biotech

Blaine Bettinger (the Genetic Genealogist) writes that some commercial test offerings are trying to sort out a way to tell you how Neandertal you are:

Once [the Max Planck] study came out, I knew it was only a matter of time before companies began offering tests that examined the percent of Neanderthal contribution to a test-taker’s genome.

This is one of the stickiest places to be a blogger. Bettinger links to a testing company's information on its product (including promotion of "Neandertal themed art" for the customer, sold at their Las Vegas gallery). Others have linked to Bettinger, drawing more attention.

I think that as a scientist, more promotion is the last thing I should be giving this company. So I won't be naming or linking to their advertising.

Ironically, the promotional material does not make any false statements of fact. The material makes it perfectly clear that the product does not test any gene variants that scientific research has shown may have come from Neandertals. Instead, the product reports on gene variants that we don't know about from Neandertals.

Huh?

You may wonder how a company can market such a product as a "Neanderthal Index". Since "Neanderthal Index" is not a scientific concept, a company can claim whatever it wants.

So what is it? According to the material, the Neanderthal Index is computed from (a very few) STR alleles shared with "archaic" populations. Those "archaic" populations aren't Neandertals, they're Basques, Turks, Syrians, and other living people. Anthropologists do not call these people "archaic", so this is not a scientific concept either. Nobody has demonstrated that the listed populations are more or less Neandertal-like than any other living people. Most of the differences between these living populations emerged during the last 10,000 years.

You'd do better putting calipers on your skull and measuring your cephalic index. At least that would tell you whether some real phenotype is Neandertal-like.

I don't imagine that customers beating down the doors for this product. I think it exists as a way of bringing attention -- Neandertals are in the headlines. That's a big reason to not give them any attention. The test has nothing whatsoever to do with Neandertals as we scientifically understand them.

Can you tell that I'm disgusted by this?

Here in my lab, we're in a very good position to say that no test today can accurately report on your individual proportion of Neandertal ancestry. Until we have characterized a broader set of gene trees than we have so far, we are really not able to give any answer about how similar any person's genome is to Neandertals. We can't say yet how heterogeneous the human population is today in its ancestry from different parts of the world during the Late Pleistocene. For the past thirty years most working geneticists completely ignored the possibility of such heterogeneity, we are only just beginning to investigate it seriously.

This kind of thing may not be why the FDA is looking to regulate personal genomics. Neandertal ancestry is not directly relevant to health. But if customers buy tests like this thinking that they are learning about Uncle Thag, just how much misinformation will they accept from other tests that purport to tell them something more important?

I wrote about the UC Berkeley genetic testing of incoming freshmen earlier this spring. The summer is halfway over and the saliva kits have been sent. Now Scientific American has a long and balanced article on the contrasting approaches to genetic testing at Berkeley and an upper-level seminar at Stanford: "Exposing the Student Body: Stanford Joins U.C. Berkeley in Controversial Genetic Testing of Students".

This is an article worth reading by anyone interested in personalized genomics or bioethics. I wouldn't have expected that university classes would be such an early battleground for genetic information, privacy rights, and junk science. But nothing about either program is unprecedented. I wrote in 2005 about genetic testing associated with a course at Penn State. As I noted in 2005, I have a lot of concerns about applying these genetic tests to students. They can have an educational effect, but not always a beneficial one.

The UC-Berkeley program actually provides vastly less information than the ancestry testing that has been applied to students in courses in the past. That's my main objection -- it's an awful lot of trouble for essentially no scientific value. I mean, they might as well just do blood types!

There's a lot in the article about the thinking of the main decision makers. I'll share these two paragraphs:

In fact, after Salari originally proposed the class last fall, a Stanford task force of about 30 basic scientists, clinical scientists, genetic professors, genetics counselors, bioethicists, legal counselors and students spent several months working through the various ethical issues and establishing safeguards to protect students. In contrast, the organizers of Berkeley's project incurred criticism because they spent hardly any time considering the potential reaction to their new orientation program.

Kimberly Tallbear, a professor of science, technology and environmental policy at Berkeley, explains that neither [Dean] Mark Schlissel nor any of the project's other organizers consulted with Berkeley's bioethics community. "Schlissel said several times they were surprised about the controversy," Tallbear says. "I said to him, 'Well doesn't that tell you that you needed input from us? Because we could have told you about the controversy and debate.'"

The article also discusses the "research study" aspect -- participants will be asked to sign an informed consent form and data will be kept. It may seem like the three genotypes provided to the students would not be very interesting as research topics. But it's not too hard to imagine psychology grad students in three years becoming very interested in research projects involving a high-risk population for binge drinking and known ALDH2 genotypes. Berkeley freshmen may be enrolling now in the first phase of a long-term research study on alcohol and sexual assault.

While I was out of town, Wired ran a long article about Google cofounder Sergey Brin and his quest to find the genetic causes of Parkinson's disease. There is much of interest here. The piece gives an account of present-day genomic research from a unique point of view.

Brin is a smart person, with a family history of Parkinson's and knowledge that he carries a risk allele. So he is directing a lot of money and attention toward new ways of approaching gene-disease associations. He is one of the major financial backers of the direct-to-consumer genomics company 23andMe, and the husband of founder Ann Wojcicki. Google, of course, has prospered by making unconventional uses of data. That's an approach that many are starting to apply to science:

Increasingly, though, scientists—especially those with a background in computing and information theory—are starting to wonder if that model could be inverted. Why not start with tons of data, a deluge of information, and then wade in, searching for patterns and correlations?

This is what Jim Gray, the late Microsoft researcher and computer scientist, called the fourth paradigm of science, the inevitable evolution away from hypothesis and toward patterns. Gray predicted that an “exaflood” of data would overwhelm scientists in all disciplines, unless they reconceived their notion of the scientific process and applied massive computing tools to engage with the data. “The world of science has changed,” Gray said in a 2007 speech—from now on, the data would come first.

I think that "fourth paradigm" probably overdignifies the approach, which looks like a regression to a naive positivism. As described in the book, The Fourth Paradigm: Data-Intensive Scientific Discovery, the idea is rather more -- a unification of theory and massive amounts of data. Data really do speak for themselves, say Fourth Paradigmers, but they speak quietly with a lot of noise drowning them out. So if you collect vast amounts of data, you have a chance to sort out the whispers of real associations from all the junk.

The article gives a vivid example:

Langston offers a case in point. Last October, the New England Journal of Medicine published the results of a massive worldwide study that explored a possible association between people with Gaucher’s disease—a genetic condition where too much fatty substances build up in the internal organs—and a risk for Parkinson’s. The study, run under the auspices of the National Institutes of Health, hewed to the highest standards and involved considerable resources and time. After years of work, it concluded that people with Parkinson’s were five times more likely to carry a Gaucher mutation.

Langston decided to see whether the 23andMe Research Initiative might be able to shed some insight on the correlation, so he rang up 23andMe’s Eriksson, and asked him to run a search. In a few minutes, Eriksson was able to identify 350 people who had the mutation responsible for Gaucher’s. A few clicks more and he was able to calculate that they were five times more likely to have Parkinson’s disease, a result practically identical to the NEJM study. All told, it took about 20 minutes. “It would’ve taken years to learn that in traditional epidemiology,” Langston says. “Even though we’re in the Wright brothers early days with this stuff, to get a result so strongly and so quickly is remarkable.”

But there are a few stumbling blocks. Unknown associations are relatively weak. Most of the phenotypes are polygenic. With heritabilities less than one, there remain unknown environmental causes of most phenotypes, which are not captured in genetic data and which may interact with different genes.

At present, I don't think anyone in genetics is really operating on a "Fourth Paradigm" level. The massive datasets are building, but few authors are working with existing population genetic theory in ways that would enhance the pattern-matching exercise. If you look through papers describing genome-wide association studies, there are a lot of bivariate statistics, and some multivariate descriptive statistics (like principal components analysis). About all the theory is case-control statistical design. Look through a paper on genetic variation and you're likely to see a STRUCTURE analysis and some coalescent simulations.

The article puts this well:

'We have no grand unified theory,' says Nicholas Eriksson, a 23andMe scientist. 'We have a lot of data.'

Genetic data today are a huge contrast from the past, both in sample size and in coverage. There are a lot of new low-hanging fruit. In the future, the easy stuff will be gone and theory will become more and more important. It remains unclear to me how much progress on health may be made by pattern-matching alone, and how much will require new theoretical advances. Given the problems explaining heritability so far, it may be that we'll need new theory sooner rather than later.

Genetic data are slowly being joined by environment data of various kinds. The article contextualizes the study of environmental variables by telling the story of the initial discovery and long use of aspirin. After it had been common in the population for a long time, researchers started to realize that it had health interactions besides followed by the slow realization that long-time use has health interactions of its own.

The second coming of aspirin is considered one of the triumphs of contemporary medical research. But to Brin, who spoke of the drug in a talk at the Parkinson’s Institute last August, the story offers a different sort of lesson—one drawn from that period after the drug was introduced but before the link to heart disease was established. During those decades, Brin notes, surely “many millions or hundreds of millions of people who took aspirin had a variety of subsequent health benefits.” But the association with aspirin was overlooked, because nobody was watching the patients. “All that data was lost,” Brin said.

The answer is simple: Collect all the data and see what percolates out of them. Heck, probably Google already has enough data about everybody based on their web searches, if they could just connect those to the 23andMe database. If you have a few years of web searches, I wonder just how much that tells you about a person's other phenotypes?

Remember (as the article points out), Google is the company that can predict flu outbreaks faster than the CDC.

Still, aside from the obvious technological progress, I'm a little more sober about the prospects of making rapid health improvements. Consider:

This approach—huge data sets and open questions—isn’t unknown in traditional epidemiology. Some of the greatest insights in medicine have emerged from enormous prospective projects like the Framingham Heart Study, which has followed 15,000 citizens of one Massachusetts town for more than 60 years, learning about everything from smoking risks to cholesterol to happiness. Since 1976, the Nurses Health Study has tracked more than 120,000 women, uncovering risks for cancer and heart disease. These studies were—and remain—rigorous, productive, fascinating, even lifesaving. They also take decades and demand hundreds of millions of dollars and hundreds of researchers. The 23andMe Parkinson’s community, by contrast, requires fewer resources and demands far less manpower. Yet it has the potential to yield just as much insight as a Framingham or a Nurses Health. It automates science, making it something that just … happens. To that end, later this month 23andMe will publish several new associations that arose out of their main database, which now includes 50,000 individuals, that hint at the power of this new scientific method.

Today's sequencing techniques make it much cheaper to do some things that used to be very expensive. But we've done a lot of gold-plated medical studies, and have more coming soon. The most important barrier to progress is not the lack of money; it is the difficulty of altering biological systems without adverse complications.

I got to return to bloggingheads.tv this week for Science Saturday, with a conversation between me and Christina Agapakis, of the Oscillator blog.

Here's a non-embedded link to the diavlog. Christina's work is in synthetic biology, and we talked a lot about the recent announcement from the J. Craig Venter Institute, with some background on what synthetic biology is, and how the newsmaking work fits into the field as a whole. I learned a lot from our conversation. We got to the Neandertal genome in the second half of our conversation, and we found several common links -- and neither of us mentioned synthetic Neandertals once!

The National Institutes of Health directorate this week announced the creation of a new database for tracking and providing public information about commercial gene tests:

The National Institutes of Health announced today that it is creating a public database that researchers, consumers, health care providers, and others can search for information submitted voluntarily by genetic test providers. The Genetic Testing Registry (GTR) aims to enhance access to information about the availability, validity, and usefulness of genetic tests.

Currently, more than 1,600 genetic tests are available to patients and consumers, but there is no single public resource that provides detailed information about them. GTR is intended to fill that gap.

It is hard to tell much from the press release, but I think it foreshadows two significant aspects of the registry. First, the NIH seems to be entering the realm of quality control:

GTR genetic test data will be integrated with information in other NIH/NCBI genetic, scientific, and medical databases to facilitate the research process. This integration will allow scientists to make, more easily and effectively, the kinds of connections that ultimately lead to discoveries and scientific advances.

This would enable NIH to provide an independent summary of whether test markers correspond to clinical studies. Second, the registry seems to be encouraging active engagement of companies in the process:

During the development process, NIH will engage with stakeholders — such as genetic test developers, test kit manufacturers, health care providers, patients, and researchers — for their insights on the best way to collect and display test information. In addition, other federal agencies, including the Food and Drug Administration and the Centers for Medicare and Medicaid Services, will be consulted.

I'm not sure what this means for the possible regulation of tests in the future. The engagement of the FDA at this point may presage greater involvement of the agency in genomic testing. The involvement of Medicare in the database seems more important, as the federal government will likely become the largest purchaser of genetic testing in the near future.

In relation to the Medicare/Medicaid involvment, I discussed candidate Obama's record on gene testing in 2008 ("Good only for entertainment value... and, of course, the government"). At that time, the main concern was standardization of records:

Providing diagnostic value for SNP screens or genome sequences will take a massive effort at standardizing information about joint gene-phenotype associations. Direct-to-consumer gene testing companies presently differentiate themselves based on the different information they provide to their customers. That approach works as long as there is little of value in the results -- the companies today are succeeding or failing on the basis of the communities of customers they are building, with the stories of customers providing the best advertisements. That's the nutrition supplement market.

But that approach will start to fail if genetic tests start to allow serious risk mitigation in health maintenance. If two companies provide divergent information to customers, in a way that impacts the customers' interactions with their physicians, I expect that the outcome will be some massive lawsuits and further federal regulation. If the government becomes the health care purchaser -- and with Medicare it already is the largest -- we can expect to see early federal intervention in this market, focused upon standardizing genetic information provided to physicians.

The creation of an NIH registry may reflect growing surveillance of the different interpretive results from these tests, with an eye toward future government purchasing protocols.

The new registry announcement is discussed in more detail by Dan Vorhaus at Genomics Law Report: "Evaluating the NIH’s New Genetic Testing Registry. " He gives some background relating to the 2008 report “U.S. System of Oversight of Genetic Testing” (PDF), commissioned by the Secretary’s Advisory Committee on Genetics, Health, and Society (SACGHS) during the Bush Administration. As Vorhaus points out:

Although the SACGHS report acknowledged that “short-term voluntary approaches” to test registration might be appropriate, it also clearly indicated that the fundamental objective was the creation of a permanent and mandatory test registry.

This important distinction has not been lost on others. In a press release celebrating the GTR, the advocacy group Genetic Alliance (whose founder, Sharon Terry, has been one of the most outspoken advocates for a mandatory registry, including making the case several months ago in this very space) applauded the NIH’s announcement while simultaneously looking forward “to the registry becoming mandatory so that we are all apprised of the quality and availability of genetic testing across the nation.” (links in original)

Probably the most important element is the involvement (for now, at the level of consultation) of Medicare. Companies that want a piece of that market will be more or less compelled to join the registry:

Depending on the degree to which purchasers of genetic tests come to rely on the GTR, inclusion in the GTR may well become a de facto requirement for any commercial genetic test provider, even if it is not converted into a legal requirement.

In the place of "purchasers" read "Medicare". And of course insurance companies have similar incentives to require tests that participate in the registry.

(via Collective Imagination blog, and Genetic Future)

I'm attending a symposium on genetics and genealogy of the African Diaspora this morning. Fatimah Jackson is here giving a very interesting talk about her genetic work in Africa and African-Americans, and in particular her idea of "ethnogenetic layering" (Jackson 2008), which is basically a strategy for describing the fine-scale makeup of present-day populations by examining their genetic ancestry from different regions of the Old World.

Part of her research has involved characterizing the regional distribution of mtDNA haplotypes within African populations. She shared some newer data with us, but I thought it worth pointing people to an earlier publication by Bert Ely, Jackson and others (2006), which gave rise to some strong insights about the poverty of current sampling of African populations.

The study reports on a sample of 3725 mtDNA sequences (HVS-I) from a diversity of sub-Saharan African populations. That's quite a massive sample of sequences, certainly on the scale that had been available earlier. It is substantially more numerous than

When a sample of 74 Gullah/Geechee mtDNA sequences were compared with the sub-Saharan database, approximately half of the mtDNAs were identical to two or more mtDNAs in the database and only seven mtDNAs matched mtDNAs from a single ethnic group. The remaining 28 mtDNAs were not identical to any sequence in the expanded database.

Similar results were obtained when the 97 African-American AFDIL mtDNAs were compared with the databases. Approximately half (49) of the mtDNAs were identical to multiple sequences in the original database. As with the Gullah/Geechee sample, fewer than 10% of the sequences matched a sequence from a single ethnic group, and 40% of the sequences did not have any perfect match in the database (Ely et al. 2006:3).

There are two aspects worth noting in those results. On the one hand, the common haplotypes -- the ones that the African-American samples were likely to have a match to -- were not regionally specific within Africa. They are shared by many ethnic groups, distributed across the continent.

On the other hand, 40% of the African-American sequences have no match among the nearly 4000 sequences taken from continental Africa. That's astounding to me, just from the standpoint of sampling. Most of the common haplotypes will emerge within a relatively small sample, so to find something you haven't already seen, you have to sample disproportionately more -- in fact, exponentially more -- individuals. You can just imagine how many tens or hundreds of thousands of sequences you would have to gather to have an adequate representation of African mtDNA for this purpose -- the purpose of finding matches for a large fraction (say, more than 90 percent) of African-American mtDNA haplotypes that originated in Africa (there are of course a substantial fraction whose recent maternal ancestry originated somewhere else).

One of the features of the symposium is a discussion of the relevance of ancestry testing. Jackson is an expert in this field and well-recognized -- she appeared in several of the "African-American Lives" episodes, for example.

With several companies and organizations now offering various kinds of ancestry tests, these have become increasingly affordable. But the results are often confusing; people don't know how to interpret them. Some of that confusion was evidenced in questions here at the symposium -- as part of a year-long discussion group, several local people submitted cheek swabs for ancestry interpretation. The results are often poor, because the sampling of recent populations is inadequate to really answer many questions. Where were today's populations 300 years ago? Have we adequately sampled the variation of present populations.

Research like Jackson's has shown that even widespread and numerous samples provide a real poverty of information about mtDNA diversity. The situation is vastly worse if we turn to autosomal variation, because the samples are smaller and more scattered.

Of course, for many anthropological purposes, the samples we have today are tremendously useful. My work on recent selection, for example, has made leaps and bounds on samples of a few hundred individuals.

But the converse case -- you take a person and ask whether you can diagnose their origin -- that task requires much larger samples to gain any statistical confidence in the general case. There may be specific haplotypes that are highly specific as to their present distribution -- but then, all of those are rare haplotypes, and you have to be lucky enough to have it within the comparative sample that the organization or company has gathered.

I'm still listening here and some of the later presentations will touch on the issues of genetic ancestry testing more directly. But I thought I would share a quote I really liked, with which Jackson ended her comments:

I'm not against genetic ancestry testing. It's fun. But in the final analysis, you have to look in the mirror, and you decide who you are.

Related posts:

Skip Gates discovers that genetic tests don't mean what he thought they meant.

Anne Wojcicki from 23andMe comments on genomics and race

Unintended consequences of genetic ancestry tests

References:

Ely B, Wilson JL, Jackson F, Jackson BA. 2006. African-American mitochondrial DNAs often match mtDNAs found in multiple African ethnic groups. BMC Biology 4:34. doi:10.1186/1741-7007-4-34

Jackson FLC. 2008. Ethnogenetic layering (EL): an alternative to the traditional race model in human variation and health disparity studies. Ann Hum Biol 35:121-144. doi:10.1080/03014460801941752

A couple of weeks ago, the Texas Tribune reported on an investigation of the archiving of blood samples taken from newborn infants: "DNA Deception".

For decades, the state has screened newborns for a variety of birth defects, pricking their heels and collecting five drops of blood on a paper card. Until 2002, the cards were thrown out after a short storage period. But starting that year, the state health department began storing blood spots indefinitely, for “research into causes of selected diseases.” Four years later, DSHS began contracting with Texas A&M University’s School of Rural Public Health to warehouse the cards, which were accumulating at a rate of 800,000 a year. State health officials never notified parents of the changes; they didn’t need consent for the birth-defect screening, so they didn’t ask for it for research purposes. The agency’s rationale was that it let parents who asked opt out of the newborn blood screening and de-identified all of the samples before shipping them off (emphasis added).

So much for informed consent. "We're from the government, and we're here to help you."

The state was sued by parents last year and rapidly settled the lawsuit before pre-trial discovery. Now, it is suspected that the state was trying to avoid drawing attention to some of the uses of the blood samples -- including several hundred which were used to develop a forensics database of mtDNA variants.

E-mails indicate that in 2003, when the agency started to release blood spots for outside research, officials knew they had a parental consent issue on their hands — but tried to avoid it. When a researcher proposed a project, the director of birth defects monitoring wrote that he’d “prefer to not have to go through” the process of getting consent. Another agency official responded that parents "never consented for blood spots to be used for research. … On the other hand, I believe [the health department] already uses (deidentified?) blood spots for some research, so that might not be a big deal.”

All states now test for metabolic disorders in newborns; the tests require only a blood spot on treated paper. The National Newborn Screening and Genetics Resource Center has more information on the specific tests and a very up-to-date list by state. It is amazing to me, as someone who has had four kids in the last ten years, just how quickly these screening programs became universal.

It is therefore hard for me to believe that Texas is going to be an exception. Surely we'll discover that some other states are archiving these blood samples instead of destroying them? Checking them out to researchers for no-consent research?