Genetic Affairs: AutoPedigree Combines AutoTree with WATO to Identify Your Potential Tree Locations

If you’re an adoptee or searching for an unknown parent or ancestor, AutoPedigree is just what you’ve been waiting for.

By now, we’re all familiar with Genetic Affairs who launched in 2018 with their signature autocluster tool. AutoCluster groups your matches into clusters by who your matches match with each other, in addition to you.

browser autocluster

A year later, in December 2019, Genetic Affairs introduced AutoTree, automated tree reconstruction based on your matches trees at Ancestry and Family Finder at Family Tree DNA, even if you don’t have a tree.

Now, Genetic Affairs has introduced AutoPedigree, a combination of the AutoTree reconstruction technology combined with WATO, What Are the Odds, as seen here at DNAPainter. WATO is a statistical probability technique developed by the DNAGeek that allows users to review possible positions in a tree for where they best fit.

Here’s the progressive functionality of how the three Genetic Affairs tools, combined, function:

  • AutoCluster groups people based on if they match you and each other
  • AutoTree finds common ancestors for trees from each cluster
  • Next, AutoTree finds the trees of all matches combined, including from trees of your DNA matches not in clusters
  • AutoPedigree checks to see if a common ancestor tree meets the minimum requirement which is (at least) 3 matches of greater to or equal to 30-40 cM. If yes, an AutoPedigree with hypotheses is created based on the common ancestor of the matching people.
  • Combined AutoPedigrees then reviews all AutoTrees and AutoPedigrees that have common ancestors and combine them into larger trees.

Let’s look at examples, beginning with DNAPainter who first implemented a form of WATO.

DNA Painter

Let’s say you’re trying to figure out how you’re related to a group of people who descend from a specific ancestral couple. This is particularly useful for someone seeking unknown parents or other unknown relationships.

DNA tools are always from the perspective of the tester, the person whose kit is being utilized.

At DNAPainter, you manually create the pedigree chart beginning with a common couple and creating branches to all of their descendants that you match.

This example at DNAPainter shows the matches with their cM amounts in yellow boxes.

xAutoPedigree DNAPainter WATO2

The tester doesn’t know where they fit in this pedigree chart, so they add other known lines and create hypothesis placeholder possibilities in light blue.

In other words, if you’re searching for your mother and you were born in 1970, you know that your mother was likely born between 1925 (if she was 45 when she gave birth to you) and 1955 (if she was 15 when she gave birth to you.) Therefore, in the family you create, you’d search for parents who could have given birth to children during those years and create hypothetical children in those tree locations.

The WATO tool then utilizes the combination of expected cMs at that position to create scores for each hypothesis position based on how closely or distantly you match other members of that extended family.

The Shared cM Project, created and recently updated by Blaine Bettinger is used as the foundation for the expected centimorgan (cM) ranges of each relationship. DNAPainter has automated the possible relationships for any given matching cM amount, here.

In the graphic above, you can see that the best hypothesis is #2 with a score of 1, followed by #4 and #5 with scores of 3 each. Hypothesis 1 has a score of 63.8979 and hypothesis 3 has a score of 383.

You’ll need to scroll to the bottom to determine which of the various hypothesis are the more likely.

Autopedigree DNAPainter calculated probability

Using DNAPainter’s WATO implementation requires you to create the pedigree tree to test the hypothesis. The benefit of this is that you can construct the actual pedigree as known based on genealogical research. The down-side, of course, is that you have to do the research to current in each line to be able to create the pedigree accurately, and that’s a long and sometimes difficult manual process.

Genetic Affairs and WATO

Genetic Affairs takes a different approach to WATO. Genetic Affairs removes the need for hand entry by scanning your matches at Ancestry and Family Tree DNA, automatically creating pedigrees based on your matches’ trees. In addition, Genetic Affairs automatically creates multiple hypotheses. You may need to utilize both approaches, meaning Genetic Affairs and DNAPainter, depending on who has tested, tree completeness at the vendors, and other factors.

The great news is that you can import the Genetic Affairs reconstructed trees into DNAPainter’s WATO tool instead of creating the pedigrees from scratch. Of course, Genetic Affairs can only use the trees someone has entered. You, on the other hand, can create a more complete tree at DNAPainter.

Combining the two tools leverages the unique and best features of both.

Genetic Affairs AutoPedigree Options

Recently, Genetic Affairs released AutoPedigree, their new tool that utilizes the reconstructed AutoTrees+WATO to place the tester in the most likely region or locations in the reconstructed tree.

Let’s take a look at an example. I’m using my own kit to see what kind of results and hypotheses exist for where I fit in the tree reconstructed from my matches and their trees.

If you actually do have a tree, the AutoTree portion will simply be counted as an equal tree to everyone else’s trees, but AutoPedigree will ignore your tree, creating hypotheses as if it doesn’t exist. That’s great for adoptees who may have hypothetical trees in progress, because that tree is disregarded.

First, sign on to your account at Genetic Affairs and select the AutoPedigree option for either Ancestry or Family Tree DNA which reconstructs trees and generates hypotheses automatically. For AutoPedigree construction, you cannot combine the results from Ancestry and FamilyTreeDNA like you can when reconstructing trees alone. You’ll need to do an AutoPedigree run for each vendor. The good news is that while Ancestry has more testers and matches, FamilyTreeDNA has many testers stretching back 20 years or so in the past who passed away before testing became available at Ancestry. Often, their testers reach back a generation or two further. You can easily transfer Ancestry (and other) results to Family Tree DNA for free to obtain more matches – step-by-step instructions here.

At Genetic Affairs, you should also consider including half-relations, especially if you are dealing with an unknown parent situation. Selecting half-relationships generates very large trees, so you might want to do the first run without, then a second run with half relationships selected.

AutoPedigree options


I ran the program and opened the resulting email with the zip file. Saving that file automatically unzips for me, displaying the following 5 files and folders.

Autopedigree cluster

Clicking on the AutoCluster HTML link reveals the now-familiar clusters, shown below.

Autopedigree clusters

I have a total of 26 clusters, only partially shown above. My first peach cluster and my 9th blue cluster are huge.

Autopedigree 26 clusters

That’s great news because it means that I have a lot to work with.

autopedigree folder

Next, you’ll want to click to open your AutoPedigree folder.

For each cluster, you’ll have a corresponding AutoPedigree file if an AutoPedigree can be generated from the trees of the people in that cluster.

My first cluster is simply too large to show successfully in blog format, so I’m selecting a smaller cluster, #21, shown below with the red arrow, with only 6 members. Why so small, you ask? In part, because I want to illustrate the fact that you really don’t need a lot of matches for the AutoPedigree tool to be useful.

Autopedigree multiple clusters

Note also that this entire group of clusters (blue through brown) has members in more than one cluster, indicated by the grey cells that mean someone is a member of at least 2 clusters. That tells me that I need to include the information from those clusters too in my analysis. Fortunately, Genetic Affairs realizes that and provides a combined AutoPedigree tool for that as well, which we will cover later in the article. Just note for now that the blue through brown clusters seem to be related to cluster 21.

Let’s look at cluster 21.

autopedigree cluster 21

In the AutoPedigree folder, you’ll see cluster files when there are trees available to create pedigrees for individual clusters. If you’re lucky, you’ll find 2 files for some clusters.

autopedigree ancestors

At the top of each cluster AutoPedigree file, Genetic Affairs shows you the home couple of the descendant group shown in the matches and their corresponding trees.

Autopedigree WATO chart

Image 1 – click to enlarge

I don’t expect you to be able to read everything in the above pedigree chart, just note the matches and arrows.

You can see three of my cousins who match, labeled with “Ancestry.” You also see branches that generate a viable hypothesis. When generating AutoPedigrees, Genetic Affairs truncates any branches that cannot result in a viable hypothesis for placing the tester in a viable location on the tree, so you may not see all matches.

Autopedigree hyp 1

Image 2 – click to enlarge

On the top branch, you’ll see hyp-1-child1 which is the first hypothesis, with the first child. Their child is hyp-2- child2, and their child is hyp-3-child3. The tester (me, in this case) cannot be the persons shown with red flags, called badges, based on how I match other people and other tree information such as birth and death dates.

Think of a stoplight, red=no, green are your best bets and the rest are yellow, meaning maybe. AutoPedigree makes no decisions, only shows you options, and calculated mathematically how probable each location is to be correct.

Remember, these “children,” meaning hypothesis 1-child 1 may or may not have actually existed. These relationships are hypothetical showing you that IF these people existed, where the tester could appear on the tree.

We know that I don’t fit on the branch above hypothesis 1, because I only match the descendant of Adam Lentz at 44.2 cM which is statistically too low for me to also inhabit that branch.

I’ve included half relationships, so we see hyp-7-child1-half too, which is a half-sibling.

The rankings for hypotheses 1, 2, and 7 all have red badges, meaning not possible, so they have a score of 0. Hypothesis 3 and 8 are possible, with a ranking of 16, respectively.

autopedigree my location

Image 3 – click to enlarge

Looking now at the next segment of the tree, you see that based on how I match my Deatsman and Hartman cousins, I can potentially fit in any portion of the tree with green badges (in the red boxes) or yellow badges.

You can also see where I actually fit in the tree. HOWEVER, that placement is from AutoTree, the tree reconstruction portion, based on the fact that I have a tree (or someone has a tree with me in it). My own tree is ignored for hypothesis generation for the AutoPedigree hypothesis generation portion.

Had my first cousins once removed through my grandfather John Ferverda’s brother, Roscoe, tested AND HAD A TREE, there would have been no question where I fit based on how I match them.

autopedigree cousins

As it turns out they did test, but provided no tree meaning that Genetic Affairs had no tree to work with.

Remember that I mentioned that my first cluster was huge. Many more matches mean that Genetic Affairs has more to work with. From that cluster, here’s an example of a hypothesis being accurate.

autopedigree correct

Image 4 – click to enlarge

You can see the hypothetical line beneath my own line, with hypothesis 104, 105, 106, 107, 108. The AutoTree portion of my tree is shown above, with my father and grandparents and my name in the green block. The AutoPedigree portion ignores my own tree, therefore generating the hypothesis that’s where I could fit with a rank of 2. And yes, that’s exactly where I fit in the tree.

In this case, there were some hypotheses ranked at 1, but they were incorrect, so be sure to evaluate all good (green) options, then yellow, in that order.

Genetic Affairs cannot work with 23andMe results for AutoPedigree because 23andMe doesn’t provide or support trees on their site. AutoClusters are integrated at MyHeritage, but not the AutoTree or AutoPedigree functions, and they cannot be run separately.

That leaves Family Tree DNA and Ancestry.

Combined AutoPedigree

After evaluating each of the AutoPedigrees generated for each cluster for which an AutoPedigree can be generated, click on the various cluster combined autopedigrees.

autopedigree combined

You can see that for cluster 1, I have 7 separate AutoPedigrees based on common ancestors that were different. I have 3 AutoPedigrees also for cluster 9, and 2 AutoPedigrees for 15, 21, and 24.

I have no AutoPedigrees for clusters 2, 3, 5, 6, 7, 8, 14, 17, 18, and 22.

Moving to the combined clusters, the numbers of which are NOT correlated to the clusters themselves, Genetic Affairs has searched trees and combined ancestors in various clusters together when common ancestors were found.

Autopedigree multiple clusters

Remember that I asked you to note that the above blue through brown clusters seem to have commonality between the clusters based on grey cell matches who are found in multiple groups? In fact, these people do share common ancestors, with a large combined AutoPedigree being generated from those multiple clusters.

I know you can’t read the tree in the image that follows. I’m only including it so you’ll see the scale of that portion of my tree that can be reconstructed from my matches with hypotheses of where I fit.

autopedigree huge

Image 5 – click to enlarge

These larger combined pedigrees are very useful to tie the clusters together and understand how you match numerous people who descend from the same larger ancestral group, further back in time.

Integration with DNAPainter

autopedigree wato file

Each AutoPedigree file and combined cluster AutoPedigree file in the AutoPedigree folder is provided in WATO format, allowing you to import them into DNAPainter’s WATO tool.

autopedigree dnapainter import

You can manually flesh out the trees based on actual genealogy in WATO at DNAPainter, manually add matches from GEDmatch, 23andMe or MyHeritage or matches from vendors where your matches trees may not exist but you know how your match connects to you.

Your AutoTree Ancestors

But wait, there’s more.

autopedigree ancestors folder

If you click on the Ancestors folder, you’ll see 5 options for tree generations 3-7.

autopedigree ancestor generations

My three-generation auto-generated reconstructed tree looks like this:

autopedigree my tree

Selecting the 5th generation level displays Jacob Lentz and Frederica Ruhle, the couple shown in the AutoCluster 21 and AutoPedigree examples earlier. The color-coding indicates the source of the ancestors in that position.

Autopedigree expanded tree

click to enlarge

You will also note that Genetic Affairs indicates how many matches I have that share this common ancestor along with which clusters to view for matches relevant to specific ancestors. How cool is this?!!

Remember that you can also import the genetic match information for each AutoTree cluster found at Family Tree DNA into DNAPainter to paint those matches on your chromosomes using DNAPainter’s Cluster Auto Painter.

If you run AutoCluster for matches at 23andMe, MyHeritage, or FamilyTreeDNA, all vendors who provide segment information, you can also import that cluster segment information into DNAPainter for chromosome painting.

However, from that list of vendors, you can only generate AutoTrees and AutoPedigrees at Family Tree DNA. Given this, it’s in your best interest for your matches to test at or upload their DNA (plus tree) to Family Tree DNA who supports trees AND provides segment information, both, and where you can run AutoTree and AutoPedigree.

Have you painted your clusters or generated AutoTrees? If you’re an adoptee or looking for an unknown parent or grandparent, the new AutoPedigree function is exactly what you need.


Genetic Affairs provides complete instructions for AutoPedigree in this newsletter, along with a user manual here, and the Facebook Genetic Affairs User Group can be found here.

I wrote the introductory article, AutoClustering by Genetic Affairs, here, and Genetic Affairs Reconstructs Trees from Genetic Clusters – Even Without Your Tree or Common Ancestors, here. You can read about DNAPainter, here.

Transfer your DNA file, for free, from Ancestry to Family Tree DNA or MyHeritage, by following the easy instructions, here.

Have fun! Your ancestors are waiting.



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Shared cM Project 2020 Analysis, Comparison & Handy Reference Charts

Recently, Blaine Bettinger published V4 of the Shared cM Project, and along with that, Jonny Perl at DNAPainter updated the associated interactive tool as well, including histograms. I wrote about that, here.

The goal of the shared cM project was and remains to document how much DNA can be expected to be shared by various individuals at specific relationship levels. This information allows matches to at least minimally “position” themselves in a general location their trees or conversely, to eliminate specific potential relationships.

Shared cM Project match data is gathered by testers submitting their match information through the submission portal, here.

When the Shared cM Project V3 was released in September 2017, I combined information from various sources and provided an analysis of that data, including the changes from the V2 release in 2016.

I’ve done the same thing this year, adding the new data to the previous release’s table.

Compiled Comparison Table

I initially compiled this table for myself, then decided to update it and share with my readers. This chart allows me to view various perspectives on shared data and relationships and in essence has all the data I might need, including multiple versions, in one place. Feel free to copy and save the table.

In the comparison table below, the relationship rows with data from various sources is shown as follows:

  • White – Shared cM Project 2016
  • Peach – Shared cM Project 2017
  • Purple – Shared cM Project 2020
  • Green – DNA Detectives chart

I don’t know if DNA Detectives still uses the “green chart” or if they have moved to the interactive DNAPainter tool. I’ve retained the numbers for historical reference regardless.

Additionally, in some places, you’ll see references to the “degree of relationship,” as in “third degree relatives always match each other.” I’ve included a “Degree of Relationship” column to the far right, but I don’t come across those “relationship degree” references often anymore either. However, it’s here for reference if you need it.

23andMe still gives relationships in percentages, so I’ve included the expected shared percent of DNA for each relationship and the actual shared range from the DNA Detectives Green Chart.

One column shows the expected shared cM amount, assuming that 50% of the DNA from each ancestor is passed on in each generation. Clearly, we know that inheritance doesn’t happen that cleanly because recombination is a random event and children do NOT inherit exactly half of each ancestor’s DNA carried by their parents, but the average should be someplace close to this number.

shared cm table 2020

click to open separately, then use your magnifier to enlarge

The first thing I noticed about V4 is that there is a LOT more data which means that the results are likely more accurate. V4 increased by 32K data points, or 147%. Bravo to everyone who participated, to Blaine for the analysis and to Jonny for automating the results at DNAPainter.


Blaine provided his white paper, here, which includes “everything you need to know” about the project, and I strongly encourage you to read it. Not only does this document explain the process and methods, it’s educational in its own right.

On the first page, Blaine discusses issues. Any time you are crowd sourcing information, you’re going to encounter challenges and errors. Blaine did remove any entries that were clearly problematic, plus an additional 1% of all entries for each category – .5% from each end meaning the largest and smallest entries. This was done in an attempt to remove the results most likely to be erroneous.

Known issues include:

  • Data entry errors – I refer to these as “clerical mutations,” but they happen and there is no way, unless the error is egregious, to know what is a typo and what is real. Obviously, a parent sharing only a 10 cM segment with a child is not possible, but other data entry errors are well within the realm of possible.
  • Incorrect relationships – Misreported or misunderstood relationships will skew the numbers. Relationships may be believed to be one type, but are actually something else. For example, a half vs full sibling, or a half vs full aunt or uncle.
  • Misunderstood Relationships – People sometimes become confused as to the difference between “half” and “removed” from time to time. I wrote a helpful article titled Quick Tip – Calculating Cousin Relationships Easily.
  • Endogamy – Endogamy occurs when a population intermarries within itself, meaning that the same ancestral DNA is present in many members of the community. This genetic result is that you may share more DNA with those cousins than you would otherwise share with cousins at the same distance without endogamy.
  • Pedigree Collapse – Pedigree collapse occurs when you find the same ancestors multiple times in your tree. The closer to current those ancestors appear, the more DNA you will potentially carry from those repeat ancestors. The difference between endogamy and pedigree collapse is that endogamy is a community event and pedigree collapse has only to do with your own tree. You might just have both, too.
  • Company Reporting Differences – Different companies report DNA in different ways in addition to having different matching thresholds. For example, Family Tree DNA includes in your match total all DNA to 1 cM that you share with a match over the matching threshold. Conversely, Ancestry has a lower matching threshold, but often strips out some matching DNA using Timber. 23andMe counts fully identical segments twice and reports the X chromosome in their totals. MyHeritage does not report the X chromosome. There is no “right” or “wrong,” or standardization, simply different approaches. Hopefully, the variances will be removed or smoothed in the averages.
  • Distant Cousin Relationships – While this isn’t really an issue, per se, it’s important to understand what is being reported beyond 2nd cousin relationships in that the only relationships used to calculate these averages is the DNA from people who DO share DNA with their more distant cousins. In other words, if you do NOT match your 3rd cousin, then your “0” shared DNA is not included in the average. Only those who do match have their matching amounts included. This means that the average is only the average of people who match, not the average of all 3rd cousins.

Challenges aside, the Shared cM Project provides genealogists with a wonderful opportunity to use the combined data of tens of thousands of relationships to estimate and better understand the relationship range of our matches.

The Shared cM Project in combination with DNAPainter provides us with a wonderful tool.


When analyzing the data, one of the first things I noticed was a very unusual entry for parent/child relationships.

We all know that children each inherit exactly half of their parent’s DNA. We expect to find an amount in the ballpark of 3400, give or take a bit for normal variances like read errors or reporting differences.

Shared cM parent child.png

click to enlarge

I did not expect to see a minimum shared cM amount for a child/parent relationship at 2376, fully 1024 cM below expected value of 3400 cM. Put bluntly, that’s simply not possible. You cannot live without one third of one of your parent’s DNA. If this data is actually accurate from someone’s account, please contact me because I want to actually see this phenomenon.

I reached out to Blaine, knowing this result is not actually possible, wondering how this would ever get through the quality control cycle at any vendor.

After some discussion, here’s Blaine’s reply:

If you look at the histogram, you’ll see that those are most likely outliers. One of my lessons for the ScP (Shared cM Project) lately is that people shouldn’t be using the data without the histograms.

People get frustrated with this, but I can’t edit data without a basis even if I think it doesn’t make sense. I have to let the data itself decide what data to remove. So I removed 1% from each relationship, the lowest 0.5% and the highest 0.5%. I could have removed more, but based on the histograms, [removing] more appeared to be removing too much valid data. As people submit more parent/child relationships these outliers/incorrect submissions will be removed. But thankfully using the histograms makes it clear.

Indeed, if you look on page 23 on Blaine’s white paper, you’ll see the following histogram of parent/child relationships submitted.

shared cm histogram.png

click to enlarge

Keep in mind that Blaine already removed any obvious errors, plus 1% of the total from either end of the spectrum. In this case, he utilized 2412 submissions, so he would have removed about 24 entries that were even further out on the data spectrum.

On the chart above, we can see that a total of about 14 are still really questionable. It’s not until we get to 3300 that these entries seem feasible. My speculation is that these people meant to type 3400 instead of 2400, and so forth.

shared cm parent grid.png

click to enlarge

The great news is that Jonny Perl at DNAPainter included the histograms so you can judge for yourself if you are in the weeds on the outlier scale by clicking on the relationship.

shared cm parent submissions.png

click to enlarge

Other relationships, like this niece/nephew relationship fit the expected bell shaped curve very nicely.

shared cm niece.png

Of course, this means that if you match your niece or nephew at 900 cM instead of the range shown above, that person is probably not your full niece or nephew – a revelation that may be difficult because of the implications for you, your parent and sibling. This would suggest that your sibling is a half sibling, not a full sibling.

Entering specific amounts of shared DNA and outputting probabilities of specific relationships is where the power of DNAPainter enters the picture. Let’s enter 900 cM and see what happens.

shared cm half niece.png

That 900 cM match is likely your half niece or nephew. Of course, this example illustrates perfectly why some relationships are entered incorrectly – especially if you don’t know that your niece or nephew is a half niece or nephew – because your sibling is a half-sibling instead of a full sibling. Some people, even after receiving results don’t realize there is a discrepancy, either because their data is on the boundary, with various relationships being possible, or because they don’t understand or internalize the genetic message.

shared cm full siblings.png

click to enlarge

This phenomenon probably explains the low minimum value for full siblings, because many of those full siblings aren’t. Let’s enter 1613 and see what DNAPainter says.

shared cm half sibling.png

You’ll notice that DNAPainter shows the 1613 cM relationship as a half-sibling.

shared cm sibling.png

And the histogram indeed shows that 1613 would be the outlier. Being larger that 1600, it would appear in the 1700 category.

shared cm half vs full.png

click to enlarge

Accurately discerning close relationships is often incredibly important to testers. In the histogram chart above, you can see that the blue and orange histograms plotted on the same chart show that there is only a very small amount of overlap between the two histograms. This suggests that some people, those in the overlap range, who believe they are full siblings are in reality half-siblings, and possibly, a few in the reverse situation as well.

What Else is Noteworthy?

First, some relationships cannot be differentiated or sorted out by using the cM data or histogram charts alone.

shared cm half vs aunt.png

click to enlarge

For example, you cannot tell the difference between half-siblings and an aunt/uncle relationship. In order to make that determination, you would need to either test or compare to additional people or use other clues such as genealogical research or geographic proximity.

Second, the ranges of many relationships are wider than they were before. Often, we see the lows being lower and the highs being higher as a result of more data.

shared cm low high.png

click to enlarge

For example, take a look at grandparents. The expected relationship is 1700 cM, the average is 1754 which is very close to the previous average numbers of 1765 and 1766. However, the minimum is now 984 and the new maximum is 2462.

Why might this be? Are ranges actually wider?

Blaine removed 1% each time, which means that in V3, 6 results would have been removed, 3 from each end, while 11 would be removed in V4. More data means that we are likely to see more outliers as entries increase, with the relationship ranges are increasingly likely to overlap on the minimum and maximum ends.

Third, it’s worth noting that several relationships share an expected amount of DNA that is equal, 12.5% which equals 850 cM, in this example.

shared cm 4 relationships.png

click to enlarge

These four relationships appear to be exactly the same, genetically. The only way to tell which one of these relationships is accurate for a given match pair, aside from age (sometimes) and opportunity, is to look at another known relationship. For example, how closely might the tester be related to a parent, sibling, aunt, uncle or first cousin, or one of their other matches. Occasionally, an X chromosome match will be enlightening as well, given the unique inheritance path of the X chromosome.

Additional known relationships help narrow unknown relationships, as might Y DNA or mitochondrial DNA testing, if appropriate. You can read about who can test for the various kinds of tests, here.

Fourth, it’s been believed for several years that all 5th degree relatives, and above, match, and the V4 data confirms that.

shared cm 5th degree.png

click to enlarge

There are no zeroes in the column for minimum DNA shared, 4th column from right.

5th degree relatives include:

  • 2nd cousins
  • 1st cousins twice removed
  • Half first cousins once removed
  • Half great-aunt/uncle

Fifth, some of your more distant cousins won’t match you, beginning with 6th degree relationships.

shared cm disagree.png

click to enlarge

At the 6th degree level, the following relationships may share no DNA above the vendor matching threshold:

  • First cousins three times removed
  • Half first cousins twice removed
  • Half second cousins
  • Second cousins once removed

You’ll notice that the various reporting models and versions don’t always agree, with earlier versions of the Shared cM Project showing zeroes in the minimum amount of DNA shared.

Sixth, at the 7th degree level, some number of people in every relationship class don’t share DNA, as indicated by the zeros in the Shared cM Minimum column.

shared cm 7th degree.png

click to enlarge

The more generations back in time that you move, the fewer cousins can be expected to match.

shared cm isogg cousin match.png

This chart from the ISOGG Wiki Cousin statistics page shows the probability of matching a cousin at a specific level based on information provided by testing companies.

Quick Reference Chart Summary

In summary, V4 of the Shared cM Project confirms that all 2nd cousins can expect to match, but beyond that in your trees, cousins may or may not match. I suspect, without evidence, that the further back in time that people are related, the less likely that the proper “cousinship level” is reported. For example, it would be easier to confuse 7th and 8th cousins as compared to 1st and 2nd cousins. Some people also confuse 8th cousins with 8 generations back in your tree. It’s not equivalent.

shared cm eighth cousin.png

click to enlarge

It’s interesting to note that Degree 17 relatives, 8th cousins, 9 generations removed from each other (counting your parents as generation 1), still match in some cases. Note that some companies and people count you as generation 1, while others count your parents as generation 1.

The estimates of autosomal matching reaching 5 or 6 generations back in time, meaning descendants of common 4 times great-grandparents will sometimes match, is accurate as far as it goes, although 5-6 generations is certainly not a line in the sand.

It would be more accurate to state that:

  • 2nd cousins, people descended from common great-grandparents, 3 generations back in time will always match
  • 4th cousins, people descended from common 3 times great grandparents, 5 generations back in time, will match about half of the time
  • 8th cousins, people descended from 7 times great grandparents, 9 generations back in time still match a small percentage of the time
  • Cousins from more distant ancestors can possibly match, but it’s unlikely and may result from a more recent unknown ancestor

I created this summary chart, combining information from the ISOGG chart and the Shared cM Project as a handy quick reference. Enjoy!

shared cm quick reference.png

click to enlarge



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Quick Tip – Calculating Cousin Relationships Easily

Lots of people struggle with figuring out exactly how two people are related.

Most genealogy programs include a relationship feature, but what if you are working with a new genetic cousin whose line isn’t yet in your genealogy software? Hopefully, that happens often!

There are also nice reference charts available, like this one provided by Legacy Tree Genealogists.

However, rather than trying to figure out who fits where, it’s easier and quicker for me to quickly sketch this out by hand on a scrap piece of paper. I can do this while looking at someone’s tree or an e-mail much more easily than I can deal with charts or software programs.

Rather than make you look at my chicken scratches, I’ve typed this into a spreadsheet with some instructions to make your life easier.

Common Couple Ancestor

This first example shows a common couple ancestor – as opposed to calculating a relationship to someone where your common ancestor’s children were half siblings because the ancestor had children by two spouses. 

Down one side, list your direct line from that ancestor couple to you.

On the other side, list your matches direct line from that ancestral couple to them.

The first generation, shown under relationship, will be siblings.

The next generation will be first cousins

The next generation will be second cousins, and so forth.

You can see that Ronald and Louise are one generation offset from each other. That’s called “once removed,” so Ronald and Louise are third cousins once removed, or 3C1R.

If Ronald’s child had tested, instead of Ronald, Ronald’s child and Louise would be third cousins twice removed, because they would be two generations offset, or 3C2R.

See how easy this is!

Half Sibling Relationships

In the circumstance where Ronald and Louise didn’t share an entire ancestral couple, meaning their common ancestor had a different spouse, the relationship looks like this:

The only difference in the relationship chart is that Jane and Joe are half siblings, not full siblings, and each generation thereafter is also “half.”

The relationship between Louise and Ronald is half third cousins once removed.

It’s easy to figure relationships using this quick methodology!

Update:  I can tell from the comments that the next question is how much DNA to these various relationships share, on average.  The chart below is from the article Concepts – Relationship Predictions, where you can read more about this topic and the chart.



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