About Jim Bartlett

I've been a genealogist since 1974; and started my first Y-DNA surname project in 2002. Autosomal DNA is a powerful tool, and I encourage all genealogists to take a DNA test.

Triangulated Group Analysis


Segment-ology TIDBIT

Let’s analyze a generic Triangulated Group (TG).  There are several facets to this analysis…

Facets related to me:

1. My DNA segment – A Triangulated Group (TG) “segment” is a specific segment of my DNA. It is defined by a Chromosome number, start and end positions, and the total Mbp. The number of SNPs included and the cMs can be obtained through look-up tables on the internet (I have not done that).

2. TG Ancestor – A TG segment of my DNA first came from a specific Ancestor of mine. I’ll call this the TG Ancestor. This TG segment was passed down through descendants of the TG Ancestor, to one of my parents and then to me. All of my Ancestors who descended from the TG Ancestor also had that TG segment in their DNA. NB: the TG Ancestor started with a full Chromosome (a big segment) which he/she passed down – the TG segment was part of that larger segment/Chromosome. This original larger segment was then whittled down through the generations, but each generation had, at least, the full TG segment. NB: A segment may be passed down, one or more generations, intact (i.e. no whittling down), but the TG segment is always intact from the TG Ancestor down to you.

3. TG segment origin – The TG Ancestor received the TG segment (usually a larger segment) through a recombination process. His/her parent recombined DNA from their two parents to create a new chromosome to pass to the TG Ancestor. At this point, in our TG Ancestor, our TG segment is made up of parts from the TG Ancestor’s parent’s two chromosomes – one from each of the TG Ancestor’s grandparents. Thus, this whole TG segment did not exist, on one chromosome, in one person, before this time. The TG Ancestor is the first person to have this particular TG segment.

4. Logic – Matches who share this full TG segment, should also share this Common TG Ancestor – because this TG segment is unique to this TG Ancestor. [It can be argued through logic, that there is a possibility of this exact same segment being created in another person – granted. But the odds are extremely low, and even more distant when you consider this happening in the small subset of your DNA Matches in a TG]

Facets related to Matches and *shared* segments:

5. Cousin segments – In general, our cousins will get somewhat different segments than we do from our Common Ancestors. Apply #2 above to a Match. Our Ancestor passed down a chromosome to their children – some of it identical, some different. The DNA segments passed down through their children and their descendants to our Matches will be randomly different. What we see through a DNA test, is the overlap created by shared DNA segments – the part of our DNA from a Common Ancestor that overlaps. We might get Chr 06: 53-86Mbp and the Match may get Chr 06: 64-97Mpb – the “shared segment” is the overlap: Chr 06: 64-86Mbp.  Our segments from Ancestors are rarely the same as our Matches’ segments from the same Ancestors, but

5. Cousin segments – In general, our cousins will get somewhat different segments than we do from our Common Ancestors. Apply #2 above to a Match. Our Ancestor passed down a chromosome to their children – some of it identical, some different. The DNA segments passed down through their children and their descendants to our Matches will be randomly different. What we see through a DNA test, is the overlap created by shared DNA segments – the part of our DNA from a Common Ancestor that overlaps. We might get Chr 06: 53-86Mbp and the Match may get Chr 06: 64-97Mpb – the “shared segment” is the overlap: Chr 06: 64-86Mbp.  Our segments from Ancestors are rarely the same as our Matches’ segments from the same Ancestors, but the TG segment is always intact from the TG Ancestor down to your Match. Always be mindful of the difference between your own DNA segments, and a “shared segment” with a Match.

6. Cousins on the TG Ancestor – these Matches may share roughly the same amount of DNA as the full TG segment, but some may well share smaller segments. It all depends on the recombinations that occurred in the generations between the TG Ancestor and the Match. Matches in a TG are already analyzed to share at least part of the TG segment with you and with other Matches.

7. Closer Cousins – these Matches also may, or may not share the full segment. Actually close cousins may share somewhat larger segments with us – beyond the scope of the TG segment. This indicates these closer cousins share more than one TG with us and a closer Common Ancestor. However, this closer Common Ancestor would have to be a descendant of the TG Ancestor. Maybe the closer Common Ancestor would be a grandparent or Great grandparent.  

8. Distant Cousins – Other Matches in the TG group may share smaller segments and be related through a more distant Ancestor of the TG Ancestor. Refer to #3 above. We could have a Match cousin related through a parent or grandparent, etc. of the TG Ancestor. In this case the Match would have only received the smaller DNA segment that was part of the full TG segment in the TG Ancestor. It is probable that Matches sharing small segments (in my case down to my 7cM threshold), could be cousins way beyond my genealogy horizon. This is particularly true with pile-up regions within a TG. The whole TG segment may come from a TG Ancestor well within my genealogy horizon, but the pile-up Matches are much more distant (or potentially false segments – a different story).

Summary – the Matches in a TG group can be cousins from many different generations, but all on the same ancestral line. The best estimated guess of relatedness to a TG segment would be a look-up of the cMs and then refer to the Shared cM Project. Generally on Chr X, the relationships may be further back.

Remember a TG segment represents your DNA – only your DNA – your DNA Matches will have a different TG.

[22BL] Segment-ology: Triangulated Group Analysis TIDBIT by Jim Bartlett 20230110

Review The Comments


A Segment-ology TIDBIT

I’d like to encourage all “Segmentologists” to periodically review the comments to my blog posts. I try to respond to every one of them and often go into more detail and/or provide suggestions for specific issues. If I may say so, there are often some more gems in the comments – including feedback from followers of this blog. I recently got a comment and elaborated on a post from over 7 years ago…

[22BK] Segment-ology: Review the Comments TIDBIT by Jim Bartlett 20221213

Sibling Crossovers


The question came up about siblings sharing the same crossover points. The answer is yes – some of them will be the same. Let’s look at this generation by generation. [There is often good discussion in the Comments to these blog posts – we are all learning on this journey. As a result of a recent comment, I decided to do a blog post about this topic]

The set-up:

1. One genome – let’s use our Mother’s side – 23 Chromosomes

2. Assume the average of 34 crossovers per generation.

3. A crossover is the point where DNA changes from one grandparent to the other grandparent, when the mother recombines her two chromosomes into a new one to pass on to a child.

4. Crossover points are random.

Mother’s DNA already has crossovers created by many of her Ancestors. She will recombine the DNA from her two parents at 34 places over the 23 chromosomes, and pass these new chromosomes to a child. Note: this means usually 0, 1, 2 or 3 crossovers per chromosome (on average 1 per 100cM). Since these crossovers are randomly formed for each egg, it would be rare for any of her children to have the same crossover from her.

The 34 new crossovers created 34+23=57 segments. These 57 segments “cover” all 23 chromosomes, from beginning to end of each one. These 57 segments are from Mother’s parents – our grandparents. All the crossover points from recombination events in prior generations are fixed (static) in the two grandparent’s DNA.

Example: Mother’s paternal DNA on Chr03 – from 47Mbp to 123Mbp has a crossover point at 68Mbp. Each of Mother’s children who got her paternal DNA that included the point at 68Mbp would include that crossover point. Mother could pass a paternal Chr03 segment 47-83Mbp to one child and paternal Chr03 59-119Mbp to another child – both of these children would have the same crossover at 68Mbp.

Note: The 68Mbp crossover could have occurred at the great-grandparent generation, OR at some previous generation.

This is a good example of why Chromosome Mapping *by generation* is important. In general Segment Triangulation results in Triangulated Groups (TGs) from different generations of ancestors. The TGs are not all from 4xG grandparents, or any other specific generation. However, if you have the Common Ancestor (CA) for your TGs, you can easily build a Chromosome Map for different generations. In my case I have 372 TGs – I know the CA side and grandparent for almost all of them – they roughly “fit” into about 114 groups (representing my 4 grandparents on both sides) on my 45 chromosomes.

Bottom line: Siblings won’t (generally) get the same crossover points from their parents, but likely will share some crossover points from grandparents and more distant Ancestors.

[05F] Segment-ology: Sibling Crossovers by Jim Bartlett 3 Dec 2022



I wanted to share one of the huge benefits of WTCB. I’ve pretty much completed the WTCB down to 20cM Matches and have added in a number of under-20cM Matches for which I had segment data (from GEDmatch, primarily, and some who had tested at the other companies). These under-20 Matches can be Clustered by looking at their over-20 Shared Matches for a consensus.

There are positives and negatives to WTCB. Overall, a large percentage of the over-20 Matches fit into very solid Clusters. But, just like a distribution curve, some of the Matches do not have many Shared Matches (a few have 0), and some just don’t seem to form a good, solid consensus. If you know me, I focus on what I *can* do – so I want to give you an example of a successful Cluster. And I want to note that this is not the best example, but it is a good one.

Here is a picture of Clusters 54 to 79 in a Super Cluster. The 281 Matches in the Super Cluster range from 20cM to 56cM (the upper threshold was 60cM for this run).

In my review of most of the Clusters and SuperClusters, I’ve found that the individual Clusters look prettier and more solid, but they do not represent a split in ancestral lines within my genealogical time frame (roughly 9 generations back; 8C level). So I have combined most of them into Cluster 54 – a total of 281 Matches.

In this Cluster I now have 3 Matches with an MRCA of A0020 (MITCHELL/UNDERWOOD couple); 12 Matches with an MRCA of A0084 (UNDERWOOD/CANNADAY) and 27 Matches with an MRCA of A0170 (CANNADAY/HILL). I also have 4 Matches who have MRCAs on different lines. The Cluster is very solid, so I suspect these 4 Matches are probably *also* related to me somewhere on my MITCHELL to UNDERWOOD to CANNADAY line. But clearly the 42 Matches on one line show a consensus!

Also within Cluster 54, I have 9 AncestryDNA Matches with segment data – they are all in Triangulated Group [17D25] – another pretty clear consensus. In DNA Painter, I could paint all 281 Matches on Chr 17, from 24 to 45Mbp. Note: In my TG spreadsheet I have over 150 Matches in TG [17D25] – 9 of them from Ancestry Matches and the rest from the other companies.  

I have Ancestors in my Tree beyond A0170 (CANNADY/HILL) which are fairly well known and also in many other Trees, and I’ve found Matches with those more distant MRCAs in other Clusters, but not in this Cluster 54. I’m coming to the conclusion that the 21Mbps in [17D25] probably came to me from either William CANNADAY 1730-1801 (A170) OR his wife, Nancy HILL 1733-1801 (A171).

But the best is yet to come. This Cluster 54 is a classic *pointer*.  I am now pretty sure that the rest of the Matches in this Cluster will have an MRCA with me on the same line. In fact, I’ve only recently found several of the MRCA Matches by building Trees back and/or looking at Unlinked Trees. Here is an example:  

In Cluster 54, I had a 36cM Match with an Unlinked Public Tree with 6 people in it. I opened it up to find only one real lead – Audrey (so I searched Ancestry for her):

BINGO! Note Audrey’s mother is a CANNADAY!! The rest was easy – I quickly found the Match’s link to A0170 (CANNADAY/HILL).

Note: I’ve had others that were just as easy; and some that took more searching and digging; and some that I threw in the towel and moved on.

The bottom line is that the WTCB tool can be very valuable in many cases. And when it works, I’ve got a Cluster which is a great MRCA-focused tool; I’m compiling consensus data for the Cluster (firming the TG and Chromosome Map), adding to the Ancestry Match Notes and helping ThruLines find more MRCAs in Private Trees.

[19Nc] Segment-ology: WTCB SITREP Nov 2022 by Jim Bartlett 20221112

WTCB Issue – Alt MRCA


I have a number of cases where the Match has an MRCA, but Clusters with a different group of Matches who clearly have a different MRCA and/or TG.  Example: A Match who has an alternate MRCA which doesn’t align with a TG. I discount the MRCA because the shared DNA segment with the Match could not have come from that MRCA. Some have a paternal TG and a maternal MRCA; some are clearly from different grandparents on the same side. I now have found two examples of such a Match who Clusters with other Matches who share the MRCA but not the TG. It is not unusual for a Match to have more than one MRCA from Colonial Virginia, but usually one is closer and the closer MRCA has a much higher probability of being the one who passed down the shared DNA segment. But “higher probability” does not mean always.  

My latest example is a Match with TG [01Y36] 14.0cM (on my mother’s side], but over half of the 23 Shared Matches have TG [17D25] (on my father’s side). [17D25] is a pretty well-established TG for me with an MRCA of A0170P (my CANNADAY/HILL ancestral couple at the 6C level). So I checked my Notes and found the Match has a ThruLine to CANNADAY/HILL. That explains why the Match Clusters with other Matches with MRCAs of A0170P.

Bottom line: Although my main objective is deep Chromosome Mapping, the ultimate goal is to get the genealogy right. In this case I want to also figure out the [01Y36] MRCA, so I must remove this Match from the A0170P-[17D25] Cluster. I also have to remind myself to follow the data – the data is talking to me, I need to listen…

[19Nb] Segment-ology: WTCB Issue Alt MRCA by Jim Bartlett 20220926

Segment Data for Ancestry Matches 2


A Segment-ology TIDBIT

My first post with this title (here) listed 4 ways to get Segment data for AncestryDNA Matches; and then added another way using GEDmatch.

Here is yet another way.

In Walking The Clusters Back (see WTCB 2022 and WTCB SITREP) I’ve now completed the analysis of all Matches over 20cM – almost all fit into one of several hundred Clusters. I’m now integrating the below 20cM Matches who have segment data (a TG ID), usually from GEDmatch – over 800 of them. Most of them have Shared Matches which usually provides a consensus on the Cluster.  In checking my Master segment spreadsheet (with all of my Match Shared Segments), I noticed a number of AncestryDNA kits which didn’t yet have a link to an Ancestry profile. It turns out that usually all the Matches with the same TG ID will be in one Cluster. It is a relatively easy task to find that Cluster (particularly with WTCB) with some of the Matches – and then review the few other TG Matches with the other Matches in that Cluster. I usually send a message to the Ancestry Match to confirm they indeed uploaded to GEDmatch (and promise to help them with the DNA if they are).

Result: more AncestryDNA Matches linked to specific DNA segments (TGs).

[22BJ] Segment-ology: Segment Data for Ancestry Matches 2 TIDBIT by Jim Bartlett 20220223


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