where society is going. Basically I think two sorts of screening going to come out. One is for rare diseases you might be a carrier for and pass on a susceptibility. And the other is common complex diseases with these small effects that we've been talking about today really. People will be told essentially whether they're in the top or bottom part of the risk spectrum for the disease. I have seen some very nice work in Estonia, for example, that highlighted risk scores for heart disease and how if someone had a BMI of say 35 - was obese - and were to lose weight, the dramatic difference they can make to their risk of heart disease if they're in that part of the population that is susceptible to heart disease in the first place due to their genetics.

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