Recently the AutoSegment ICW tool on Genetic Affairs for 23andMe and FamilyTreeDNA profiles has received significant enhancements. In short, if the DNA matches linked to overlapping segments are not shared matches (for FTDNA) or do not triangulate (for 23andme), it can be presumed that these segments are related on opposite parent sides.

An AutoSegment analysis first collects and groups all the segments that overlap on each chromosome. Next shared matches for these segments are collected, and the shared matches are used to group overlapping segments, to make segment clusters. These clusters are used to generate the AutoSegment ICW cluster, which is found at the top of the window of the HTML file. 

Earlier segments that were part of an overlapping segment cluster, but did not have any shared matches with the DNA matches of the other segments were discarded, but now with the new enhancements these segments are kept, and the data are available along with the ICW groups in a table.  The table is brightly colored to indicate where there are ICW clusters and where there are additional segments.  Another feature of this table is the built-in ICW matrix (similar to the FTDNA ICW matrix) that shows the segments. Clicking on ‘more info’ brings up the matrix where grey cells indicate when one of the segments that was not in the cluster is a shared match with some of the members of the cluster. Finally, this table of all the segments can be entered into DNA Painter’s Cluster Auto Painter (CAP) to show the triangulated clusters as well as the segments that do not match. Since I have another blog post about AutoSegment ICW cluster, this post will primarily be about these new segment clusters.

Also note that although the name suggests otherwise, the AutoSegment ICW on 23andme actually employs triangulation data, since the quality of actual triangulation data is better as compared to ICW data (especially linked to high cM matches that share multiple segments).

In a nutshell, this is the set of different steps employed by AutoSegment ICW and the newest addition to the tool:

• Getting DNA match list until it reaches the lowest cM setting.
• Getting the segments (or in the case of 23andme, the user-provider match file with all segment data).
• Clustering of segments, finding overlapping segment clusters.
• Identify which matches are part of overlapping segment clusters
• Download ICW matches for DNA matches linked to segment clusters
• Redo the segment clustering but use the ICW data, overlapping segments are discarded if the underlying matches are not ICW
• Link matches together if they share a segment in a segment cluster and create a network of DNA matches.
• Perform AutoCluster clustering and create the chart
• so until now, this is the regular AutoCluster ICW – now comes the new part
• Redo the segment clustering and identify overlapping segment clusters
• Examine the segment clusters and check if all DNA matches underlying the segments are ICW, if this is all true, it’s a green segment cluster
• Segment clusters for which not all segments are triangulating are clustered, to see if we can identify 2 or more separate segment clusters
• The separate segment clusters from the previous step are used to color the segment clusters
• Create an ICW matrix page per segment cluster, color the ICW information with the same information from the segment cluster colors from the table
• Add the table with colored segment clusters to the main HTML created for the regular AutoCluster ICW

23andMe AutoSegment ICW

Figure 1 shows the files that are produced by the AutoSegment ICW analysis for 23andMe data. The first HTML file that is listed here is the AutoSegment ICW AutoCluster which is typically displayed. The second file, the Excel one, is the spreadsheet version of this same AutoCluster, which is very useful for reading the match names when the HTML AutoCluster is very large. The third file, which has ‘no-chart’ just before the HTML contains all the results of the AutoCluster html but without the AutoCluster at the top. This file is most useful when the cluster is so large that your computer has trouble displaying it. The fourth and last file that ends with ‘segment_clusters’ contains the new, enhanced segment clusters that are used for DNA Painter Cluster Auto Painter.

Clicking on the first html file in Figure 1 brings up the AutoCluster at the top and all of the information including the new segment clusters table. The original AutoSegment ICW cluster is shown in Figure 2.

Scrolling down the page next is the Chromosome segment statistics per AutoSegment cluster. Clicking on one of the AutoSegment clusters lists all the DNA matches in that cluster as well as matches that are in other clusters and have grey cells to a match in the first cluster. Continuing down the page is the list of all the matches in each of the clusters found in the AutoSegment ICW cluster from the top of the window, as well as the individual segment cluster information. All of these features have been explained in more detail in previous blog posts.

Next is the Complete Segment Cluster Information, which is one of the new features. Previously any segments in an overlapping segment cluster that did not have shared matches with the other DNA matches linked to the other segments in the cluster were discarded. Also, matches that did not fit into a cluster, even if there was some overlap, but only to one or two people in the cluster, were not included. Now all matches are used for this table. The matches are color coded so that triangulated matches, matches that share the same segment of the same chromosome and also are shared (ICW) with others in that cluster, are given the same color in the table. Figure 3 shows an example from this colored table.

Looking at the data in this table first is the cluster number from the AutoSegment cluster, which was at the top of the window. Ann and Sue are in cluster 23 and Trish is in cluster 1. The next number in the table is the cluster number that will be used when these data are put into DNA Painter CAP. All three of them are on chromosome 5, and their start, end, SNPs and cM values are given. Trish is my known paternal second cousin. Looking at this table with a different color for Trish than for Ann and Sue, I would say that Ann and Sue triangulate and do not match Trish. Clicking on ‘more info’ in the upper left of the table brings up the matrix for these three matches, see figure 4.

The triangulation matrix confirms that Trish does not match Ann and Sue. Since I know Trish is on my paternal side, this would lead me to believe Ann and Sue are maternal.

Another new feature of the colored table is that it can be imported into DNA Painter using the Cluster Auto Painter (CAP). It’s always been possible to import the main AutoSegment ICW cluster using CAP, but now all of the segments can be imported. Clinking on ‘Cluster Auto Painter’ at the top of the colored table brings up CAP in DNA Painter, shown in Figure 7.

Select the tester’s gender and then choose the file. The HTML, shown in Figure 1, that ends with ‘segment_clusters’ is the one that contains the segment data from the colored table. It’s still possible to import the main HTML file into CAP, but that file will not hold all segment clusters. Figure 6 shows the clusters after importing the file that ends with segment_clusters.

One thing to note is that each segment cluster is in a separate cluster. That includes the single segments that were once part of an overlapping segment cluster but do not have triangulation or ICW evidence to be part of the main segment cluster. For example, I share nineteen segments with my cousin Trish, but they are in different locations on various chromosomes. Figure 7 shows some of these segments.

Since Trish and I triangulate with different matches on each of these segments this provides an easy way to group our triangulated matches for each chromosome.

Using the CAP results we can see chromosome 5, which we saw in Figures 3 and 4, Ann and Sue match each other, but did not match Trish. Initially, all of the results are shown as ‘shared or both.’ Since Trish is known as paternal I can change her cluster 4 to paternal. Because she does not match Ann and Sue I can change their cluster 3 to maternal.

Figure 8 shows both clusters as ‘shared or both.’ Figure 9 has the results after moving Trish’s cluster to paternal and Ann and Sue’s cluster to maternal.

FTDNA AutoSegment ICW

The AutoSegment ICW directory for FTDNA contains two more directories that were not present in the 23andMe one. Since matches on FTDNA might have posted a tree with their DNA results, the ancestors and tree directories are included here. The files for the AutoSegment ICW cluster, both as HTML and Excel, the AutoSegment results with ‘no_chart,’ and the ‘segment_clusters’ are the same as for the 23andMe data.

Other than trees and ancestors the displayed results for FTDNA are the same as for 23andMe. Figure 8 shows a more complex match list in the colored table. The first match in the list is my paternal second cousin on my Dad’s father’s side, who I will call Frank. There are a number of matches to Frank on chromosome 20 in this cluster. The matches in blue triangulate with Frank on chromosome 20. Using the matrix for this table entry we can determine the relationship for the yellow, green and red clusters. Clicking on ‘more info’ brings up the matrix in Figure 12.

Frank is part of the large grouping as well as having grey cells, indicated matches, to other matches on chromosome 20. Fred and Dan are brothers and match Fred and Joe in the blue cluster, and Karl matches Frank and several others in the blue cluster. The only ones who do not match anyone in the blue cluster are Jim and Van. Since Frank is a known paternal second cousin, Jim and Van must be maternal. Looking at the surnames Jim listed on FTDNA I can tell that his ancestors were from Germany. My mother’s entire family was from Germany. Unfortunately, neither Jim nor Van have a family tree so it would not be easy to try and find the connection to my mother’s family.

Using CAP with this FTDNA cluster we can look at these clusters in DNA Painter.

Summary

The new addition to the AutoSegment ICW tool for FTDNA and 23andme provides all the information that was available before and adds important new features. Now all the segment data is included which shows matches that are grey cells to the main triangulated cluster as well as showing DNA matches that did not fit into the cluster. The matches that do not fit are likely the opposite side of your family.

For example, if the cluster of triangulated matches is on your maternal side, and there are other DNA matches that do not belong in the cluster, they are likely paternal. This can provide valuable hints for searching for family members that might have been overlooked before.

Another application of this enhancement might be the ability to assist researchers in obtaining information about each of the tested parents (e.g., in the case of adoptees, Does, or perpetrators). For example, if a certain ethnicity of DNA matches is found to be often different as compared to the matches linked to the opposite-sided segments. Following this approach, it might be possible to identify parents that are linked to an ethnicity that is underrepresented in the DNA database. In this scenario, almost no opposite-sided segment clusters are present because there are almost no DNA matches on the side of the underrepresented parent. If there are opposite-sided segment clusters, these might provide some essential clues to the ethnicity of the parents.

The newest feature on Genetic Affairs website is AutoKinship.  The most amazing thing about AutoKinship is that it generates a tree using only your DNA matches and the shared DNA between your matches.  It doesn’t require you or any of your matches to have a tree. On the Genetic Affairs website there are 2 ways to run AutoKinship, an automated analysis for 23andme or a manual analysis for MyHeritage or GEDmatch matches. Recently Roberta Estes wrote a blog describing the use of AutoKinship for 23andme.  This blog will describe using the manual AutoKinship at Genetic Affairs using MyHeritage DNA matches.

Figure 1. The two AutoKinship approaches on Genetic Affairs.

The start point of this analysis is the MyHeritage AutoCluster clustering. Starting with the MyHeritage cluster my second cousin, Trish¹ is found in cluster 6, but she has several grey cells to cluster 1. Cluster 7 has several additional known cousins.

Cluster 1

A close up of cluster 1 is shown in figure 3.

From the information provided on MyHeritage for the matches in cluster 1 two of them live in Australia, two in England, a couple in the US and the rest in Ireland.  Trish and my great grandparents, Thomas Byrnes and Bridget Fenton, both came from Ireland so I’m interested in finding the connection there.

To set up for AutoKinship I took the HTML from this MyHeritage cluster and converted it to an Excel file using the “Transform AutoCluster HTML to Excel” under the “Analysis” menu at Genetic Affairs.

The Excel file has several tabs in it.  The first is a list of my matches.  The second tab is a list of the shared matches.  The next tab has the matches in cluster 1 followed by a tab with the shared matches for cluster 1.  This continues then for all of the clusters with a list of matches showing the cM that the match shares with me and whatever notes I’ve written about the match, followed by a tab that has those matches with the names of all their shared matches in the cluster.  Matches from grey cells are not included in the cluster matches but do show up in the shared matches list for that cluster.  Figure 5 shows the match list for cluster 1.

Part of the shared match list for cluster 1 is in figure 6.

Using the shared match list, I then went to MyHeritage and got the number of cM that these matches share with each other.

The shared match list in MyHeritage shows how much DNA Mary shares with me, but it also shows the amount that she shares with each of our shared matches. These data are needed for AutoKinship. I’ve circled two of these amounts in red in figure 7. These shared centiMorgans are copied and pasted into column C of ‘icw_1’ list.

Because Trish was a shared match to several of these matches she already shows up in the shared match list, but not in the match list (see figure 5), so I needed to add her to the match list. On MyHeritage Trish is listed as Patricia Ann Harris, her formal name.  AutoKinship needs the names exactly as they appear at MyHeritage so that it can find the correct person in the cluster.

I also need to add Trish and myself to the shared match list.  I copied the table from the match list, shown in figure 9, and added that to the shared match list.

I could run AutoKinship with the information that I have now, but I can also add a known genealogical tree (in the WATO format) for the known relationship between Trish and me.  Our common ancestors are Thomas Byrnes and Bridget Fenton, our second great grandparents. Using the WATO tree insures that Trish and I are placed correctly relative to each other. The amount of DNA that we share is on the high side for second cousins, and it could be labeled as first cousins once removed. Since we know the relationship it’s better to set it with a WATO tree.

With the WATO tree I need to use the same exact names for Trish and myself that MyHeritage uses, or they will not be recognized as the same people.  Also on the WATO tree I need to add the shared cM that MyHeritage has for Trish and use 0 cM for myself.

To use the WATO tree with AutoKinship I downloaded the WATO tree.  Do not use the ‘save image’ as that will create an image of the tree and not what is actually in the tree.

Figure 12. Download the WATO tree to use with AutoKinship.

Now everything is ready to run the manual AutoKinship.

For name of tested person, I entered my name exactly as it is on MyHeritage.  The default is for 10 trees. You can select more if you want, and they are listed from highest probability down to lower ones.  The first few would be the most likely.  Maximum difference in generation refers to the difference between the tested person and their matches.  The default is 2 generations which would include people in my generation or my parent’s or my children’s generations.  Since I don’t know how all the matches are related to me this is likely a good value.  If I were to set it to 3 generations that would indicate that some of the matches could be in my grandparents or grandchildren’s generation. Looking at the ages the matches have indicated in MyHeritage gives me an idea that 3 generations is not needed here. ‘Set generation of tested person’ lets you set the generation level for yourself if you’ve set the generation of some of your matches.  This is especially helpful if you know how some of the matches in the list fit in your tree and are a different generation.  This is data from MyHeritage so I want to use the MyHeritage probabilities.  And I’ve loaded the WATO tree for Trish and me.

There are two ways that the data can be entered.  However, the bulk import is so much easier! Just copy and paste the match data from the Excel file.  In this case it’s cluster 1 so the tab with the data has a ‘1’ on it for the Bulk Input DNA matches data.

Then copy and paste the shared match list from the ‘icw_1’ tab into the Import shared matches data screen.

Next I clicked on “Perform AutoKinship Analysis”.  A zip file is sent to my email which I then downloaded, saved to my computer and unziped.  The first autokinship.html file is the landing page and has the highest probability.  The autokinship.xlsx lists the match file in one tab and all the shared matches in another tab.  I’d used 10 as the max number of trees.  Tree1.html is identical to the landing page tree.  The other 9 are trees with lower probability. WATO trees of the 10 probability trees are also provided.

Figure 18 shows the first tree using WATO for Trish and me and setting only myself as generation 0.

The WATO puts Trish and me into our correct second cousin relationship.  However, Sarah, Sue and Joe Smith being our grandparent’s level seems unlikely, since they list their age range on their MyHeritage page, and they are in the same range as Trish and me.

Next I ran the AutoKinship setting Joe Smith as generation 0 as well as having set myself as generation 0 using the ‘set generation level of tested person’ showed in figure 13.  To set generation 0 for Joe Smith I added 0 in Column C next to Joe’s name in the match Excel file (see figure 19) and used that match file in the AutoKinship.

The landingpage AutoKinship tree for the analysis that has Joe listed as generation 0 is shown in figure 20. There is a notation of gen 0 by both Joe’s and my names in the AutoKinship tree.

In the AutoKinship tree clicking on the person’s name brings up a box that summarizes all of their matches and the amount of DNA charged both as centiMorgans and percentage. This is shown for Joe Smith in figure 21.

One interesting thing that jumps out at me is the relationship between Joe Smith, Sue and Sarah is the same in both AutoKinship trees.  On MyHeritage Sue only has a tree of 1, so that doesn’t provide any information.  However, her son Frank has a small tree but indicates his mother’s maiden name was Smith.  Sarah also has a small tree and indicates her mother’s maiden name was Smith.  From their shared DNA it appears that those connections are through Joe’s grandfather and great grandfather on the Smith side of his family.

Bridget Fenton’s mother, our second great grandmother was Johanna O’Brien.  Bridget was born in 1853 in Limerick and was Johanna’s only child born in Ireland.  All her other children were born in the United States and lived there their entire lives.  We don’t know who Johanna’s parents or her siblings were.  It appears that at least one generation is missing here, since Johanna cannot be person #1 in the tree.

In following Joe Smith’s family back starting with the tree he had and looking up Irish civil birth records and Catholic baptismal records I discovered that his great grandfather, born 1851, married an O’Brien who was born in 1850.  They would be in the same generation as Bridget, born 1853.  And both this O’Brien and Bridget Fenton were baptized at the same Catholic parish in Limerick.  Unfortunately, the records haven’t survived far enough back to give either my second great grandmother, Johanna’s or Joe’s second great grandfather’s baptismal records.  My hypothesis is that they were siblings (see figure 22).  There is another occurrence of O’Brien in Joe’s tree on his mother’s side.  It’s quite possible that Trish and I match on his mother’s side as well, and we just haven’t found that connection yet.

Looking at the others in cluster 1 I’ve messaged with Mary.  She and her brother, Bob, are second cousins to Joe Smith but not on the Smith line.  Mary’s grandmother is sister to Joe’s grandfather. I’ve not found enough records to generate a hypothesis for her connection to Trish and me since it is different than our connection to Joe.  Meagan and Barbara are cousins to each other.  They have a private tree and did not reply to messages.  So I have no idea how they connect.  Ann has a small tree but all of the people in her tree live in the US.  I have not messaged her at this time.

Cluster 7

Cluster 7, shown in figure 23, has several known cousins in it. 

Carl is a known second cousin on my Dad’s father’s side, and Carol and Andy are known second cousins twice removed. Our Barry family came from Kilkenny, Ireland.  Edward Barry married Pauline Fröhlich from Baden after both families had immigrated to Evans, Erie, New York.   Figure 24 shows the WATO tree for this side of my family.

With both a second cousin and second cousins twice removed the generation indicator in Genetic Affairs AutoKinship setup becomes very important.  If Carl and I are set to generation 0, then Carol and Andy would be -2 since twice removed is 2 generations past Carl and me.  Figure 25 shows the cluster 7 match tab.  It is worth noting that Carol’s and Andy’s setting is based on where they are in relation to our common ancestor and not on when they were born. Their births are both within in a few years of my daughter’s birth.

The AutoKinship cluster for cluster 7 is shown in figure 26.

Looking at the information the matches provided on their MyHeritage post Laura has ancestors in Nova Scotia and Prussia.  Richard has ancestors from England, several counties in Ireland and Newfoundland, and Mae has ancestors from England and Ireland and specifically from County Kilkenny in Ireland.  Based on immigrating information for the Barry family, specially not finding any passenger list to the United States, a cousin’s family that immigrated through Canada, and several DNA matches that live in Ontario, the Newfoundland and Nova Scotia are not surprising. Our hypothesis is that the family immigrated to Erie, NY, a short distance from Buffalo, an international entry location, after arriving by ship from Ireland to Canada.  Since Ireland to Canada would have been within the British Commonwealth there would have been no passenger lists for the journey. 

Conclusion

First there was a DNA match.  Then shared matches gave a hint to the family connection.  A triangulated match provided a second hint.  Next AutoClusters grouped these shared matches together to hint of the relationship between them.  And now AutoKinship provides the biggest hint by suggesting how the family tree is connected!

1. Trish has given me permission to use her real name. No other living people are identified by their real names in this blog.

The other day several of us where having a discussion about AutoCluster, which employs In Common With (ICW) matches, and AutoSegment cluster, which employs overlapping segments and triangulated segment.  Triangulated segments, where you and some of your matches share the same segment on the same location of a particular chromosome, indicate that you share a common ancestor. A segment of DNA can only come from one ancestor. Then it’s a matter of determining which ancestor gave you that segment.

ICW clusters are groups of matches that share many if not all matches but they do not necessarily share one common ancestor.  The question then came up if matches are in a cluster together, doesn’t that automatically mean they all share a most recent common ancestor (MRCA).  Like so many things, it depends. 

In many cases I’ve been able to classify an ICW cluster to a specific grandparent or great grandparent.  But then there are some where there’s a mixture of generations, a great grandparent and that person’s parents, for example.  Those seem to be the ones that I see most often.  There is a common ancestor, but not everyone in the cluster has the same most recent common ancestor.  Recent being the key word there.

My family is such that I have a large number of unknown DNA matches.  I’m an only child.  My parents did not do DNA testing.  My closest known cousins are 2nd cousins.  Most often I’m looking for fourth or more distant cousins. 

Maternal 3rd and 4th Great Grandparents

The AutoCluster in figure 1 is from 23andMe.  I found person A early on as a DNA match.  She has tested on several sites.  We emailed back and forth, and I was able to add her to my tree.  The next one I found on 23andMe was person E.  We also emailed, and I was able to place him in my tree.  Filling in other descendants from the same line with the help of cousin’s trees I had all the others in my tree before they showed up as DNA matches.  A mini tree that shows how they are all connected to me is in figure 2.

This is my mother’s mother’s side of the family.  I’m circled in the tree in figure 2.  My grandmother, Louise Wolff, was the daughter of Jacob Wolff and Anna Marie Briel. Both were born in Marburg, Germany and immigrated to Richmond, VA.  A number of Anna’s cousins on her mother’s side had already immigrated to Richmond.  In the tree Anna Briel’s parents were Phillip Briel and Elizabeth Schaaf, my 2nd great grandparents. All but one of the matches in the cluster descend from Phillip and Elizabeth.  Person H descends from Elizabeth Schaaf’s parents Matthaus Schaaf and Anna Kuntz.

I like to use the Cluster Auto Painter (CAP) and add my cluster results to DNA Painter.  Figure 3 shows the segments from this cluster.

The segments are labeled the same as in the cluster (figure 1) and the tree (figure 2).  Each segment of DNA that you inherit comes from only one ancestor.  Most of the time I name them for ancestor couple because I don’t know which of the couple gave the segment to me.

The segments on chromosome 10 and 12 are where person H matches me.  Because the MRCA between H and me are my 2nd great grandparents, I would move those to a new group named for Matthaus Schaaf and Anna Kuntz.  Then I’d change the cluster name for the rest of these matches to Phillip Briel and Elizabeth Schaaf. Clearly A, B, C, and D on chromosome 10 got that segment that they share with me from my 2nd great grandmother, Elizabeth Schaaf, and she got it, as did H from her parents.  I name my groups based on couples, and I have no way, at the point, to tell if that segment came from Elizabeth’s father or her mother.

How was I able to figure all of this out?  First one of my Schaaf 4th cousin has been researching and documenting the family a lot longer than I have.  A lot of the information in my tree came from her research.  When I noticed a match to person A first on FTDNA, I emailed her.  That helped me fill in some of the living people in her part of the family.  Person E matched on 23andMe, and because of his triangulating with A and me, I knew he was in this part of this same family.  I messaged him, and he helped me fill in his family.  When person B appeared I knew right away where she fit because her mother and mine had been good friends.  So really it came down to having a good, filled out tree and matches who replied and shared information with me.

Paternal – Somewhere on the Byrnes line

I don’t always have such luck in getting replies to messages.  Figure 4 shows a perfectly filled AutoCluster from 23andMe that my paternal 2nd cousin, Trish, is in.

The 3 others in the cluster are a father, his brother and his son.  The son was the first to show up as a match to me, sharing 36 cM.  According to the shared cM project 36 cM is the average for 4th cousins, so that would be around the 3rd great grandfather level.  His father also shares 36 cM and his uncle shares 39 cM.  He’d added a greeting on his page and wanted people to message him.  He also indicated that he lives in the same city where I grew up.  I messaged him 3 years ago but never got a reply.  About a year later his uncle showed up as a match.  I messaged him 2 years ago and again no reply.  By now Trish had tested and I could see that they matched her, so I knew it was on my father’s mother’s side.  The father only showed up recently, and I messaged him last week.  He also added 3 ancestor surnames.  Unfortunately the surname of my matches here is rather common.  I tried looking for each of them on Ancestry and found over 900 members with the same name.  Then I tried looking for trees with combinations of their surname and the 3 surnames the father had listed.  Still that didn’t help.  

The 3 of them share 2 segments of DNA with me.  They triangulate with Trish on chromosome 17.  I know that triangulated segments with Trish could be Byrnes, Fenton, Lillis, Shannon or O’Brien.  I’ve not resolved which is the chromosome 17 segment.  The other segment that the 3 of them and I share is on chromosome 1, see figure 5.  I do know something about that segment!  Four years ago I found 6 people on GEDmatch that triangulated with each other and me on that segment and emailed them.  I heard back from several of them.  One of their grandmother’s was a Byrnes, and several of them had ancestors from County Galway near the County Roscommon border!  Thomas Byrnes, Trish and my great grandfather was from County Roscommon, but we don’t know exactly where in Roscommon.  This gives us a hint to where he lived. I’ve not been able to find a baptismal record for Thomas, so I know he lived in an area where the baptismal records have not survived.  All I can say for this cluster is that I know at least 1 of the segments is on our Byrnes line, and it’s likely around the 3rd great grandparent level or more distant. Since Trish and my MRCA are our great grandparents, there are at least 2 different MRCA in this cluster.

Dave’s Paternal Grandmother’s Line

My husband Dave has a large number of known cousins on his paternal grandmother Marti side, and many of them have done DNA tests. Dave’s Aunt Mary worked on the family tree for many years, and we can trace back several generations.  Figure 6 shows one of Dave’s AutoClusters from MyHeritage.

At first I thought this was going to be similar to my Schaaf one since there’s a combination of 2nd and 3rd great grandparents.  But as soon as I drew out the tree I knew something was different here.  Figures 7 and 8 show the trees for this cluster.

Dave’s paternal grandmother Harriett’s father was Jacob Marti, son of Jacob Marti and Anna Fritz.  The elder Jacob’s parents were Adam Marti and Elizabeth Schnell.  Now the problem coms in that match K descends from Veronica Stamm, who is Anna Fritz’s mother.  After Veronica’s husband, Johann Fritz died, she remarried and had daughter Rose, who was a half sister to Anna Fritz.  Match K descends from Rose.

Dave and matches B through H MRCA are his 2nd great grandparents, Jacob Marti and Anna Fritz.  His MRCA with matches A and J are Jacob’s parents, Adam Marti and Elizabeth Schnell, and his MRCA with match K is his 3rd great grandmother, Victoria Stamm.  Person K matches B through H with Victoria Stamm as their MRCA.   All of this would be very well as long as K doesn’t match A or J.  However, K does match A. There has to be some more distant connection between Victoria Stamm’s family and the Marti family.  Matches A and K share 32 cM and do not triangulate with Dave. From the shared cM project 32 cM would be in the 4th to 5th cousin or more distant range. Veronica Stamm was born 1811, so the MRCA ancestor here is the 1700s.  All of these families were living in the same village in Switzerland at that time, so it’s quite possible that there were other earlier marriages in the family that we don’t know about.

Conclusion

This started from a question about whether or not all the matches in an AutoCluster were from one most recent common ancestor.  In my experience and the examples I have shown here, they are not.  There is a family line, such as the paternal grandmother, that all the matches follow, but there are typically several generations of ancestors present in the cluster.  How do you figure out the exact connections?  What I’ve found is having a detailed tree, matches that also have detailed trees, as well as matches that will reply to messages and share family information with you are important to helping to find that common ancestor.

Considering the fact that the clustering was performed using shared matches, this conclusion perhaps should not be a surprise. Shared match data is usually a mixture of DNA matches that share the same or another segment as compared to you. However, the AutoSegment ICW, which is available for FTDNA and 23andMe, and AutoSegment for GEDmatch, which employs triangulated data, looks for overlapping segments that are on the same side. Therefore, by using these clusters, we should be able to obtain clusters of matches that share the same or several DNA segments and therefore share a common ancestor. The AutoSegment ICW clusters will be explored in a future blog.

Both Dave and Trish have giving me permission to use their real names.  All other living people’s names are hidden for privacy.

GEDmatch now offers the improved AutoClustering tools that will generate AutoTrees from the gedcoms of DNA matches, and if there are high enough DNA matches with extensive trees, additional trees might be generated by AutoPedigree. All of these new features were developed in collaboration with Evert-Jan Blom of Genetic Affairs and are available from the Tier 1 features on GEDmatch. One of these is ‘Clusters, Single Kit input, Basic Version’ but it’s considerable more than the basic cluster GEDmatch used to offer. Another way of using the new AutoTree feature is based on using tag groups in the Tier 1 ‘One-to-Many Comparison Beta.’ Both of these will be examined here.

Clusters, Single Kit input

The first of these is the ‘Clusters, Single Kit input, Basic Version.’ There are now more input parameters than there used to be which allows more control over the results you obtain. The opening screen for the “Clusters” item in Tier 1 is shown in figure 1.  The “Work Flow info Toggle” gives suggestions on running your AutoCluster and AutoTree.  First you’d want to run the cluster, next go back to the entry screen and run the Auto Tree, and finally go back and run the segment data. 

I usually think of the values that appear in the opening screen as a bit of a default for my first run.  Number of kits is set to 100.  Other options are 250 or 500 kits.  The lower threshold is set for 15 cM, which is what I’d likely use.  But the upper threshold is 50 cM.  I’m not sure that the range 15 – 50 cM would be very useful for most people.  I like to set the upper threshold based on what matches I want included in the cluster or which ones I want to exclude.  For example, I’d want to include cousin matches, but most likely not want to include siblings or nephews/nieces.  The information from the ‘i’ button on ‘minimum overlap’ is shown in figure 2.

This minimum overlap is related to ‘Overlap’ seen in the One-to-Many comparison.  Figure 3 shows the One-to-Many for Judy, my sister-in-law.

Overlap numbers that are less than 100,000 are colored various shades of pink or red.  Judy tested at 23andMe.  Her brother tested at both 23andMe and at Ancestry.  Notice how his Ancestry migration – F2 -A is pale pink whereas his 23andMe one is over 100,000.  Sister 1 only tested at 23andMe, but sister 2 tested at both Ancestry and 23andMe.  All the matches from 23andMe are over 100,000 overlap whereas the ones from Ancestry are not and are pink or red.  I definitely want to include matches that tested at other companies in my analyses. To me this is one of the huge benefits of GEDmatch that I can compare matches from different testing companies and especially from Ancestry since they do not provide a chromosome browser.  Looking at this list tells me that 40,000 is the value I want to use here for Judy’s data.

‘Include Segment Detail’ “performs 1-to-1 on all pairs of kits. Includes a Triangulation check.  Generally more accurate results,” see figure 4.  When this analysis is run a triangle appears in matches that triangulate.  ‘Auto Tree’ option first will add a tree symbol to your cluster and allow you to see the match’s gedcom pedigree.  However, if there are high DNA matches with extensive trees you can also get AutoPedigree.

The ‘Cut off Year’ refers to the earlier date seen in the tree.  The default is 1700 CE, but it can be lowered to 1500 CE.

Example

Using my sister-in-law, Judy’s¹ GEDmatch data I wanted to include Sue, her 3C1R.  Figure 3 shows Sue matches Judy with 92.8 cM in the 1 to many match list.  I want to include all the various testing companies so I set the overlap parameter to 40,000.   Using 100 kits and running the cluster from 15 cM to 100 cM should include her, but it didn’t.  The range of matches that were in the clusters were 26.4 to 27.1 cM.  It appears that GEDmatch is using some modification of the clustering algorithm that they used previously which favored the smaller cM values.  After trying several combinations of values I obtained a cluster with Sue, Judy’s 3C1R, by using 25 to 500 cM and 40,000 overlap with 100 kits. It’s important to view the cluster order ‘by Cluster number’ in order to get the cleanest results.  The clusters for this analysis are shown in figure 5.

One thing you might notice are clusters that are more sparse as compared to other clustering results. This is because the clustering settings have been changed to generate larger clusters. This should improve the identification of common ancestors when they would not be found if the clusters are more condensed.

Next I ran the Auto Tree.  In Chrome hitting the back arrow on the browser twice will bring up the original screen with the parameter I’d used intact.  Often several more matches are shown in the clusters with Auto Tree, and a tree icon is shown for matches that have a gedcom on GEDmatch.  See figure 6.

Now both Sue, the 3C1R and her daughter, Mary are showing in the cluster, whereas Mary wasn’t showing in the earlier cluster in figure 5. Neither of them has a gedcom, but I know  they are related to Judy through her second great grandparents on her paternal grandmother’s side.  There are a number of trees here to explore. I recognized D. McFury surname’s as I’ve seen it with another match on 23andMe.  There was no tree on 23andMe so I was very interested to see the gedcom and hopefully figure out the connection to Judy.  Part of the gedcom is shown in figure 7. Ellen Talbot is D. McFury’s 2nd great grandmother. Judy’s 2nd great grandmother is Jane Talbot who married Richard Coleman.  This match is on her Talbot 2nd great grandmother’s line.  Two things in this gedcom jumped out at me.  Ellen Talbot was from County Kilkenny, Ireland, which is where my Barry family lived. Kilkenny is the one place in Ireland that I’ve done extensive research.  The other is that Ellen’s daughter Nancy Anna died in Kalamazoo County, MI.  Judy’s 2nd great grandfather, Richard Coleman lived in Kent County, MI and is buried in Ada, MI which is only about 70 miles (113 km) from Kalamazoo. In fall 2019 I spent a day in Kent County researching the Coleman family and plan a trip back there when I’m visiting in MI and the libraries are open again.

D. McFury shares 35 cM with Judy.  Looking at the Shared cM Project on DNA Painter that could be 3rd cousin or perhaps more likely 4th cousin.  Now I need to build out the tree and try and find the most recent common ancestor between Jane Talbot and Ellen Talbot.

At the top of the screen with the clusters and trees there are several new options, shown in figure 8.

Selecting the ‘AutoTree AutoCluster Analysis’ tab brings up an explanation of how the AutoCluster and AutoTrees are calculated.  At the bottom of this explanation is a table.  See figure 9. 

In the table each cluster is listed and any information that has been obtained from the cluster.  Looking at cluster 3 the information for the tree icon indicates that clicking on the tree icon will display the tree connecting the matches in the cluster.  This tree is shown in figure 10.  Location indicates there are 5 locations for this tree.  Clicking on the gedcom icon will download a gedcom for this cluster. Surnames lists all the surnames found in gedcom for the cluster.

Below the tree is a list of all the people and locations in more detail, shown in figure 11. The table shows the gedcom number found on GEDmatch, the names of the ancestors, their birth and death location, the descendants, their GEDmatch kit number, their name and the shared cM.  All the kit numbers and living people are blocked out for privacy in the figure.  Clicking on the location brings up Google map showing the exact location!

Below this table are detailed tables of all the locations found and the ancestors who lived in them for the matches and the primary kit.  Figure 12 shows which ancestors from this cluster lived in Grand Rapids, MI, as well as Judy’s ancestors who lived there.  These tables add a great deal to the information I’d seen in the gedcom. It lists all the information in one place, as opposed to spread out in the gedcom, and it does the exact comparison of specific locations that are common to the different gedcoms.

Next I ran the ‘Include Segment Data’.  The clusters go back to what was seen for the original clusters but this time with triangles in the clusters to indicate triangulated matches as shown in figure 13.

Sue, who is 3C1R, triangulates with other known cousin who are descendants of Judy’s paternal grandmother’s side of the family.  These are seen in the red cluster. The  two matches in the first orange cluster triangulate but I do not know how they connect to Judy’s family.  The green cluster is on her maternal grandfather’s side, as is the pink cluster. The brown cluster is on her maternal grandmother’s side.  The mustard cluster is where D. McFury is and would be on paternal grandfather’s side.  Both the turquoise and orange clusters are on maternal grandfather’s side of the family.

Tag Groups

Another method to get AutoTrees and potentially AutoPedigree is based on tag groups. From the home page I selected ‘View/Change/Delete your profile (password, email, groups),’ see figure 14. Then I selected ‘Tag Group Management,’ and figure 15 appeared.

Since I wanted to make a tag group for Judy using her matching kits that contain gedcoms I used Judy1 for the description. I selected green for the color and clicked ‘Add Tag Group.’ Next I went back to the main page by selecting ‘Home’ and select ‘One-to-Many DNA Comparison Beta’ under Tier1. The One-to-Many entry screen is shown in figure 16. After entering Judy’s kit number, I selected 2500 as the ‘limit’ for the number of kits to include and clicked ‘Search.’

Once the list of matching kits is displayed, I clicked ‘Select all with GEDComs.’ That resulted in about 250 matches. Then I selected ‘Visualization Options’ which brings up the screen shown in figure 17, and there I selected ‘Tag Groups’ which let met add the 253 matches that had gedcoms to Judy1 tag group.

The end results of adding the 253 matches to Judy1 tag group is shown in figure 18.

I next went back to the screen in figure 17 and selected ‘Clustering’ which brings up the screen shown in figure 19.

Since I’ve only included kits that have gedcoms I selected ‘Auto Tree’. I can also select ‘Include Segment Detail’ if I want to see which of the matches triangulate. An ‘Overlap’ of 100,000 would likely only include kits that were tested at 23andMe, which is where Judy tested, so I again changed that to 40,000 in order to include all the testing companies. I’ll used 15 – 1000 cM for the range and selected ‘Cluster’. The top of the resulting screen is shown in figure 20 and the top part of the cluster is shown in figure 21.

Next I looked at the ‘AutoTree AutoCluster Analysis’ tab to get the list showing common ancestors and common locations, as shown in figure 22.

Notice in figure 22 how all of the clusters have locations listed because they all have gecoms, but not all of them show common ancestors. Very often finding ancestors of DNA matches that live in the same location as your ancestors did can help to either make a connection or at least help identify which part of your family is likely in the connection. I decided to look at the tree for cluster 22, which is shown in figure 23.

Looking at the list of common locations associated with cluster 22 and this AutoTree points to the common ancestors that are listed in the table below the AutoTree and shown in figure 24.

Jane Lutey and Simon Hocking are Judy’s 5th great grandparents. They are also Ann’s and her brother, Mark’s 4th great grandparents. In the AutoTree shown in figure 23 Martha Murrish and James Hocking are Judy’s great grandparents. Judy’s mother was Virginia Hocking. Virginia’s paternal ancestors were miners in Cornwall, England until the mines closed. Some of the miners immigrated to Minnesota, which is what Judy’s great grandparents, Martha Murrish and James Hocking did. Other miners immigrated to Australia, which is what Judy’s 5C1R, Ann’s and Mark’s great grandparents, John Hocking and Margaret Oats, did. John and Margaret are the parents of Caroline Jane Hocking shown in the AutoTree in figure 23. There is a second Hocking line that connects Judy with Ann and Mark, which makes the shared cM value higher than would be expected for a 5C1R. Ann was one of the first matches on GEDmatch that we found, and we’ve been emailing ever since.

Summary

There are two exciting new features on GEDmatch Tier 1 that were developed in collaboration with Evert-Jan Blom of Genetic Affairs. Both of them use your DNA matches and specifically the matches that have gedcoms to find common locations and common ancestors. Both of these techniques have been described in this blog post.

1. Judy has given me permission to use her real name. All other names of living people are either hidden or fictitious names have been used. All kit numbers have been hidden.

For over two years Genetic Affairs has provided the AutoCluster reports in the HTML format. For some of these reports, it would be interesting to extract the DNA match information as well as the shared matches. This information could provide a head start for people that would like to run a manual AutoCluster analysis. Imagine transforming an old Ancestry analysis to Excel, add the most recent (shared) matches, and re-run the AutoCluster analysis using CSV files.

Another scenario would involve the MyHeritage AutoClusters analysis which does not allow you to change the maximum and minimum cM setting. It runs from 400 cM down for about 100 matches and to whatever minimum cM that gives. Recently I was looking at my sister-in-law¹, Barb’s matches, and noticed that she had a known first cousin once removed (1C1R) who had tested. I was very interested in seeing her shared matches since we know something about Barb’s mother’s side of the family. But to my dismay, this match, Grace, matched Barb at over 400 cM and so wasn’t included in the cluster. Well, I can manually look through all 90 of her matches, or normally I’d think of Leeds method, but I know this is Barb’s mother’s father’s side of the family, and I’m more interested in who the matches are and how they connect to others in the family.

I wanted to follow Grace’s matches and compare them with the rest of the AutoCluster. There’s a new feature on Genetic Affairs that lets me do just that. ‘Transform AutoCluster HTML’, as shown in figure 1, lets me take the existing AutoCluster HTML report and convert it to an Excel file with 2 tabs. One tab has the matches with the name of the match and the cM they share. The second tab has the shared matches for all the people in the match list. Using this I can add Grace and her matches to the match list with their cM values and save that tab as a new CSV file. Then I can add Grace and her matches to the shared match list and again save that tab as a new CSV file. Next, I can run a CSV AutoCluster (as explained in an earlier blog post) using my two new CSV files and generate a cluster that contains Grace and her matches along with all the matches that were in the original MyHeritage AutoClusters.

Looking at the MyHeritage AutoCluster I noticed that it went down to 30 cM. So I’d want to find Grace’s matches that also go to 30 cM to be consistent. Looking at Barb’s matches and selecting Grace I could then see her shared matches with Barb. The shared match list on MyHeritage doesn’t always go by the highest match first and appears to have highest matches to Grace in some cases before the highest shared matches to Barb, so I went down the list making a new Excel file of the match names and the cM shared with Barb. I went down to the point where matches I was seeing were less than 20 cM. At that point I skimmed the rest of the list for any matches over 30 cM and didn’t see any. So I was pretty sure I’d found them all.

Next I downloaded the Excel file from Barb’s MyHeritage AutoCluster, see figure 2, so I could add the new matches I’d found with Grace. Even though Barb and Grace had 90 shared matches only 10 of them were over 30 cM. Then I looked at those 10 matches to see if they matched others in this little group of 10. A couple of things I noticed that turned out to be important. One is to the copy the name exactly as they listed it in MyHeritage. You and I might think that John Smith and John SMITH are the same, but the comparison won’t see it that way. The other problem I had was in writing Robert F. Jones but he had Robert F Jones without the period after the initial. Thus it took me a couple runs, because of these minor, careless mistakes to get the AutoCluster that I wanted.

Figure 3 shows the new AutoCluster with Grace in cluster #1 along with her matches. As it turned out 2 of those 10 matches did not have any shared matches over 30 cM so they were not included. That first cluster is all on the Barb’s mother’s side of family side as well as the matches in the second cluster.

Summary

Being able to convert my old AutoCluster to an Excel file and then adding new matches is a great new feature. It will be a lot easier to update older clusters with additional information without having to redo the entire cluster as a manual csv cluster.

1. Barb has given me permission to use her real name.

A year ago I was in Salt Lake City researching in the Family History Library and getting ready for RootsTech to start.   I was very fortunate to be able to attend RootsTech for the first time.  One of the highlights was getting to meet Leisa Byrne and Jonny Perl in person!  We’d been working on the DNA Painter user group for over 2 years and ‘chatting’ by email. Here’s a photo of us at the DNA Painter booth last year. Jonny is on the left, Leisa’s on the right, and I’m in the middle.

This year with the pandemic RootsTech is all virtual and FREE!!!  From numbers I’ve seen over 430,000 people have registered so far. If you’ve not registered yet click on RootsTech to go to the registration page.

Also on the registration page are the Main Stage Streaming Schedule and the list of All English Sessions.  For sections in other languages there will be subtitles in English on the screen. So we should be able to enjoy any of the presentations in a language that we are comfortable with. All of the presentations are prerecorded and will be available for a year, so you’ll have plenty of time to view as many as you want.  The virtual Expo Hall opens at 5:00 PM Mountain Standard Time (MST) Wednesday, February 24.  Many of the exhibitors will have special sales associated with RootsTech Connect, and there will likely be games to play at the booths just like there were in Salt Lake City last year.  There will be FamilySearch employees and volunteers available during the conference for questions.  It will be in the ‘Ask Me Anything’ tab on the RootsTech page.  I’ll be there helping there on Thursday and part of Friday. Because this is a world-wide event you will be able to access all the conference features and the volunteers all day and all night until 9:00 PM MST Saturday, February 27 when the conference ends.   

I have three short presentations on DNA Painter that will be in the DNA Booth.  They will be shown for the first time on Saturday: ‘Getting Started with DNA Painter’ is at 5:00AM MST; ‘Adding My Heritage Data to DNA Painter’ is at 12:00 MST; and ‘Adding 23andMe Data to DNA Painter’ at 2:00 MST. I plan to be online to answer any questions that come up during that first showing for each of these.

This should be a very exciting conference. I’m looking forward to it!! Hope to see you there!