The screenshot to the right is from my updated 23andMe results. The inference that my ancestry is from “Chittagong Division” is 100% correct. More precisely, my family is from Comilla. There are some cases where consumer genomics tells you exactly what you know, and this is one of those. 23andMe has an excellent method to infer ancestry, and the power of a massive database. If you want to see how they do it, check out their new preprint. It’s pretty fancy.

What would be more informative is if they let me see what it means to be from Chittagong Division compared to other Bengalis. Or to be Bengali compared to other South Asians. You probably already know your recent history through basic family genealogy, but what do these results tell you about your deep history and your relatedness to global populations?

Of course, realistically even the largest direct-to-consumer genomics companies can only deliver so much, because they are simultaneously serving millions of customers. A custom approach isn’t a feasible ask, even if it is what many consumers are longing for. Little surprise then that people have been reaching out to me about anomalies in their results for over a decade. People like me who got no value-added information (as in: they already know where their great-grandparents were born) reach out too. We’re driven to know more.

That is why this coming week I’m offering a first workshop through Speakeasy titled Analyze Your 23andMe and Ancestry Data (and N.B. it didn’t make the title but I’ve prepared everything to work right on results from Family Tree DNA as well). It’s Wednesday, Jan 27, 5pm PT/8 pm ET. 

Here’s how it will work. You’ll arrive in class with your (or a friend’s or relative’s) 23andMe, Ancestry or Family Tree DNA raw data. Before class you’ll get a zip folder with all the files and utilities you need for class. You’ll have downloaded R & R Studio if you don’t already have them (I include instructions, but don’t worry, this is quick and easy!). And you’ll want to decide whether to Zoom into the meeting on one device and access your data on the other (or work on a two-screen setup if you’re on a desktop); neither is essential, but I’d consider them nice to have. 

At that point you’re ready for the workshop. And we’ll get straight into digging into your data. You can come to class with a question or questions about your ancestry. Or I can help you zero in on what might be interesting given what you already know.

Over the course of the hour, I’ll guide you through the use of three tools. No lecture, just hands-on doing, with your own actual data. Two of your tools are open-source utilities written by academics for their colleagues that have been in wide use in genomics for over a decade. Even long-time readers who aren’t here for the genetics will recognize Plink and Admixture, which I’ve referenced on this blog thousands of times. 

In addition to easing you right into using these core tools of the trade (without any of the usual slow initial learning curve), I have built an automated pipeline just for participants in the class. This is your third tool, which will save you untold startup hours no matter who you are. I’ve created an automated workflow so that you can input your raw data from any of those three DTC genetics companies and analyze it (including automatically generating formatted output) against your choice of reference populations (a library of which I’ve also prepared for you) and 2. automatically plot and visualize your results in a flexible, customizable format.

I have written all the scripts for you in order to create a custom, automated pipeline. This draws on my years of experience using these tools and guiding others. Then, the bespoke reference population library of human genomes I’ve curated for this purpose instantly equips you to measure your relatedness to any branch or branches of humanity (you get 5000 human genotypes culled from public datasets and selected to represent 250 distinct populations, on a quarter of a million markers (SNPs).

And in class I will teach you and guide you through using your toolkit in real time. Getting started in Plink and Admixture can entail hours of trouble-shooting and false starts. A decade into using them, I know the quirks and idiosyncrasies of these programs all too well; and that’s why I’ve built a pipeline that allows you to leapfrog over those slow early steps and get right into your (or any) genetic data. Building and merging a reference panel from publicly available sources is time-consuming and a headache and the individuals don’t come clearly labeled by population. I’ve got everything ready and clearly identified for you. With these two headstarts, and lots of pointers about best practices along the way, you’ll be asking and answering (and outputting visualizations of) your own questions in your first hour.

By the end of the workshop, you’ll have the skills and the tools to analyze genotypes against world population data. You’ll be able to use Plink, Admixture and the pipeline I’ve created for you on any DTC genomic results from those main three companies. You’ll have both the curated reference library and the pipeline I built for you… And the know-how to use them to your ends. You’ll also have reference cheat-sheets to remember how to do everything you tried in the workshop (I don’t want you having to take notes when you can be learning by doing!) 

Who is this for? You. I promise. My goal with this project is to make it accessible and easy for everyone with basic personal computing literacy. Not programming, not command line, not R. Just be comfortable on your computer. (And for this iteration, you need to be on a Mac or Ubuntu/Linux OS. I’m still working out a kink in Windows, so DM me or comment below if you’d like to trial it on Windows once I get that working.) 

I want to reach people who aren’t geneticists. I want to reach people who think they can’t do this. I want to show curious people who have never heard of any of the tools I’m naming that they can still delve into their DNA on their own terms the first time they sit down and try. (I did a trial run of the course with a crew of friends recently. Everyone did great, including the two who were anxious beforehand. And let’s be real: if you’re thinking you’re not tech-savvy enough, it probably says less about your actual tech skills than it does about your friends/family and how tech-nerdy they are!)

But enough about you. Let’s get back to me and what I did with my 23andMe results. To answer the question of what it means to be “Bengali from Chittagong” I analyzed myself against only a few populations and compared myself to the Bengalis in the 1000 Genomes. 

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The great Chinese genetic database

China Is Collecting DNA From Tens of Millions of Men and Boys, Using U.S. Equipment:

The police in China are collecting blood samples from men and boys from across the country to build a genetic map of its roughly 700 million males, giving the authorities a powerful new tool for their emerging high-tech surveillance state.

They have swept across the country since late 2017 to collect enough samples to build a vast DNA database, according to a new study published on Wednesday by the Australian Strategic Policy Institute, a research organization, based on documents also reviewed by The New York Times. With this database, the authorities would be able to track down a man’s male relatives using only that man’s blood, saliva or other genetic material.

An American company, Thermo Fisher, is helping: The Massachusetts company has sold testing kits to the Chinese police tailored to their specifications. American lawmakers have criticized Thermo Fisher for selling equipment to the Chinese authorities, but the company has defended its business.

I don’t have much to say, though you should read the piece. This is a vision of a particular future. I am obviously concerned, but I think I have to frankly “grade on a curve” here. The Chinese state already has an incredible amount of power and control over its citizens. The genetic angle is much more of a movement on the margins than a qualitative change in anything. Genes are not magic, but phone tracking is.

As for the involvement of American companies, I don’t know what to say. Have you stopped buying Chinese products?

Genetics got personal in this decade

In the spring of 2010 I began to “eat my own dog-food.” By this, I mean that I entered the world of “personal genomics.” I ordered a bunch of kits from 23andMe for myself and my family.

I didn’t have too many strong expectations of surprises. One thing though I did suspect: my parents would differ some in ancestry. My mother had family lore of someone of “Chinese” background in the 19th-century.

What did I find out? First I got my Y and mtDNA results. I was at a Japanese restaurant in Japantown in San Francisco when I got the email. My Y was R1a, and my mtDNA was U2b. I was a bit surprised by the mtDNA. Bangladesh is 80% macrohaplogroup M. The Y wasn’t as surprising. I knew a substantial minority of Bengalis were R1a from the literature. But it was cool knowing for certain.

What personal genomics in the 2010s has done is making the abstract concrete. The general personal. It’s now part of the mainstream. In 2010 personal genomics was very niche, and it’s not anymore.

Another thing that 23andMe told me is that my parents are very similar genome-wide. Depending on how you calculate it they are between 10 and 20 percent East Asian (their results are highly correlated using the same parameters). This surprised me. Whatever the family legends were, my parents are pretty generic East Bengalis.

This year, DNA from an ancient woman of the Indus Valley Civilization was analyzed from Rakhighari. It turns out she was U2b!

So on the paternal side my lineage extends back to the Eurasian steppe, and the Sintashta-Andronovo cultural horizon. But on the maternal side, it is deeply rooted northwest South Asia, with the Indus Valley Civilization. That’s a pretty cool duet of facts to learn in this decade about myself.

Note: If you want to download my VCF generated from high coverage whole genome sequencing, here is the link.

Forensic genetics after Golden State Killer

It’s been a year and a half since the Golden State Killer was arrested. That was a big day in the genetics community, as genealogy was leveraged for forensics in a big way. One of the people who I began to have discussions with regarding this development was my friend David Mittelman. Since then David has started his own forensic genetics company, Othram.

He moves fast!

But there’s a major issue with any project moving forward into this space: the strange ethical grayland of genomic databases. A lot of the breakthroughs are coming through GED Match, a site that feels like it stepped out of the late 1990s, with both the innocence and design sense of that period. You’ve probably read about the fire which the proprietors of GED Match have come under due to confusions about terms of use. Curtis Rogers, a co-founder of GED Match, thinks it’s a “distraction.” Certainly, it has been for him.

GED Match is great, and the founders tried to do great things with the best of aims. But the world comes at you fast.

As someone who has put their own genotype into the public domain, I’m not super worried about privacy. Yaniv Erlich of MyHeritage was one of those aggressively asserting that he would be happy for people to solve violent crime with his genotype when the Golden State Killer was caught. Many of us feel that way, though not all of us.

To get at the forensic and criminal justice aspect of genomics, and around some of the ethical hurdles of prior databases, David’s company has created a new database, DNA Solves. Since it was designed and coded this year it definitely feels 2019. I uploaded some of my raw genotype data and it was very easy and quick. The FAQ is explicit in what the aim here is. Othram is a forensic genetics firm that gains from public buy-in, but the current options are not optimal. Everyone is worried that GED Match will get shut down. There need to be alternatives out there.

This database is aimed only at helping law enforcement. There’s no public search. And, David told me they’re only going to return matches, not the whole genotype. This is basically a tool that allows people to want to get involved to remain involved.

If you are as open about your genes as I am, I’d recommend checking it out.

(in the near future they will begin providing “reports” to people who volunteer to upload to get the database bigger)

Note: Dante is telling me that my sample is being sequenced. I will be posting my whole genome online soon (I promised about a decade ago that I’d do this if I got WGS).

How related should you expect relatives to be?

Like many Americans in the year 2018 I’ve got a whole pedigree plugged into personal genomic services. I’m talking from grandchild to grandparent to great-aunt/uncles. A non-trivial pedigree. So we as a family look closely at these patterns, and we’re not surprised at this point to see really high correlations in some cases compared to what you’d expect (or low).

This means that you can see empirically the variation between relatives of the same nominal degree of separation from a person of interest. For example, each of my children’s’ grandparents contributes 25% of their autosomal genome without any prior information. But I actually know the variation of contribution empirically. For example, my father is enriched in my daughter. My mother is my sons.

The sample principle applies to siblings. Though they should be 50% related on their autosomal genome, it turns out there is variation. I’ve seen some papers large data sets (e.g., 20,000 sibling pairs) which gives a standard deviation of 3.7% in relatedness. But what about other degrees of relation?

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On the whole genomics will not be individually transformative…for now

A new piece in The Guardian, ‘Your father’s not your father’: when DNA tests reveal more than you bargained for, is one of the two major genres in writings on personal genomics in the media right now (there are exceptions). First, there is the genre where genetics doesn’t do anything for you. It’s a waste of money! Second, there is the genre where genetics rocks our whole world, and it’s dangerous to one’s own self-identity. And so on. Basically, the two optimum peaks in this field of journalism are between banal and sinister.

In response to this, I stated that for most people personal genomics will probably have an impact somewhere in the middle. To be fair, someone reading the headline of the comment I co-authored in Genome Biology, Consumer genomics will change your life, whether you get tested or not, may wonder as the seeming contradiction.

But it’s not really there. On the aggregate social level genomics is going to have a non-trivial impact on health and lifestyle. This is a large proportion of our GDP. So it’s “kind of a big deal” in that sense. But, for many individuals, the outcomes will be quite modest. For a small minority of individuals, there will be real and important medical consequences. In these cases, the outcomes are a big deal. But for most people, genetic dispositions and risks are diffuse, of modest effect, and often backloaded in one’s life. Even though it will impact most of society in the near future, it’s touch will be gentle.

An analogy here can be made with BMI or body-mass-index. As an individual predictor and statistic, it leaves a lot to be desired. But, for public health scientists and officials aggregate BMI distributions are critical to getting a sense of the landscape.

Finally, this is focusing on genomics where we read the sequence (or get back genotype results). The next stage that might really be game-changing is the write revolution. CRISPR genetic engineering. In the 2020s I assume that CRISPR applications will mostly be in critical health contexts (e.g., “fixing” Mendelian diseases), or in non-human contexts (e.g., agricultural genetics). Like genomics, the ubiquity of genetic engineering will be kind of a big deal economically in the aggregate, but it won’t be a big deal for individuals.

If you are a transhumanist or whatever they call themselves now, one can imagine a scenario where a large portion of the population starts “re-writing” themselves. That would be both a huge aggregate and individual impact. But we’re a long way from that….

There could be 100 million genotyping kits sold by January 1st 2020

The figure to the right is from the comment David Mittelman and I wrote for Genome Biology, Consumer genomics will change your life, whether you get tested or not. The original numbers are from ISOGG, which does a great job collating information from a variety of sources. When final revisions for the comment were due, we only found data up to 5/1/2018.

That being said, I thought it would be useful to generate a chart where I combined and smoothed the results from the various companies. It is clear that the period after 2016 is when you see massive takeoff and adoption, driven first by Ancestry, but later by 23andMe joining the race. The other companies have been increasing their sales as well, with new players such as MyHeritage making a big play.

All this makes me wonder: what does the future have it store? Year-to-year the total number of kits in circulation were doubling in 2013 and 2014. That rate dropped to ~1.6-fold increases in 2015 and 2016. A lot of this is due to 23andMe turning away from customer acquisition (more marketing always leads to more sales). With 23andMe competing with Ancestry again in 2017 one saw a >2.5-fold increase in the number of kits sold.

My back-of-the-envelope calculations indicate that around 1.8 million kits were being sold per month between the big players in the first in the first 4 months of 2018. That’s about 18 million kits this year. That means 29 million kits total in circulation by January 1st of 2019. The wildcard here though is that this space is “consumer”, which means that a disproportionate number of kits are going to be sold between Halloween and Christmas. Extrapolating from the period between January 1st to May 1st, as I’m doing above, could be way too conservative.

The sales in markets outside of the USA, along with customer acquisition through marketing, need to keep increasing up until January 1st of 2020 for there to be 100 million kits sold. But I think it’s very possible. I’m on the bubble of saying even likely. The wholesale price of arrays (the chips) keeps decreasing, so the price point of the consumer product is also decreasing. This isn’t a situation where the market is growing linearly, it’s exponential. A few positive shocks here and there 100 million by January 1st of 2020 may seem conservative.

Addendum: There has been some confusion in the media between sequencing and genotyping platforms. These are different technologies. Genotyping platforms, SNP-arrays, are targeting a genome-wide subset of polymorphisms. 23andMe’s current chip seems to probe about 630,000 markers. The whole genome consists of 3 billion bases. In the 2020s sequencing will probably replace targeted genotyping arrays in consumer products, but it will probably really come to the fore first in the medical space.

Consumer Genomics in 2018, beyond the future’s threshold

In 2013 David Mittelman and I wrote Rumors of the death of consumer genomics are greatly exaggerated. This was in the wake of the FDA controversy with 23andMe, and continuing worries about DNA and privacy. Today David and I came out with a new comment in Genome BiologyConsumer genomics will change your life, whether you get tested or not.

Really transformative technology becomes beneath comment. As long as we’re having to comment about genomics, it isn’t really mainstream. But I think in 2018 it is much clearer that the 2020s will see legitimate mainstreaming. The numbers speak for themselves. I hadn’t realized in a visceral manner how much had changed since our original comment came out. It’s pretty much an order of magnitude shift.

My hypothesis for why 23andMe plateaued for a while at ~1 million is that that was the sample size which maximized the statistical power they wanted to catch loci of particular effect sizes. In the initial years, 23andMe was not just buying customers with marketing, it was subsidizing the array costs. Today Illumina SNP arrays are well under $50 (some people say less than $25) wholesale, so I think at some point in early 2017 they realized even though 10 million wasn’t worth much to them in comparison to 1 million for GWAS, they were going to lose the luster of being “market leader” to Ancestry, who were acquiring customers at a massive clip through their marketing (my understanding is that at some point Illumina was having issues processing the samples that Ancestry was returning to them it was at such high scale; higher than Ancestry had anticipated!).

At least today we can explore personal genomics

A very long piece on the “personal genomics industry.” Lots of quotes from my boss Spencer Wells, since he has been in the game so long.

The piece covers all the bases. I actually think some of the criticisms of direct-to-consumer genetics are on base. I just don’t think they’re insoluble problems, or problems so large that that should discourage the industry from growing. I think part of the problem is that many of the people journalists can talk to who can comment on the industry are based in academia, and academia has a different focus when it comes to comes to genetics than the nascent industry. For rational reasons academics need to be very careful when it comes to ethics. Consumer products I think are somewhat different.

But I do think we need to reflect how far we’ve come in 10 years. Back in the 2000s when I was reading stuff on Y, mtDNA and autosomal studies, I honestly didn’t imagine that I would know my own haplogroups and genome-wide ancestry decomposition. It seemed like science fiction. That all changed rather rapidly over a few years, and I purchased kits in the early years when the price was still high. Today it’s a mass industry, with a sub-$100 price point in many cases.

Yes, there are plenty of cautions and worries we need to consider. But the future is already the present, and the horse has left the stable.