Tag Archive for 'public health'

Identical twins usually do not die from the same thing

Over at Nature News, Erika Check Hayden has a post about a recent Science Translational Medicine paper by Bert Vogelstein and colleages looking at the potential predictive power of genetics. The take-home message from the study (or at least the message that has been taken home by, e.g., this NYT article) is that DNA does not perfectly determine which disease or diseases you may get in the future. This take home message is true, and to me relatively obvious (in the same way that smoking doesn’t perfectly determine lung cancer, or body weight and dietary health doesn’t perfectly determine diabetes status).

A lot of researchers have had a pretty negative reaction to this paper (see Erika’s storify of the twitter coverage). There are lots of legitimate criticism (see Erika’s post for details), but to be honest I suspect that a lot of this is a mixture of indignation and sour grapes that this paper, a not particularly original or particularly well done attempt to answer a question that many other people have answered before, got so much press (including a feature in the NYT). A very large number of people have tried to quantify the potential predictive power of genetics for a number of years – why was there no news feature for me and Jeff, or David Clayton, or Naomi Wray and Peter Visccher, or any of the other large number of stat-gen folks who have been doing exactly these studies for years. ANGER RISING and so forth.

But of course, the reason is relatively obvious. Continue reading ‘Identical twins usually do not die from the same thing’

A decade of genomics, 60 new genomes, parenthood and sharing genetic data, and more on data return

To celebrate 10 years since the back-to-back publications of complete human genomes in Science and Nature, Science has published series of articles looking back at the last 10 years of genomics, and forward to the future. The article contains short essays from Francis Collins and Craig Venter, the former talking about some of the successes of medical sequencing (including giving a name and photograph to the exome-sequenced IBD patient I discussed a few weeks ago), and the latter discussing how far we still have to go before genomics can reach its potential. Baylor’s Richard Gibbs talks about how the large-scale technical discipline of genomics and the biological subject of genetics are starting to re-merge, after the Human Genome Project saw the two diverging, and there is an oddly inspiring comment from theologian Ronald Cole-Turning about how genomics is redefining our vision of humanity.

Of particular interest is an article by Eliot Marshall on why genomics hasn’t yet had a large effect on medical practice, and what needs to be done to allow the genomic revolution to trickle into medical care. He argues that scientists and doctors need to meet each other half way; scientists need to focus more on showing the direct clinical utility of genomics, whereas doctors need to be more ready to accept new technologies and discoveries, and adapt the way they practice medicine to make full use of them. [LJ]

Continue reading ‘A decade of genomics, 60 new genomes, parenthood and sharing genetic data, and more on data return’

Friday Links

Over at 23andMe’s blog The Spittoon, Stanford genetics professor Uta Francke has a great point-by-point dissection of the new policy statement of the European Society of Human Genetics on direct-to-consumer genetic testing. Francke doesn’t shirk from explaining that this statement should be seen in the context of an ongoing turf war between traditional geneticists and DTC upstarts:

For example, organizations like ESHG, the American Society of Human Genetics (ASHG), and the American College of Medical Genetics (ACMG) were created to represent the interests of their professional membership, similar to the American Medical Associations (AMA) for physicians. Any claims to act in the public interest by protecting people from potentially damaging genetic information may reflect a fear of the new consumer-driven healthcare system that, as in the case of DTC genetic testing, may circumvent the professional establishment. Insisting on individual professional counseling in the pre-testing and post-testing phase can be interpreted as an attempt to ensure continued involvement of board-certified genetics professionals. [DM]

In PLoS Genetics, researchers report that a SNP associated with risk for colorectal cancer likely exerts its effect by modifying the expression level of a nearby gene. Previous studies on a different region associated with both prostate and colorectal cancer revealed a similar mechanism (see here, for example). This contributes to growing molecular evidence that SNPs with long-range regulatory effects might be generally important in disease. [JP]

Kevin Davies’ superb new book The $1000 Genome is now available on Amazon. Keith Robison has already posted his review, and I’ll have my full thoughts here on Genomes Unzipped soon – but for now I’ll just say that Davies’ long and extensive experience working the genomics beat really pays off, with both a wealth of inside information and juicy anecdotes about the key players in the field. Well worth a read for anyone interested in modern genomics (i.e. everyone reading this post). [DM]

Dan Koboldt of MassGenomics has a useful review of the recent Cold Spring Harbor Personal Genomes meeting. He pulls out four key themes emerging from the conference: new estimates of human mutation rates, more sequencing of cancer genomes, studies of genome regulation and epigenetics, and an explosion of exome sequencing in both severe and common diseases. [DM]

Our own Dan Vorhaus reports on the utterly bizarre decision by Health and Human Services (HHS) Secretary Kathleen Sebelius and NIH Director Francis Collins to discontinue the Secretary’s Advisory Committee on Genetics, Health, & Society (SACGHS). Inexplicably, Sebelius and Collins argued that “the major topics related to genetic and genomic technologies had been successfully addressed by the committee through its comprehensive reports and recommendations over the years”. Vorhaus notes diplomatically that “it is clear that even those issues SACGHS investigated in detail have not been resolved with any meaningful degree of finality”. [DM]

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]

How well can a screening test predict disease risk?

We can usually be pretty confident that if our genotyping results say we carry a certain genetic variant, we really do carry that variant.  So why doesn’t that necessarily equate to a confident prediction about disease risk?  As Caroline outlined in her previous post on the risks and benefits of population screening, the results of screening tests (genetic or otherwise) may not provide a definitive diagnosis.  Test results often just categorise people as being at high or low risk of disease.  In this post, we’ll look at some of the ways we assess the predictive ability of a diagnostic test, and why the results we get from them are usually probabilistic.

Continue reading ‘How well can a screening test predict disease risk?’

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