Author Archive for Carl Anderson

‘Disguised’ heritability, changes ahead to marketing of personal genomics, deciphering developmental disorders

Even though genome-wide association studies (GWAS) have identified many loci associated with complex disease, much disease heritability is still unexplained, or “missing”. But what if rather than being missing, some of the heritability was “disguised”. This is the term put forward by Chris Spencer and collegues to describe the proportion of heritability that we miss because SNPs (imperfectly) correlated to the causal variant (“tag SNPs”) are used to estimate explained heritability rather than causal variants themselves. Reassuringly, their simulations show that for the vast majority of loci detected via GWAS the risk estimated from the best tag SNP is very close to the truth. They also show that, occasionally, fine mapping of GWAS loci will identify causal variants with considerably higher risk and this is more likely if the true effect of the locus is large. The figure above, taken from their paper, shows that for estimated relative risks in the range 1.2–1.3, there is approximately a 38% chance that the true relative risk exceeds 1.4 and a 10% chance that it is over 2. The consequence of all of this for personal genomics is that disease risk could be much greater than currently thought for those individuals who, for a given disease, carry a large number of common risk variants. [CAA]

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At odds with disease risk estimates

It's all a game of Risk!

The first thing I did when I received my genotyping results from 23andMe was log on to their website and take a look at my estimated disease risks. For most people, these estimates are one of the primary reasons for buying a direct to consumer (DTC) genetics kit. But how accurate are these disease risk estimates? How robust is the information that goes into calculating them? In a previous post I focused on how odds ratios (the ratio of the odds of disease if allele A is carried as opposed to allele B) can vary across different populations, environments and age groups and, as a consequence, affect disease risk estimates.  It turns out that even if we forget about these concerns for a moment, getting an accurate estimate of disease risk is far from straightforward. One of the primary challenges is deciding which disease loci to include in the risk prediction and in this post I will investigate the effect this decision can have on risk estimates.

To help me in my quest, I will use ulcerative colitis (UC) as an example throughout the post, estimating Genomes Unzipped members’ risk for the disease as I go. Ulcerative colitis is one of two common forms of autoimmune infllammatory bowel disease and I have selected it not on the basis of any special properties (either genetic or biological) but because I am familiar with the genetics of the disease having worked on it extensively.

The table below gives our ulcerative colitis risks according to 23andMe. The numbers in the table represent the percentage of people 23andMe would expect to suffer from UC given our genotype data (after taking our sex and ethnicity into account). The colours highlight individuals who fall into 23andMe’s “increased risk” (red) or “decreased risk” (blue) categories based on comparisons with the average risk (males: 0.77%; females 0.51%). As far as I am aware none of us actually do suffer from UC.
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People Have A Right To Access Their Own Genetic Information

This week has seen another FDA meeting seeking guidance on how to regulate direct-to-consumer (DTC) genetic tests in the US. The meeting itself has been covered by GNZ bloggers Daniel at Genetic Future and Dan at Genomics Law Report, and its apparent outcome has sparked furious debate elsewhere. The discussion among the “independent” panel convened at the meeting appeared to converge on the proposal that all health-related genomic tests should be ordered and reported through physicians. However, the outcomes of the meeting in terms of FDA policy remain unclear, and one FDA official has indicated that decisions about the availability of genetic tests will be made on a test-by-test basis.

There is no doubt that the appropriate regulation of personal genomics tests is a complex issue, and there is a diversity of opinion about how best to achieve it within GNZ (as there is throughout the genomics community). However, there are several points we agree on:

  • Individuals have a fundamental right to access information about themselves, including genetic information. While it is important to also consider the accuracy, interpretation, validity and utility of tests, this underlying principle should guide policy.
  • There is currently no evidence that DTC genetic tests pose a danger to consumers. A recent study of over 2,000 participants in DTC testing concluded that “testing did not result in any measurable short-term changes in psychological health”. In the absence of any evidence of harm there is no justification for restricting individual autonomy.
  • DNA does not have magical powers, and does not require special treatment simply by virtue of being DNA. Genetic exceptionalism – the idea that genetics must be treated as special under the law – is an inappropriate basis for policy-making. Tests should be regulated appropriately based on their predictive power, utility and potential for harm, all of which are related concepts.
  • As DNA sequencing becomes cheaper, the line between medical and non-medical testing will continue to blur. Excessive regulation of health-related genetic tests could also unncessarily hinder the ability of people to access their entire genome sequences for other purposes (such as genetic genealogy).
  • Most clinicians do not have the appropriate knowledge to interpret genomic tests, particularly in healthy individuals. This point is almost universally agreed, even by the FDA, and has certainly been the experience of some of the GNZ members upon taking our genetic results to doctors. Physicians in general are therefore a strange choice for ‘guardians of the genome’.
  • Most early adopters of DTC genetic tests are sufficiently well-informed to understand the implications of a genomic test and interpret the results correctly. Putting a general physician between these informed individuals and their own genomes is paternalistic and unnecessary.

While the outcome of the FDA’s deliberations remain uncertain, it is clear that there will be intensive lobbying against any attempt at excessive legislation. In the worst case scenario, the fledgling and innovative personal genomics market could be crushed by the FDA. However, there is still plenty of room for a measured approach that enforces test accuracy, punishes false claims and promotes informed choices by consumers, without reducing the ability of responsible companies to continue to operate and innovate.

We urge others in the genomics community to make their voices heard on these issues. Let the FDA – and, if you’re based in the USA, your political representatives – know that regulation of genetic testing should be based on evidence, not fear, and that any attempt to unreasonably restrict your access to your own genetic information is unacceptable.

Are synthetic associations a man-made phenomenon?

Early last year David Goldstein and colleagues published a provocative paper claiming that many GWAS associations are driven not by common variants of modest effect (the canonical common disease – common variant hypothesis underpinning GWAS) but instead by a local cluster of lower frequency  variants that have much bigger effects on disease risk. They dubbed this hypothesized phenomenon “synthetic association” and the term quickly became a genetics buzzword. The paper was widely discussed in both the specialist and mainstream media, and caused quite a stir among academic statistical geneticists.

That debate has been re-opened today by a set of Perspectives in PLoS Biology: a rebuttal by us (Carl & Jeff) and our colleagues at Sanger, a rebuttal by Naomi Wray, Shaun Purcell and Peter Visscher, a rebuttal to the rebuttals by David Goldstein and an editorial by Robert Shields to tie it all together.

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Friday Links

A quick note about the Reader Survey; we are going to stop taking responses at the end of Saturday (Pacific Time). If you haven’t already done so, please fill out the survey now.

A couple of interesting articles this week on the Personal Genome Project and public genomics in general. Mark Henderson at the Times has an opinion piece (behind a paywall, I’m afraid) about Misha Angrist‘s book Here Is A Human Being (see also this review from The Intersection), and in the Duke Magazine Mary Carmichael has an in-depth feature on the work of George Church, with some interesting history of the early days of the PGP.

One aspect that comes out of these articles is how those who take part in public genomics projects are starting to own the unknown unknowns. They accept that they cannot anticipate all the risks of making their data public, but are willing to take the risk of exposing themselves to these unknown risks, and in doing so turn them into knowns. Another aspect is the sheer number of individuals who want to sign up to have their data published online: 15,000 people have expressed interesting in being part of the PGP, despite initial NIH concerns the no-one would want to take part at all. This also chimes with research presented at ASHG this year, showing that members of the public are more concerned with contributing to scientific knowledge, and, crucially, getting access to their own genetic data than they are about the potential risks that such data could expose them too. [LJ]

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Friday Links

We’ve been pretty quiet here at Genomes Unzipped for the last couple of weeks, while many of us prepared for and attended the American Society of Human Genetics meeting in Washington DC last week. The meeting was (as usual) an exhausting barrage of presentations on human genetics; you can get a flavour of the new results presented either by scrolling back through the Twitter coverage, or reading the summary posts by Luke Jostins (who found himself explaining why Eric Lander was wrong about epistasis and missing heritability) and Larry Parnell. Shirley Wu also has an excellent recap of the meeting, including an analysis of the Twitter coverage.

My overall impression of the meeting – and this was a view shared by many other attendees I’ve spoken to – was  that 2010 was a year of transition. The numbers were impressive: Stacy Gabriel at the Broad Institute blithely reported that their pipeline is now theoretically capable of sequencing up to 800 exomes (complete sequence of the protein-coding regions of the genome) per week, and the 1000 Genomes Project has now completed sequencing of over 1,000 whole genomes at low coverage. Projects that would have seemed mind-blowing even a year ago seem almost mundane now, an indication of just how fast sequencing technology has moved in the last twelve months.
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Friday Links

The largest genome-wide association study ever undertaken was published in Nature this week. The appropriately named Genetic Investigation of ANthropocentric Traits (GIANT) consortium combined data from 183,727 individuals and identified around 180 loci influencing human height. The loci were enriched with genes underlying skeletal growth and other relevant biological pathways. Interestingly, these 180 loci are estimated to only account for 10% of the phenotypic variation in height (or around 12.5% of the heritability). [CAA]

Christophe Lambert from Golden Helix has an excellent, thorough post looking at the importance of careful experimental design in large-scale genetic association studies. In particular, Lambert focuses on the need for randomising samples across experimental batches: if you have some batches containing entirely cases and others entirely controls, then the all-too-pervasive spectre of batch effects can easily create false positive associations. In many cases batch effects can be recognised and corrected for post hoc (Lambert cites a good example from the original WTCCC study), but in other cases a failure to perform the right quality controls can have devastating consequences (Lambert cites the recent longevity GWAS paper in Science). I’d be interested to hear from my more GWAS-savvy colleagues (Carl, Jeff) whether randomisation is standard procedure in most large GWAS now. [DM]

We managed to miss this out last week, but the current issue of Nature Genetics has a strange and wonderful paper on breast cancer genetics. The study looked at 2838 individuals with BRCA1 mutations that strongly predispose to breast cancer, and looked for non-BRCA1 variants associations with breast cancer in this group. They found an associated variant of chromosome 19, and replicated it in another 5986 BRCA1 carriers (where do they find this many BRCA1 carriers?). To top it all off, they looked at this variant in another 6800 breast cancer patients without BRCA1 mutations, and found no association. However, when they stratified their samples into ER+ and ER- associations, they found associations in both, but going in opposite directions! The variant predisposes people to ER- cancer, but is protective against ER+, and taken together they pretty much perfectly balance out. [LJ]

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.

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