Genomic ancestry tests are not cons, part 1

As someone who is part of the personal genomics sector, I keep track of media representations of the industry very closely. There is the good and the bad, some justified and some not.

But there is one aspect which I need to weigh in on because it is close to my interests and professional focus, and it is one where I have a lot of experience: ancestry inference on human data.

Periodically I see in my Twitter timeline an article shared by a biologist which is filled with either misrepresentation, confusions, and even falsehoods. Of course, some of the criticisms are correct. The problem is that when you mix truth and falsehood or sober analysis and critique with sensationalism the whole product is debased.

I’m going to address some of the most basic errors and misimpressions. This post is “part 1” because I might have follow-ups, as I feel like this is a situation where I have to put out fires periodically, as people write about things they don’t know about, and then those articles get widely shared to a credulous public.

First, if an article mentions STRs or microsatellites or a test with fewer than 1,000 markers in a direct to consumer genomic context, ignore the article. This is like an piece where the author dismisses air travel because it’s noisy due to propeller-driven planes. Propeller-driven planes are a very small niche. Similarly, the major direct to consumer firms which have sold close to ~10 million kits do not use STRs or microsatellites, very much a technology for the 1990s and 2000s. Any mention of STRs or microsatellites or low-density analyses indicate the journalist didn’t do their homework, or simply don’t care to be accurate.

Second, there is constant harping on the fact that different companies give different results. This is because tests don’t really give results as much is interpretations. The raw results consist of your genotype. On the major SNP-chip platforms this will be a file on the order of 20 MBs. The companies could provide this as the product, but most humans have difficulty grokking over 100,000 variables.

So what’s the solution? The same that scientists have been using for decades: reduce the variation into a much smaller set of elements which are human digestible, often through tables or visualization.

For example, consider a raw data set consisting of my three genotypes from 23andMe, Ancestry, and Family Tree DNA. Merged with public data these are ~201,000 single nucleotide markers. You can download the plink formatted data yourself and look at it. The PCA below shows where my three genotypes are positioned, by the Tamil South Asians. Observe that my genotypes are basically at the same point:

The differences between the different companies have nothing to do with the raw data, because with hundreds of thousands of markers they capture enough of the relevant between population differences in my genome (do you need to flip a coin 1 million times after you’ve flipped it 100,000 times to get a sense of whether it is fair?). The law of large numbers is kicking in at this point, with genotyping errors on the order of 0.5% not being sufficient to differentiate the files.

Sure enough raw genotype files of the three services match pretty closely. 99.99% for Family Tree DNA and 23andMe, 99.7% for Family Tree DNA and Ancestry, and 99.6% for Ancestry and 23andMe. For whatever reason Ancestry is the outlier here. My personal experience looking at genotype data from Illumina chips is that most are pretty high quality, but it’s not shocking to see instances with 0.5% no call or bad call rates. For phylogenetic purposes if the errors are not systematic it’s not a big deal.

The identity to other populations is consistent. About 74% to Tamils. 72-73% for other Eurasians. 71% for the Surui, an isolated Amazonian group. And 69% to Yoruba. Observe that this recapitulates the phylogenetic history of what we know for the population which I am from, Bengalis. The greater the genetic distance between two populations due to distinct evolutionary histories the greater the genetic divergence. This is not rocket science. This gets to the point that the raw results make a lot more sense when you integrate and synthesize them with other information you have. Most customers are not going into the process of getting a personal genomic ancestry test blind…but that causes pitfalls as well as opportunities.

But most people do not receive statistics of the form:

SNP Identity
You Yoruba 0.69
You German 0.72
You Japanese 0.73
You Tamil 0.74

Mind you, this is informative. It’s basically saying I am most genetically distant from Yoruba and closer in sequence to Tamils. But this is somewhat thin gruel for most people. Consider the below which is a zoom in of PC 2 vs. PC 4. I am blue and the purple/pink are Tamils, and the population at the bottom left are East Asians.

If you looked at enough PCA plots it will become rather clear I am shifted toward East Asians in comparison to most other South Asians. The high identity that I have with Japanese and Dai is due in part to the fact that I have relatively recent admixture from an East Asian population, above and beyond what is typical in South Asians. Remember, all three of my genotypes are basically on the same spot on PCA plots. That’s because they’re basically the same. Genotyping error is rather low.

How do we summarize this sort of information for a regular person? The standard method today is giving people a set of proportions with specific population labels. Why? People seem to understand population labels and proportions, but can be confused by PCA plots. Additionally, the methods that give out populations and proportions are often better at capturing pulse admixture events relatively recent in time than PCA, and for most consumers of ancestry services, this is an area that they are particularly focused on (i.e., Americans).

An easy way to make one’s genetic variation comprehensible to the general public is to model them as a mixture of various populations that they already know of. So consider the ones above in the plink file. I ran ADMIXTURE in supervised model progressively removing populations for my three genotypes. The results are below.

  Dai Druze German Japanese Papuan Sardinian Surui Tamil Yoruba
Razib23andMe 11% 3% 8% 4% 1% 0% 1% 73% 1%
RazibAncestry 10% 2% 8% 4% 1% 0% 1% 73% 1%
RazibFTDNA 11% 2% 8% 3% 1% 0% 1% 72% 1%
  Dai Druze German Japanese Papuan Sardinian Surui Tamil  
Razib23andMe 11% 3% 8% 4% 1% 0% 1% 73%  
RazibAncestry 10% 3% 8% 4% 1% 0% 1% 74%  
RazibFTDNA 11% 3% 8% 3% 1% 0% 1% 73%  
  Dai Druze Japanese Papuan Surui Tamil      
Razib23andMe 10% 9% 4% 1% 1% 74%      
RazibAncestry 10% 9% 4% 1% 1% 75%      
RazibFTDNA 11% 9% 4% 1% 1% 74%      
  Dai Japanese Surui Tamil          
Razib23andMe 11% 4% 1% 84%          
RazibAncestry 10% 4% 1% 85%          
RazibFTDNA 11% 3% 1% 84%        

Please observe again that they are broadly congruent. These methods exhibit a stochastic element, so there is some noise baked into the cake, but with 200,000+ markers and a robust number of reference populations the results come out the same across all methods (also, 23andMe and Family Tree DNA seem to correlate a bit more, which makes sense since these two genotypes are more similar to each other than they are to Ancestry).

Observe that until I remove all other West Eurasian populations the Tamil fraction in my putative ancestry is rather consistent. Why? Because my ancestry is mostly Tamil-like, but social and historical evidence would point to the likelihood of some exogenous Indo-Aryan component. Additionally, seeing as how very little of my ancestry could be modeled as West African removing that population had almost no impact.

When there were three West Eurasian populations, Germans, Druze, and Sardinians, the rank order was in that sequence. Removing Germans and Sardinians and the Druze picked up most of that ancestral component. This a supervised method, so I’m assigning the empirical populations as reified clusters which can be used to reconstitute the variation you see in my own genotype. No matter what I put into the reference data, the method tries its best to assign proportions to populations.

The question then comes into the stage of subtle choices one makes to obtain the most informative inferences for the customer. These are not always matters of different results in terms of accuracy or precision, but often of presentation. If West Eurasian populations are removed entirely, my Tamil fraction inflates. That’s the closest to the West Eurasian populations left in the data. In contrast, the East Asian fraction remains the same because I’ve left the two proxy populations in the data (I rigged the die here because I know I have Tibeto-Burman admixture which is a combination of Northeast and Southeast Asian).

Let’s do something different. I’m going to swap out the West Eurasian populations with equivalents.

  Armenians Dai French_Basque Japanese Mandenka Surui Sweden Tamil
Razib23andMe 6% 11% 0% 4% 1% 1% 5% 72%
RazibAncestry 5% 11% 0% 4% 1% 1% 5% 73%
RazibFTDNA 6% 11% 0% 4% 1% 1% 5% 72%
German Papuan Yoruba          
Razib23andMe 68% 20% 13%          
RazibAncestry 68% 20% 13%          
RazibFTDNA 68% 20% 13%          
French_Basque Tamil            
Razib23andMe 8% 92%            
RazibAncestry 7% 93%            
RazibFTDNA 8% 92%            
Tamil Yoruba            
Razib23andMe 97% 3%            
RazibAncestry 97% 3%            
RazibFTDNA 97% 3%          

I have no ancestry from French Basque, but I do have ancestry from Armenians and Swedes in this model. Why? If you keep track of the most recent population genomic ancestry this all makes sense. But if you don’t, well, it’s harder to unpack. This is part of the problem with these sorts of tests: how to make it comprehensible to the public while maintaining fidelity to the latest research.

This is not always easy, and differences between companies in terms of interpretation are not invidious as some of the press reports would have you think, but a matter of difficult choices and trade-offs one needs to make to give value to customers. True, this could all be ironed out if there was a ministry of genetic interpretation and a rectification of names in relation to population clusters, but right now there isn’t. This allows for both brand differentiation and engenders confusion.

In most of the models with a good number of populations, my Tamil ancestry is in the low 70s. Notice then that some of these results are relatively robust to the populations one specifies. Some of the patterns are so striking and clear that one would have to work really hard to iron them out and mask them in interpretation. But what happens when I remove Tamils and include populations I’m only distantly related to? This is a ridiculous model, but the algorithm tries its best. My affinity is greatest to Germans, both because of shared ancestry, and in the case of Papuans, their relatively high drift from other East Eurasians and Denisovan ancestry. But both Papuan and Yoruba ancestry are assigned because I’m clearly not 100% German, and I share alleles with both these populations. In models where there are not enough populations to “soak up” an individual’s variation, but you include Africans, it is not uncommon for African ancestry to show up at low fractions. If you take Europeans, Africans, and East Asians, and force two populations out of this mix, then Europeans are invariably modeled as a mix of Africans and East Asians, with greater affinity to the latter.

Even when you model my ancestry as Tamil or Yoruba, you see that there is a Yoruba residual. I have too much genetic variation that comes from groups not closely related to the variation you find in Tamils to eliminate this residual.

Just adding a few populations fixes this problem:

  Dai Tamil Yoruba  
Razib23andMe 14% 83% 2%  
RazibAncestry 14% 84% 2%  
RazibFTDNA 14% 83% 2%  
  Dai German Tamil Yoruba
Razib23andMe 15% 10% 74% 1%
RazibAncestry 14% 9% 75% 1%
RazibFTDNA 15% 10% 74% 1%

Notice how my Tamil fraction is almost the same as when I had included in many more reference populations. Why? My ancestral history is complex, like most humans, but it’s not that complex. The goal for public comprehensibility is to reduce the complexity into digestible units which give insight.

Of course, I could just say read Inference of Population Structure Using Multilocus Genotype Data. The basic framework was laid out in that paper 17 years ago for model-based clustering of the sort that is very common in direct to consumer services (some use machine learning and do local ancestry decomposition across the chromosome, but really the frameworks are an extension of the original logic). But that’s not feasible for most people, including journalists.

Consider this piece at Gizmodo, Why a DNA Test Is Actually a Really Bad Gift. I pretty much disagree with a lot of the privacy concerns, seeing as how I’ve had my public genotype downloadable for seven years. But this portion jumped out at me: “Ancestry tests are based on sound science, but variables in data sets and algorithms mean results are probabilities, not facts, as many people expect.”

Yes, there are probabilities involved. But if a DNA test using the number of markers above tells you you are 20% Sub-Saharan African and 80% European in ancestry, that probability is of the same sort of confidence of you determining that a coin flip is fair after 100,000 flips. True, you can’t be totally sure after 100,000 flips that you have a fair coin, but you can be pretty confident. With hundreds of thousands of markers, a quantum of 20% Sub-Saharan African in a person of predominantly European heritage is an inference made with a degree of confidence that verges upon certitude within a percentage or so.

As for the idea that they are not “facts.” I don’t even know what that means in this context. And I doubt the journalist does either. Which is one of my main gripes with these sorts of stories: unless they talk to a small subset of scientists the journalists just don’t know what they are talking about when it comes to the statistical genetics.

Finally, there is the issue about what does it even mean to be % percent of population X, Y, or Z? Even many biologists routinely reify and confuse the population clusters with something real and concrete in a Platonic sense. But deep down when you think about it we all need to recall we’re collapsing genealogies of many different segments of DNA into broad coarse summaries when we say “population.” And populations themselves are by their nature often somewhat open and subject to blending and flow with others. A population genomic understanding of structure does not bring into clarity Platonic facts, but it gives one instruments and tools to smoke out historical insight.

The truth, in this case, is not a thing in and of itself, but a dynamic which refines our intuitions of a fundamentally alien process of Mendelian assortment and segregation.

Razib Khan’s raw genotype data on 23andMe, Family Tree DNA, Geno 2.0 and Ancestry

It has been a while since I posted an update on my genotype. Since then I’ve been tested on most of the major platforms. I don’t see any harm in releasing this to the public or researchers who want to look at it (though I don’t know why anyone would).

You can download all the files here.

Having my genotypes public is pretty useful for me. If I inquire about someone’s genetics oftentimes people get weirdly defense and ask “what about you?” I Just invite them to look at my raw data and analyze it for themselves! I’m not a hypocrite about this.

Over the years I’ve had researchers inquire about my ethnicity when they stumble upon my genotype on platforms such as openSNP. So in full disclosure, most of my ancestry is pretty standard eastern Bengali. I’m more East Asian shifted than most Bangladeshi samples in the 1000 Genomes project, but then my family is from Comilla, in the far east of eastern Bengal (anyone who cares, my Y is of course R1a1a-Z93 and my mtDNA U2b).

As before I’ll put the genotype under a Creative Commons license:Creative Commons License

$9.99 to get into the Helix exome ecosystem

Will try to keep self-interested product placement to a minimum normally, but I thought I’d pass on that Helix has a $100 off sale for the next 72 hours. That means that the company I work for has a Neanderthal app on sale for $9.99. The regular price is $29.99, and added $80.00 for exome+ sequencing if you aren’t in the Helix database (which most people are not).

The upshot here is that the $9.99 will get you an exome+ sequence, which at some point in early 2018 you can download for $600. But if you don’t want to download it it’s a great way to get into the ecosystem on-the-cheap.

I assume most of my readers know what the exome is, but it’s basically the portion of your genome which is directly translated into functional proteins. That’s about ~1% of the genome, or ~30,000,000 bases. This is a major expansion on the SNP-chip platforms which are DTC which are in the 500,000 to 1,000,000 SNP range.

Anyway, not sure this will be appealing to readers who need a full download of data. But if you are the type who is more interested in getting applications related to your genome, this is a pretty good deal at a sub-$10 price point.

Note: To my knowledge only ships to USA currently.


Four years ago my friend David Mittelman and I wrote Rumors of the death of consumer genomics are greatly exaggerated. The context was the FDA crackdown on 23andMe. Was the industry moribund before it began? The title gives away our opinion. We were personally invested. David and I were both working for Family Tree DNA, which is part of the broader industry. But we were sincere too.

Both of us have moved on to other things. But we still stand by our original vision. And to a great extent, we think we had it right. The consumer genomics segment in DTC is now nearing 10 million individuals genotyped (Ancestry itself seems to have gone north of 5 million alone).

One of the things that we observed in the Genome Biology piece is that personal genomics was still looking for a “killer app”, like the iPhone. Since then the Helix startup has been attempting to create an ecosystem for genomics with a variety of apps. Though ancestry has driven nearly ten million sales, there still isn’t something as ubiquitous as the iPhone. We’re still searching, but I think we’ll get there. Data in search of utility….

David and I are still evangelizing in this space, and together with another friend we came up with an idea: DNAGeeks. We’re starting with t-shirts because it’s something everyone understands, but also can relay our (and your) passion about genomics. We started with “Haplotees.” Basically the most common Y and mtDNA lineages. This might seem silly to some, but it’s something a lot of people have an interest in, and it’s also a way to get ‘regular people’ interested in genetics. Genealogy isn’t scary, and it’s accessible.

We are also field-testing other ideas. If there is a demand we might roll out a GNXP t-shirt (logo only?). The website is obscure enough that it won’t make sense to a lot of people. But perhaps it will make sense to the people who you want it to make sense to!

Anyway, as they say, “keep watching this space!” We don’t know where DNAGeeks is going, but we’re aiming to have fun with genomics and make a little money too.

When journalists get out of their depth on genetic genealogy

For some reason The New York Times tasked Gina Kolata to cover genetic genealogy and its societal ramifications, With a Simple DNA Test, Family Histories Are Rewritten. The problem here is that to my knowledge Kolata doesn’t cover this as part of her beat, and so isn’t well equipped to write an accurate and in depth piece on the topic in relation to the science.

This is a general problem in journalism. I notice it most often when it comes to genetics (a topic I know a lot about for professional reasons) and the Middle East and Islam (topics I know a lot about because I’m interested in them). It’s unfortunate, but it has also made me a lot more skeptical of journalists whose track record I’m unfamiliar with.* To give a contrasting example, Christine Kenneally is a journalist without a background in genetics who nevertheless is immersed in genetic genealogy, so that she could have written this sort of piece without objection from the likes of me (she did write a book on the topic, The Invisible History of the Human Race: How DNA and History Shape Our Identities and Our Futures, which I had a small role in fact-checking).

What are the problems with the Kolata piece? I think the biggest issue is that she didn’t go in to test any particular proposition, and leaned on the wrong person for the science. She quotes Joe Pickrell, who knows this stuff like the back of his hand. But more space is given to Jonathan Marks, an anthropologist who is quite opinionated and voluble, and so probably a “good source” for any journalist.

Marks seems well respected in anthropology from what I can tell, but he’s also the person who put up a picture of L. L. Cavalli-Sforza juxtaposed with a photo of Josef Mengele in the late 1990s during a presentation at Stanford. Perhaps this is why anthropologists respect him, I don’t know, but I do not like him because of his nasty tactics (I wouldn’t be surprised if Marks had power he would make sure people like me were put in political prison camps, his rhetoric is often so unhinged).

Marks’ quotes wouldn’t be much of an issue if Kolata could figure out when he’s making sense, and when he’s just bullshitting. But she can’t. For example:

…“tells me I’m 95 percent Ashkenazi Jewish and 5 percent Korean, is that really different from 100 percent Ashkenazi Jewish and zero percent Korean?”

The precise numbers offered by some testing services raise eyebrows among genetics researchers. “It’s all privatized science, and the algorithms are not generally available for peer review,” Dr. Marks said.

The part about precise numbers is an issue, though a lot less of an issue with high density SNP-chips (the real issue is sensitivity to reference population and other such parameters). But if a modern test says you are 95 percent Ashkenazi Jewish and 5 percent Korean it really is different from 100% Ashkenazi. Someone who comes up as 5% Korean against an Ashkenazi Jewish background is most definitely of some East Asian heritage. In the early 2000s with ancestrally informative markers and microsatellite based tests you’d get somewhat weird results like this, but with the methods used by the major DTC companies (and in academia) today these sorts of proportions are just not reported as false positives. Marks may not know because this isn’t his area, but Pickrell would have. Kolata probably did not think to double-check with him, but that’s because she isn’t able to smell out tendentious assertions. She has no feel for the science, and is flying blind.

Second, Marks notes that the science is privatized, and it isn’t totally open. But it’s just false that the algorithms are not generally available for peer review. All the details of the pipeline are not downloadable on GitHub, but the core ancestry estimation methods are well known. Eric Durand, who wrote the originally 23andMe ancestry composition methodology presented on it at ASHG 2013. I know because I was there during his session.

You can find a white paper for 23andMe’s method and Ancestry‘s. Not everything is as transparent as open science would dictate (though there are scientific papers and publications which also mask or hide elements which make reproducibility difficult), but most geneticists with domain experience can figure out what’s going on and it if it is legitimate. It is. The people who work at the major DTC companies often come out of academia, and are known to academic scientists. This isn’t blackbox voodoo science like “soccer genomics.”

Then Marks says this really weird thing:

“That’s why their ads always specify that this is for recreational purposes only: lawyer-speak for, ‘These results have no scientific standing.’”

Actually, it’s lawyer-speak for “do not sue us, as we aren’t providing you actionable information.” Perhaps I’m ignorant, but lawyers don’t get to define “scientific standing”.

The problem, which is real, is that the public is sometimes not entirely clear on what the science is saying. This is a problem of communication from the companies to the public. I’ve even been in scientific sessions where geneticists who don’t work in population genomics have weak intuition on what the results mean!

Earlier Kolata states:

Scientists simply do not have good data on the genetic characteristics of particular countries in, say, East Africa or East Asia. Even in more developed regions, distinguishing between Polish and, for instance, Russian heritage is inexact at best.

This is not totally true. We have good data now on China and Japan. Korea also has some data. Using haplotype-based methods you can do a lot of interesting things, including distinguish someone who is Polish from Russian. But these methods are computationally expensive and require lots of information on the reference samples (Living DNA does this for British people). The point is that the science is there. Reading this sort of article is just going to confuse people.

On the other hand a lot of Kolata’s piece is more human interest. The standard stuff about finding long lost relatives, or discovering your father isn’t your father. These are fine and not objectionable factually, though they’ve been done extensively before and elsewhere. I actually enjoyed the material in the second half of the piece, which had only a tenuous connection to scientific detail. I just wish these sorts of articles represented the science correctly.

Addendum: Just so you know, three journalists who regularly cover topics I can make strong judgments on, and are always pretty accurate: Carl Zimmer, Antonio Regalado, and Ewen Callaway.

* I don’t follow Kolata very closely, but to be frank I’ve heard from scientist friends long ago that she parachutes into topics, and gets a lot of things wrong. Though I can only speak on this particular piece.

The cultural revolution that will happen in China

If you’ve been hiding under a rock, please see Baby Genome Sequencing for Sale in China:

A Boston-based DNA sequencing company is offering to decode the complete genomes of newborns in China, leading some to ask how much parents should know about their children’s genes at birth.

Veritas Genetics says the test, ordered by a doctor, will report back on 950 serious early- and later-life disease risks, 200 genes connected to drug reactions, and more than 100 physical traits a child is likely to have.

Called myBabyGenome, the service costs $1,500 and could help identify serious hidden problems in newborns, the company says.

Obviously a $1,500 price point is beyond most Chinese. But the total fertility rate in China today is 1.56, and almost certainly lower among urban elites who could actually afford this service. Considering that genome is with you for life, more or less, $1,500 isn’t really that much.

For years there have been many fly by night genetics companies who have some presence in East Asia. They come and go. The combination of a lax regulatory environment and the allure of “genes” means that there is some money to be made. Veritas in contrast is a serious company. This is heralding the reality that widespread sequencing is going to go primetime.

Of course there is skepticism. Fortunately the piece doesn’t talk much to people who express Leon Kass style FUD. The objections are scientific:

But some doctors say the plan is a huge overstep. “I think it’s vastly premature to peddle a completely unproven set of data, especially to a vulnerable population like neonates,” says Jim Evans, a professor of genetics at the University of North Carolina Chapel Hill.

The problem is that the risk posed by many disease genes remains uncertain. Even if a child has a mutation in a gene, he or she may never be affected, prompting debate among doctors about whether it’s useful to inform parents.

These are real points, but I don’t see why sequencing should not be allowed as a choice by parents nevertheless. And widespread adoption of sequencing would actually allow us to start obtaining more data that we could use to make better decisions in the future. The genomics space for a while has had a bit of a chicken & egg problem. The sequencing is starting to get there, but we still need to get phenotypes. And this seems like one way to get the public involved and feel like they have a stake.

Like in vitro fertilization I see neonatal sequencing as inevitable in regards to whether it will be ubiquitous. It will be common. And “ethical problems” won’t really come up in the future because people will be comfortable with it. Right now there’s a fear factor.

This is in sharp contrast with CRISPR. That’s a technology that will have real consequences, and like male circumcision may be difficult to reverse for someone who as an adult was modified in infancy. In contrast a genome sequence is just more information.

For years may people have speculated that genetic testing technology will really begin to take off in Asia because for various reasons Americans and Europeans are wary. To me this is a signal that that might be occurring, depending on how successful Veritas’ venture is.

This is perhaps the most important point:

But Veritas will not reveal everything, in recognition that not all the information in the genome is appropriate to give parents right away. For instance, it won’t tell them about a gene that can strongly predispose people to Alzheimer’s in old age.

Instead, Veritas says, it will retain the rest of a newborn’s genome data and let parents purchase further information at a later date.

I think this may be more common than we think. Most people don’t want to deal with a ~3 GB text file. That being said, I think they should in the future have an option for people to buy their data outright.

Addendum: I met Robert Green at SxSW. Nice fellow. One thing that he mentioned was widespread opposition among physicians. I suspect one dynamic that MDs won’t like in the near future is decentralization of the distribution of medical services away from their own profession. This is part of it.

10 million DTC dense marker genotypes by end of 2017?

Today I got an email from 23andMe that they’d hit the 2 million customer mark. Since they reached their goal of 1 million kits purchased the company seems to have taken its foot off the pedal of customer base growth to focus on other things (in particular, how to get phenotypic data from those who have been genotyped). In contrast Ancestry has been growing at a faster rate of late. After talking to Spencer Wells (who was there at the beginning of the birth of this sector) we estimated that the direct-to-consumer genotyping kit business is now north of 5 million individuals served. Probably closer to 6 or 7 million, depending on the numbers you assume for the various companies (I’m counting autosomal only).

This pretty awesome. Each of these firm’s genotype in the range of 100,000 to 1 million variant markers, or single nucleotide base pairs. 20 years ago this would have been an incredible achievement, but today we’re all excited about long-read sequencing from Oxford Nanopore. SNP-chips are almost ho-hum.

But though sequencing is the cutting edge, the final frontier and terminal technology of reading your DNA code, genotyping in humans will be around for a while because of cost. At ASHG last year a medical geneticist was claiming price points in bulk for high density SNP-chips are in the range of the low tens of dollars per unit. A good high coverage genome sequence is still many times more expensive (perhaps an order of magnitude ore more depending on who you believe). It also can impose more data processing costs than a SNP-chip in my experience.

Here’s a slide from Spencer:

I suspect genotyping will go S-shaped before 2025 after explosive growth in genotyping. Some people will opt-out. A minority of the population, but a substantial proportion. At the other extreme of the preference distribution you will have those who will start getting sequenced. Researchers will begin talk about genotyping platforms like they talk about microarrays (yes, I know at places like the Broad they already talk about genotyping like that, but we can’t all be like the Broad!).

Here’s an article from 2007 on 23andMe in Wired. They’re excited about paying $1,000 genotyping services…the cost now of the cheapest high quality (30x) whole genome sequences. Though 23andMe has a higher price point for its medical services, many of the companies are pushing their genotyping+ancestry below $100, a value it had stabilized at for a few years. Family Tree DNA has a father’s day sale for $69 right now. Ancestry looks to be $79. The Israel company MyHeritage is also pushing a $69 sale price (the CSO there is advertising that he’s hiring human geneticists, just so you know). It seems very likely that a $50 price point is within site in the next few years as SNP-chip costs become trivial and all the expenses are on the data storage/processing and visualization costs. I think psychologically for many people paying $50 is not cheap, but it is definitely not expensive. $100 feels expensive.

Ultimately I do wonder if I was a bit too optimistic that 50% of the US population will be sequenced at 30x by 2025. But the dynamic is quite likely to change rapidly because of a technological shift as the sector goes through a productivity uptick. We’re talking about exponential growth, which humans have weak intuition about….

Addendum: Go into the archives of Genomes Unzipped and reach the older posts. Those guys knew where we were heading…and we’re pretty much there.