Archive for the 'Disease Risk' Category

My genome, unzipped

As part of the Personal Genome Project (PGP), my genome was recently sequenced by Complete Genomics. My PGP profile, including the sequence, is here, and their report on my genome is here. As I play around with the best ways to analyze these data, I’ll write additional posts, but for now I’ve noticed only one thing: I’m almost surprised by how unsurprising my full genome sequence is.

According the the PGP’s genome annotator, I have two variants of “high” clinical relevance. The first is the APOE4 allele, which Luke had already reported that I carry. The second is a variant that causes alpha-1-antitrypsin deficiency, which is also typed by 23andMe.

Of course, this is all quite reassuring. Long-time readers will remember that last year I was briefly worried that I might have Brugada syndrome. I do not carry any of the known pathogenic mutations (modulo worries about false negatives); this of course is now unsurprising, but would have been really nice information to have, say, when I was talking with a cardiologist last year.

Dozens of new IBD genes, but can they predict disease?

Out in Nature this week is a paper by three Genomes Unzipped authors reporting 71 new genetic associations with inflammatory bowel disease (IBD). This breaks the record for the largest number of associations for any common disease, and includes many new and interesting biological insights that you should all go and read about in the paper itself (pay-to-access I’m afraid) or on the Sanger Institute’s website.

One thing that we did not discuss in the paper was genetic prediction of IBD (i.e. using the risk variants we have discovered to predict who will or will not develop the disease). In this post I want to outline some of the situations in which we have considered using genetic risk prediction of IBD, and discuss whether any of them would actually work in practice.

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The week that I worried I had a rare genetic disease

I recently had a series of moderately unpleasant health problems, which eventually led to my being tested for a rare, and potentially very serious, genetic disease (for worried parties: the test was negative). I thought I would share this anecdote because, first, it’s the only time I’ve wished I had more genetic information about myself in a medical setting, and second, because it illustrates the sorts of gaps in medical knowledge that could be aided by routine genome sequencing.

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Genetic risk prediction in complex disease

I thought I’d point out a review article in Human Molecular Genetics that just came out in (open access) preprint form by Luke and myself on genetic risk prediction in complex disease. In it we discuss some of the strengths and weaknesses of genetic and risk prediction compared to classical epidemiological predictors, different statistical modelling considerations, and the effect of GWAS on prediction. Readers of this space might find the conclusion of some interest, where we consider some of the societal aspects of trying to bring the interpretation of genomes into mainstream medical practice.

Visualizing the impact of APOE4 on ageing

Recently, Luke reported that I am a carrier of the E4 allele at the gene APOE; this gives me approximately double the average risk for late-onset Alzheimer’s disease. I didn’t think too much about this–it’s only double the risk, and in any case I’m 28 years old. But I recently came across the below plot, by Nick Eriksson (I’ve re-plotted it). It shows the frequency of the APOE4 allele plotted against average age in 15 cohorts of “cognitively normal elders” (data from here).

If we assume that these 15 cohorts are all from relatively similar populations, the interpretation of this is that, between the ages of 70 and 85, people with my genotype go from being cognitively normal elders to not (due to Alzheimer’s, another form of dementia, or death) at a rate about twice that of people who don’t carry the E4 allele [1]. This, of course, is exactly what I knew before (that E4 carriers have double the risk of Alzheimer’s), but seeing this visually is quite striking.

[1] Could the drop in APOE4 allele frequency could be mostly due to E4/E4 homozygotes (i.e. people not of my genotype)? If we assume an initial allele frequency of 20% and Hardy-Weinburg equilibrium, then a fifth of the APOE4 alleles are present in homozygotes. So even if all of these individual developed Alzheimer’s, then this would drop the allele frequency from 20% to ~16%. The observed drop in allele frequency is much greater than that.

A case study in personal genomics

I have no strong family history of any disease, despite having 7 blood aunts and uncles and countless cousins. So when I sent my spit off to 23andMe at the start of the Genomes Unzipped project, I was expecting something very similar to Caroline’s experience: a 5% increase in risk here, a 2% decrease in risk there, nothing that would really tell my anything about my health.

However, this was not my experience. Along with a pretty interesting Y haplogroup, I also had three unexpected and potentially worrying health results. I am a cystic fibrosis carrier, a hemochromatosis compound heterozygote, and have a strongly elevated risk of age-related macular degeneration. This cocktail of genetic disease certainly was not what I came to the test expecting!

After some thinking, I decided to take my test results to my GP, and see if there was any advice or testing he would recommend. In the end, my GP referred me to a clinical geneticist, which started a cascade of appointments which in turn led to a number of important changes in how I treat my own health.

What was most interesting is how the whole experience got me thinking about my health as something I am in charge of. I have since made a number of important life-style changes, some of them directly related to my genotyping results, some more generally to improve my overall health.

The point of this post is just to go through some of the experiences, what I have learned about specific conditions, and what changes I have made to my life since. In some sense, I feel like my experience is a case-study in what good outcomes can come from personal genomics, both for specific conditions, and more generally for how genetic data can change your own approach to your health.

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Calculating your Alzheimer’s risk

For many diseases we have very little ability to determine who is at high or low risk; the risk factors are unreplicated, complicated, or understudied. However, for other diseases we can do much better. Alzheimer’s disease is a form of senile dementia that is characterised by abnormal clustering of proteins in the brain (right). We know a number of important risk factors for Alzheimer’s, and knowing your own risk factors may seriously change your estimate of the chance of developing the disease. But how can you calculate this risk?

This is going to be somewhat of an information deluge, as I go through everything to think about when you predict a complex disease, including how to calculate genetic and environmental risks, and how important these risks are, both individually and all together. I will demonstrate all of the calculations on the various GNZ contributors, and in particular how I have worked out my own risk.

I’ll measure the risks in terms of odds ratios; you may want to read the introduction to Carl’s post from earlier this year to refresh your mind on what this means. I will also use the disease probability; this is simply the chance of developing Alzheimer’s, or equally, the percentage of people with this set of risk factors who will develop the disease.

Also note that an important factor to consider is the baseline lifetime risk, the total proportion of people who will develop Alzheimer’s before they die. I am going to use a lifetime risk of 9% for men and 17% for women, taken from an Alzheimer’s Association report, but getting a good estimate of this is actually very difficult, and will vary from country to country.

If you want to know more about Alzheimer’s, including prevention, diagnosis and treatment, you can read about the disease on the Mayo Clinic or NHS Choices websites.

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