What can be done to reduce this burden? Increasingly sophisticated methods for detecting disease in embryos during pregnancy will help, and these have recently taken another step forward with the development of accurate, non-invasive methods based on analysing foetal DNA in the blood of pregnant mothers (an article in the BMJ this week demonstrates the feasibility of this approach for a non-Mendelian disease, Down syndrome; and the same group showed late last year that this approach can also be applied to effectively any known disease-causing mutation). Yet these approaches detect disease after pregnancy has already begun.

Disease mutations can also be detected in embryos prior to implantation, for prospective parents undergoing IVF. But IVF remains an expensive, arduous and invasive procedure, and thus a weapon of last resort for most parents-in-waiting; as Armand Leroi notes drily in an exceptional 2006 article in EMBO Reports: “nature has contrived a cheap, easy and enjoyable way to conceive a child; IVF is none of these things.” (While Leroi goes on to argue that the challenges of IVF are less severe for young couples with no fertility problems, it still seems fairly implausible that this will become the default mode of reproduction in the near future.)

However, for some classes of Mendelian disease it’s possible to move the screening one step back. Recessive diseases are insidious things. The mutations that cause them lurk undetected – each of us carry perhaps 5 to 10 of them – as their carriers are protected by the presence of a healthy second copy of the affected gene. These mutations can thus wait silently for generation after generation, until a carrier is unlucky enough to fall for someone who carries the same mutation, or another mutation in the same gene. The children of such a couple will each have a 25% chance of inheriting one damaged copy of the gene from each parent and thus developing the disease.

The ability of a recessive mutation to pass silently from generation to generation means that many children born with recessive diseases have no family history. And while certain marriage practices (notably serial first-cousin marriage) can dramatically increase the risk of having a child with a recessive disease, these diseases can also explode into appearance in families with no obvious risk factors.

However, the fact that both parents must carry mutations in the same gene to pass a recessive disease to their children raises the possibility of detecting risk before a couple has even conceived children. For instance, one could screen both members of a couple for a panel of known mutations, an approach currently offered by US company Counsyl (disclaimer: my wife and I both accepted free tests from Counsyl in 2009). However, while a panel containing all known Mendelian mutations could detect a substantial fraction of all genetic disease (Leroi again), it can never eliminate the risk, because many Mendelian mutations remain undiscovered. However, one could go one step further: rather than simply look for known mutations, one could examine the entire sequence of all genes known to be associated with Mendelian diseases, and thus identify new mutations lurking in the same gene.

In an article published today in Science Translational Medicine a group of US researchers describe a high-throughput approach for doing precisely that.

Rather than review the technical aspects of the paper in great detail, I’ll just hit the main points I took from it:

• The 448 genes selected for the screening panel are the product of walking a political tightrope. While the authors indicate that the marginal costs of adding extra sequence mean that the optimal cost-benefit ratio comes from including a very broad range of diseases, they have excluded diseases that might trigger controversy (such as deafness and adult-onset disorders).
• The authors do a commendable job of comparing multiple technologies for capturing disease genes and for sequencing the captured DNA; the final product is a combination of Agilent SureSelect for sequence capture and Illumina HiSeq for sequencing.
• This approach allowed them to detect ~95% of the genetic variants in their target genes with very high accuracy. In other words, they might miss around 5% of disease-causing mutations in these genes, but the ones they find are probably real.
• Perhaps the single most important message from the paper, which I hope to expand on in a future post, is that disease mutations reported in the literature are depressingly enriched for false positives. The authors suggest that 27% of mutations in their samples that overlap with entries the largest database, the Human Gene Mutation Database, turned out to be the result of sequencing errors or mistakes in the literature (e.g. common polymorphisms that have been falsely reported to be disease-causing).
• The researchers found that the 104 sequenced samples contained on average 2.8 known disease-causing mutations in the surveyed genes. This fits with expectations: each of us is likely walking around with 5-10 of these severe disease-causing variants in total, which we will only know about if (1) we get our genomes sequenced, or (2) we’re unlucky enough to have children with a partner who carries mutations in the same gene.
• The authors estimate the cost of their test at $378, which they note is “approximating that expended on treatment of severe recessive childhood disorders per U.S. live birth”. In other words, offering this type of screen across the US population as a whole would be roughly cost-neutral from a healthcare stand-point, while simultaneously reducing the number of children dying from genetic diseases.

Given the plummeting costs of sequencing and the economies of scale, the cost-benefit ratio for this type of screening panel will continue to drop. In this context it seems inevitable that some form of sequencing approach will ultimately be implemented as routine for young parents-to-be.

Matt’s underlying argument is that while sequencing costs will continue to drop, obtaining a complete genome sequence that is sufficiently accurate for medical interpretation will require additional expenses (increased sequence coverage to ensure accuracy, all of the computation required to stitch the raw data into a useable form, and paying doctors to perform the interpretation) that will keep the cost of medical sequencing well above the arbimagical US$1,000 threshold. Instead, Herper argues, we will likely see medical-grade genomes stay above $10,000, or at least above the $2,000 currently forked out for MRI scans.

There’s certainly some depressing truth here. I believe Herper is right that if you intend to access your genome sequence via a traditional medical route, it will certainly cost more than $1000 for the foreseeable future – if indeed you can get access to it at all, which is by no means guaranteed. The costs of clinical sequencing will include even more overheads than Herper notes in his short post: for instance, even as the accuracy of high-throughput sequencing technology improves, there will still be a need for variants with major medical impact to be independently validated in clinical labs, and custom assays don’t come cheap.

However, many of these extra costs of clinical sequencing will be further inflated by regulatory demands (at least some of which will be arbitrary and pointless), and many will only apply if you obtain your genome through the medical system. Individuals with the motivation to seek alternative routes will be able to obtain a perfectly serviceable genome sequence at a substantially lower price: they’ll have to be cautious in how they interpret the results, of course (although, importantly, this is also true of a medical test), but it will be possible to obtain substantial and potentially extremely useful information from your own genome without having to pass through the clinical toll-booths.

The key obstacle will be the development of cheap (or free), intuitive tools for annotating large-scale genetic information. Purchasing the sequencing itself will be trivial: even if the FDA succeeds in crushing innovation in direct-to-consumer genetic testing in the US, there will be plenty of companies abroad (especially in East Asia) willing to convert a mailed saliva sample into an assembled genome sequence. What the individual needs to do with that sequence is to (1) validate that the sequence they receive is in fact their own genome; (2) extract medically useful variants; (3) confirm that these variants are real; and (4) figure out how that information should be used to make health and lifestyle decisions.

Only one of these steps (the final one) will require direct consultation with the medical profession. The first will require comparing the sequence with independent genetic data (such as a genome scan from a company like 23andMe) to check that the two match the same individual (i.e. you), and also to provide an indicator of the global quality of the sequence. The second step is currently extremely challenging, but we can expect tremendous innovation in genome interpretation software and databases of functional variants over the next few years that will gradually simplify and improve this process. The third step will require sending another DNA sample to a company that does affordable, custom assays of a small number of genetic regions of interest using an independent technology. And the final step will involve discussing the results with everyone who might be able to tell you something useful about them, including your family and your doctor.

None of this is simple, but it will become easier with time. As the retail costs of sequencing drops, a substantial niche will develop for innovators providing affordable, intuitive, accurate interpretation tools (embryonic versions already exist: see, for instance, Promethease or Enlis Genomics). Open-source academic software built for large-scale sequencing projects will be adapted for use by non-specialists. The increasing availability of large-scale computing power (for instance, via Amazon EC2), coupled with this intuitive software, will make even compute-intensive analyses available to the educated, motivated lay-person. (Incidentally, tracking and fostering the development of these tools is one of the motivations behind the Genomes Unzipped project – and you’ll hear more about our plans in this area in 2011).

Done carefully, there’s no reason why a DIY genome couldn’t be every bit as useful (or indeed as useless, in many cases) as one obtained through the doctor-as-gatekeeper route. As Dan Vorhaus argued after last year’s sample mix-up at 23andMe, it is likely that clever DIY genomicists will be better at picking up certain kinds of errors (such as sample swaps) than clinical labs would. In terms of accuracy, while retail genomes may not reach the same quality standards as those generated by clinical labs, increased competition and innovation in the direct-to-consumer space will mean they’re unlikely to lag too far behind – and judicious use of independent validation of important variants would in most cases raise the reliability to a level is equivalent to, or even higher than, a medical-grade test. Of course, for the very small number of genetic variants per genome that might require urgent, serious action – such as BRCA1 breast cancer-associated mutations – individuals can always fork out for a clinical test.

What proportion of people will take this DIY route? This isn’t for everyone. Those wealthy enough to blithely fork out $10,000 for a medical genome interpretation, or sufficiently unwell to be able to convince their insurance company or public health system to pay for it, will by and large simply take the expensive medical route. Of the remainder, relatively few people will be sufficiently motivated to develop the background knowledge required to make sense of their genome, even if the analysis software is relatively intuitive. But for the non-trivial fraction of the population who want to know about their genomes, but don’t want to pay the inflated costs associated with medical-grade sequencing – and I know this is a category that many readers of Genetic Future fall into – this will be an attractive and feasible option.

In addition, there will be advantages to the DIY approach beyond the lower cost. People who actively engage in the process of constructing useful information from raw sequence data – regardless of how intuitive the software is for doing it – will automatically learn important lessons about the nature of genetics (just as anyone who has given more than a casual glance at their own 23andMe profile has automatically learnt something important about the probabilistic nature of genetic risk factors for common diseases). They will also have opportunities to ask and answer fascinating questions that would be irrelevant in a purely medical consultation about your genome: for instance, what does your genetic information tell you about your ancestry?

They’ll be able to ask these questions because they will own the data. How easy do you think it will be to obtain your raw genome sequence from your doctor to use to satisfy your own curiosity? How many forms and disclaimers will you need to sign? How many times will you need to listen to someone tell you that the data files are just too large, that the formats are inaccessible to lay-people, that your request is extremely unusual and will need to be considered for months by a hospital committee in the name of “health data privacy”? Anyone who has ever tried to get access to their own medical records will know how tedious and shrouded in unnecessary mystery this process can be; imagine how much larger the obstacles will loom when the system has the additional excuse of large, complex file formats to throw in your path.

Importantly, your genome is just one contributor to your present and future health. DIY genomics will, I hope, be part of a larger ongoing trend towards individuals taking greater personal responsibility for tracking and maintaining their own wellness – a task, incidentally, which modern healthcare systems are spectacularly ill-equipped to perform, something that seems unlikely to change substantively in the near future. As Western populations age, this broader shift of responsibility will be essential for healthcare systems to survive.

But I digress. My point here is simply this: Herper is perfectly correct that the overheads imposed (for a mixture of valid and arbitrary reasons) by medical-grade testing will ensure that clinical genomes remain expensive. But for those of us willing to learn the skills required to go outside the system, the $1000 genome is rapidly approaching. We just need to be ready to make the most of what it contains – and to reap the benefits of accessing that information as an active, engaged participant rather than a passive recipient.

These claims would be fascinating, if true. However, while the article makes some (scattered) valid points, its central claim (that the results of GWAS suggest that genetics plays little or no role in the causation of common diseases) is entirely false, and the authors rely on a combination of distortions and statistical misunderstandings to make their case.

In a comment over at the Huffington Post found by Keith Grimaldi, one of the authors explains the key messages and motivations of his analysis:

We have just reported that genetics now demonstrat­es that genes cannot be the cause of common diseases:

http://www­.bioscienc­eresource.­org/commen­taries/art­icle.php?i­d=46

That means environmen­t must be the entire cause of ill health, i.e. junk food, pollution, lack of exercise, etc. The reason we wrote an article about human genetics (when we are a food and agricultur­e website) is that we believe that if people live right, agricultur­e and therefore the planet will more or less fix itself. [my emphasis]

This quote is illuminating in a number of ways. Firstly, it shows that there is no nuance in this argument: the authors aren’t attempting to argue that genes play a smaller role in common disease than geneticists expected, but rather that genetics plays no role whatsoever. 

For each disease, even if a person was born with every known ‘bad’ (or ‘good’) genetic variant, which is statistically highly unlikely, their probability of contracting the disease would still only be minimally altered from the average.

Studies of human twins estimate heritability (h²) by calculating disease incidence in monozygotic (genetically identical) twins versus dizygotic (fraternal) twins (who share 50% of their DNA). If monozygotic twin pairs share disorders more frequently than do dizygotic twins, it is presumed that a genetic factor must be involved. A problem arises, however, when the number resulting from this calculation is considered to be an estimate of the relative contribution of genes and environment over the whole population (and environment) from which the twins were selected. This is because the measurements are done in a series of pairwise comparisons, meaning that only the variation within each twin pair is actually being measured. Consequently, the method implicitly defines as environment only the difference within each twin pair. Since each twin pair normally shares location, parenting styles, food, schooling, etc., much of the environmental variability that exists between individuals in the wider population is de facto excluded from the analysis. In other words, heritability (h²), when calculated this way, fails to adequately incorporate environmental variation and inflates the relative importance of genes. [my emphasis]

The last fifteen years, coinciding with the rise of medical genetics, have seen unprecedented sums of money directed at medical research. At the same time, research on pollution, nutrition and epidemiology has not benefited in any comparable way.

[...]

This same mindset is accurately reflected in the media where even strong environmental links to disease often receive little attention, while speculative genetic associations can be front page news.

Even as a direct beneficiary of money thrown at medical genetics over the last five years, and someone who blogs entirely about news in the genetic domain, I freely acknowledge that these criticisms have merit. Genetic dissection of common disease is valuable, and will be (and indeed already has been) fruitful in generating new therapies, but it is nonetheless true that research into environmental risk factors and interventions to minimise morbidity is woefully under-funded and under-reported relative to its potential benefit.

Personal genomics company 23andMe has made some fairly major announcements this week: a brand new chip, a new product strategy (including a monthly subscription fee), and yet another discount push. What do these changes mean for existing and new customers?

The new chip

• Increased coverage of drug metabolizing enzymes and transporters (DMET) as well as other genes associated with response to various drugs. 
• Increased coverage of gene markers associated with Cystic Fibrosis and other Mendelian diseases such as Tay-Sachs. 
• Denser coverage of the Human Leukocyte Antigen region, which contains genes related to many autoimmune conditions.

I summarised some of the key motivations for members of the group in my Unzipped announcement post:

• we want to share the results of scientific analysis of our own genomes, and as proponents of open data access most of us believe that doing good science means releasing complete data for others to investigate;
   
• we hope that releasing our data publicly will help to guide useful discussions about genetic privacy and the benefits, risks and limitations of genetic information in general;
   
• many of us believe that the ideal resource for genetic research is large open-access, non-anonymous research databases such as the Personal Genome Project, and that sharing linked genetic and trait information openly with the wider community is a public good – and we hope that our own experiences will encourage others to participate in open research projects;
   
• we all believe that many of the fears expressed about the dangers of genetic information are exaggerated, and see this project as an opportunity to have a constructive public discussion about the truth behind these fears;
   
• given the ease with which a dedicated snoop could obtain genetic information surreptitiously (via shed skin, hair or saliva, for instance), some of us argue that the whole notion of genetic privacy is illusory anyway – while releasing our data online makes it easier for people to get hold of it, this is a difference of degree rather than kind.

In my second big piece of news for the day, I’m pleased to announce that Genetic Future will shortly be moving to a  the brand new Wired Science Blogs network.