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Genetics of Smokers (Jan 2019)

Posted in Discussions, English, tagged , , , , , , , , , , , , , , , , , , , , , , , , on 12/01/2019| Leave a Comment »

smoking_genetics_gwas_mesut_erzurumluoglu
A ‘Circos’ plot (with three concentric circular ‘Manhattan’ plots) presenting results from our latest genetic association study of smoking behaviour – showing some (not all) regions in our genome that are associated with smoking behaviour (Erzurumluoglu, Liu, Jackson et al, 2019). SI: Smoking initiation – whether they smoke or not; CPD: Cigarettes per day – how many cigarettes do they smoke per day; SC: Smoking cessation – whether they’ve stopped smoking after starting. Labels in the outer circle show the name of the nearest gene to the identified variants. X-axis: Genomic positions of the variants in the human genome (chromosome numbers, 1-22, in the outer circle), Y-axis: Statistical significance of the genetic variants in this study – higher the peak, greater the significance. Red peaks are the newly identified regions in the genome, and the blue ones were identified by previous groups. Image source: Molecular Psychiatry

I believe that all scientists should be bloggers and that they should spare some thought and time to explain their research to interested non-scientists without using technical jargon. This is going to be my attempt at one; hopefully it’ll be a nice and short read.

We’ve just published a paper in one of the top molecular psychiatry journals (well, named Molecular Psychiatry 🙂 ) where we tried to identify genetic variants that (directly or indirectly) affect (i) whether a person starts smoking or not, and once initiated, (ii) whether they smoke more. The paper is titled: Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. It is ‘open access’ so anyone with access to the internet can read the paper without paying a single penny.

If you can understand the paper, great! If not, I will now try my best to explain some of the key points of the paper:

Why is it important?

Smoking causes all sorts of diseases, including respiratory diseases such as chronic obstructive pulmonary disease (which causes 1 in 20 of all deaths globally; more stats here) and lung cancer – which causes ~1 in 5 of all cancer deaths (more stats here). Therefore understanding what causes individuals to smoke is very important. A deeper understanding can help us develop therapies/interventions that help smokers to stop and have a massive impact on reducing the financial, health and emotional burden of smoking-related diseases.

Genes and Smoking? What!?

There are currently around fifty genetic variants that are identified to be associated with various smoking behaviours and we identified 40 of them in our latest study, including two on the X-chromosome which is potentially very interesting. There are probably hundreds more to be found*. So, it’s hard to comprehend but yes, our genes – given the environment – can affect whether we start smoking or not, and whether we’ll smoke heavier or not. This is not to say our genes determine whether we smoke or not so that we can’t do anything about it.

There are three main take-home messages:

1- I have to start by re-iterating the “given the environment” comment above. If there was no such thing as cigarettes or tobacco in the world, there would be no smoking. If none of our friends or family members smoked, we’re probably not going to smoke no matter what genetic variants we inherit. So the ‘environment’ you’re brought up in is by far the most important reason why you may start smoking.

2- I have to also underline the term “associated“. What we’re identifying are correlations so we don’t know whether these genetic variants are directly or indirectly affecting the smoking behaviour of individuals – bearing in mind that some might be statistical artefacts. Some of the genetic variants are more apparently related to smoking than others though: for example, variants in genes coding for nicotine receptors cause them to function less efficiently so more nicotine is needed to induce ‘that happy feeling‘ that smokers get. Other variants can directly or indirectly affect the educational attainment of an individual, which in turn can affect whether someone smokes or not. I’d highly recommend reading the ‘FAQ’ by the Social Science Genetic Association Consortium (link below) which fantastically explains the caveats that comes with these types of genetic association studies.

3- Last but not least, there are many (I mean many!) non-smokers who have these genetic variants. I haven’t got any data on this but I’m almost 100% sure that all of us have at least one of these variants – but a large majority of people in the world (~80%) don’t smoke.

Closing remarks

To identify these genetic variants, we had to analyse the genetic data of over 620k people. To then identify which genes and biological pathways these variants may be affecting, we queried many genetic, biochemical and protein databases. We’ve been working on this study for over 2 years.

Finally, this study would not be possible (i) without the participants of over 60 studies, especially of UK Biobank – who’ve contributed ~400k of the total 622k, and (ii) without a huge scientific collaboration. The study was led by groups located at the University of Leicester, University of Cambridge, University of Minnesota and Penn State University – with contribution by researchers from >100 different institutions.

It will be interesting to see what, if any, impact these findings will have. We hope that there will be at least one gene within our paper that turns out to be a target for an effective smoking cessation drug.

Further reading

1- FAQs about “Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment” – a must read in my opinion

2- Smoking ‘is down to your genes’ – a useful commentary on the NHS website on an older study

3- 9 reasons why many people started smoking in the past – a nice read

4- Genetics and Smoking – an academic paper, so quite technical

5- Causal Diagrams: Draw Your Assumptions Before Your Conclusions – a fantastic course on ‘Cause and Effect’ by Prof. Miguel Hernan at Harvard University

6- Searching for “Breathtaking” genes – my earlier blog post on genetic association studies

Data access

The full results can be downloaded from here

*in fact we know that there is another paper in press that has identified a lot more associations than we have

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Diseases: Prevention, therapy, and cure (Nov 2018)

Posted in Discussions, English, tagged , , , , , , , , , , , , , , , , , , , , , , , , on 06/11/2018| 2 Comments »

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We – as a group – carried out the largest genome-wide association study to identify genetic variants that are associated with decreased lung function and increased risk of chronic obstructive pulmonary disease. We hope that our findings will ultimately lead to the identification of effective drug targets for COPD. Image source: University of Leicester

I remember reading somewhere that ‘if you get asked the same question three times, then write a blog post about it’. That’s what I’ve been doing so far, and the purpose of this blog post is the same: to try and provide an answer to a commonly asked question. (Important note: my answers are in no way authoritative and only meant for interested non-scientists)

As a ‘Genetic Epidemiologist’, I constantly get asked what I do and what my (replace ‘my’ with ‘our’, as I do everything within a team) research can lead to. Please see my previous post ‘Searching for “Breathtaking” genes. Literally!‘ and My Research page for short answers to these questions. In tandem to these, I am constantly asked ‘why we can’t find a ‘cure’ for (noncommunicable) diseases that affect/will affect most of us such as obesity, diabetes, cancer, COPD – although there are many scientific advancements?’. I looked around for a straight forward example, but couldn’t find one (probably didn’t look hard enough!). So I decided to write my own.

I will first try and put the question into context: We do have ‘therapies’ and ‘preventive measures’ for most diseases and sometimes making that distinction from ‘a cure’ answers their question. For example, coronary heart disease (CHD) is a major cause of death both in the UK and worldwide (see NHS page for details) but we know how we can prevent many CHD cases (e.g. lowering cholesterol, stopping smoking, regular exercise) and treat CHD patients (e.g. statins, aspirin, ACE inhibitors). However, there are currently there are no ‘cures’ for CHD. So once a person is diagnosed with CHD, it is currently impossible to cure them from it, but doctors can offer quite a few options to make their life better.

I then gave it some thought about why finding a ‘cure’ was so hard for most diseases, and came up with the below analogy of a river/sea, water dam, and a nicely functioning village/city (excuse my awful drawing!).

The first figure below sets the scene: there’s a water dam that’s keeping the river from flooding and damaging the nice village/city next to it. Now please read the caption of the below figure to make sense of how they’re related to a disease.

Prevention

The river/sea is the combination of your genetic risk (e.g. you could have inherited genetic variants from your parents that increased your chances of type-2 diabetes) and environmental exposures (e.g. for type-2 diabetes, that would be being obese, eating high sugar content diet, smoking). The water dam is your immune system and/or mechanisms in your body which tame the sea of risk factors to ensure that everything in your body work fine (e.g. pancreatic islet cells have beta cells which produce insulin to lower your glucose levels back to normal levels – which would be damaging to the body’s organs if it stayed high).

So to ‘prevent’ a disease (well, flooding in this case), we could (i) make the water dam taller, (ii) make the dam stronger, and (iii) do regular checks to patch any damage done to the dam. To provide an example, for type-2 diabetes, point (i) could correspond to being ‘fit’ (or playing with your genes, which currently isn’t possible), point (ii) could correspond to staying ‘fit’, and point (iii) could correspond to having regular check-ups to see whether any preventive measures are necessary. Hope that made sense. If not, please stop reading immediately and look for other blog posts on the subject matter 🙂

Using the figure below, I wanted to then move to ‘therapy’. So as you can see, the river has flooded i.e. this individual has the disease (e.g. type-2 diabetes as above). The water dam is now not doing a good job of stopping the river and the city is in danger of being destroyed. But we have treatments: (i) The (badly drawn) water pumping trucks suck up excess water, and (ii) we have now built a second (smaller) dam to protect the houses and/or slow the flow of the water. Again, to provide an example using type-2 diabetes, water pumping trucks could be analogous to insulin or metformin injections, and the smaller dams could be changing current diet to a ‘low sugar’ version. This way we can alleviate the effects of the current and future ‘floods’.

Therapy

Analogy for therapy/treatment – after being diagnosed with the disease

Finally, we move on to our main question: ‘the cure’. Using the same analogy as above, as the water dam is now dysfunctional, the only way to stop future ‘floods’ would be to design a sewage system that can mop up all water that could come towards the city. Of course the water dam and ‘old city’ was destroyed/damaged due to past floods, so we’d need to build a new functioning city to take over the job of the old one. A related real example (off the top of my head) could be to remove the damaged tissues and replace them with new ones. Genetic engineering (using CRISPR/Cas9) and/or stem cell techniques are likely to offer useful options in the future.

Cure

Analogy for cure – after being diagnosed with the disease

Hopefully it is now clear that the measures taken to prevent or treat the disease, cannot be used to cure the disease. E.g. you can build another dam in place of the old one, but the city is already destroyed so that’s not going to be of any use in curing the disease.

So to sum up, diseases like obesity, cancer, COPD are very complex diseases – in fact they’re called ‘complex diseases’ in the literature – and understanding their underlying biology is very hard (e.g. hundreds of genes and environmental exposures could combine to cause them). We’re currently identifying many causal variants but turning these findings into ‘cures’ is a challenge that we have not been able to crack yet. However, it is clear that the methods that we currently use to identify preventive measures and therapies cannot be used to identify cures.

I hope that was helpful. I’d be very happy to read your comments/suggestions and share credit with contributing scientists. Thanks for reading!

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World lung health study allows scientists to better predict your chance of developing COPD (Feb 2017)

Posted in Discussions, English, tagged , , , , , , , , , , , , , , , , , , , , , , , , , on 06/02/2017| Leave a Comment »

bileve_qt_paper_3_lung_function_traits_concentric_circos

Breathtaking genes: A ‘Circos’ plot depicting how chronic obstructive pulmonary disease (COPD) has become a global concern – the 3rd biggest killer, defined by poor lung function. Our work shows that many parts of our DNA play a role in our lung health. Peaks in red are newly discovered regions, and the blue ones were previously identified by other groups. Millions of genetic variants from tens of thousands of individuals were analysed in this study. The identified genes will help us understand why some of us have better lung function, and lead to the identification of drug targets of potential relevance to COPD.

A press release was issued by the University of Leicester Press Office on 6 February 2017 about a study that I was also heavily involved in (please click on links below for details):

“Breakthrough advance offers the potential to defuse a ‘ticking timebomb’ for serious lung disease, including for over 1 billion smokers worldwide (source: World lung health study allows scientists to predict your chance of developing deadly disease — University of Leicester)”

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The study has received a lot of attention from the media, with articles appearing in large media outlets such as BBC News, The Independent and MSN News. If you’re interested in the details, please read the paper published in Nature Genetics.

If interested in reading about the area of Genetic Epidemiology itself, please have a look at my (previously published) blog post about the matter: Searching for “Breath taking” genes. Literally!

Details on Circos plot* (above): FEV1: Forced expiratory lung volume in 1 second; FVC: Forced lung volume capacity; FEV1/FVC: the ratio of the two measurements. Labels in the outer circle show the name of the nearest gene to the newly identified (red) variants. X-axis: Genomic position of variant in genome (chromosome number in the outer circle), Y-axis: Statistical significance of variant in this study (higher the peak the greater the significance).

*The figure is a more artistic version of Figure 1 (Manhattan plot) in the above mentioned academic paper. It did not make it into the final manuscript published in Nature Genetics (6th Feb 2017) as it was found to be “confusing” by one of the reviewers – and the editor agreed. 😦 However, the plot was shortlisted (title: Breathtaking genes) and displayed in the Images of Research exhibition (9th Feb 2017) organised by the University of Leicester. 😉

 

My role in the Wain et al paper mentioned above: I led the ‘functional follow-up’ of the identified associated variants (e.g. mining eQTL datasets, biological pathway analyses, identify druggable genes, pleiotropy, protein-protein interactions) and appropriately visualise the GWAS results (various Manhattan and Circos plots). I was part of the core bioinformatics team of three in Leicester – alongside Dr. Nick Shrine and Dr. Maria Soler-Artigas.

 

References:

Wain LV et al., Published online 6th Feb 2017. Genome-wide association analyses for lung function and chronic obstructive pulmonary disease identify new loci and potential druggable targets. Nature Genetics. URL: https://www.nature.com/articles/ng.3787

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Searching for “Breathtaking” genes. Literally! (Jun 2016)

Posted in Discussions, English, tagged , , , , , , , , , , , , , , , , , , , , , on 08/06/2016| 5 Comments »

Difference between the lung of a COPD patient and an unaffected one. Image taken from NHLBI website (click on image to access the source)

Difference between the lung of a COPD patient and an unaffected one. Image taken from the NHLBI website (one of the leading institutes in providing information on various diseases; click on image to access the source)

Many of us will either suffer or have a relative/friend who suffers from a disease called Chronic Obstructive Pulmonary Disease (COPD, click on link for details) which is a progressive respiratory disease characterised by decreasing lung function (struggling to inhale/exhale air, irreversible airflow obstruction), very likely accompanied by chronic infections. COPD has a prevalence of over 2% in the UK population (corresponding to approx. 1 million in the UK, probably a lower bound estimate due to many undiagnosed cases; this figure is approx. 16 million in the USA) and is currently the third biggest killer in the world (only behind cancers and heart-related diseases) – costing the lives of millions (in the USA alone, number of deaths attributed to COPD is over 100 thousand); and the health services, billions of pounds.

Contrary to the well-known genetic disorders such as Cystic Fibrosis and Huntington’s disease, which are diseases caused entirely by a person’s genetic makeup and caused by mutations in a single gene, COPD is a (very!) complex disease with many genes and environmental factors (e.g. smoking, pollutants) contributing to the development/progression of the disease. This complexity makes it much harder to dissect the causes and find potential (genetic) targets for cures or therapies. However, we do know that smoking is by far the biggest risk factor with up to 90% of those who go on to develop clinically significant COPD being smokers. But only a minority (<25%) of all smokers develop COPD, indicating the strong role genetics can play in the progression of this disorder. Also not all COPD patients are smokers (up to 25% in some populations), indicating that – at least in some patients – genetics can play a rather determining role. I must stress that all the statistics I provide here can vary considerably from population to population due to different lifestyles and genetic backgrounds.

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I – together with a large group of collaborators – search for genetic predictors of lung function, which helps us to identify which individuals are more likely to develop the disease and potentially understand the underlying biology/pathology of respiratory diseases such as COPD and asthma, and related traits such as smoking behaviour. To do this, we carry out what is called a genome-wide association study (GWAS, click on link for details), where we obtain the genetic data (millions of data points) from tens of thousands of COPD (or asthma) patients and ‘controls’ (people with normal lung function). To ensure that our results are not biased by different ethnicities, life styles and related individuals, we collect all the relevant information about the participants and make sure that we control for them in the statistical models that we use. GWASs have been extremely successful in the identification of successful targets for other diseases and have led to the field of Genetic Epidemiology (GE, click on link for details) to come to the fore of population-based medicine. GE requires extensive understanding of Statistics (needed to make sense of the very large datasets), Bioinformatics (application of computer software to the management of large biological data), Programming (needed to change data formats, manage very large data), Genetics (needed for interpretation of results) and Epidemiology (branch of medicine which deals with how often diseases occur in different groups of people, and why); thus requires inter-disciplinary collaborations.

GWAS results are traditionally presented with a Manhattan plot (due to its resemblance of the city's skyline) where the genetic variants corresponding to the dots above the top grey line (representing P values less than 5e-7 i.e. 0.0000005) are usually followed up with additional studies to validate their plausibility. Image taken from Wikipedia (click on image to access source)

GWAS results are traditionally presented with a Manhattan plot (due to its resemblance of the city’s skyline) where the genetic variants corresponding to the dots above the top grey line (representing P-values less than 5e-8 i.e. 0.00000005) are usually followed up with additional studies to validate their plausibility. Image taken from Wikipedia (click on image to access source)

The inferences we make from these studies can shed light in to which genes and biological pathways play key roles in causing COPD. We then follow up these newly identified genes and pathways to analyse whether there are molecules which could be used to target these and be potential drugs for treating COPD patients. Our results can be of immense help to Pharmaceutical companies (and ultimately to patients), as many clinical trials initiated without genetic line of evidence have failed, costing the public and these companies billions of pounds.

As smoking is the biggest risk factor for respiratory diseases like COPD, I am – also with the contribution of many collaborators – in the process of analysing whether some people are more likely to start smoking, stop after starting, and smoke more than usual when they start smoking. The results can have huge implications as many people struggle to stop smoking, and when they do, research suggests that up to 90% (figure differs between populations) of them start to smoke again within the first year after quitting. Smoking is not only a huge contributor to the risk of developing COPD, but also to lung (biggest killer amongst all cancers), mouth, throat, kidney, liver, pancreas, stomach and colon cancer (not an exhaustive list). In the UK alone, these cancers cause the slow and painful death of tens of thousands, alongside a huge psychological and financial burden on the families and public resources.

The “lung” and the short of it (stealing a phrase thought up by my colleagues at the University of Leicester, click on link to see who they are) is that COPD is a disease that is going to affect many of us, and any useful finding which leads to cures and/or therapies could increase the life years of COPD patients and affect the lives of thousands of people directly, and millions indirectly (e.g. families of COPD sufferers, cost to the NHS). Finding targets to help people stop smoking can potentially have even bigger implications as many continue to smoke, despite huge efforts and funding allocated to smoking prevention and cessation.

A nice TED talk about the world of Data science and Genetic Epidemiology

Addition to post (09/02/17): A Circos plot presenting results from our latest lung function GWAS (Wain et al, 2017; Nature Genetics) was shortlisted (title: Breathtaking genes) and displayed in the Images of Research exhibition (9th Feb 2017) organised by the University of Leicester

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