Using ChatGPT to Streamline DNA Analysis in My Coleman Research

November 10, 2025 ~ Patricia Coleman ~ Leave a comment

David Coleman, my husband’s third great grandfather, was born about 1801 somewhere in Ireland. This information comes from the 1850 Federal Census — the only record we have includes these details about him. He married Catherine, likely in Ireland, though her maiden name is unknown. By 1820 they were in Saratoga, New York, where oldest son James was born.

For more than sixty years Mary Coleman Kennedy searched for David’s birth location. The family was Catholic, but few baptismal records from 1801 have survived in Ireland.

About eight-years ago Dave and three of his sisters took DNA tests. Using their data, I followed Blaine Bettinger’s five-part blog posts on visual phasing to determine which DNA segments came from each grandparent. Since I’m on a Mac, I did the analysis manually using Keynote and Excel, and then imported the phased segments into DNA Painter.

Below is Dave’s visual phasing (VP) showing the Coleman segments he inherited from his grandfather, Herbert Coleman, Sr.

Whenever one of the siblings has a DNA match, I compare the match’s chromosome location in DNA Painter to the four grandparents segments. This allows me to determine whether the match is maternal or paternal — and often which grandparent line it belongs to.

Over the last eight years, we’ve collected a large number of DNA matches. The example below shows chromosome 10, where the blue segment represents Coleman DNA, and the green segment represents the paternal grandmother’s Marti DNA.

The matches whose segments fall under the Coleman blue could help me locate David Coleman (born ~1801) in Ireland. Since they are DNA, there must be a family connection somewhere back in time. If I can identify where those matches’ ancestors lived in Ireland it might point to the area where David himself lived.

Earlier I used this same method to locate my great-grandfather Thomas Byrnes’ birthplace in County Roscommon, Ireland — research that was published in The Septs in April 2024. Starting with DNA from my second cousin and me, I traces Thomas three generations back. Beginning with Dave and his sister’s grandfather,we would also need to go back three generations to reach their third great grandfather, David (born ~1801).

For the Byrnes research, the earliest records were Griffith’s Valuation (1847 —1864). With luck, some of these Coleman DNA matches have earlier, surviving baptismal records that could finally identify David’s birthplace.

The biggest challenge has been isolating only the Coleman-line matches from the DNA Painter data. Although DNA Painter allows me to download a csv file for each chromosome, it’s cumbersome to filter through hundreds of matches manually.

For example. Dave has 100 matches just on chromosome 1. The gold segments under the blue Coleman line represent triangulated matches from MyHeritage and the pink ones are triangulated matches from 23andMe.

There was no easy way to extract just the matches under the Coleman line — until I asked ChatGPT to write an Excel macro for me!

This was my first prompt to ChatGPT on the problem: Here’s a new problem to work on. Family lore says David Coleman was born about 1801 somewhere in Ireland. We don’t know where. I was to use locations of DNA matches to find where he likely lived. Here are 2 csv files. C.csv has the locations of Coleman segments on various chromosomes. There is a header row. Column A had chr# the chromosome number. Column B has the start and Column C the end of the segment. P.csv file has all of the paternal data. For the comparison A2 of file P must equal A2 of file C. Then look at Column B of file P.csv until you find a number greater than or equal to the number in B2 of file C. Remember that row. Now look at Column C of file P and scroll till you find a number less than or equal to the number in C2 of file C. Now take that the data you found and copy those rows to a new csv file.

The c.csv file has the Coleman segments from Dave’s VP file for the various chromosomes.

The p.csv file has all of the paternal matches. It’s ChatGPT’s job to create an Excel macro for Mac that will collect only the Coleman matches.

There were a few issues initially. I’m on the free version of ChatGPT and had hit the end of the day.

ChatGPT’s reply: It seems like I can’t do more advanced data analysis right now. Please try again later. If you want, I can still walk you through the exact steps to run this matching process in Excel or in a simple Python script on your computer so you can get the output immediately. Would you like me to give you those step-by-step instructions?

So I asked for an Excel macro that would run on Mac. This was the first macro iteration.

ChatGPT’s reply: Assumptions

You have two sheets in the workbook:
Both have headers in row 1
Column A = Chromosome, Column B = Start, Column C = End
You want results on a new sheet “Matches”

I changed from the two separate csv files and made them two sheets in one Excel file. And then ran the macro.

My next prompt: I got it to run – it only found the 4 occurrences of Coleman, not all the data within the start and end range, which is what I want. Once you find the start value in P that matches the one in C, then copy all the rows of data you find until the end value in P matches the end value in C. would you change the macro to do that?

This was marco version 2. My prompt: Still not working – let’s try something else. You’ve got the chr# in C and P matches perfectly. Now let’s not look at Start or End. In column F under the header ‘Match’ in file C it has text Coleman. With the chr# selected – look in column F of the P file and find text Coleman. Copy that row into new match sheet and then copy all the rows under it, that still have chr# matching until you find the text Marti in column F in sheet P. Then move to the next row in sheet C and repeat this. If you do not find text Marti, but the chr# no longer matches, then also move to the next row.

ChatGPT’s reply: Got it — so now the logic becomes:

Use the chromosome number in C and P to match
In C, column F (“Match”) must contain “Coleman”.
Once we have the chromosome number, look in P column F for “Coleman” on that chromosome.
Copy that row from P to Matches sheet.
Then keep copying the following rows (same chromosome) until column F says “Marti” — then stop.
If “Marti” isn’t found, stop when the chromosome changes.
Move to the next “Coleman” in C and repeat.

This was macro version 3. My prompt: Almost perfect! several chr like chr 2 have more than one segment. The macro duplicated all the matches on chr 2. When the macro finds the first occurrence of Coleman in column F for a chr# it needs to remember the start that was in column B of that row. then it goes on and stops at Marti and starts at Coleman again if chr# is the same as before and column B has larger start value. It should not go back to a lower sOkay — the duplication on chr 2 means my current loop is checking every “Coleman” in P for a given chr in C, even if it’s already been handled from another row in C.

ChatGPT’s reply: To fix that, we need to:

Loop through C in order.
For each row in C, find only the matching Coleman in P that has
- the same chr number,
- the exact same Start as in C column B,
Then copy from there until Marti or chr changes.

That way, we never re-run the same Coleman block twice for the same start value.

My prompt: That worked!!

This entire conversation — and the four versions of the macro it took to get it working —lasted about an hour and forty-five minutes. I can not even estimate the time it would have taken me to do this myself!

Of course, you always want to verify anything a chat bot gives you. In this case, I already had a csv file in the correct format for DNA Painter, so I could easily add it back into DNA Painter for a visual check.

The only match that might have fallen outside the selected region was at the bottom of the image — for Aunt Mary. Her parents are Dave’s grandparents, Herbert Coleman and Harriett Marti. Since her match could reasonably extend a bit beyond the region of interest, that doesn’t concern me.

This project was a great example of how AI can enhance traditional genealogical research. What once felt tedious became manageable — and even fun — with a little help from ChatGPT. There’s still plenty of work ahead before I can pinpoint where David Coleman (born about 1801 in Ireland) came from, but these DNA-based tools have moved me one step closer.

I shared this example as part of my presentation, “Enhancing Genealogy Research with AI Assistant,” at the Minnesota North Star Conference in Eagan, MN on 25 October 2025.

New Copy and Paste Option in AutoLineage

November 6, 2025November 8, 2025 ~ Patricia Coleman ~ Leave a comment

Great news for AutoLineage users! You no longer have to use “Save Page As” to import your Ancestry matches and shared matches—there’s a faster way with simple copy and paste. Instead of saving files to your computer, you can now copy your match or shared match page in Ancestry and paste it directly into AutoLineage in another browser tab.

Log into AutoLineage, set up a profile, and select ‘Generic’ as the DNA test. Under Import Matches select ‘Copy Paste Wizard.

That brings up the Wizard.

The Wizard can be used for Matches and Shared Matches. It will show the Existing Matches, and Existing Shared Matches. You will see these numbers change as you add matches and shared matches. After you select and copy your Ancestry data you add it to the Wizard with the blue ‘Paste Data’ button. The list of New Imported Matches or New Imported Matches will update.

Ancestry’s match page initially shows 20 matches. If you scroll to the bottom of that page, you can change that setting to 50 matches. To make things more efficient that number can be changed to 100 matches per page. At the top of the window is the URL for the match page and it says ‘ItemsPerPage=50’ That 50 can be changed to 100, which makes copying the match data twice as fast!

**Match data set at 50 matches per page.**

Changing that 50 to 100 makes copying the match data twice as fast!

After changing match collection to 100 matches per page.

‘Select All’ under the Edit menu, or Ctrl A in Windows, or Cmd A on Mac will highlight all the matches on the page.

Select ‘Copy’ (Ctrl C or Cmd C) to copy all the data on the page and then ‘Paste’ it into the AutoLineage ‘Copy Paste Wizard.’

**After pasting 100 matches into the Copy Paste Wizard.**

You’ll notice both the ‘Total Imported Matches’ and the ‘New Imported Matches’ should 100. At this point you can ‘Save and Close Wizard’ or go back to the tab with Dave’s Match data and select another page of matches. If there are no (shared) matches appearing in the wizard, go back to the page and try to select and copy the data from the top of the page.

Next collect shared matches. Dave paternal Aunt Mary tested at Ancestry. She has 14 pages of shared matches. Each page can be selected, copied and added to the ‘Copy Paste Wizard.’

**Shared matches added to the Copy Paste Wizard.**

When shared matches are added to the Wizard, if a match hadn’t been collected before, the wizard adds that match to Dave’s matches as well. Four hundred of Dave’s matches had originally been added. The lowest cM for them was 25, but you’ll notice in the list of Mary’s shared matches several matches at 6 or 7 cM. The Wizard automatically adds these to Dave’s matches as well. Looking at ‘New Imported Matches’ shows there were 180 new matches that had values lower than cM that matched Dave and his Aunt Mary, that were added along with the shared matches.

Especially with Copy – Paste, it’s a good idea to backup the AutoLineage data after adding matches and shared matches. The backup option is at the bottom of the ‘DNA Test Overview’ page. This saves the data as a JSON file that can be loaded back into AutoLineage (under import matches).

**Backup for matches and shared matches.**

Once you have enough matches and shared matches you can run clustering. There are several new features in clustering.

Unweighted clustering is the default cluster setting that AutoLineage (and Genetic Affairs in general) has always employed. Weighted clustering is a new way of clustering that considers the amount of DNA between shared matches. This first image is of Dave’s matches in an unweighted, dense cluster. The various lines of the family are labeled. You’ll notice the green, red, purple brown, and pink clusters have a lot of grey cells that connect to paternal Aunt Mary, and Dave’s full sister connects to the both the paternal and maternal clusters, as would be expected.

**Unweighted, dense cluster of Dave’s data.**

The graph images are clearer now too. This is the one for the unweighted cluster. You can clearly see how Dave’s sister connects their maternal and paternal matches and how all the paternal matches are connected to Aunt Mary.

**Graph visualization for unweighted, dense cluster.**

This next image is of the weighted, dense cluster of Dave’s data. In this cluster a lot more of Dave’s close family members are seen, and the paternal grandmother side splits into 2 clusters. The orange cluster’s most recent common ancestor (MRCA) are Dave’s paternal third great grandparents, while the green cluster MRCA are his paternal second great grandparents. Here again sisters and nieces have grey cells to both paternal and maternal matches.

When you have a cluster you can run the AutoKinship tool, which is able to reconstruct trees based on shared DNA (and optionally MRCA data). From viewing the clusters select ‘Matches’ at the top of the window. This brings up a list of the matches in the clusters, which you can download to Excel if you want. You can also select the cluster that you want to use for AutoKinship.

The brown cluster was selected for AutoKinship and again brings up the actual cluster.

Clicking on ‘Matches’ again brings up the screen where you can select AutoKinship.

The list shows that Dave will be included along with the people in cluster 5.

You can choose to add known relationships, or provide generational information between matches, before running AutoKinship if you wish. Or import trees for the matches and search for common ancestors. Both known relationships and MRCA information will be integrated in the reconstructed trees. Five trees were generated. This one perfectly located the matches in their family relationship. The MRCA are Dave’s great grandparents, John Coleman and Mary Ann Duff.

**An AutoKinship tree of the matches in the brown cluster.**

Conclusion

The new Copy Paste Wizard is a real game-changer for AutoLineage users working with Ancestry data. No more saving pages or juggling multiple files ,with just a few quick copy-and-paste steps, you can bring in both your matches and shared matches directly into AutoLineage. It’s fast, simple, and surprisingly satisfying to watch those numbers update as new matches appear!

I tested the Wizard with my own Ancestry data and found it worked beautifully. Within minutes, I had all my matches imported, ready for clustering and AutoKinship analysis. The process felt seamless compared to the old ‘Save Page As’ method.

Combined with the new weighted clustering and improved visualization options, this streamlined workflow helps turn raw match data into clear, meaningful family patterns and reconstructed trees.

Exhaustive MRCA Analysis for GEDmatch Matches

May 15, 2025May 26, 2025 ~ Patricia Coleman ~ Leave a comment

A new import tool has recently been added to AutoLineage, enabling the use of GEDmatch Tier 1 data. This powerful addition allows users to identify potential common ancestors based on tree data linked to up to 7,500 matches. Unlike existing tools (even the ones on GEDmatch) that typically only analyze trees for a small number of top DNA matches, this new capability dramatically expands the scope of common ancestor analysis.

Start by selecting the One-To-Many – Full Version.

Enter your kit number and select 7500 matches for the comparison. Click on ‘Search’ and a message will come up for you to wait while the data is collected. When the site has loaded completely, save this page using the ‘Save Page As…’ command. Make sure the complete site is loaded, it might take a while to fully process.

Enter kit number and select 7500 matches.

Next click on the ‘Select all with Gedcoms’ command. This will select all DNA matches that have a linked GEDCOM.

At the top of the list of matches and gedcoms is the ‘Visualization Options.’ Click on that next.

List of visualization tools for the matches and gedcoms.

Select ‘GEDCOM’ that I’ve circled in red in the figure above, which will bring up the tools.

Select the ‘Find matching GEDCOMs’ which brings up a new tab with a detailed list of the gedcoms. This page will allow for linking the DNA matches (represented by the kit numbers) to their GEDCOM trees as well as which root person they represent.

‘Save Page As….’ using the filename that GEDmatch gives it. Make sure that the page is fully saved before the next step. Then go back to the previous page and select ‘Find Matching GEDCOMs (anc)’. This step takes some time to come up. It will quickly display a heading, but you don’t want to save the page until after that X, that I’ve circled in the figure changes to a circle with an arrow and the table has been filled in.

The anc2 page before it has collected the data.

The anc2 page when all the data has been collected.

Once the page is complete, save it using ‘Save Page As….’ again using the filename that GEDmatch gives it.

Now you are ready to add the matches and their trees to the AutoLineage profile. If you’ve not made a profile yet, log into Genetic Affairs, select AutoLineage (local) and click on register a new profile.

Creating a new profile on AutoLineage.

After the profile has been created, select the register DNA test.

Next select the GEDmatch as the DNA test.

Next select Import matches and the Wizard will appear. The ‘One to many HTML file’ is what you want to select.

Wizard to import the matches from GEDmatch.

Clicking on the blue question mark explains what you want to collect, which you’ve already done.

Instructions for collecting the 7500 GEDmatch match files.

The list of matches is then displayed.

The next step is to add the gedcoms that you collected. Go to ‘Tree Management’ and select ‘Import trees.’

Clicking on the blue question mark gives the detailed instructions for using this GEDmatch import tool.

The trees are now automatically linked to the matches and with the matches and linked gedcoms in place, you can run ‘Find Common Ancestors.’ Select Judy’s profile and go to the overview page. Select the find common ancestor button.

I know that Judy’s closest matches are her siblings and nieces. Then her next highest match is less than 100 cM. So I ran the ‘Find Common Ancestors’ wizard down to a birth year of 1700.

FInd Common Ancestors Wizard set for 1700 birth year.

After waiting for quite some time, a large number of trees where found. The first one, shown here, connects Judy and her siblings to their fifth cousins once removed. Judy’s mother was a Hocking, whose family was originally from Cornwall, England. Some of the Hocking family immigrated to Australia, and Judy’s side immigrated to the United States. The Hocking line is well documented by the Hocking Descendants Society based on Australia.

Judy’s family tree on the Hocking side..

In many cases when I find unknown matches and want to find the connection to the tester, I start building a private, unsearchable tree on Ancestry. After a short while Ancestry provides hints to records and other trees, and I also research other sources for records and information to make the connection. Luckily, a new GEDCOM export capability in AutoLineage can facilitate the tree building!

On the ‘Tree Management’ ‘Tree Overview’ Page scroll to the bottom to ‘Manage Tree Data’ and select ‘Export Trees to Gedcom.’ This option will generate a new GEDCOM file using all of the trees that have been found, and optionally you can add additional DNA matches that do not have trees. All trees and DNA matches are provided in the GEDCOM as floating trees or floating individuals. In addition, tree persons linked to DNA matches as well as the individual DNA matches are provided with the DNA match tag, allowing for easy retrieval when searching for people.

Profiles for Judy and another tester, Linda, are available. Judy’s profile has 898 trees that came from GEDmatch, which is the only test site I’ve added to Judy’s profile currently. The wizard shows trees linked to available profiles as well as showing all trees (the all-trees option). Moreover, it’s possible to deselect individual trees if they do not need further researching in Ancestry.

I’ve checked the ‘Integrate DNA matches’ which allows me to add up to 1000 additional DNA matches that are not associated to an existing tree. These would show up in the resulting gedcom as floating trees of one person.

I selected 100 additional DNA matches and clicked on the export. It takes some time for the wizard to finish and it puts the gedcom, named ‘example.ged’ into your download folder.

Next in your Ancestry ‘Trees’ menu, scroll down to ‘My Trees.’

Select ‘Upload a gedcom file’ and add the ‘example.ged’ file. You can then name the tree whatever you like. I unclicked the ‘make the tree public’ and after it was processed I turned off the ‘make tree searchable’ under the Privacy settings.

It took several hours for Judy’s large gedcom to finish processing.

List of all the people there were in the example.ged file.

Since I want to find details of Judy’s third great grandfather, David Coleman, who was born somewhere in Ireland around 1801, I searched Coleman.

It appears there are two listings for David Coleman born around 1801 in Ireland. David, born in 1835 is his son. Carefully review the trees before merging them, or all the people in the tree will be duplicated. David has 16 hints and potential parents that someone has found. In the list of hints there are several US Federal Census, homestead records in Michigan and a death record for his daughter.

Summary

Using GEDmatch Tier 1 One-To-Many – Full Version, selecting 7500 matches and then finding the gedcoms that these matches have provides an easy way to find common ancestors using AutoLineage. This approach also works for profiles that do not have a linked tree, in that scenario, the common ancestors will be identified using only the trees of DNA matches.

The new export to GEDCOM functionality provides an easy method to boost your tree finding efforts, without the need to manually copy existing tree data.

More Shortcuts for AutoLineage

March 12, 2025March 19, 2025 ~ Patricia Coleman ~ Leave a comment

Often your research is focused on a specific part of your family line. This was the case with Mary who wanted to explain her father’s Collins line. Where had the Collins family lived in Ireland? When did the immigrate to the United States? And could we find more cousins on the Collins line? She had her DNA on Ancestry where we could select and study only her “Paternal” matches on her father’s Collins line. Mary’s family tree provided the surname of paternal grandmother, and great grandmothers that we’d want to avoid.

We started out by selected “Paternal” matches on her Ancestry DNA.

Selecting Mary’s paternal matches on her Ancestry DNA.

After scrolling down the page, we set the number of matches on the page to 50.

Recently Paul Weaver had commented on the Genetic Affairs Facebook page how that number could be changed to a maximum of 100, by editing the number in the URL of the page.

Ancestry URL where you can change the 50 matches to 100.

We saved several pages of the close matches first.

Next, we added the “Common Ancestor” filter, which is found at the top of the match list.

Filter to find matches that say “Common Ancestor.”

We saved the pages of matches that said Common Ancestor. We added CA for Common Ancestors to the filename to help keep track of what we had saved.A

Since AutoLineage keeps track of the matches that have been added, there’s no worry of duplicating a match.

I make a list of the matches when I start looking at shared matches. I write down the match name, the number of pages of shared matches, notes and cM. This helps keep track of which matches I’ve seen and having the name there makes it easier to spell the name correctly on the gedcom when I save it. If the match is on the grandmother’s side, and not one that I’m saving I still write it down and add grandmother in the notes. This avoids having to look at that match again.

Excel worksheet that is printed and used to document matches viewed.

By looking at the Common Ancestors that Ancestry had found we could determine which matches we wanted to save based on Mary’s tree. If they were on the Collins line, we wanted their tree and their shared matches. First,we’d click on “Tree” and then “Expand Tree” using right click to have the expanded tree open in a new tab.

Elizabeth’s tree that will be expanded in a new tab.

Then on the match’s page we clicked on “Shared Matches.”

While those are coming up, we’d go to the tab with the tree and save the gedcom using One2Tree in my Chrome extension.

Elizabeth’s tree that will be saved to a gedcom.

After the gedcom is saved to the Download folder, we rename it with the match’s name, copy the name and add the gedcom to Mary’s gedcom folder.

By then the shared matches were up. We’d sort the shared matches to show the closest match to this shared match.

Sorting shared matches to show Elizabeth’s closest matches.

Often that would give hints to the family relationship. Elizabeth’s closest matches are first cousins once removed, but often there are siblings or nieces and nephews which provide a lot of information for connecting the family trees.

Next, we’d scroll to the bottom of the page and record to the number of pages of shared matches on my list.

Number of pages of shared marches for Elizabeth.

Here Elizabeth only has one page of shared matches. Using Command S on Mac (Control S on Windows) and we saved the first page of shared matches, pasting in the match’s name that was saved when we named the gedcom, and adding p1 when there are multiply pages for this match.

Once a number of pages of matches and shared matches have been saved and added to AutoLineage, we ran clustering to get an idea of where things were. Under Mary’s profile we selected Generic to bring up the page of commands available and then scrolled down to Perform Clustering Analysis.

Getting ready to run Clustering analysis.

We ran the cluster with all the available matches and normal settings.

The cluster gives us some ideas of where we want to collect shared matches to fill in more connections for the cluster and also which trees would be useful for when we run Find Common Ancestors on AutoLineage.

Back on Ancestry, after collecting all the matches that said “Common Ancestors” and were on the Collins line, we removed that filter and “Search” for matches that have Collins in their trees. This will exclude any matches that don’t have trees, but since trees are needed for Finding Common Ancestors in AutoLineage, the matches who don’t have trees are often not that useful.

Again, saving those pages of matches. It’s likely that we’ve already added some of these matches and trees. While AutoLineage will ignore matches I’ve already added, it would let us add a tree more than once, and that is something we want to avoid. We set up AutoLineage DNA matches page on one tab and the Trees page on a second tab.

Sean K is listed as DNA match and has 8 In Common With matches.

Sean K has a tree already on AutoLineage.

Here we can see that Sean K match has already been uploaded to AutoLineage and he has 8 In Common With matches. His tree is also loaded, so we can skip over him to the next match that has Collins in their tree. This saves a lot of repeating something that has already done a few days earlier.

Once a good number of matches and trees have been added we can find Common Ancestors. Starting on the page of Mary’s profile, we scroll down to near the bottom of the page and select “Find Common Ancestors” on the left side of the page.

Clicking on Find Common Ancestors brings up the Wizard where we can make any changes that we want. Since Mary’s ancestors were born before the 1800 birthdate that is in the wizard, we changed it to 1750.

When the wizard completes its calculations the reconstructed trees appear. We obtain the beginnings of a nice reconstructed tree. In the tree below tester Mary, and matches Elizabeth, Doreen, and J have been connected back to their common ancestors, William Collins and Katherine Liston.

In conclusion if you are looking for Ancestry DNA matches that are on one specific line in your family, you can set up AutoLineage concentrating on that line. First select paternal or maternal depending on where the line of interest is in your family. Next look at matches that Ancestry labels “Common Ancestor.” You’ll want to compare the surnames found here to the names in your family tree to avoid common ancestors who are not on your specific line. Then remove the “Common Ancestor” filter and “Search” on trees that have the family surname or interest. AutoLineage will prevent you from adding a DNA match a second time, but you have to make sure that you don’t duplicate a tree. Checking for the match’s name in Trees under Tree Management before adding a tree can ensure that you don’t duplicate one.

Thanks to Mary for the use of her DNA data. If you’d like some help with your DNA contact me at info@patriciacolemangenealogy.com

A Shortcut for Saving Pro Tools Shared Matches

November 23, 2024November 27, 2024 ~ Patricia Coleman ~ Leave a comment

If you have been collecting your Pro Tools shared matches for AutoLineage, you know it can take a bit of time. I’ve found another shortcut to speed up this collection. First I check if the match has a tree and look at the tree. If it’s only a few people and all are private, there’s no point in making a gedcom of that. But when there is a tree I make a gedcom on One2Tree. Before I save the match’s name on the gedcom, I copy the name. Then when I go to do the shared matches I’ve already got the name and then only have to add p for page. Since I was already on the match’s page, I select shared matches and am ready to collect them, as I describe below.

This is especially useful when the match has multiple pages of shared matches. The figure below shows match lists for Joyce, who has nine pages of shared matches and three of the pages of David’s shared matches. With these shared matches it is important to keep the folder as well as htm file that you add to AutoLineage. If you delete the folder, the htm file will also be deleted.

Directory showing some of the files of shared matches.

Tiffany is a match to Dave and she has 6 pages of matches. I start out by saving her first page.

Saving Tiffany’s first page of shared matches.

Before saving the file I copy the match name and p, which I’ll use for all of the matches other pages and just add the page number.

Copying the match’s name and p for the page number.

Then I watch as the circle, that is in the upper right bar of Chrome, fills in around the arrow to make sure that the file has been completely saved

On the left is the circle that is drawn as the file is being saved. On the right is the look after the file is saved.

Once the file is saved I position my mouse next to the arrow and change to the next page. I then leave the mouse there for the rest of the files. I also set my left hand so I can use Command S and Command V for the rest of Tiffany’s pages. I’m on a Mac and Command S saves the page in Chrome and Command V pastes the beginning of the filename that I copied earlier. On a Window computer the commands would be Control S and Control V.

By just pivoting my fingers I can reach Command V.

Finger pivoted to reach Command V.

Once the file for the first page of Tiffany’s matches is saved, I mouse click to page 2. Then Command S brings up the ‘Save page as’ dialog. Command V copies in Tiffany p. I move my right hand and select 2, to make the file say ‘Tiffany p2’ and use the Return key to save the file. As the files are added to my directory I can see what has already been added which makes it easy to not overwrite a file.

Directory showing the files that have already been saved.

The figure below shows all six pages of Tiffany’s shared matches. The first five pages have twenty matches each and the last page has nine matches. You’ll noticed that all six pages were saved in under two minutes.

Directory showing all six pages of Tiffany’s shared matches.

Summary

I have found this to be a lot faster than having to go up to the File menu to get ‘Save page as’ and also to have to type the filename over each time. I’ve also found several matches with up to fourteen pages of shared matches. Especially when you’re trying to save a lot of shared matches to build out your clusters and subsequently the reconstructed trees, saving any time on the data collection is a bonus.

Convert Ancestry ProTools to Trees using AutoLineage and AutoKinship!

November 3, 2024November 4, 2024 ~ Patricia Coleman ~ Leave a comment

From ProTools to AutoKinship tree using matches shared by my cousin, Tish¹, and me.

Genetic Affairs has unveiled a powerful new feature in AutoLineage—now you can apply AutoKinship to each individual cluster generated from Ancestry ProTools shared matches, unlocking a whole new level of insight into your family connections! It invokes the functionality of AutoKinship on the site directly at no additional cost, to provide reconstructed trees based on the shared DNA between shared matches.

In a previous blog post, I described how to get started with Ancestry and AutoLineage. In short, these are the steps involved. First, a profile is created or selected after which the same is done for a DNA test. Select a generic DNA test in order to process Ancestry matches. Next, we import DNA matches by saving each match page (make sure to set the number of matches to 50) to an HTML file. Repeat until you have enough matches. Next, we download shared matches for the matches of interest followed by a clustering analysis. Once we obtain the clusters, we can select an individual cluster for which we want to have a reconstructed AutoKinship tree.

In this blog post I will demonstrate how I cluster the shared matches of my 2nd cousin Trish. For this purpose, I will visit each DNA match page in the shared matches list with Trish and download their shared matches.

First, I imported my direct DNA matches. Next, I visited Trish her match page and downloaded the shared matches by using the Chrome “Save page as” to save the shared matches. Trish and I have three pages of shared matches, and I saved them as Trish-p1, Trish-p2 and Trish-p3. I used the “Import shared matches” to add these matches that Trish and I share. Next, I visit each DNA match in the shared matches with Trish and download the shared matches for each match.

In order to get a cluster that only showed Trish and my shared matches, I selected “DNA matches” from my DNA test linked to Ancestry page and then clicked on Trish’s name.

Clicking on “perform clustering analysis” brings up the Cluster wizard. Again, this clustering approach will only take all DNA matches that are shared with Trish, basically allowing me to only cluster matches that share an ancestral connection with myself and my 2nd Trish.

Trish has 42 matches that she shares with me.

Clusters of Trish’s and my shared matches.

Notice the cM values listed in each of the colored cells in the cluster. These are the result of the new Ancestry ProTools data!

Trish and I share great grandparents, Thomas Byrnes and Bridget Fenton.

Thomas was born in County Roscommon, Ireland and Bridget in County Limerick. They met and married in Virginia. From building out family trees we know that the orange cluster has ancestors from Counties Roscommon and Mayo, and the green cluster has matches with surname Burns. Those matches would be on Thomas’ side. The brown cluster has ancestors who lived in County Limerick and would be on Bridget’s side. Very few of the matches in the large red cluster have any trees or other family information.

Clicking on “Matches” at the top of the screen brings up a table with the list of matches in each cluster.

The next step is to click on cluster 3, which brings up just the red cluster. Again clicking on “Matches” at the top of the screen brings up the list of matches for cluster 3 only.. In the upper left of the screen is the “Run AutoKinship” button.

Cluster 3 matches and AuroKinship button.

Once AutoKinship analysis starts a message appears at the bottom of the screen telling you that the results will be sent to your email. The zip file should be saved to the computer and then unzipped. There are ten probability trees in the AutoKinship.

AutoKinship tree gives hints as to how matches can be related based on the amounts of shared DNA. At the top of the tree beau is listed as child of M, and M and P are listed as siblings. This is identical to what Ancestry said about these matches. But not every relationship is correct. Trish and I are second cousins, but we share a larger amount of DNA than would be expected compared to the average for second cousins, and we’re often miss assigned as first cousins or first cousins once removed. However, the AutoKinship tree can be used to get valuable hints for connections for the matches.

At the bottom of the page with the first AutoKinship tree is a matrix of the DNA matches used for the tree. This matrix can be downloading into Excel and saved.

Part of the DNA matrix for the red cluster.

In conclusion being able to organize you Ancestry ProTool matches into a probability tree and generate a matrix provides addition methods for viewing your shared matches.

Note that the current AutoKinship approach will be replaced with a more versatile version that will run directly in AutoLineage. This version will allow for using pre-determined relationships or integration of known trees into the reconstructed trees.

Patricia Harris Anthony, Trish, has given me permission to use her real name and her data.

Linking GEDmatch and FTDNA – AutoLineage for iGG

May 7, 2024 ~ Patricia Coleman ~ 6 Comments

AutoLineage is a powerful tool that allows you to cluster your matches at a particular testing site but also to find common ancestors across multiple sites. In this blog, we will discuss a scenario when only data from FamilyTreeDNA (FTDNA) and GEDmatch can be used as sources, for instance for an investigative genetic genealogy search.

An example reconstructed tree using only FTDNA and GEDmatch data.

We will be importing and analyzing data from FTDNA and GEDmatch, and use the common ancestor identification tool to find common ancestors. Also, we’ll show how to use the hints tool to get insights about ancestral lines that could hold a potential common ancestor.

From the landing page select ‘Register a new Profile’ in the left panel. Start AutoLineage by making a profile for the case you are investigating and for whom DNA matches are obtained. In this blog, we shall be using Dave as an example.

Starting a new profile.

Family Tree DNA

Next, a DNA test is registered.

The first thing to do is to import Dave’s matches from FTDNA. Click on the ‘import matches’ button.

Flowchart showing Import matches.

Each testing site has its own list of methods for collecting matches.

Options for uploading matches from FTDNA.

Here we are using the first one, a CSV file from Genetic Affairs. Clicking on the question mark explains what file this option needs.

Explanation of the CSV from Genetic Affairs.

For this option, we first ran the AutoCluster analysis with the AutoTree feature enabled for FTDNA on Genetic Affairs.

Starting AutoCluster with AutoTree enabled on Genetic Affairs.

After starting the analysis, an email appeared with a download link. Alternatively, this link is also available in the notification section on the members page of Genetic Affairs (top right corner, under the bell icon).

After unzipping the results file, we found all the data we needed for AutoLineage. In the Gephi folder, two files are present; nodes.csv and edges.csv. The nodes file contains the matches that would be selected in the dialog box of the ‘Wizard – import matches’.

The imported DNA matches are shown after the import process has been completed.

Clicking on ‘DNA test overview’ at the top brought back the flowchart where we selected ‘Import shared matches.’. Shared matches are required for the clustering process.

The wizard explains that the edges.csv file from the Gephi folder is used for the shared matches.

After importing the shared matches, which can take some time, the number of shared matches for each match is now displayed in the table.

Matches list showing the number of shared matches each has.

With matches and shared matches available, we ran the cluster analysis for FTDNA. Going back to the ‘DNA test overview’ brings up the flowchart where we selected ‘Cluster matches.’

The ‘Clustering wizard’ allows us to select the range of matches for the clustering, which type of clustering, and which color scheme should be used.

We selected to run all of Dave’s matches in the clustering analysis. After selecting the “start clustering” button, the clustering chart appears.

GEDmatch

For the GEDmatch data, we added data from an AutoKinship analysis (Tier 1).

Going back to Dave’s profile page we registered another DNA test and selected GEDmatch from the drop-down list. Next we ‘import matches.’ An unzipped AutoKinship report also has a Gephi folder containing the nodes.csv and edges.csv files. Nodes.csv contains Dave’s matches, and edges.csv contains the shared matches. Now these matches can be clustered. We ran 500 kits for AutoKinship and 362 of these have shared matches.

Adding Trees

Now it’s time to add trees. The trees are typically associated with the matches, but they are managed independently of the DNA testing site. Clicking on ‘Home’ in the left panel brings up a list that includes ‘Tree Management.’ Clicking on ‘Tree Management’ brings up the flowchart for Trees.

In ‘Gather Trees’ we clicked on ‘Import Trees’ which brought up the ‘Tree Wizard.’ There is a setting for the trees associated with the GEDmatch DNA matches, and a different setting for trees associated with the FTDNA DNA matches. In both cases, they retrieve tree information from the ‘match’ files associated with the GEDmatch AutoKinship or the FTDNA AutoTree AutoCluster.

‘Import tree wizard’ showing the two options for importing the trees associated with both testing sites.

In both scenarios, we selected all the HTML files in the match folder of the unzipped AutoCluster FTDNA report or unzipped AutoKinship GEDmatch report. Using these methods also ensured that the trees were automatically associated with the FTDNA and GEDmatch DNA matches

The GEDmatch AutoKinship folder contents.

The list of trees inside the matches folder.

Typically, iGG profiles are not associated with trees, but in our example, Dave does have a tree, and it needs to be linked to his profile. For other cases where the tester does not have a tree, such as people looking for birth parents, this step would be skipped, and you’d go on to ‘Find Common Ancestor’ from the various DNA matches.

To link Dave’s profile to a tree, we visited the tree management and select Dave in the ‘link profiles to tree’ section (alternatively, go to the tree view in Dave’s profile and associate the tree from there).

We clicked on Dave’s name and brought up the Wizard to link Dave’s profile to his tree. Next we searched for Dave’s tree in the name field.

Selecting Dave brought up his tree where we selected him as the root person of the tree.

Selecting Dave as the root person in his tree.

This brought up Dave’s tree. Notice the person image over Dave’s name that tells the tree is connected to his profile.

Dave’s tree. Names of living persons or those who died since 1973 have been hidden,

Going back to Dave’s profile page we scrolled down to ‘Find Common Ancestors’ near the bottom of the page. There are now two DNA tests associated to his profile, and some of the DNA matches are linked to trees that we imported. We selected the ‘find common ancestor’ button to start the common ancestor wizard.

Running the analysis with the default birth year of 1800 identified eighty-seven common ancestors for Dave across FTDNA and GEDmatch. A problem arises when a match is on both sites and has a tree at both locations. The match on the right of the reconstructed tree shows both FTDNA and GEDmatch. She and her tree are duplicated which causes brown squares, indicating that tree persons are duplicated. Seeing a lot of these brown squares is often indicative of a problem.

DNA match who is on both sites and has trees on both.

Clicking on ‘Linked trees’ shows the trees that are attached.

Right-clicking on the tree brought it up in a new tab. Since the GEDMatch tree has more people we kept that one and deleted the FTDNA one. Next, we attached Rachel’s FTDNA DNA match to the remaining GEDmatch tree.

Connecting Rachel’s FTDNA match to her GEDmatch tree.

Rachel shared 30 cM with Dave at FTDNA and 27.7 cM at GEDmatch. Looking through the list of matches in the Wizard there’s only one Rachel that matches Dave at FTDNA with 30 cM, so we selected that one.

Clicking on the DNA symbol over Rachel’s name shows she is connected to both GEDmatch and FTDNA matches to Dave.

Finding Hints

Since Rachel was the only match on both FTDNA and GEDmatch we used her tree and ‘calculate hints.’

Calculate hints is basically a common ancestor identification with a focus on the tree that is shown on the screen. The tool compares the names and dates in Rachel’s tree with all the other trees that have been loaded into AutoLineage. It can take a minute or so for this calculation depending on how many trees there are. Contrary to the regular common ancestor tool, the hints tool also shows hints about shared surnames and/or surnames and first names.

The calculate hints from Rachel’s tree showed a yellow hint for Phyllis May Thompson and for Grace Tonkin. Yellowish hints indicates a shared surname and might mean that there might be something useful farther back in time. So we expanded the tree out first from Phyllis. The common surname, Barrett, was found in another DNA match’s tree, but the dates were several hundred years different. Next we looked at the hint for Grace.

Grace Tonkin’s hint showed that Dave had several people in his tree with the Tonkin surname, but her date is more recent than the people in Dave’s tree. We followed the Tonkin line past Grace in Rachel’s tree and found that Thomas Tonkin’s dates were in the same range as those in Dave’s tree.

Thomas Tonkin’s wife, Margaret Hattam, also has a hint.

Looking at Margaret Hattam’s hint we find that Dave’s tree has several Hattam ancestors.

Margaret Hattam was born in 1785 and Dave has an ancestor, Mary Hattam born in 1783. When we added Margaret’s parents to our tree, and rerun the hints tool we found new green hints linked to Margaret’s parents.

John Hattam and Elizabeth Eddy both have green hints.

Looking at John Hattam’s hint we found that he is in both Rachel’s tree and Dave’s tree. We’ve found the common ancestor!

John Hattam in both Rachel’s and Dave’s trees.

Since John Hattam had a birth date of 1754, we changed the birth year to 1700 in the parameter of the ‘Find Common Ancestors’ wizard and then reran ‘Find Common Ancestors.’

Changing the birth year in the Find common ancestor Wizard.

Now a total of fifty-four ancestors were found, and the reconstructed tree showed the connection between Dave and Rachel. They have common fourth great-grandparents, John Hattam and Elizabeth Eddy.

Reconstructed tree connecting Dave’s and Rachel’s trees.

Dave’s tree is showing brown squares which often means there is a duplicate tree or a date problem, however for Dave’s family this is correct. He has two Hocking lines. John Hocking’s parents were James Monteith Hocking and Martha Murrish. James Monteith is one of the Hocking lines, and Martha’s mother was Eliza Hocking, which is the other Hocking line. They both go back to John Hattam and Elizabeth Eddy, so they are Dave’s fourth great-grandparents twice!

Summary

We ran AutoLineage using only FTDNA and GEDmatch data to look for common ancestors to Dave and his matches. AutoLineage generated clusters for the FTDNA matches and also for the GEDmatch matches. Trees from both testing sites were loaded and attached to the DNA matches. Next, we identified common ancestors across all of the data. We found one match who had a tree on both sites. By associating her DNA from both sites to one tree and deleting the extra tree we avoided her showing as a common ancestor to herself. Next, we ran hints on her tree to get clues on which ancestral lines to prioritize for our research. After expanding the tree, and re-running the hints tool, we found the common ancestor. This led us to find that she and Dave had common fourth great-grandparents!

RootsTech Week 2024

March 15, 2024 ~ Patricia Coleman ~ Leave a comment

I arrived in Salt Lake City Saturday 24 February. Arriving on Saturday is convenient as it gives me Sunday to sleep in and get organized for the week. Leisa Byrne had already arrived, and we recreated some of the things we’d done before RootsTech in 2020, starting off with brunch at Eva’s Bakery.

Then we went on to Harmons grocery store to pick up some snacks and items to have for lunch while working in the library Monday through Wednesday.

Sunday afternoon we went to the organ recital at the Tabernacle.

The lights changed colors with the different songs that the organist placed. It’s such a beautiful location and magnificent organ.

We were staying at the Little America Hotel this year. I was surprised at the pansies that were blooming all over the place. It was still pretty cold. They had beds of purple ones and others of yellow ones.

Purple pansies blooming at Little America Hotel.

Yellow pansies blooming at Little America Hotel.

Little America Hotel was at the end of the free Trax line which made getting around to the library and the Salt Palace very easy. Both the blue and green Trax lines went to Temple Square, which was the exit we took for both the library and the Salt Palace. But once we got on the red line by mistake and ended up having to walk farther!

Monday was FamilySearch Library day! We arrived just before the library opened and joined the crowd of people waiting to get in. There had been a little snow overnight.

I was surprised at how crowded the library was for this early in RootsTech week.

Monday through Wednesday was spent working in the library. After being there three times I think I may have finally found all the Kent County, Michigan land records that exist from the 1850s. I know that Dave’s third great grandfather, David Coleman, was listed in Lyndon, Washtenaw County in the Michigan 1845 census. Now I’ve found that the family purchased land in 1846 in Kent County. Many of the land deeds were lost in a fire in 1860, but some of the indexes still exist.

Wednesday afternoon the RootsTech free golf cart shuttle was already running between the library and the Salt Palace, and we got a ride to go and pick up our badges.

RootsTech map showing the presentation rooms.

Things were still quiet in the halls on Wednesday, but there was plenty of activity in the Expo Hall getting the booths set up.

Grand Ball ready for presentations that would begin at 8:00 AM Thursday.

We headed to the DNA Painter booth to see if help was needed on the setup, but Jonny and James had it all done.

One of the signs at the DNA Painter booth.

The RootsTech souvenir booth was open Wednesday afternoon, and I had to get a new RootsTech T-shirt.

Thursday presentations started at 8:00 AM, and the Expo opened at 9:00 AM. I heard there was a huge crowd waiting outside the Expo before it opened, and we were swamped with people! I think it was the busiest day. None of us ever got any lunch. I did manage to get a quick photo of the DNA Painter shirt, that we had new for the booth this year. Thursday the expo was open until 7:00PM. It was a long day.

Things settled a bit as the conference went on, and I was able to attend some presentations and visit several booths.

String quartet playing in front of the FamilySearch book.

There was often musical entertainment in the Central Park area.

The Ancestry booth was to the left of Central Park.

MyHeritage booth was to the right of Central Park.

MyHeritage had a booth set up where you could upload your raw DNA right onsite. They offered the DNA tools for free if you uploaded during the time of RootsTech.

MyHeritage booth to upload your raw DNA.

FamilyTreeDNA was to the left of FamilySearch.

DNA Painter was near the FamilyTreeDNA booth. We were able to get a group photo.

Left to right: James Nunn, Leisa Byrne, me and Jonny Perl.

LegacyFamilyTree Webinars was in the row of booths beside DNA Painter.

Farther down the aisle was the Board for Certification of Genealogists.

Association of Professional Genealogist had a booth as well.

I managed to get a photo of the GEDmatch booth when no one was there.

Last year at RootsTech I purchased Cite-Builder which is a program that lets you generate citations. With the premium account you can generate your own and save them on the site. When I had a question about one of their structures last year, it was Jenny who answered my email. It was great to meet her person this year.

It was great to get to meet Nathan in person.

RootsTech is also a great place to see friends that you’ve not seen for a year.

With Roberta Estes before her presentation.

At the end of Roberta’s presentation she told us about her newest book, that hadn’t been released in time for RootsTech. It’s due to come out in March or April 2024.

Roberta’s book due out in March-April 2024.

It’s always a bit of a rush to fly home on Sunday after RootsTech, but staying another day doesn’t make much sense since the library isn’t open on Sunday. There was more snow Saturday overnight.

Snow on the mountains as seen from the airport Sunday morning.

I had several hours layover in Minneapolis and spent the time putting in a couple presentation proposals for upcoming conferences and organizing my notes from the library.