My recent blogpost on Walking The Clusters Back (WTCB) is here. This is an advanced topic and process which involves homework before you start. The idea is to start with your closest known Matches in several Clusters (maybe 5 to 10). Then gradually decrease the lower cM Threshold in order to “follow” the known Ancestors in your Tree; back to Most Recent Common Ancestors (MRCAs) with Matches; and impute these “Root Ancestors” (your parent, grandparent, etc) to the other Matches in each Cluster. The linked blogpost provides a spreadsheet template. In practice the initial Cluster runs are fairly easy, but the number of Matches roughly doubles with each run. It soon becomes a time consuming, head-scratching, process (take regular breaks….).

Bottom line: It’s working very well – the Clusters in each new “run” carry over Root Ancestors from previous runs, and the addition of new Matches sometimes extends Root Ancestors.

My plan is to post several more blogs about the details of the process, and the insights I’ve gained doing it. But in this post I want to provide you with two tables of my experience, so far.

1. Summary of Cluster Runs:

I title the Cluster Run by the Lower cM (Lo cM) Threshold. You can see I started with 80cM and reduced by increments down to 23cM. I also adjusted the Upper cM (Hi cM) downward to cull out the closest Matches which become confusing. The Time column is the time it took DNAGedcom Client to produce the 3 data documents (after the initial gathering process). These different Cluster Runs resulted in the number of Matches and Clusters shown. I rolled up some of the very small Clusters – usually, but not always, into the main Cluster in a Super Cluster. At the end of my analysis, I had the number of Clusters in the Net CL column.

I note that although the *average* size of Clusters grows, slightly with each decrease in the lower cM Threshold, this hides that fact that a number of Clusters are in the 50-100+ range. The average stays low because there are a lot of 3-10 Match Clusters. The 250 Clusters in the 23cM run, is approaching the 372 Triangulated Groups I have already established over all of my DNA. My TGs also range from small to large groups…

2. Listing of Root Ancestors in Clusters:

G2 is the parent generation of Root Ancestors – always a 2 or 3; G3 is the grandparent generation (3, 4, 5, or 6); etc. The Ahnentafel numbers represent my Ancestors out to, but not including, a consensus Most Recent Common Ancestor (MRCA) couple between me and the Matches in each Cluster. Many of these MRCAs are from Ancestry ThruLines and were validated by me (I find about 5% of my ThruLines hints are incorrect and they are not included in my Notes or in these Clusters). I’ve done the “homework” of including all the validated ThruLInes MRCAs in the Match Notes, which makes the above List possible.

The above 228 Clusters are about 90% of my 250 net Clusters for this 23cM run (the other 10% don’t have a clear consensus – these cases are usually resolved in the next Cluster run).

The Root Ancestors shown above are a huge benefit in several ways:

A. finding and validating more MRCAs with Matches in each Cluster – there is a clear focus on who I’m looking for, and generally where and when.

B. identifying MRCAs that do not “fit” – they stick out like sore thumbs, in that their RAs clearly do not match the consensus. Sometimes this is resolved because the Match is related to me more than one way, and another way does “fit”. In all these cases I need to revisit the MRCA conclusion. This is a significant Quality Control “opportunity”.

C. Identify other problems: MPEs, ThruLines Potential Ancestors, hypotheses, etc. The RAs in each Cluster really narrow down the alternatives.

So this is a SITREP (Situation Report) of where I am now. The 20cM run is next – adding over 2,500 new Matches (many with MRCAs) – this may take weeks to sort out. Then I will take perhaps an additional 1,000 ThruLines under 20cM and see if I can manually include them in Clusters (if they have Shared Matches). And along the way, I want to post more details and insights about this WTCB process.

[19Na] Segment-ology: WTCB SITREP by Jim Bartlett 20220821

Walking the Clusters Back (WTCB) 2022


An Advanced Segment-ology Topic


This will be a longer and more detailed post than usual. The process I’ll outline takes a lot of precise and detailed work. And preparation work. You have to decide if it’s worth it for your objectives.

I’ve tried several blog posts about Walking The Clusters Back. In my opinion, they all failed. I was trying to find a sweet spot that would give us groups of Matches at each generation. That generally works at the grandparent level (the Leeds Method works in most cases to provide 4 columns for 4 grandparents), but the Clusters quickly get jumbled up as the cM Threshold decreases. I should have known better. Each Cluster still tends to focus on an Ancestor, but the different Clusters have Ancestors of different generations. The Clusters sort of mirror the Shared cM Project – as the cM value decreases, the Shared DNA Segments come from a wider and wider range of generations. The overlap of possible relationships grows. The Shared cM Segment pattern gets more and more jumbled – just like the Clusters.

So if we can’t use brute force on the data, lets go with the flow, and develop a process that tracks Clusters – by tracking the Matches in them. As the cM Threshold is decreased, the number of Matches being Clustered increases. This results, generally, in more Clusters with more Matches in them.


Overall WTCB Process:

1. Run a Cluster report with a high cM Threshold (say 80 or 90cM) to get at least four Clusters that you can identify a consensus Most Recent Common Ancestor (MRCA) in each Cluster.

2. From information in the Match Notes, determine the consensus Root Ancestors (RA) in each Cluster. The RAs start with your parent, and include your Ancestors out to, but not including, the consensus MRCA for each Cluster. These RAs should “fit” all the known Matches in the Cluster.

3. Impute (copy) these RAs to all the other Matches in the Cluster.

4. Repeat for all Clusters

5. Run a new Cluster report with a lower Threshold.

6. Combine the Matches from the previous and the new Cluster reports into one spreadsheet.

7. Sort on Match names.

8. Merge the duplicate Matches into one Match (much more on this step later)

9. Return to step 2, and continue…

10. Gradually reduce the upper cM Threshold, to cull out the closest Matches – this fine tunes the MRCA of the Cluster.

As part of this overview, I must provide a warning:  there is a lot of homework required before you can start this process – see the Homework section below. The Cluster runs include the Notes for each Match. These Notes should include  “known” Match MRCAs and cousinships [including multiple MRCAs], and any TG IDs, that  you have determined. This is the information you need to populate your Master WTCB Spreadsheet. This is your source for RAs.

In the paragraphs to follow, I’ll offer a spreadsheet template, and specific steps to accomplish the steps in each Cluster run. It’s a repetitive process that I have tweaked to make it as standard and efficient as I can. The number of Matches about doubles with each Cluster run – so the work gets harder. I’ve also incorporated my short cuts and tips into the steps…

I started with an 80cM Threshold and found 8 Clusters – I was confident I knew the consensus MRCA for each Cluster. More importantly, I knew the Root Ancestors for each Cluster (the RA being the parent and grandparent and sometimes more) back to, but not including, the MRCA). As I lowered the cM Threshold (usually by 10cM at first) and ran a new Cluster run, I found the number of Matches about doubled and the number of Clusters increased. The increases were not in a predictable way, but the Clusters grew in size (more Matches) and slowly, but surely, pushed the RAs out to more distant MRCAs.  

I’m now confident this process works. By that I mean for each new WTCB Cluster, we get some RAs which point to the MRCA of the Cluster; and this MRCA is very close to the MRCA we’ll find with each of our Matches in the Cluster. A strong, helpful, clue…


Some *essential* homework is required before you try this:


1. Test at Ancestry and build your Tree out (as much as you can to 7xG grandparents where possible [you only need Ancestors (use standard names); birth/death dates/places]. AncestryDNA needs this information for ThruLines to work. See some of my posts on ThruLines starting here.

2. Link yourself to your Tree – this let’s AncestryDNA do it’s magic with ThruLines and other hints.

3. Find as many MRCAs as you can – some are close low hanging fruit; many will be via ThruLines (which will find MRCAs in Private, but searchable, Trees); some you’ll find in Unlinked Trees (which ThruLines does not review).

4. Add what you find to the Notes of your Matches – see my blogpost here.

5. Review: It Is Iterative here – the goal is to get info into the Notes of your Matches.

6. It is very important that you have information in the Notes of as many Matches over 20cM as possible.


1. Subscribe to DNAGEDcom Client (DGC) (you can subscribe for one month to try it out). See links in this blogpost.

2. Click on the DCG Icon and Log in.

3. Set up your folder (you’ll access this folder regularly in the WTCB process)

IMPORTANTDo not go beyond this point until you have completed the ANCESTRY TREE & MATCH NOTES Homework – we need the data in the Match Notes before we gather it in the next step!

4. Gather Matches and ICW from 20cM to 400cM (ignore Trees and Ethnicity for now – they are not needed for this WTCB process, and they can always be gathered later). This gathering process may take a day or more (depending on the number of Matches you have). I think the % Complete indicator is based on gathering all of your Match, so it may be misleading, and the gathering process will finish somewhat sooner.

5. This process will store several files on your computer:

        a. m_yourname CSV file of your Matches with lots of information about each one, including your Notes, URLs to the Match and their Tree, Shared cMs, etc. This file is a gold mine by itself – I highly recommend you save a Working Copy of this file in Excel – it’s very useful.

        b. icw_yourname CSV file – this is a large file used by the Clustering program

        c. DNAGedcom Data Base File – where all the data is stored

6. The Clustering reports are run separately. Each run takes about a minute (not a typo), and produces 3 reports:

        a. clm3d_yourname_[date,time,threshold string]_clusters CSV file – a list

        b.  clm3d_yourname_[date,time,threshold string] Excel file – includes a TAB you’ll use. [I make a copy of this file – appending the word “Working” – to use in WTCB.

        c. clm3d_yourname_ date,time,threshold string]  HTML doc – the colorful display.


The last part of our homework assignment is to set up a Master WTCB spreadsheet template.

There are 3 features about this spreadsheet template:

1. It is a tool, to incrementally follow and interpret the data.

2. It is the culmination of many variations I have tried. It is fairly easy to set up, and it offers a lot of flexibility.

2. A standard spreadsheet will help me explain the various steps later in this post. Of course, you are free to use any format you want. In fact, I encourage feedback on improvements to this Master spreadsheet, or the whole WTCB process.

Here is a sample of my Master WTCB Spreadsheet with some data:


1. This is from the initial CL 80cM Cluster run, and there are columns to the right for the 70cM Cluster results from the next run.

2. I have Notes for all of these close Matches – they were in the AncestryDNA Match Notes and then captured by the DGC Cluster program.

3. The data in the known columns was from the Notes

4. The data in the ROOT ANCESTORS columns was derived from the known data, and then imputed (copied) to the other Matches in the Cluster.

Teaser:  these 5 Clusters have Root Ancestors from three of the four grandparents (4, 6 and 7) and CL 3 and CL 4 appear to be splitting to Great-grandparents 8 and 9. The Walk has started!

Here is a list of the 10 columns from the DGC Cluster run spreadsheet and where 9 of them go in the Master spreadsheet:

Note: the CL [B] and Super CL [C] columns copy to different columns in the Master spreadsheet, depending on the Cluster cM Threshold.

Here is a list of the 49 columns from the Master spreadsheet – with a brief description of each:

This covers the Homework section. Get ready to Walk The Clusters Back…

WTCB Master Spreadsheet overview:

Let’s divide the process into several stages for each Cluster run – details later:

1. Run a Cluster report at DGC; copy the data to the Master Spreadsheet; do some additional housekeeping chores to get the Master Spreadsheet Ready.

2. Merge duplicate Match rows (not with the initial run, but needed with subsequent runs, after the previous Matches are added to the spreadsheet)

3. Sort the Master Spreadsheet to show the Cluster Groups.

4. Type information into columns L, M and N from the Match Notes (when available).

5. Analyze each cluster, and fill in Root Ancestors (RAs) for all (in columns F-K as needed)

6. Save Master WTCB Spreadsheet for this run.

There are some other “details” I’ll explain as I expand on each of these stages below.

WTCB Master Spreadsheet details:

Here are the details for each stage:

1. Run a Cluster report at DGC; copy the data to the Master Spreadsheet; do some additional housekeeping chores to get the Master Spreadsheet Ready.

        a. At DGC, click on the Autosomal TAB and select the Collins Leeds Method (CLM).

        b. Select the Thresholds (start with 80cM and subtract 10cM for each of the next few runs); leave the upper limit at 400cM, and reduce that in later runs. I uncheck Paint Midline & Include Ancestors. Then click on the Run Grouping bar. It takes about a minute to produce the three files.

        c. Open the file:  clm3d_yourname_[date,time,threshold string] Excel file. I make a copy of this file – appending the word “Working” and save it in Excel format. Open the second TAB labeled Data.

        d. Open the Master Spreadsheet and save it with the cM Threshold number (e.g. 80cM) append to the file name.

        e For the next 4 steps – make sure you copy to the same blank row at the bottom of your Master Spreadsheet, so the columns line up properly with the Matches.

        f. Copy columns B and C to the appropriate Master spreadsheet columns (this would be Q and R for the first 80cM run – it shifts with each subsequent run)

        g Copy columns D, E and F to Master columns A, B and C

        h. Copy columns G , H and I to Master columns AU, AV and AW

        i. Copy column J to Master column P

        j. In the appropriate order column [S for the first run], type a 1 for the first Match, then drag this down to the last Match to create a series. [I sometimes want to recreate the original Cluster order]

        k. Use a new row to create a Header. In column O type: CLUSTER 80cM (or whatever the cM Threshold is for that run). Type 0 in the appropriate CL column and 0 in the appropriate order column. Yellow highlight this row.

        l. Use another new row to create another Header. In column P type: CLUSTER RUN 80cM (or whatever the cM Threshold is for that run). Type 1 in the appropriate CL column and 0 in the order column. Highlight this row in light grey. Copy this row so there is one for each Cluster. Drag the 1 in the CL column down to fill in the series – this provides a numbered header for each Cluster.

2. Merge duplicate Match rows. Note: this step is not used for the first (80cM) Cluster run – there is only one set of Matches, so merging is not required. In subsequent runs, the prior Matches are in the Master Spreadsheet (all those above 80cM, in the second run), and all the Matches from the new Cluster run will be added to the Master Spreadsheet (all those above 70cM). This means the prior Matches (above 80cM) will be duplicated, but in the new run they will only have Cluster data from the new (70cM) run in the CL and CL Super columns. This step will merge the duplicated Matches into one row with prior and new CL and Super CL data (and the order numbers); and delete the other row.  Here we go…

        a. Tip – sort the Spreadsheet by C [cM]. This puts all of the new, smaller cM, Matches at the top.

        b. Highlight the rest of the Match rows and sort by Name and cM and CL [for the current run]. This puts all the duplicate Matches together, with the ones from the new run (with a value in the CL column) on top.

        c. a view of the spreadsheet at this point – showing the duplicate Matches on the left, and the CL 70, Super, and Order data that needs to be copied down one row. Notice also that the Matches below 80cM are still in this sort. This is OK, but be careful dragging the data down. By using the Tip above, these can be sorted out, which makes this merging step a little easier.

        c. Copy (or drag) the 3 cells (CL, CL Super and order number data) down one row and paste it into the same columns (to add it to the duplicate Match who already has some data from previous runs). Then delete the Match which you just copied from. [Tip: an alternative to deleting the rows one-by-one, is to type an x in the order cell of the top Match and later sort the spreadsheet by that column and deleting all the rows with an x.]

        d. Continue to the bottom of the Matches in this Cluster run – a boring task .

3. Sort the Master Spreadsheet to show the Cluster Groups.

        a. Highlight all the rows of the spreadsheet (under the main header) and sort on CL and order – columns Q and S in the first run – it shifts to the right with subsequent Cluster runs.

        b. You should now have a nice looking WTCB Master Spreadsheet with a group of Matches under each grey CLUSTER RUN Header – see the sample above.  You’re ready to start working with the data.

4. Type information into columns L, M and N from the Match Notes (when available).

        a. Work down the spreadsheet, looking at the information in the Notes. For Matches with known MRCAs and/or TGs, type in the MRCA Ahnen in column L; cousinship in Column M and TG ID in column N

        b. This is populating the Master Spreadsheet with data from the Notes – this is why the Notes Homework (before running the DGC gathering program) is so important.

5. Analyze each cluster, and fill in Root Ancestors (RAs) for all (in columns F-K as needed)

        a. This is where the Ahnen system shines (using numbers instead of typing out the Ancestor names); and descendants are always half the father’s Ahnentafel, so we can easily work from the MRCA Ahnen back down to a parent)

        b.  Fill in the Root Ancestor Ahnentafel numbers where an MRCA is known. Note: The “Root Ancestor(s)” are the closest ones to you – NOT including the MRCA Couple. The basic RA is a parent – using Ahnentafels, this would be a 2 or 3 (father or mother). The next RA (at Generation 3) must be a grandparent – a 4, 5, 6, or 7 – 4 and 5 are the parents of 2; 6 and 7 are the parents of 3. The line of descent (and most probably the shared DNA segment) comes from the MRCA to you along this path.

        c. Use judgment to determine the consensus RAs that would apply to all the Matches with known MRCAs. Note: if there is a Match who is clearly inconsistent with the rest, ignore or move that Match row (to a different Cluster or a “time out” area at the bottom of the spreadsheet).

        d. Copy these consensus RAs to all the Matches in the Cluster. The concept here is that each Cluster is formed around an Ancestor, and that all the Matches would have these same RAs. The stronger the Cluster consensus is, the stronger the case for the same RAs. There may be some Match anomalies, but by Walking The Clusters Back, I’ve found that the same RAs are almost always consistent.

        e. A very few Matches in a Cluster may be at odds with the consensus. This may be due to an incorrect MRCA (it happens to me and to ThruLines). It may also be due to the Matches having multiple MRCAs with me, and/or multiple segments. Check the DGC HTML file to see if there are grey cells that link the Match to another Cluster(s). When a Match appears to me to be “better” in a linked Cluster, I move their row to that other Cluster in the spreadsheet and change the CL number to match (I leave the order number in case I want relook at the original Cluster list.)

6. Save Master WTCB Spreadsheet for this run. For each Cluster run, I save the Master Spreadsheet with a new descriptor added to the file name – like 70cM.

This is a repetitive process – go back to #1, and run a new Cluster report – keep going….

Objectives of WTCB

Identify root ancestors for Clusters, and, by inference for all the Matches in them. This provides a pointer when investigating any Clustered Match. It gives direction (names, dates, places) when building a Match’s Tree back; to finding an MRCA with any Match; to researching Brick Walls.


1. Start with a high cM Threshold, say 80cM or 90cM. I have found that reducing the cM threshold by 10cM about doubles the number of Matches in the next run – to a point. The shift from a 50cM threshold to a 40cM threshold added much more than double – so I back tracked and started using a 5cM reduction to get a 45cM run. Similarly when I got to the 30cM range, I then reduced by 4cM, then 3cM, then 2cM (for a final run with a 20cM threshold.

2. A very few Matches turned out to be anomalies – they did not “fit” in the Cluster they were assigned by DGC, based on the MRCA we had. If they had a grey cell link to another Cluster with a good fit, I moved them to that Cluster. If they didn’t appear to fit any grey cell Cluster, I moved them to a “time out” section at the bottom of the spreadsheet, with an X in the CL column. These very few Matches probably had an issue with the MRCA, that I needed to investigate. They were in “time out” so they didn’t “taint” the Cluster analysis – I could look at them later. The Cluster is talking to you – try to understand what the message is.

3. The Clusters *tend* toward a single MRCA, as the upper cM Threshold is decreased.

4. Do not be afraid to move a Match from one Cluster to another. Review alternate “grey” cells in the the HTML Cluster diagram. If a Match has, say, 5 squares in a Cluster, and several grey links to another Cluster (which other Cluster is a much better “fit”), I would not hesitate to move that Match. Usually this will resolve itself in subsequent Cluster runs.

5. Excel Macro – for the task of copying 3 cells from a Match from a new Cluster run and pasting it into that Match from the previous Cluster run, and then deleting the first Match. Here are the steps:

        a. Go to File > Options > Customize the Ribbon > add “Developer” to the Main Tabs

        b. In the spreadsheet, insure “Use Relative References” is ON [highlighted]

        c. Position cursor on the CL cell of the top Match;

        c. Click Record Macro [fill in the popup – the only critical thing is a letter for the Macro]

        a. Highlight the three numbers [in CL, Super CL, and order columns]

        b. Control-C to copy that data

        c. Click on next cell to the right

        d. Type: x [this will let you easily delete all these rows later)

        e. Click on the CL cell in the next row (this should be the same Match from previous run)

        f. Control-V to paste the data into three cells

        g. Click on the CL cell in the next row [to preposition the curser for the next Match]

        h. Click Stop Macro.

        i. Save Spreadsheet with Macro Enabled

        j. Good luck – it took me several tries to get it right. Practice on a spreadsheet copy.

6. Special Note: Some close Matches have multiple MRCAs with me. They may well be related though multiple Clusters. I make duplicate copy of that Match and add it to other Clusters per the gray cells. Once moved I adjust the CL and super columns per their new Clusters. Use judgment, but I think after about two cycles with the multiple copies of close Matches (closer than the Cluster Root Ancestors indicate), they can be eliminated from future Clusters. They have done their job of solidifying the root ancestors in other Matches.

7. I also think the maximum/upper cM Threshold needs to be reduced as the Clusters evolve. We don’t need the higher cM/closer Matches – they have already passed on their Root Ancestors to the Clusters in the Master Spreadsheet. They should be dropped from the Spreadsheet. I put an X in the CL column to remind me they are no longer needed.

8. Some Matches wind up in singleton Clusters – this is silly, a Match doesn’t form a Cluster with itself. And most of the time these Matches show a grey link to another Cluster. I move (Ctrl-X; Ctrl-V) the Match row to the other Cluster and change the CL cell to match that Cluster (so they will sort with that Cluster in the future). I sometimes also move Matches out of very small Clusters when that seems appropriate. Most of the time subsequent Cluster runs resolve these issues.

9, If a Cluster goes through several iterations without any indication of a more distant RAs, there may be an MPE or brick wall involved –a strong potential clue from the data.

Manual WTCB Process

If all of this is overwhelming, you can try a few iterations using manual Clustering. Start with the Leeds Method that results in 4 Clusters, one for each grandparent. So in these 4 Clusters you already have two Root Ancestors for each [2-4, 2-5, 3-6 and 3-7, using Ahnentafels]. Find your Matches who are in the 80 to 90cM range and manually Cluster them. Start by seeing which ones are Shared Matches with the ones in the 4 Clusters – that automatically gives each one the same two Root Ancestors as the Cluster they share {actually the Matches they share). Now, from the information you know about these new Matches, do any have an MRCA at the 2xG grandparent level – this would give you the next Root Ancestor – for that Match, and that Matches shared Matches. Keep dropping the cM Threshold, checking Shared Matches for Cluster affinity, and using the Matches with MRCAs to tease out the next Root Ancestor for each Cluster. This is workable with a small number of Matches, but when you have 500 or 1,000 Matches to work with, you will yearn for automated Clustering…

Tracking RAs

Some results so far:

At 60cM run: 11 Clusters: with generation 5 (G5) RAs:

        Paternal RAs: 8, 8, 9, 9, 10, and 11; Maternal RAs: 12,, 12, 12, 13 and 14/15

            -The last Cluster, 14/15, is my maternal grandmother whose immigrant parents had two brothers married two sisters resulting in few Matches, those are hard to separate until I can get more distant Matches.

            -I’m happy with this spread – it includes Clusters for all 8 of my Great grandparents. The WTCB is working…

            – The 70cM run had 47 Matches in 8 Clusters; 60cM run had 75 Matches in 11 Clusters. Roughly double the number of Matches (and analytical review work) in 3 additional Clusters. My experience is that the doubling of Matches with each 10cM decrease in Threshold continues…

At 50cM run: 128 Matches in 24 Clusters (net, after moving several singleton Matches to Clusters they shared with other Matches).

        Paternal RAs: 8, 8, 17; 9, 9, 9, 9, 9, 18; 10, 10, 11, 22; Maternal RAs: 12, 24, 24, 24, 24; 26, 26, 26, 27, 27 and one 14/15.

            -These are broken apart quite nicely, I think. And the uneven nature of the splits (not cleanly by generation like the 4 grandparents often do); illustrates the folly of trying to find a sweet spot in the Thresholds to result in one specific generation (like we get with grandparents). I should have expected this – beyond the grandparent level the Shared cM Project shows growing overlap of cM values for a growing range of cousinships. So, this WTCB process just lives with that, and tracks the Matches as the Clusters grow in size and split apart – Walking The Clusters Back!.


Ahnen – abbreviation for Ahnentafel number – a system of numbers to represent our Ancestors [e.g. 2 for father; 13 for mother’s father’s mother] – see also this blogpost.

CL – Cluster, or Cluster Run [usually combined with a number representing the lower cM Threshold]

Czn – Cousinship – how we are related to a Match. Second Cousin is abbreviated 2C; 5th cousin once removed: 5C1R.

DGC – DNAGEDcom Client – an automated Clustering program – runs from your computer.

MRCA – Most Recent Common Ancestor – this is usually a couple that you and a Match have in common. Usually represented by the Ahnentafel of the husband, but we really don’t know which parent (husband or wife in the MRCA couple) the shared DNA came from.

RA – Root Ancestor – the Ancestors you have leading up to the MRCA. This should always include your parent and grandparent (each is a RA). During the WTCB process, the number of RAs will generally increase (adding generations) and increasing the ancestral “focus” for each Cluster.

TG ID – Triangulate Group Identification Code – see this blogpost.

WTCB – Walking The Clusters Back – the process discussed in the post which helps determine the MRCA of most Clusters – sort of a Leeds method on steroids.

Final Thoughts

This WTCB process uses the power of Clustering to link large groups of Matches to specific areas of your Ancestry. As the process develops, the Clusters become more and more precise on the path back to an MRCA. There are only two options for each Cluster going back another generation – going back on the paternal side or the maternal side. Larger Clusters with more distant Matches, tease this information out of the data. The Homework is essential – recording what you know in the Notes; and the work is sometimes tedious; but the end result is very powerful.

I’m confident this process will tell us some Root Ancestors for all of our Matches down to 20cM. Just think what we could do with those clues…

Feedback on this process and suggestions for improvement are welcome.

[19N] Segment-ology: Walking The Clusters Back by Jim Bartlett 20220822

It is Iterative

A Segment-ology TIDBIT

Genetic Genealogy is a very iterative process – particularly at AncestryDNA. The more you find out, the more the AncestryDNA Tools feed back new clues to you.

Here is an overview:

Genetic Genealogy Process at Ancestry DNA

Let’s take it step-by-step and see how it snowballs…

1. First build your Tree and link it to your DNA results. This is very important because AncestryDNA keys off of this information, and your DNA Matches, to provide you with strong clues. Even if you don’t know much about your ancestry, list yourself and the Ancestors you know with their birth/marriage/death dates/places. Even one or two generations can often link you into AncestryDNA’s giant Tree. Focus on adding the Ancestors you know. Later you can add pictures, records, children, etc.

2. Work your way down your DNA Match list (your closest Matches are at the top of the list) and try to determine how you are related to them. Find the “low hanging fruit” first. Continue to work on other Close Matches who have Trees. Check out any/all ThruLines. You are looking for Common Ancestors (CAs) with your Matches.

3. Verify! All the CAs you find should be verified by you – both the individual Ancestors as well as the line of descent down to the Match.

4. When you are satisfied that the Match really descends from your CA, add the line of descent and the Match (as a living person) into your Tree. I find that this step tends to generate more ThruLInes. Note that ThruLines finds lines and Matches in Private Trees – something we cannot do.

5. Add the CA information to the Match’s Notes. This keeps track of it for you, AND, since the Notes are visible in a Shared Match List, it helps build consensus for CAs in Clusters. The information you put into the Notes is very valuable. More on Notes in this blogpost here.

6. For each Match you are working on, always look at the Shared Match List. The Shared Match Notes will tell you when there is a consensus; they add confidence as you add more and more notes pointing to the same CA.

7. Also use the Shared Match Lists to build Clusters – either automated Clusters including many of your Matches or a Manual Cluster focused on a few Matches for a particular objective. See my DIY Clustering blogpost here.

8. Clusters have been shown to group on an Ancestor. If you see this congruence in the Notes, you can input a *clue* in the Notes of other Matches in the Cluster. (The *clue* is not rock solid evidence). This includes Matches with No or Private Trees. The Clusters and Notes often provide an Ancestor “pointer” for these other Matches – which is sometimes the only information you have about them.

9. For Matches with *clues* in their Notes, see if you can build out their Tree to the CA. Building out Trees is one of best genealogy Tools, and it’s somewhat more efficient when you have a target CA (with known surname and timeframe and location). At this point, cycle back to Step 3 and verify the line and Step 4 add it to your Tree and Step 5 add the information in the Match’s Notes.

10. Additionally, you can search your Match list for Surnames and Places. See my blog post here. This search returns a list of your Matches, who share DNA with you, and who have a large enough Tree which includes the Surname in their Ancestry – that’s a pretty efficient method of finding CAs. You might have to build out the Match Tree, and you’ll need to do Steps 3, 4 and 5 again.

This whole process adds information to your Tree and to the Match Notes. These in turn lead to more ThruLines CAs, which continues this iterative process.

[22BI] Segment-ology: It is Iterative TIDBIT by Jim Bartlett 20220731

Segment Data for Ancestry Matches


A Segment-ology TIDBIT

Genetic Genealogy has two main parts: genetic – the Shared DNA Segments; and genealogy – the Most Recent Common Ancestor (MRCA) with a Match. In a perfect world we link a Match and his/her Shared DNA Segment to the MRCA who passed it down to both of us.

Shared DNA Segments can be found for Matches at 23andMe, FamilyTreeDNA, MyHeritage and (by uploading our raw DNA data file) at GEDmatch. Unfortunately, none of those companies have nearly as many good Trees as Ancestry has. So finding MRCAs is hard.

Finding MRCAs is best done at AncestryDNA – many more people have tested there, and more of our Matches have good, in depth, Trees there. Unfortunately, AncestryDNA does not provide the precise Shared DNA Segment data that the other companies do.

The best outcome are Matches with MRCAs and Shared DNA Segments. I’ve run out of patience looking for MRCAs at FTDNA, MyHeritage, 23andMe and GEDmatch. Instead I am now looking for DNA segment data for my thousands of Matches at Ancestry with MRCAs.

This post will cover ways to get Segment data for AncestryDNA Matches – there are several:

1. Click on the Match name to bring up their profile – some have already uploaded to GEDmatch and list their Kit number in their profile.

2. Message the DNA Matches and request, suggest, cajole them to upload their raw DNA data to GEDmatch. I wrote a blogpost, here, about doing this. I’ve messaged many Matches requesting that they upload to GEDmatch. A few have…  The best results occur when I include my email address and promise to report back my findings and to help them with autosomal DNA.

3. Ask the DNA Match if they have tested at one of the other companies, and what is their user name there. Some have…  I’ve tested at all the companies and can usually find them.

4. Try to find the Ancestry user name at GEDmatch or vice versa. It sometimes works.

However, in looking at my GEDmatch One-to-Many list, I see many more Ancestry kits, that I have not yet linked to Ancestry names. Many folks use use very different names.

NB: Large segments (say over 30cM) will usually be about the same cM at AncestryDNA and the testing companies/GEDmatch. However, many segments below 30cM have been “Timbered”, and Ancestry then reports a smaller segment than the other companies report. You can always click on the “segment” line on their Match page and see what the “unweighted” cM value is – this is usually fairly comparable to what you see at GEDmatch. It’s a good idea to check this when there is an apparent discrepancy.

A better way – a Segment-ology TIDBIT

1. At GEDmatch Tier 1, run the One-to-Many list. When I set the limit to 1,000 Matches, the smallest Match shares 22.6cM – a good place to start.  NB: By default, this list is sorted with the Matches with the most shared DNA at the top.

2. Sort the list on the Source column (it has the source of the DNA test data)

3. Scroll down the list to the beginning of the Ancestry kits. NB: these Ancestry Matches are still listed with the largest total cM at the top.

4. Work down this Ancestry list one by one, trying to find the Match at Ancestry. The closest ones at the top of the GEDmatch One-to-Many list are usually the easiest to find near the top of your AncestryDNA list of DNA Matches. Usually the largest Matches (most cM) will have the same total Shared DNA cM at GEDmatch and AncestryDNA – so even if the names are different, it’s often easy to find the right one at AncestryDNA.

5. As you go down the list, the AncestryDNA cM total tends to be smaller than the GEDmatch total, due to the Timber down-weighting. NB: you can always click on a Match’s AncestryDNA cM total to see what the unweighted total would be – it is usually pretty close to the GEDmatch total.

6. By working down both lists (the GEDmatch list and the Ancestry list), I’ve found they are roughly in the same order. And, through a combination of cM amount, user names and email addresses, I’ve been able to find most of the top GEDmatch Matches at Ancestry. If there is some doubt, I’ll look at the Shared Matches at Ancestry to see if any grouping would provide a clue. UPDATE: GEDmatch info puts the Match in a TG – look in that TG for other Ancestry Matches, then search Ancestry for one of those Matches and scroll down their Shared Matches for a likely link (this is generally a somewhat shorter list).

So far I’ve been able to link over 90% of my top GEDmatch kits with my Ancestry Matches. It’s easy to determine the TG at GEDmatch, and I put the TG ID in the Match Notes. Even if I cannot determine an MRCA with the Match at Ancestry, the Notes are invaluable in the Shared Match lists – they clearly form Clusters in most cases.

In just a few hours, I’ve been able to link over 100 Ancestry Matches to TGs. It will get harder as the segments get smaller and more scrolling is necessary at Ancestry to find a “fit”. But this process is worth the work, IMO, as it adds TGs to Matches at Ancestry. It adds evidence about the true ancestral line for each TG.

[22BH] Segment-ology: Segment Data for Ancestry Matches TIDBIT by Jim Bartlett 20220706

Finding Bio-Ancestors

After exhausting all genealogy research, try letting your DNA Matches identify your bio-Ancestor(s).

Think about any bio-Ancestor (bA) for a moment…

1. It doesn’t really matter how or why this bio-Ancestor is known or unknown to you – adoptee, foster child, brick-wall, no records, family story, found under a rock, etc., etc. The issue is you are stuck.

2. However, the bio-Ancestor absolutely had a bio-father and a bio-mother! This is key!

3. These two bio-parents had their own bio-parents – four more Ancestors of the bA; there were eight Great grandparents of the bA; 16 2xG grandparents; etc. The bA had a full Tree of Ancestors, just like you and me.

Yes, there are some negative factors that may come into play (each situation is different):

1. The bA, and/or his/her Tree of Ancestors, may be from outside the US.

2. The bA, and/or his/her Tree of Ancestors, may be from communities in the US with few records and Trees going back – think African Americans, Native Americans, Jews, etc.

3. With every generation going back, the search/research gets harder! The records are harder to find. DNA Matches with deep Trees are harder to find.

But… the bA did have a full Tree of Ancestors. Although not guaranteed, most of those Ancestors were known and left records. Most of those Ancestors had multiple children and multiple descendants – and, almost certainly, multiple descendants who took a DNA test and are Matches to you. Although you don’t know how you are related to them, yet, these Matches exist – they are in your Match list and in your Cluster Groups or Triangulated Groups.

Most of us will have tens of thousands of DNA Matches at AncestryDNA. Some of them will have good Trees going back enough generations to Ancestors of the bA. In my experience, atDNA “works” back to 7xG grandparents (8th cousin level) – I have over 1,000 Matches with Common Ancestors with me at the 8C level. Ancestry’s ThruLines, finds Common Ancestors at the 6C level – their (now defunct) Circles routinely found cousins out to the 8C level. You should have many Matches who are related to you out to the 8C level. The issue is not with the DNA testing, it’s finding Matches with good Trees that go back that far.

So how do we find a bA that we know little or nothing about. We let our Matches identify them! Let our Matches identify the bA or the bA’s parents and/or grandparents! If our target bA is a Great grandparent, we need to find Matches who are about 2nd cousins (2C). These 2C would usually be from a Great grandparent couple. To more precisely target the bA, we’d need 3Cs who descended from a 2xGreat grandparent couple who were the parents of the bA. Or if we don’t have enough 3C, we’d need 4C Matches from the grandparents of the bA.

Each situation is different, but the concept is the same: look at the Trees of Matches for Common Ancestors among the Matches. It helps, a lot, if we can identify, and cull out, Matches who are clearly from a different line than the bA. Or, to put it in a positive sense, we’d like to work with Matches we believe are related to us through the bA line. This is where groups and grouping play an important role. If we can identify Clusters or Triangulated Groups (TGs) from other lines (or ethnicities), we can cull them out. Perhaps we have already determined the grandparent for each of our groups – using the Leeds Method or TGs with known cousins in them.  

The concept here is that each group is focused on an Ancestor (or ancestral line) – whether we know that line or not! We need to look at the available Trees and determine the CAs among the Matches in each group. We are actually looking for information that we didn’t know before, often, but not necessarily, new Ancestors.

The point is that we need to clear our minds, and let the Matches tell us their Common Ancestors.

Here are three graphics that may help you:

1. This is a Crib Sheet to help get oriented:

Column 2 is you and your Ancestors:

Column 1 shows the relationship of Matches who would nominally be related to you. Example:  2C Matches would be related from your Common Great grandparents.

Column 3 shows how many of each type of Ancestor. Example: we all have 32 3xGreat grandparents.

Column 4 is the average birth year for each level of Ancestors (in my case for this chart). You should amend this column based on the average birth years of your Ancestors. This works out to about 30 years per generation in my case.

Column 5 is the number of Triangulated Groups that would be expected to “pass through” each Ancestor. Example: all of my 372 TGs are in me. About 47 of those TGs probably passed through each of my 8 Great grandparents. This provides a rough order of magnitude of the number of groups which might be available for each level of Ancestor. The total number of Clusters would depend on the lower cM threshold.

2. This is the Crib Sheet combined with Ancestral levels – in this case showing a Target bio-Ancestor (TBA in the chart) who is a Great grandparent of the DNA Test Taker (DTT):

The key points here include the F (father) and M (mother) of the TBA; and the TBA’s four grandparents; and the oval that highlights a lot of additional Ancestors of the TBA. All of these Ancestors, as yet unknown to the DTT, are Ancestors of the TBA – AND all are Ancestors of the DTT! These Ancestors are NOT known to the DTT, but they ARE known to many of DTT’s Matches.

3. This figure shows some DTT 3C, 4C and 5C Matches from several Groups (Clusters or TGs):

In general, the Matches with a Common Ancestor to one of the TBA’s grandparents will be in one group.

As the four grandparents are identified, we usually see grandparent marriages, and then, somewhere, there is usually a marriage between their children. This is a strong indication that the TBA (the bio-Ancestor) is a child of that marriage.

This process is part work, part logic, part luck. As I mentioned before, each situation is different. But this concept should provide a framework for finding Ancestors who had children and left records.

Once we have a probable set of parents and/or grandparents, we can usually build the rest of the Tree back using basic genealogy skills. And the DTT in this case should also find DNA Matches who share Common Ancestors from the 8 Great grandparents of the TBA and the 16 2xGreat grandparents and the 32 3xGreat grandparents (provided, of course, they each had multiple children and descendants).

Recommendation: When looking for a bio-Ancestor this way, wipe your mind clean of any pre-conceived ideas, from any source. Rely solely on the input of your Matches. After you have a “solution”, you need to think of ways to validate (or disprove) it.

This is a concept, and not a guarantee. But hopefully it will get you beyond where you were, it will provide new family lines to research, and new Ancestors who should show up as Common Ancestors with your Matches.

Summary: Focus on the red oval above the bio-Ancestor – there are lots of Ancestors in that oval that many of our Matches probably know about. Determine potential DNA Match groups that include the bio-Ancestor and find the Common Ancestor within each group. Look for intermarriages within each group. Let our Matches tell us our Common Ancestors!

[30B] Segment-ology: Finding BioAncestors by Jim Bartlett 20220602

What Is Your Objective?

A Segment-ology TIDBIT

To paraphrase Lewis Carroll: If you don’t have an objective, any path will take you there.

I sometimes think about this with respect to genetic genealogy. Over 30 million people have taken a DNA test – there are probably many objectives, whether stated or not.

Over the past few years our community has developed a range of tools; and folks ask: “Which tool should I use? Which one is best for me?” Well… it depends. It depends on your objective(s).

You may have several objectives… I’m asking you to write one down. Edit your objective statement until it’s clear and concise. Make a simple sentence. Then we can think about the tools… which ones will help you achieve your objective.

Let’s look at some examples of objectives and the tools we might use. NB: These are *my opinions*.

1. Determine my ethnicity (aka admixture, heritage, geographic mix, etc) percentages. You only need reading skills for this one. Take a test and read the results.

2. Find my close cousins who have done DNA testing. Test at AncestryDNA and build a Tree of your Ancestors there. Then look at the ThruLines program for Matches who share a Common Ancestor with you (back as far as 7 generations). Also test at the other three major companies – you never know where a close cousin might test. For extra credit: verify their line of descent; contact them and share information.

3. Determine a bio-parent. Let your Matches provide a pointer. At AncestryDNA, list your top Matches with Trees; list their ancestors; analyze these lists to find 4 families; build the families down and find two intermarriages – who are probably the grandparents. Sometimes it’s as easy as it looks, sometimes the answer is elusive. The point is: for this objective you often don’t need any of the Clustering or Triangulation tools. Your objective is very close, and your close Matches often have the information you need.

4. Organize (group) my Matches on my Ancestors. Use Shared Match Clustering and/or DNA Segment Triangulation methods. Both of these methods work very well. It’s still up to you to determine the Common Ancestor within each group. The point is: These methods group your Matches on Ancestors.

5. Determine which segments came from which Ancestors. This is Chromosome Mapping. We need DNA segment Triangulation to map the DNA – a lot of grouping work using data from GEDmatch, MyHeritage, 23andMe and FamilyTreeDNA. We need to determine a lot of Common Ancestors with a wide range of Match cousins – a lot of genealogy work (best done, IMO, at AncestryDNA – and often involving extending Matches’ Trees). We need to build the evidence for each ancestral line & segment combination. The point is: Several tools are needed – and it’s a major project.

Recommendation: Write down an objective statement. Then select the tools that will get you to your objective most effectively. For most of us, genetic genealogy is a hobby – so use whatever tools work best for you, and have fun.

 [22BG] Segment-ology: What Is Your Objective? TIDBIT by Jim Bartlett 20220508

Endogamy – Get the Facts!

Endogamy is sort of like a scare word for genetic genealogy. It raises a specter of something terrible that will cloud your Ancestry or your DNA. We often see Cluster diagrams with few, big blobs of color that are not very helpful. Oh, woe is me, I have endogamy…

I say: just the facts, Ma’am…  [which Joe Friday never really said]

Endogamy (and/or Pedigree Collapse) affects each person differently, but in a very specific way, in each case. Our DNA is fixed at conception – each of us has a very precise crossover points in our DNA, separating contributions from various Ancestors. We have a very specific set of DNA segments from our Ancestors. Any endogamy has already had its effect on our DNA, if any. Let’s just find out – let’s find out the facts!

1. Upload to GEDmatch.

2. Click on the free program: Are your parents related?

3. Read the intro paragraph

4. Enter you Kit#

5. Submit

6. View the results – green or red (match or no match)

Did GEDmatch find any substantial (over 7cM) green segments, in your DNA?

NO? Then, even if you had some endogamy or pedigree collapse in you Ancestry, you did not get any identical DNA segments from your parents. This means there is no effect when comparing with your Matches – every shared DNA segment with a Match will be from your father’s DNA or your mother’s DNA – no confusion!

YES? Then you do have some effect of endogamy or pedigree collapse in your DNA. BUT, it is confined specifically to the green segments. Write those segments down! Remember those segments! Put those segments into your master segment spreadsheet (two copies: one for each chromosome)! In DNA Painter: add those segments (one on each side) to highlight these areas.

All the rest of your DNA is free from the effects of endogamy or pedigree collapse. All other shared DNA segments with a Match will be from your father’s DNA or your mother’s DNA – no confusion!

Get the facts for your DNA! Free up as much of your DNA as you can.

IMPORTANT EPILOGUE – This blog post addressed the genetic, or DNA, part of endogamy. TGs are more definitive than Clusters, for example. But, it did not necessarily lighten the load on the genealogy side of our hobby. The issues of which Ancestor to link to which DNA segment (TG) remain.

[16F] Segment-ology: Endogamy – Get the Facts! by Jim Bartlett 20220425 EPILOGUE added 20220426

Phasing Your Ancestors’ DNA?

Actually, it’s pretty easy to phase some of your Ancestors’ DNA. If you’ve ever formed a Triangulated Group (TG), you’ve already done it!

I’ve posted before that TGs are phased data. Here. We don’t really care what the SNP values are (A or C or G or T), just that they “the ones” on one chromosome. We know this because all of our Matches in a TG have the same SNP values. That’s how we get the Shared DNA Segments and a TG. We are all in agreement about the segment represented by our matching SNPs.

One of the outcomes of Triangulation is to determine an Ancestral Line that passed down the TG segment to you. This is usually accomplished by determining multiple Match-cousins in the TG with Common Ancestors (CAs) to you on one Ancestral Line. This pretty much confirms the DNA came down through those Ancestors.

So, given a TG with a confirmed Ancestral line, each of your Ancestors in that line, had to have the TG DNA segment in their DNA. They had to have that same phased DNA on one of their Chromosomes, in order to pass it down to you and a Match.

If you are just dying to know the ACGTs of this phased DNA, you’d need to collaborate with some of your Matches in the TG (it doesn’t necessarily need to be the ones with the CAs – they ALL should have the same phased SNPs). From your and your Match’s raw DNA data, compare all of the SNP pairs from the start to the end of the TG, and, for each position, list the single SNP that is the same.

Most of us won’t do this, but we can be content knowing that the data is phased in the TG and is identified by the Matches with specific shared DNA segments. We know the Chromosome, the side, the start position and the end position – the phased data is locked in.

 But that’s not all…

Each of our Matches in a TG almost certainly got a different DNA segment down their Ancestral line – often starting sooner (on the Chromosome) or ending later. Their TG would be different – usually adding some Matches to their TG and not including some of the Matches I had in my TG – and many of the same Matches will still be included. In effect their TG is “offset” some from mine. And their TG is also phased data. And each such Match TG may add more to the phased data of the Most Distant Common Ancestor, and often some of the intermediate Ancestors, depending on where the Match ties into your line.

And that’s not all, either…

Most of our Ancestors are linked to multiple TGs. I have 372 TGs that cover my DNA. That means, on average, about 1/4, or 93 of my TGs come through each of my grandparents. Put another way, my TGs would “cover” about 1/4 of the DNA of each of my grandparents – with my data alone, I could determine phased data for 1/4 of each of my grandparents. Even each 3xG grandparent would average 10 TGs.

And there is more…

My siblings and cousins have DNA that I don’t have, from each of my Ancestors. Their TGs could document more phased data in my Ancestors

There is a limit…

Generally, a parent, or any Ancestor, does not pass down all their DNA to their children – some is lost, forever.

So, quite a journey…  And like most journeys, we need to take it a step at a time. The first step is to Triangulate your own genome. And then work on linking each TG back to a CA (and thus to the Ancestral Line down to yourself).

[14A] Segment-ology: Phasing Your Ancestors’ DNA? by Jim Bartlett 20220420

Insights on Clustering vs Triangulation

A Segmentology TIDBIT

Triangulated Groups will cover an Ancestral line; Clusters tend to focus on a Common Ancestor.

Think about this for a moment. A Triangulated Group is formed around one segment of your DNA. This segment of DNA was passed down to you from your mother or your father. This segment of DNA was first formed in one of your Ancestors – as a part of their DNA which was passed down through a line of your Ancestors to you. Let’s call this Ancestor your “first” Ancestor, with respect to the DNA represented by the TG segment. Generally:  this TG segment probably started as part of a somewhat larger segment of DNA in that “first” Ancestor; it probably got whittled down by recombination along its journey down to you; and portions of the larger segment were also passed down through several children of this “first” Ancestor to other people who became your Matches (because they shared this DNA with you). The bottom line is that you may have a first cousin (1C) who shares part (or all) of this TG segment of DNA with you. You may also have a 3C or a 5C or an 8C who shares part of this TG segment of DNA with you. The point is that within a TG, you may well have Matches who are cousins over a wide range – back to any of your Ancestors between your parent and the “first” Ancestor to have the TG segment DNA. In fact, among your TG Matches there may be cousins beyond your “first” Ancestor – these Match-cousins would share smaller pieces of the TG segment that came from Ancestors of your “first” Ancestor.

Now let’s shift and think about Clusters. Clusters are formed from Matches who are Shared Matches with each other.  Each of the Shared Matches in a Cluster *tend* to match most of the other Shared Matches in the Cluster. That’s why we see Cluster diagrams with squares which are almost solid – most Matches match most of the other Matches. This usually happens when the Matches have the same Common Ancestor. Think about the LEEDS method – the focus is on four Clusters, each one representing a different one of your four grandparents. As the lower cM threshold is reduced, more Matches are included in the analysis, and more Clusters are formed. These Clusters *tend* to drift away from grandparents and form around more distant Ancestors. Although it is not a “rule” or “requirement”, it does seem that each cluster is centered on a specific Ancestor. However, sometimes a Match in a Cluster may be related through an Ancestor a generation closer or farther than most of the Matches. This is because the range of relationships is not rigidly tied to cMs – the smaller the cMs in a Cluster, the larger the range of possibilities. This is also due to the fact that a close Match will be included in one of the Clusters – unless the upper cM limit on Clustering is lowered to preclude close cousins. Beyond the 4-generation LEEDS Clusters, the Clusters with smaller and smaller cMs, get more and more “messy” with more and more exceptions to the one Ancestor per Cluster concept.  But the “tendency” remains: the Clusters “tend” to form with Matches who have the same Common Ancestor. NB: if you want the Clusters to point to one Common Ancestor, you should either adjust the upper cM limit, or manually cull out Matches who are clearly closer cousins.

A few years ago, I Clustered all of my FTDNA Matches (roughly 8,000 of them). I had already Triangulated them into about 370 TGs. I got about 350 Clusters. In both cases there were about 5% of the Matches who didn’t Cluster or Triangulate – these Matches were all under 15cM (most under 10cM) and were the same Matches in both cases – they were false Matches. There was very close to 100% correlation between the Cluster and the TGs (in other words the Matches in each Cluster had the same TG). My conclusion was/is that the Cluster Common Ancestor was the same as the “first” Ancestor for the TGs [I only wish I knew, for sure, who that CA was…]

Bottom line: you should be able to “Walk the Ancestor Back” with different Matches in a TG; and you should see most of your Matches in a Cluster as cousins with the same Common Ancestor (with maybe a few Matches being a little closer or farther cousins).

[22BE] Segment-ology: Insights on Clustering vs Triangulation TIDBIT by Jim Bartlett 20220413

How Many NPEs Do You Have?

This post is intended to get you to think about the possibilities in your own Tree – it is not about the term NPE (there are many variations); and it is not about the historical percentage (there are NPEs, but not many good studies on the NPE percentage). For this blog, I’m using NPE to mean an Ancestor who by most accounts is in our Tree, but it turns out that “Ancestor” is not our biological Ancestor.

Three assumptions for this post (in order to get to the thought process):

1. NPE is a paper-trail/genealogy Ancestor, who is not the biological Ancestor.

2. The rate is 2% – there are many studies (cf: )

3. I’m going to count each “box” in our Tree as an Ancestor (recognizing that there may be some Pedigree Collapse and/or Endogamy)

The mumber of our Ancestors doubles every generation. At 8 generations back we have 256 Ancestors at that generation and 510 total Ancestors all together. At a 2% NPE rate overall, that would mean about 10 of our Ancestors were NPEs. If you read the ISOGG article above, you’ll find a slew of reasons for NPE (in addition to infidelity). As a genealogist with many Colonial Ancestors, I believe the instance of one or both parents dying in those times, and someone else raising the orphaned children as their own (with no paperwork) was more prevalent than we know. And probably many other variations…

So if the paper-trail doesn’t help, how would we ever know? I think our DNA Matches can sometimes give us a clue.

From my experience tracking Matches (see: ), I can tell you there is a clear correlation between family size and number of DNA Matches – the larger the number of surviving children (with families of their own) the more DNA Matches we’ll have. And the obverse: with a “skinny” family of only one child, we get no cousins, and if there are two children (one is our Ancestor), any Matches would descend from the other child, and there would be far fewer Matches. Some other factors may also come into play, such as distance back to the Ancestor and recent immigration, so we must take those into consideration as well.

So here are two scenarios to think about:

1. A large family with relatively few descendant Matches – maybe one or both of the “parents” are NPEs.

2. A Surname comes up repeatedly in Matches – say within a Shared Match Cluster, or a Triangulated DNA Group. I now have two of these – in each case virtually all of the Matches are from the same Tree. Clearly, I should descend from this Surname – I just need to find out how (and if it involves an NPE or a Brick Wall).

As I said at the top of this post, this is just to get us all to think – to keep an open mind – to follow the DNA signals – and to understand that we probably have some NPEs in our Tree. Watch for the clues….

[30B] Segment-ology: How Many NPEs Do You Have? – by Jim Bartlett 20220325