Ancestry Composition:

The Basics

Different DNA markers are found in different places across the world, and every marker has its own characteristic pattern of geographical variation. The 23andMe Ancestry Composition algorithm combines information about these patterns with the unique set of DNA markers in your genome to estimate your genetic ancestry.

Here's an example of a haplogroup, one special kind of DNA marker, that illustrates the idea. This map shows the frequency of the maternal haplogroup H around the world. Haplogroup H is very common in Europe, is also found in Africa and Asia, and is never seen in people native to Australia or the Americas.

The association between this marker and geographic location works in two ways: If you know you have European ancestry, we know that there's a decent chance you have the H haplogroup. And if you have the H haplogroup, we know that your genetic history likely includes at least one European ancestor.

Just on the basis of this one DNA marker, we can't locate your ancestry with much precision. Fortunately, we measure hundreds of thousands of DNA markers on the 23andMe platform. If we combine the evidence from many markers, each of which offers a little bit of information about where in the world you're from, we can develop a clear overall picture.

Wrinkle #1: People Usually Have Multiple Ancestries

If all of your DNA came from one place in the world, figuring out where you're from would be very easy and very accurate. Recent research has suggested that, for a European person whose whole family comes from the same place, genetic analysis can locate their ancestral home within a range of around 100 miles!

For most people, though, it just isn't true that all their ancestry comes from one place. The technical word for this is admixture — the genetic mixing together of previously separate populations. For example, it's common for people of European descent to have ancestry from all around Europe. Another example is Latino people, who typically have Native American, European, and often some African DNA.

Our Ancestry Composition algorithm handles the challenge of admixture by breaking up your chromosomes into short, non-overlapping, adjacent windows, like boxcars in a train. If the windows are small enough, then it is safe to assume that you inherited all the DNA in each window from a single parent, grandparent, great-grandparent, etc., going back many generations.

Wrinkle #2: We Don't Know Which DNA Comes From Which Parent

Recall that for each of your 23 chromosome pairs, one chromosome in each pair comes from your mom and the other from your dad. Genotyping chips like the one 23andMe uses don't capture information about which markers came from which parent.

Here's a quick example to illustrate this point. Say, for a short stretch of Chromosome 1, you inherited the following genotypes at three consecutive DNA markers:

from Dad: A-T-C

from Mom: G-T-A

When we look at your raw 23andMe data in this spot on Chromosome 1, we'll see the following:

You: A/G - T/T - A/C

The genotypes where you inherited different variants from mom and dad — in this case, the markers on the ends — are jumbled up. There are two possible genotypes that are consistent with the raw data, and we don't know which one is your actual DNA sequence. It could be:

A-T-A

G-T-C

which happens to be wrong, or it could be:

A-T-C

G-T-A

which is right. The technical term for knowing which markers belong on the same chromosome together is phasing. DNA data like our raw data is called unphased.

So what? This matters because we can learn more from long runs of many DNA markers together than we can learn from individual DNA markers alone. In the above example, the combination A-T-C will generally say more about your ancestry than the A, T, and C say when they are considered separately. Luckily, we can use statistical methods to estimate the phasing of your chromosomes. After your raw data are phased, the algorithm for calculating Ancestry Composition is executed separately on each phased chromosome.

The Ancestry Composition Algorithm

Overview

There are several different ways to estimate your ancestry using your DNA. The approach we take at 23andMe is simple but powerful.

As described above, we use a computational method to estimate the phasing of your chromosomes. Next, we break up the chromosomes into short windows, and we compare your DNA sequence in each window to the DNA in the same window in our reference datasets. We assign your DNA to the ancestry whose reference DNA it's most similar to, and then we process those assignments computationally to "smooth" them out. All of the steps in this process are described in more detail in the following sections.

Step 1: Phasing

Recall wrinkle #2 above. For each customer, we measure a set of genotypes (pairs of alleles). But what we really want is a pair of haplotypes for each chromosome. That is, we want to figure out the series of alleles present on each of your two copies of, for example, chromosome 7: one you received from your mother and one you received from your father. To do so, we first build a very large "phasing reference panel" using data from hundreds of thousands of customers. We then use Eagle (Loh et al., 2016) to phase these individuals jointly. Eagle uses sophisticated statistics and a very clever algorithm to do this. Once we have phased this large collection of customers, we can use the information inferred to efficiently phase new customers.

Step 2: Window Classification

After phasing your chromosomes, we segment them into consecutive windows containing ~300 genetic markers each. We measure between 6,445 and 36,734 markers per chromosome, which translates to 21 to 122 windows, depending on the chromosome's length. We consider each window in turn and compare your DNA to the reference datasets to determine which ancestry most closely corresponds to your DNA.

There are many ways to assign ancestry to DNA segments based on reference data, and we tried several. The best-performing option was a well-known classification tool called a support vector machine, or SVM. An SVM can "learn" different ancestry classifications based on a set of training examples and then assign new DNA segments to a learned category.

In the case of Ancestry Composition, we train the SVM with reference DNA sequences and tell it which ancestry population those sequences are from. Then, when we look at the DNA from a 23andMe customer with unknown ancestry (like you), we can ask the SVM to classify your DNA for us based on the reference datasets.

We chose an Ancestry Composition algorithm based on SVMs because it performed the best out of all the techniques that we tried. SVMs are also very fast, which is critical for a large and growing database.

Step 3: Smoothing

The SVM classifies each window of your genome independently, creating a "first draft" version of your ancestry result. We use another computational process, called the smoother, to smooth this raw SVM output. The smoother uses a version of a well-known mathematical tool called a Hidden Markov Model to correct, or “smooth,” two kinds of errors. Hidden Markov Models are used to analyze sequential data, like biological sequences or recorded speech. As an example, suppose we had three ancestry populations: X, Y, and Z. An example of output from the SVM might look like this:

chromosome 1, parent 1: X - X - X - Z - Z - Z - Y - Z

chromosome 1, parent 2: Z - Z - Z - X - X - X - X - X

The first kind of error the smoother corrects is an unusual assignment in the middle of a run of similar assignments. In the first line above, there's a run of Z's, interrupted by a single Y:  Z — Z — Z — Y — Z. It's possible that the lone Y was a close call between Y and Z that went the wrong way. If that were the case, the smoother could correct it to Z — Z — Z — Z — Z.

The second kind of error the smoother corrects arises from the phasing step. Phasing algorithms can make phasing mistakes known as switch errors, where they mix up the DNA of one parent with that of another. The smoother can switch the ancestry assignments between your mother and your father if it detects one of these errors. In this example, there may be a switch error after the fourth window. If the switch were reversed, then the runs of X's and the runs of Z's would stay together. In our simplified example, the smoother might output something like this:

chromosome 1, parent 1: Z - Z - Z - Z - Z - Z - Z - Z

chromosome 1, parent 2: X - X - X - X - X - X - X - X

This example illustrates the purpose of the smoother. But with real data the picture is much messier, and the answers are rarely so clean. So instead of assigning a single ancestry to each window like we did in this example, the smoother estimates the probabilities of each Ancestry Composition population matching each window of DNA. The following picture shows a concrete example:

This is the output of the smoother analysis of one copy of chromosome 2. Starting on the left, there is a short run of pink, then a wider run of green, then another run of pink. In this chart, pink is the color for Sub-Saharan African ancestry, and green is the color for Indigenous American. The y-axis runs from 0 to 100 percent, and it shows the probability that the DNA in that region of the chromosome comes from each Ancestry Composition population. These pink and green regions fill the entire vertical space of the graph, which means that we are 100 percent confident that the DNA in those regions has Sub-Saharan African and Indigenous American genetic ancestry, respectively.

The next region to the right — between positions 50 and 100 on the x-axis — is a stretch of multi-colored blue. The thickest strip at the bottom is dark teal, which is the color for British & Irish. This segment of DNA has somewhere between a 50 percent chance and a 60 percent chance of reflecting from British & Irish ancestry. The other shades of blue show that the same DNA segment also has a chance of reflecting Italian, Iberian, or French & German ancestry. If you think back to the haplogroup example above, this result makes sense: it is normal for a DNA marker to match reference DNA from lots of places, even if it matches some places better than others. In this example, the result shows that this DNA segment matches reference DNA from all over Europe. We can very confidently conclude that this stretch of DNA reflects European ancestry, but the evidence isn't strong enough to assign it to one specific region of Europe with high confidence.

Step 4: Aggregation & Reporting

As a last step, we summarize the results to display them in your Chromosome Painting. The way we do this is to apply a threshold to the probability plot:

The horizontal line in this image shows a 70 percent confidence threshold, which we will talk about for this example. You can view your own Chromosome Painting at different confidence thresholds ranging from 50 percent (speculative) to 90 percent (conservative).

We look across the entire chromosome and ask whether any ancestry has an estimated probability exceeding the specified threshold (in this case 70 percent). In this example, with the exception of the blue European stretch, the ancestry estimates exceed 70 percent over the majority of the chromosome. Each region contributes to your overall Ancestry Composition in proportion to its size: For example, the green Indigenous American segment near the end of this plot makes up about 0.26 percent of the entire genome. Even though there is some probability that the segment comes from a different population, the Indigenous American proportion exceeds the 70 percent threshold, and so we add 0.26 percent Indigenous American to the overall Ancestry Composition at this threshold.

In the case of the European segment, no single ancestry exceeds the 70 percent threshold, so we don't assign that DNA to any fine-grained ancestries. Instead, we refer to our hierarchy of ancestries. There is a Broadly Northern European ancestry that includes four fine-level ancestries: British & Irish, Scandinavian, Finnish, and French & German. When we add up the contributions of each of these subgroups, if the total Broadly Northern European contribution exceeds the 70 percent threshold, then we will report the region as Broadly Northern European.

In this example, the Broadly Northern European reference populations still don't exceed the 70 percent threshold, but the combined probabilities of all the European populations do. So this region is assigned Broadly European ancestry.

We use broad Ancestry Composition categories to avoid making assumptions about your ancestry when your DNA matches several different country-level populations. In regions where no ancestry — including the broad ancestries — exceeds the specified threshold, we report Unassigned ancestry. You can see the entire ancestry hierarchy in your Ancestry Composition report by clicking "See all tested populations."

Testing & Validation

Ancestry Composition includes a lot of steps, and each step has to be tested. We've discussed a few of those tests already while explaining our algorithm. In this section, we want to share some actual test results to give a sense of how well Ancestry Composition works. This section focuses on the final test we run, because that integrates the performance of each of the steps into an overall picture.

This test looks at two classic measures of model performance, called precision and recall. These are the standard measurements that researchers use to test how well a prediction system works. Precision answers the question "When the system predicts that a piece of DNA comes from population A, how often is the DNA actually from population A?" Recall answers the question "Of the pieces of DNA that actually are from population A, how often does the system correctly predict that they are from population A?"

There is a tradeoff between precision and recall, so we have to strike a balance between them. A high-precision, low-recall system will be extremely picky about assigning, say, Scandinavian ancestry. The system will only assign DNA as Scandinavian when it's very, very sure. That will yield high precision — since the assignment of Scandinavian is always correct — but low recall, because a lot of true Scandinavian ancestry is left unassigned.

With a low-precision, high-recall system the opposite problem exists. In this case, the system is liberal with assignments of Scandinavian ancestry. Any time a piece of DNA might be Scandinavian, it is assigned that ancestry. This will yield high recall, as all genuine Scandinavian DNA will be assigned correctly, but low precision, because non-Scandinavian DNA will often be assigned as Scandinavian incorrectly.

The ideal system has both high precision and high recall, but that may be impossible in real life. Let's see how Ancestry Composition performs on these metrics. For this quality control test, we set apart 20 percent of the reference database, about 2,800 individuals of known ancestry. We trained and ran the entire Ancestry Composition pipeline on the other 80 percent of the reference individuals. Then we treated the other 20 percent like new 23andMe customers and used our Ancestry Composition pipeline to calculate their ancestry. Since we know these people's true ancestries, we can check to see how accurate their Ancestry Composition results are. We ran this test five times, with a different 20 percent held out each time, and then averaged across the five tests to give the following results:

This table shows that our precision numbers are very high across the board, mostly above 90 percent, and rarely dipping below 80 percent. That means that when the system assigns an ancestry to a piece of DNA, that assignment is very likely to be accurate. You can also see that as you move up from the sub-regional level (e.g. British & Irish) to the regional level (e.g. Northern European) to the continental level (e.g. European), the precision approaches 100 percent.

It's also important to realize that poor recall doesn't mean bad results. Some populations, like Sardinian, are just hard to tell apart from others. When Ancestry Composition fails to assign Sardinian DNA, this doesn't mean that DNA is incorrectly assigned to something else, like Italian. If it were, then the Italian population would have poor precision. Instead, Ancestry Composition often assigns Sardinian DNA to the Broadly Southern European or Broadly European populations.