Tag Archive for 'genetic epidemiology'

Inbreeding, Genetic Disease and the Royal Wedding

Today is, of course, the day of the Royal Wedding, with new blood entering the British royal line, and the hope of new heirs to our throne. And of course the question on the lips of all British geneticists is: will there be any new royal genetic diseases in this crop? The European royal lines have always been prone to the odd loss-of-function mutation. An unlucky mutation in Queen Victoria’s Factor IX gene caused a nasty case X-linked Haemophilia B in her male descendants (a mutation that was only mapped in 2009 by sequencing the bones of the murdered Romanov branch). Luckily for them, this mutation hasn’t been observed in any of Victoria’s descendants lately; while it can hide undetected in women, this obviously doesn’t apply to William. More systemic genetic problems have been the result of heavy inbreeding; Charles II of Spain, with his distressingly bushy family tree (left), suffered from severe Habsburg jaw, along with a host of other genetic complaints.

In terms of inbreeding, there has been a bunch of digging around in the press to find the closest common ancestor of William and Kate: Channel Four turned up fourteen and fifteenth cousinships, and the Daily Mail managed to find a eleventh cousinship. For comparison, William’s parents Diana and Charles were also 11th cousins, and the Queen and Prince Philip were a far more regal 2nd cousins once removed. Eleventh cousins share on average 60-parts-per-billion of DNA, or about 180bp (although with wide variation due to the spotty nature of meiotic recombination: in fact, 99.5% of 11th cousins will share no stretches of DNA through recent descent at all, while the remaining 0.5% will typically share tens of thousands of bases). Given that the average person harbours about 10 recessive diseases, this gives about a 1 in 1.6 million chance of Kate and Will’s offspring developing a royal disease due to a piece of DNA shared between them. So, not very likely then.

In fact, eleventh cousins is a pretty low degree of relatedness, by the standard of these things. A study of inbreeding in European populations found that couples from the UK are, on average, as genetically related as 6th cousins (the study looked at inbreeding in Scots, and in children of one Orkadian and one non-Orkadian. No English people, but I would be very suprised if we differed significantly). 6th cousins share about 0.006% of their DNA, and thus have about a 0.06% chance of developing a genetic disease via a common ancestor. Giving that the Royal Family are better than most at genealogy, we can probably conclude that the royal couple are less closely related than the average UK couple, and thus their children are less likely than most to suffer from a genetic disease. Good news for them, bad news for geneticists, perhaps?

Estimating heritability using twins

Last week, a post went up on the Bioscience Resource Project blog entited The Great DNA Data Deficit. This is another in a long string of “Death of GWAS” posts that have appeared around the last year. The authors claim that because GWAS has failed to identify many “major disease genes”, i.e. high frequency variants with large effect on disease, it was therefore not worthwhile; this is all old stuff, that I have discussed elsewhere (see also my “Standard GWAS Disclaimer” below). In this case, the authors argue that the genetic contribution to complex disease has been massively overestimated, and in fact genetics does not play as large a part in disease as we believe.

The one particularly new thing about this article is that they actually look at the foundation for beliefs about missing heritability; the twin studies of identical and non-identical twins from which we get our estimates of the heritability of disease. I approve of this: I think all those who are interested in the genetics of disease should be fluent in the methodology of twin studies. However, in this case, the authors come to the rather odd conclusion that heritability measures are largely useless, based on a small statistical misunderstanding of how such studies are done.

I thought I would use this opportunity to explain, in relative detail, where we get our estimates of heritability from, why they are generally well-measured and robust, and real issues need to be considered when interpreting twin study results. This post is going to contain a little bit of maths, but don’t worry if it scares you a little, you only really need to get the gist.
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Digging deeper into my disease risk

When Daniel first asked me if I wanted to be involved in Genomes Unzipped, I was one of the more hesitant participants.  I weighed up the pros and cons, but in the end what sold me was that after almost a decade of curiosity I finally had the opportunity to find out my genotype for the hereditary haemochromatosis (HH) variants in the gene HFE.  But things didn’t unfold quite how I’d expected, and I’m still left with some unanswered questions about HH in my family.

Continue reading ‘Digging deeper into my disease risk’

Getting even with the odds ratio

In the recent report from the US Government Accountability Office on direct-to-consumer genetic tests, much was made of the fact that risk predictions from DTC genetic tests may not be applicable to individuals from all ethnic groups. This observation was not new to the report – it has been commented on by numerous critics ever since the inception of the personal genomics industry.

So, why does risk prediction accuracy vary between individuals and what can be done to combat this? Are the DTC companies really to blame?

To explore these questions it is first necessary to understand what is meant by the odds ratio (OR). In genetic case-control association studies the OR typically represents the ratio of the odds of disease if allele A is carried compared to if allele B is carried. If all else is equal, genetic loci with a higher OR are more informative for disease prediction – so getting an accurate estimate is extremely important if prediction underpins your business model. However, getting an accurate estimate of OR is far from easy because many, often unmeasured, factors can cause OR estimates to vary. In this post I will try to break down the concept of a single, fixed odds ratio for a disease association, and highlight a number of factors that can cause odds ratios to vary using examples from the scientific literature.

Continue reading ‘Getting even with the odds ratio’

Friday Links

Over at Your Genetic Genealogist, CeCe Moore talks about investigating evidence of low-level Ashkenazi Jewish descent in her 23andMe data. What I like about this story is how much digging CeCe did; after one tool threw up a “14% Ashkenazi” result, she looked for similar evidence in 23andMe’s tool. She then did the same analysis on her mother’s DNA, finding no apparant Ashkenazi heritage, and to top it all off got her paternal uncle genotyped, which showed even greater Ashkenazi similarity. [LJ]

A paper out in PLoS Medicine looks at the interaction between genetics and physical activity in obesity. The take-home message is pretty well summarized in the figure to the left; genetic predispositions are less important in determining BMI for those who do frequency physical excercise than for those who remain inactive. This illustrates the importance of including non-genetic risk factors in disease prediction; not only because they are very important in their own right (the paper demonstrates that physical activity is about as predictive of BMI as known genetic factors), but also because information on environmental influences allows better calibration of genetic risk. [LJ]

Trends in Genetics have published an opinion piece in their most recent issue outlining the types of genetic variants we might expect to see for common human diseases (defined by allele frequency and risk), and how exome and whole-genome sequencing could be used to find them.  They give a brief, relatively jargon-free, overview of gene-mapping techniques that have been previously used, and discuss how sequencing can take this research further, particularly for the previously less tractable category of low-frequency variants that confer a moderate level of disease risk. [KIM]

More Sanger shout outs this week; Sanger Institute postdoc Liz Murchison, along with the rest of the Cancer Genome Project, have announced the sequencing of the Tasmanian Devil genome. The CGP is interested in the Tasmanian Devil due to a rare, odd and nasty facial cancer, which is passed from Devil to Devil by biting. In fact, all the tumours are descended from the tumour of one individual; 20 years or so on, and 80% of the Devil population has been wiped out by the disease. As well as a healthy genome, the team also sequenced two tumour genomes, in the hope of learning more about what mutations made the cells go tumours, and what makes the cancer so unique.

I have to say, this isn’t going to be an easy job; assembling a high-quality reference genome of an under-studied organism is a lot of work, especially using Illumina’s short read technology, and identifying and making sense of tumour mutations is equally difficult. Add to this the fact that the tumour genome is from a different individual to the healthy individual, this all adds up to a project of unprecedented scope. On the other hand, the key to saving a species from extinction could rest on this sticky bioinformatics problem, and if anyone is in the position to deal with it, it’s the Cancer Genome Project. [LJ]

Tasmanian Devil image from Wikimedia Commons.

Friday Links

A lot of the Genomes Unzipped crew seem to be away on holiday at the moment, so today’s Links post may lack the the authorial diversity that you’re accustomed to.

I just got around to reading the August addition of PLoS Genetics, and found a valuable study from the Keck School of Medicine in California. They authors looked at the effect of known common variants in five American ethnic groups (European, African, Hawaiian, Latino and Japanese Americans), to assess how similar or different the effects sizes were across the groups.

The authors calculated odds ratios for each variant in each ethnic group, and looked for evidence of heterogeneity in odds ratios. They find that, in general, the odds ratios tend to show surprisingly little variation between ethnic groups; the direction of risk was the same in almost all cases, and the mean odds ratio was roughly equal across populations (the authors note that this pretty effectively shoots down David Goldstein’s “synthetic association” theory of common variation). One interesting exception was that the effect size of the known T2D variants was significantly larger in Japanese Americans, who had a mean odds ratio of 1.20, compared to 1.08-1.13 for other ethnic groups. The graph to the left shows the distribution of odds ratios in European and Japanese Americans.

These sorts of datasets will be very useful for personal genomics in the future, as a decade of European-centered genetics research has left non-Europeans somewhat in the lurch with regards to disease risk predictions. However, the problem with the approach in this paper is that even this in large a study (6k cases, 7k controls) the error bounds on the odds ratios within each group are still pretty large. [LJ]

Over at the Guardian Science Blog, Dorothy Bishop explains the difference between learning that a trait is heritable (e.g. from twin studies), and mapping a specific gene “for” a trait (e.g. via GWAS). Her conclusion is worth repeating:

The main message is that we need to be aware of the small effect of most individual genes on human traits. The idea that we can test for a single gene that causes musical talent, optimism or intelligence is just plain wrong. Even where reliable associations are found, they don’t correspond to the kind of major influences that we learned about in school biology. And we need to realise that twin studies, which consider the total effect of a person’s genetic makeup on a trait, often give very different results from molecular studies of individual genes.

There are also interesting questions to be asked about why there is such a gap between heritabilities estimated by twin studies, and the heritability that can be explained by GWAS results. That is, however, is a question for another day. [LJ]

Another article just released in PLoS Genetics provides a powerful illustration of just how routine whole-genome sequencing is now becoming for researchers: the authors report on complete, high-coverage genome sequence data for twenty individuals. The samples included 10 haemophilia patients and 10 controls, taken as part of a larger study looking at the genetic factors underlying resistance to HIV infection. While this is still a small sample size by the standards of modern genomics, there are a few interesting insights that can be gleaned from the data: for instance, the researchers argue from their data that each individual has complete inactivation of 165 protein-coding genes due to genetic variants predicted to disrupt gene function. I’ll be following up on this claim in a future post. [DM]

Finally, a quick shout-out to our fellow Sanger researchers, including Verneri Anttila and Aarno Palotie, along with everyone else in the International Headache Genetics Consortium, for finding the first robust genetic association to migrane. They looked at 3,279 cases and >10k controls (and another 3,202 cases to check their results), and found that the variant rs1835740 was significantly associated with the disease.

To tie in with the above story, in the region of 40-65% of variation in migraine is heritable, but only about 2% of this was explained by the rs1835740 variant. However, explaining heritability isn’t the main point of GWAS studies: a little follow-up found that rs1835740 was correlated with expression of the gene MTDH, which in turn suggests a defect in glutamate transport; hopefully this new discovery will help shed some light on the etiology of the disease. [LJ]

Friday Links

Two exciting-looking new science blogging collectives have been announced this week. The Public Library of Science launched a new blogging collective, including personal genomics blogger Misha Angrist, and the Guardian newspaper has launched its Guardian Science Blogs network, including Dr Evan Harris, ex-MP for Oxford West and long time supporter of the role of science in public policy. I’m pretty excited about these new blogs, but it does stand to increase my RSS load significantly. [LJ]

In this month’s issue of European Journal of Human Genetics, Yang, Visscher and Wray contribute to the discussion around the aetiology of common complex diseases.  They demonstrate that the existence of a large number of sporadic cases (instances where a patient has no first, second, or third-degree relatives with the disease) is not incompatible with a polygenic model of disease.  A little less hot-off-the-press are two opinion pieces on genetic testing regulation from the August issue of Nature.  Arthur Beaudet argues that stringent government regulation should be applied to genotyping/sequencing, and interpretation should be the exclusive domain of the medical profession.  Gail Javitt takes a different view, arguing that genetic tests to be treated in the same way as other medical tests and that the level of regulation imposed should be determined by medical relevance of the outcome. [KIM]

Finally, Procreation News; our very own Daniel MacArthur and Ilana Fisher have recently given birth to a baby boy (the picture to the left may be a little out of date). Daniel made the announcement on Twitter, and also had this to say:

After careful inspection, I’ve decided that my six-day-old son is the most remarkable human being to have ever lived.

Due to double blinding, neither we nor Daniel know whether he has an actual or placebo baby, so we can’t yet assess the significance of this claim. Watch this space! [LJ]

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