AutoClustering My Wife’s Aunt Lorraine’s AncestryDNA Results

AutoClustering is working well. I have previously run an autocluster report for Lorraine’s sister Virginia:

Here are some comparisons:

Virginia’s number of 4th cousins or closer and her SAHs are as of today and I did her autocluster about a month ago. I changed the upper limit for Lorraine to 600 cM because I was having trouble identifying some of the clusters. I had set the lower limit down to 12 because I was looking for distant Butler relatives.

Lorraine’s AutoCluster

Since the time I ran Virginia’s autocluster, the clusters have been arranged differently to show connections between the clusters. This has been a very helpful innovation.

Adding Names to Lorraine’s Clusters

I’ll start with a table:

This table starts with each of Lorraine’s clusters. That is followed by the top match name in the cluster and the amount that top match has in cMs. I just need to fill in which grandparent side each cluster belongs to and which common ancestors the cluster seems to point to,

Lorraine’s Ancestors

These are some of the ancestors that I will pick from:

I am interested mostly in the top part of the tree. The bottom part is where most of the matches will be. The bottom represents the maternal French Canadian side.

Name Those Clusters

To get the ball rolling, I’ll start with Fred. I have have been in touch with Fred who a second cousin on Lorraine’s Pouliot maternal grandparent side:

Turns out that is Lorraine’s largest Cluster:

That’s a lot of Pouliot’s. These could be all descended from a certain common ancestor along the Pouliot or Fortin Lines.

The Second Largest Cluster: LeFevre

Sandra shows up a lot in my analyses. Here she is:

Sandra is also in Lorraine’s Cluster 1:

Skipping Down to Clusters 34 and 35: Kerivan and Butler

These are the Clusters I am more interested in.

Clusters 34 and 35 are the purple and tan Clusters. They show a lot of connections between those two Clusters.

Cluster 34 – Kerivan

Amanda is the first person in Cluster 34, but she has no tree. Donna is the third match in Cluster 34. Here is the paternal side of her tree:

Turns out Donna is a second cousin to Lorraine also:

Cluster 35 – Butler

The top match for Lorraine in her Cluster 35 is Barbara. Barbara has a short tree:

Here is Barbara in a tree with other Butlers:

 

 

She shows up as Lorraine’s 2nd cousin. What is interesting about Cluster 35 is that it includes Butlers from Cincinnati. My guess is that they are related this way:

There is a branch on the left of Cincinnati Butlers headed by a George Butler born about 1826. My wife’s ancestor Edward Butler was also living in Cincinnati for a while. His first son was named George – perhaps after the Cincinnati Georg Butler. I haven’t worked out all the details yet, but the DNA is showing a definite connection.

Lorraine’s Cluster Summary

Here are the bones of Lorraine’s clusters:

It is possible that there are 33 French Canadian Clusters and 3 Irish Clusters. I would have to look at all the clusters to be sure. However, as I scan the clusters, it looks like that could be the case. Here is my best guess:

That means that finding the 1/2 Irish side among the French Canadian half, is like looking for a needle in a haystack.

Comparing Lorraine’s Clusters to Virginia’s Clusters

Here is a comparison of the two sisters’ clusters:

This shows that Virginia split in two both of Lorraine’s Clusters 34 and 35. Here are some of the clusters that I tried to identify for Virginia:

So with that comparison and looking at some of Lorraine’s Shared Ancestor HInts at AncestryNDA give me this cluster chart for Lorraine:

It is possible that Cluster 16 is wrong based on the placement within Pouliot’s.

Summary and Conclusions

  • Lorraine’s AutoCluster Chart looked like a mess at first but seemed to sort out between her four grandparents.
  • I didn’t look at why there were so many matches between the Kerivan and Butler Lines.
  • I compared Lorraine’s Clusters to her sister Virginia’s Clusters
  • The new ordering of clusters makes a lot of sense and makes the identification and organization of clusters much clearer.

 

 

 

 

 

A New Look for AutoClusters

I ran an AutoCluster and was surprised by the new look. I ran autocluster for my sister Lori:

The old look organized the clusters by how many were in the cluster. This newer, more logical approach organizes the clusters better to take into account the little gray dots.

Lori’s First 6 Clusters

It seems like these clusters could be related. There are four gray boxes connecting the small Cluster 2 to Cluster 1. There is one gray cluster connecting the red Cluster 3 to the green Cluster 2. And so on. I can tell that Lori’s orange Cluster 1 contains many 2nd cousins on my Hartley side and slightly more distant Snell relatives.

Lori’s Irish Clusters 8 Through 18

This has taken a lot of the guesswork away. I like that.

  • The top left Clusters 8 and 9 (green and blue) contain some of my matches with Frazer ancestry.
  • Green Cluster 12 has someone who I believe matches on a McMaster/Frazer Line.
  • Cluster 15 between purple and pink is an important match that goes to my Clarke/Spratt Lines. They also match on McMaster. They are swimming in a sea of what I believe to be other Irish matches.
  • The last lower right cluster contains the Spratt name where I have a brick wall.

Here is my Irish portion of my tree:

The clusters virtually mimic my tree which has Frazer at the top and Spratt at the bottom.

Lori’s Known and Unknown Clusters

Just by looking at Lori’s clusters I can tell the following:

  • Clusters 1-6: Paternal Hartley 2nd cousins back to Massachusetts Colonial times
  • Cluster 7: Maternal Nicholson/Ellis [Sheffield, England to Philadelphia]
  • Clusters 8-18: Frazer, McMaster, Clarke and Spratt from Ireland
  • Cluster 27: Maternal Lentz/Nicholson
  • Clusters 28 and 29: Maternal Rathfelder ancestors back to Latvia

That leaves just Clusters 19 through 26 which are not obvious. That leaves only 8 unknown clusters.

Comparing Lori’s Clusters to My Mom’s and My Siblings’ Clusters

Here is how Lori’s clusters compare to her mom’s:

Cluster 3 was a surprise as that was in Lori’s paternal Hartley grouping above and it matches one of my mother’s clusters. I’ll won’t assign that as a maternal or paternal cluster for now.

Here is what I get when I compare Lori’s four other siblings who have tested at AncestryDNA:

 

  • I gave Lori’s paternal Massachusetts grouping a blue color and her paternal Irish grouping a green color.
  • Lori has two new clusters where she doesn’t match anyone else’s clusters. These are Clusters 19 and 25. I assume that they are paternal clusters as they don’t match with her mother’s clusters.
  • My ancestors from Ireland were Protestant and married Protestants for the most part. This resulted in some inter-marriage of families. I assume that this is why Jon’s Cluster 6 is reflected in Lori’s Clusters 8, 9 and 10. Sharon’s Clusters 11 and 18 each show up in more than one of Lori’s clusters, etc.

Fleshing Out Lori’s Hartley and Frazer Mega-Clusters

Hartley – Colonial Massachusetts

The Hartley Clusters in blue seem to go quickly from 2nd cousins to Colonial Massachusetts. I still haven’t looked at Cluster 3 which is oddly shared with my mother. I suspect that it is indeed a paternal cluster as it is a lower numbered cluster for my sister and Jon than for my mother. Also there is a connection between Lori’s Cluster 3 and her Cluster 2.

Frazer – Ireland

Cluster 17 is interesting as the match with Keith goes back to two McMaster common ancestors:

With this information, I could go back to Sharon’s Cluster 15. I see that Keith is not Sharon’s largest match in her Cluster 15, but he is in that Cluster, so I can fine tune Sharon’s Cluster 15 to McMaster.

Lori’s New Clusters 19 and 25

There are only two people in Cluster 19. Their trees are not extensive and the match numbers are not impressive. I will just call this cluster paternal for now.

Cluster 25 and Peter

Peter is interesting as he shows one of his grandparents from Australia. If this match is on my Hartley side, that could go back to my English Hartley’s. I am interested in Peter’s Howarth ancestry as it could be linked to my Howorth ancestry from Lancashire, England. I just need to build out Peter’s tree

Peter’s Howarth Line goes back from Australia then to Ireland then to Rochdale, England where my Howorths were from. However, he also has an Irish Whiteside in there. I may be related to the Whiteside family. At this point, I’m leaning toward Howarth/Howorth in Rochdale, but I’ll just say it’s a paternal match for now.

Done with Lori’s Clusters – For Now

This is about as much as I have patience for right now. I had originally thought that Sue at Lori’s Cluster 26 was Massachusetts Colonial, but Sue uploaded her results to gedmatch and that showed that she matched us on our Frazer side.

Summary and Conclusions

  • Lori was the first autocluster that I have looked at with the new mega-clustering feature. This put our birds of a feather ancestors together.
  • This new rendering of the clusters helped me to see how my paternal Hartley and Frazer ancestors related to each other.
  • Two small maternal clusters showed relationships which confirmed a suspected Latvian ancestor cluster.
  • Cross-referencing Lori’s clusters to my mom’s and her siblings’ clusters helped to fine-tune these clusters.
  • Lori had two unique clusters. However, they were difficult to nail down past being paternal clusters.

 

Comparing Four Siblings’ AncestryDNA AutoCluster Results

In my previous Blog, I compared my AncestryDNA AutoCluster results to two of my siblings, Jon and Heidi. In this Blog, I will look at Sharon’s results:

For the previous three siblings, I looked at matches between 25 and 600 cM. For Sharon, I lowered the upper limit to 300 cM. This was to eliminate my 1st cousin’s daughter’s results.

Here are my sibling comparisons:

By bringing Sharon’s upper limit down to 300 cM, I eliminated my father’s first cousin, a daughter of a maternal first cousin and a paternal 2nd cousin. However, many of my paternal second cousins have tested.

Comparing My Clusters to My Three Siblings’ Clusters

Rather than trying to figure out each of Sharon’s clusters, I will compare her clusters to mine. To do this, I compared my clusters to Sharon’s in MS Access. This just saves time. The Query in Access looks like this:

 

I connected our two tables by the identifier. This is the identifier of the different AncestryDNA matches. Then I chose my clusters and Sharon’s clusters and I grouped them to get rid of duplicates. That Query resulted in this:

This is a lot easier than going through Sharon’s clusters one by one. The above table tells me a few things:

  • 13 of Sharon’s 18  clusters can be identified in my clusters.
  • I split Sharon’s Cluster 1 into my Clusters 1 and 2.
  • Sharon splits my Cluster 21 into her Clusters 3 and 18.

Here is how Sharon looks on my cluster list:

Sharon matches me on my Cluster 32 and 34 where Jon and Heidi did not.

Further Insight on My Cluster 32.

I have two matches in my Cluster 32. Sharon has three.  Of those people, Louisa, in my Cluster 32 has a private tree but told me that we match on Simon Hathaway born 1711 and Hannah Clifton. Sharon’s additional person in her Cluster 13 is Gloria:

Gloria has a fairly good size tree which includes a Hathaway:

I wonder if Gloria’s Florida Hathaway is related to my Massachusetts Hathaway ancestors? To find this out, I need to build out Gloria’s Hathaway Line. Ancestry’s suggestions for Gloria’s tree matched up to Rufus Jefferson Pitts, but then I ran into a snag:

Gloria had Susan Hathaway for Rufus’ mother and Ancestry had Rebecca Pate. Here is the 1880 Census which seems to support the Rebecca theory:

I also found 10 Ancestry Trees. Three had Susan Hathaway as Rufus’ mother and seven had Rebecca Pate. After searching a bit, I found this narrative at Ancestry concerning Rufus’ father, John Gilbert Pitts:

This appears to resolve the discrepancy.  Unfortunately, I couldn’t find out more about this Hathaway family.

Sharon’s Clusters Compared to Her Three Siblings’ Clusters

If I sort Sharon’s Clusters, I get this:

I’ll change this around and compare Sharon to her three siblings:

Sharon’s “new” clusters are 5 and 10. These are not shared by her siblings. Here are Sharon’s clusters sorted by size:

By cross-referencing, I get this:

Sharon’s “New” Clusters 5 and 10

That leaves two clusters to figure out. I’ll start with Cluster 5. Debra on Sharon’s match list has a family tree. However, I can’t tell how she might match. She has ancestors from a lot of the same places as my mother. I can tell that Cluster 5 is maternal due to Shared Matches with my mother.

Sharon’s Cluster 10

This cluster appears to be paternal based on a lack of Shared Matches with my mother. I note that Sharon has a match with Catherine who has  a good tree and is on Gedmatch. Based on Chromosome mapping, I can tell that Catherine matches on our Frazer side. This side has ancestors in Ireland and so does Catherine.

Sharon and Catherine’s match is at the beginning of the Chromosome where Sharon matches Catherine on the Frazer (blue) side. Note that Heidi should match there also. Jim did not test at Ancestry. In fact, Heidi does match Catherine at Gedmatch by slightly more than Sharon. For some reason, Ancestry has shaved some DNA off Heidi and Catherine’s match to just below the 25 cM that I chose for the clusters.

Here is one of Catherine’s Irish ancestors who lived in the vicinity of my Irish ancestors:

Here are the final (for now) results:

Sharon has a lot of Frazer clusters.

More Summaries

It seemed like Sharon and I had a lot of Frazer matches. Sharon had the most proportionately. It would be difficult to deduce much from the maternal side as the numbers are low there. Jon had the fewest maternal clusters. It would be worthwhile to see which clusters only Jon had at some point.

Next up I’ll look at my Mom’s clusters. Then perhaps my other sister’s.

Summary and Conclusions

  • By cross-referencing Sharon’s clusters with other existing clusters, I was able to speed up the cluster identification process.
  • Sharon had two clusters that her other three siblings did not have. One was maternal and unidentified so far. Sharon’s other new cluster was on the Frazer quarter of ancestors and likely goes back Ireland where one of my brick wall areas is on the Clarke/Spratt Lines.
  • I looked at percentages of clusters to see how the siblings compared to each other.
  • I tried to connect genealogically to the Hathaway family to one of the matches in a cluster, but got stuck.

 

 

 

 

 

 

Comparing My AncestryDNA AutoClustering To Two of My Siblings’ Clusters

I looked at my sister Heidi’s AncestryDNA clusters here and my brother Jon’s here. I used the bottom level of 25 cM and top-level of 600 cM for their matches. This resulted in 20 clusters for Jon and 23 clusters for Heidi. This is what my clusters look like at those same levels:

I have 37 clusters. These clusters are proportional to the number of 4th cousins or closer that we have a AncestryDNA.

In my previous tries at looking at my clusters, I chose match levels that resulted in first 5 clusters and then 76 clusters. 37 clusters seems like a good number, plus it will give me a good comparison to my two siblings.

Starting to Identify My Clusters

The thought behind clusters is that a group of clusters probably indicates a group of ancestors that are all along a particular line. I have sorted my clusters by DNA match. I have my top match in the cluster followed by the match amount in cM. Then I have the cluster number, my grandparent line and then common ancestors or other notes:

I continued the cluster numbers down in the order of their match levels. Cluster 17 is out-of-order in a sense. There are only 4 people in the Cluster 17 and one is my maternal 1st cousin’s daughter. So those matches could be on either side of my maternal grandparents.

Cluster 10 and Gladys

Here is how I am related to Gladys:

That’s assuming that I have this tree right. Gladys is my third cousin, once removed. We have a double Frazer ancestry. That makes me wonder how I am related to the other people in Cluster 10:

Ancestry shows me at the same relationship to Gladys as with the rest of the group. However, I’m not so sure about that.

I tried building out Debra’s tree and found some Irish ancestors:

Little and Burns were both from Ireland if I drew Debra’s tree correctly.

Here is John’s tree:

John appears to have two Burns Lines in his tree. So that is something to keep in mind in case this is more than a coincidence.

It looks like my top 5-10 clusters are all on the Frazer side.

Some Relatives from Russia?

My Rathfelder ancestors lived in a German Colony in Latvia. In Cluster 11, I have some relatives that were from a German Colony in Russia about 1,000 miles away from Latvia. I have read of at least one connection between the two colonies. These are my top 20 clusters. I seem to favor the Frazer side as more than half of the clusters are Frazer clusters.

Diving Further Into the Unknown Clusters

The next Cluster is 30. One of the two people in that Cluster, Howard, has a tree with this person:

This looks like the same Hannah that I have in my tree:

Hannah’s grandparents would be Howard and my common ancestors: Samuel Snell and Mary Head.

Cluster 16 and a New Ancestor Discovery

I can see this from this table from AutoCluster:

A note that I had put under my AncestryDNA match Bobby turned out to be helpful.

I have no idea who Seymore is and have no known ancestors in this area of the country. I suspect that we may have common ancestors in England or Ireland. From what I can tell, this match is on my Frazer grandparent side.

My Last Seven Clusters

Here is a summary of all my clusters:

My Clusters Compared to My Brother’s and Sisters’ Clusters

I wold like to see how my clusters compare to my brother Jon’s and my sister Heidi’s. It looks like my matches tend to the Frazer side. The process was a bit annoying, so I took the data files into MS Access and compared them there. I came out with this comparison:

This shows where my clusters are equivalent to Jon’s or Heidi’s.

Here is the same chart by match size:

 

I match Cheryl at 69.1 cM but her Cluster has no match with Jon or Heidi.

This shows that I split Heidi and Jon’s Cluster 1 into two. They are now my Clusters 2 and 1. Likewise, I split Jon’s Cluster 20 and Heidi’s Cluster 15 in two. They are my Clusters 21 and 15. One theory is that I am related on both common ancestral lines and Heiid and Jon are related on only one. My assumption is that my Cluster 15 is related to my Cluster 21. I split Jon’s Cluster 6 into my Clusters 10 and 28. However, I don’t match Heidi on my Cluster 28. I had already determined that My Clusters 10 ane 28 involved the same Frazer couple.

My maternal Cluster 17 is described by Jon’s cluster 8 and Heidi’s Cluster 14. These are not helpful because they are on my mother’s maternal and paternal sides. I need to lower the thresholds so Taylor does not show in the matches. She is a 1st cousin once removed, so she is related on both of my maternal grandparent sides.

Summary and Conclusions

  • I have 37 clusters. I matched Jon on 14 of my clusters and my sister Heidi on 19 of my clusters. This sounds about right as we should match half our our sibling’s DNA and I have more clusters than they do.
  • I liked running AutoCluster with a top cuttoff of 600 cM to get an idea of how to sort the clusters. However, once those clusters are sorted, it is good to lower the top cutoff. I lowered my top cutoff for my next run with my sister Sharon to 300 cM and got good results.
  • I like sorting the clusters by match size. This should put the more recent matches that are easier to identify at the top of the list.
  • I like to compare my clusters to my siblings’ clusters to see where I match amd where I don’t. I was also able to see where my clusters split my siblings’ clusters in two in some cases.

AutoClustering My Sister’s AncestryDNA

It seems like AncestryDNA is best suited for AutoClustering. Which is good, because many people have tested at AncestryDNA. In my previous Blog, I autoclustered my brother Jon. I was able to cross-reference his clusters to ones I had found for myself. In some cases there was no cross-reference. In some cases, my brother’s clusters helped identify my own clusters. In this Blog, I’ll look at my sister Heidi’s clusters at Ancestry.

Heidi’s Clusters look like this:

I have left out the names on the top and left for privacy. I like using 600 cM for a top limit and 25 cM for a bottom limit. For Heidi, this gives her 23 clusters. Heidi has 403 4th cousins or closer. My brother Jon has 381 4th cousins or closer at AncestryDNA and he had 20 clusters using the same upper and lower match limits that I used for Heidi.

Nigel – a Non-Clustered Match

First, I’ll mention Nigel. He is the first one on the AutoCluster Report who is mentioned as not being clustered. I think that this is significant. Nigel matches Heidi at 66 cM. This is a very high match for a 5th cousin once removed. Here is the Shared Ancestry Hint between Nigel and Heidi:

The match is high for our family, but not with other descendants of this couple. As a result, Nigel and Heidi are not in a cluster.

Clusters By the Numbers

By this, I mean that I like to look at the highest matched clusters first. These are easiest to identify. Cluster 1 has the most people in it and the closest matches. This is because I have a lot of second cousins from my prolific Hartley/Snell great grandparents.

Heidi’s Clusters 1, 14 and 7

Here Heidi’s results are below and my brother Jon’s are above. What is interesting is that the top matches in Heidi’s and Jon’s first clusters are the same. However, for the Taylor match, the clusters point to different grandparent lines. This could partially be because Taylor is the daughter of our first cousin. Taylor matches us on both maternal grandparent lines.

Here is a tree with Nigel who I mentioned above:

Taylor is Cindy’s daughter. I find it interesting that there is a Cluster 14 and 7. Cluster 7 is Nicholson, but not Lentz. Cluster 14 is Nicholson and Lentz, but as Cluster 7 is already Nicholson, does this mean that Cluster 14 favors the Lentz side?

Heidi’s Clusters 10, 5 and 2

Heidi already has more maternal clusters than my brother Jon. Gladys is an interesting match. The common ancestors between Gladys and me were both Frazer’s. From what I can tell two first cousin Frazer’s married each other.

Heidi’s Next Three Clusters – More Obscure?

One would expect the clusters to represent more obscure common ancestors as the match levels go down.

Here are the common ancestors for one of the people in Cluster 15 (William McMaster and Margaret Frazer):

This goes back to about 1790, so back to my 4th great-grandparents.

Here are my Parker/Hatch 4th great-grandparents:

They lived in Nantucket and Isaac had a whaling boat repair business there.

Cluster 9 goes into a black hole where I am stuck. This is likely on my Clarke or Spratt Line. Cluster 9 is also Heidi’s 9th cluster by size and already I am getting stuck identifying the ancestors.

That makes sense, though, because Jane Spratt above is my 2nd great-grandmother and I don’t know who her parents were. Two more generations out from Spratt would result in 3 new surnames that I don’t know about (or could only make guesses at).

Heidi’s Clusters 16, 17 and 18

These next three clusters came in order:

Anthony Snell is interesting as he fought in the US Revolutionary War. I don’t have specific common ancestors for Clusters 17 and 18. This brings us past the halfway point for Heidi’s clusters.

More Clusters for Heidi – The Brick Wall Zone

The bottom clusters for Heidi should be in the area where I am stuck on the genealogical paper trail side.

The question marks show that I am not sure who the common ancestors are for the above clusters. I have done some work on Heidi’s Cluster 21 matches. Here is my best shot at finding common ancestors at Cluster 21:

 

Here are the rest of the clusters:

In my brother Jon’s clusters, I only saw two maternal clusters out of his 20. Here Heidi has 7 maternal clusters out of her 23.

Here is how Heidi’s clusters compare with my brother Jon’s:

10 out of Heidi’s 23 Clusters had no corresponding cluster with her brother Jon. Two other of Heidi’s clusters (14 and 11) were not a perfect match with one of Jon’s clusters.

Summary and Conclustions

  • Heidi had about 30% maternal clusters compared to her brother Jon’s 10% maternal clusters
  • It was interesting to look at the specific ancestors that were in the clusters (when I was able to identify them). I was able to identify 10 ancestral couples
  • Many of Heidi’s clusters were not equivalent to her brother Jon’s clusters. This means that it is helpful to look at the different results for the different siblings.
  • Heidi’s clusters offer another piece of the puzzle in breaking down some of my family’s genalogical brick walls.

 

 

 

AutoClustering My Brother at Ancestry

AutoClustering fans are happy that Genetic Affairs has the AncestryDNA autoclustering working again. I ran a report this morning for my brother Jon. I used an upper limit of 600 cM and lower limit of 25 cM. This gave me a manageable 20 Clusters.

I had been trying to get a similar autocluster for myself, but had trouble getting it work for me. First, I notice that there appears to be a connection between Clusters 1 and 2 based on the grey squares.

Clustering By Size

I like to cluster by match size. That means that I sort my cluster list by largest match:

I push the cM arrow twice. This should put the arrow pointing down which will put the larger matches on the top. The highest match in this case is also Cluster 1 with the most people in it. Many of these people are my Hartley/Snell relatives who have tested at AncestryDNA.

After that, I see my Clusters 8 and 4.

Clusters 1, 8 and 4

Cluster 1 is easy. This has many of my Hartley 2nd cousins. They descend from Hartley and Snell. I know one of the more distant relatives in this group descends from the Snell side only. The Snell side gets back to Colonial Massachusetts. My second great grandfather Isaiah Hatch Snell was born in 1837.

The top match in Cluster 8 is my 1st cousin’s daughter:

That would normally only identify this Cluster as maternal. However, in this case, I know that I am related to Otis on the Schwechheimer and Gangnus Lines. These two families lived in a German Colony in Latvia, where some of the families intermingled. Our common Schwechheimer ancestor was born in 1772.

Cluster 4: Nicholson/Ellis

This Cluster is lead by Carolyn. I have been in touch with Carolyn and Joan and know that they both descend from Nicholson and Ellis. They were both from Sheffield, England on my mother’s side. William Nicholson was born in 1836.

Here is a summary so far:

This is good news. Out of the top three clusters, I have three out of my four grandparents represented. I know common ancestors.

The Next Three Clusters: 9, 6 and 18

Cluster 9 gives me my fourth grandparent side. The match is with Ron. Our common ancestors are Clarke and Spratt on my Frazer grandparent side. Our common ancestor Thomas Clarke was born about 1823.

Cluster 6 is on my Frazer/Frazer side. Clarke/Spratt is from the mother of my Frazer grandmother’s side. Frazer is from her paternal side. This line goes back a ways, but it has been well researched.

Cluster 18 has only two people in it, but it is a great cluster as it represents my Pilgrim ancestry. The first match in the Cluster and I descend from Harvey Bradford, who is a descendant of William Bradford from the Mayflower. Harvey Bradford was born in 1809.

Here is a summary of Jon’s top six Clusters:

The pink represents maternal and blue is paternal. Frazer/Frazer means that I had two Frazer ancestors who married each other.

Clusters 5, 10 and 14

At some point these Clusters will be more difficult to nail down.

Cluster 5 appears to center in on my Parker ancestors who lived on Cape Cod and Nantucket.

Cluster 10 has some Spratt names. This name is my biggest brick wall. My Spratt ancestor died young in County Sligo, Ireland and I can’t find much information about her.

Cluster 14 is not obvious to me. YK and John have a shared match with Gladys from Cluster 6. The third person has a Frazer tree. I would say that Cluster 14 is another flavor of my intermarried Frazer Lines.

So while Cluster 14 was not obvious at first, I was able to figure it out through Shared Matches.

Clusters 7, 11, and 2

I am now getting deeper into the less obvious clusters.

Some people in Cluster 7 match Ron. Ron and I share Clarke, Spratt and McMaster heritage back in Ireland.

I have been in touch with Patricia from Cluster 11. She has uploaded to Gedmatch. The match is definitely on my Frazer side and that should hark back to Ireland. My guess is the Clarke/Spratt Lines.

Cluster 2

Cluster 2 is a large one with connection to my Hartley 2nd cousins in Cluster #1 based on the gray squares. Just because there are many in a cluster does not mean that the cluster is easy to identify. This is the 12th cluster by size of match. There are 18 members in the Cluster. Peter has the highest match to Jon. Peter also has 62 Shared Matches at AncestryDNA.

Next, I’ll look at some of the trees from Cluster 2 Members. Candy has this ancestor in her tree:

This is her only listed ancestor in the area where my colonial Massachusetts ancestors lived. Looking at another Ancestry Tree, I find these parents for Betsey:

I see only one Swift in my genealogical list, but many Wing’s. So that is a possibility.

Another Cluster 2 person has Wing in his ancestry and other surnames from the area around SE Massachusetts where my ancestors lived.

Cross-referencing Jon’s Cluster 2

Next, I’ll look at my Clusters to see where Jon’s Cluster 2 people are. Peter is Jon’s top match in Cluster 2. Peter is in my Cluster 1. In my previous Blog, I identified my Cluster 1 as my Colonial Massachusetts matches. In fact, the first 12 in Jon’s Cluster 2 are in my Cluster 1.

William is in my Cluster 1, but falls below the 25 cM level for Jon. William also has a Wareham ancestor:

There are other possibilities.

Here is my 8th cousin Linda from my Cluster 1:

According to Ancestry, Linda and I match at 23.8 cM and we are 8th cousins with common ancestors in the 1660’s. Right now, this couple is as good a guess as any other.  However, this couple is out nine generations from Linda and me. At that level, I would have 32 couples that would be possibilities. These 32 are just my Massachusetts Colonial ancestors who lived around that time.  All I have to do is disprove the other 31 couples or link my Cluster 1 members or Jon’s Cluster 2 to Finney and Warren.

Here is a summary of my top 12 Clusters:

At this point, I could give up or forge on into the unknown.

Forging On Into the Unknown with Clusters 3, 19 and 12

I’m at a loss for Cluster 3. For one thing, this is my brother Jon’s Cluster and I don’t have many notes on his matches. Perhaps a cross-reference to my clusters would help. Unfortunately, none of the people in Jon’s Cluster 3 are in any of my clusters. It’s a mystery. I suppose autoclustering more siblings may help.

Kitty from Jon’s Cluster 19 is in my Cluster 24

Bonnie is in Jon’s Cluster 12. Again I don’t see any of Jon’s Cluster 12 members in any of my clusters. Bonnie has a Hulme ancestor from Manchester, England that might be worth pursuing.

Jon’s Last Five Clusters

I recognize Jon’s Cluster 20. One member has a McMaster ancestor that I believe is related on McMaster and Frazer sides. If I am right, our common ancestor William McMaster was born about 1790.

Cluster 13

None of Jon’s Cluster 13 members match my clusters. Fortunately Catriona who has a private tree is on Gedmatch and I can tell she is related on my Frazer grandparent side.

Cluster 15

Jon has a Shared Ancestor Hint here, so that makes things easier:

This match is also part of a Snell and a Luther Circle at AncestryDNA. This is another of Jon’s Clusters where I have no members in my clusters.

Cluster 16

I don’t see anyone in Jon’s Cluster 16 that is in any of my clusters.

Jon’s 20 Cluster Summary

By Cluster:

Comparing Jon’s Clusters To MIne

I was able to cross-reference Jon’s clusters to mine in most cases. However, 30% of the time, Jon’s clusters were not found among my clusters. Also some of Jon’s clusters that I was able to decipher more or less, I had not figured out on my clusters. Finally, Jon has a match with someone who goes back to our most recent male Bradford. This is a match that I don’t have, but the cluster is one that has been identified.

Summary and Conclusions

  • I autoclustered my brother Jon’s matches at a lower level of 25 cM and upper level of 600 cM. That was a good level for Jon and resulted in 20 Clusters
  • I looked at Jon’s clusters starting with the largest matches. The higher match clusters were easy to figure out. At about halfway down the list, the common ancestors began to get more difficult to figure out.
  • I was able to find many common ancestors. I tried finding common ancestors for one of my Colonial Massachusetts clusters, but that was difficult.
  • Many of Jon’s clusters with matches near the last half of Jon’s list had no corresponding cluster for my matches. I found this to be interesting. This would lead me to look at more of my sibling clusters.
  • 18 of 20 (90%) of Jon’s clusters were on his paternal side.
  • Finally, I cross-referenced Jon’s clusters to many of my own clusters. This showed where Jon’s clusters did or did not match mine. In some cases, Jon’s clusters identified some of my own clusters that I had not figured out yet.

 

 

Ancestry AutoClustering Back in Action

I noticed that the Genetic Affairs Facebook site had a recent post. They said that as a Christmas present Ancestry AutoClustering was back in operation with some new controls to limit problems with the autoclustering. Ancestry AutoClustering has been popular. That is because AncestryDNA has the largest database of DNA-tested people but they are lacking in analytical tools.

My AncestryDNA AutoCluster

When AutoCluster first came out, I tried it at the low default settings. I wrote a Blog about those results here. Here are my annotated results:

 

I was impressed with the results and even though my clusters were small based on the default parameters, I liked the simplicity of the five clusters.

Here is my latest try at autoclustering. Now I used defaults that were 600 cM on the high end and 9 cM on the low end:

Now I have gone from five clusters to 76.

My Genealogy and Deciphering Some of the 76 Clusters

This tree goes to 16 branches. I suspect that 76 branches could go back at least two or three more generations than above. I have a lot of Hartley relatives as my great-grandparents had 13 children. My great-grandmother Snell had colonial Massachusetts ancestry. That means that I have a lot of 2nd cousins.

My Hartley 2nd Cousins

My Hartley 2nd cousins are not found in Cluster 1, but in Cluster 4:

These are my top 13 clusters. In my previous analysis, the present Cluster 4 was #1. By expanding the matches out to more distant matches, the new Clusters 1-3 beat out my former #1 Hartley 2nd cousin Cluster. Along with my 2nd cousins in Cluster 4 above are a few more distant cousins.

Massachusetts Colonial Ancestors – Cluster 1

Cluster 1 appears to include many of my more distant Colonial Massachusetts ancestors going back past my Hartley side. My closest match in Cluster 4 is my father’s cousin Joyce. My closest match on Cluster 1 is Jonathan – a relative of Joyce. Previously, Jonathan was in my old Cluster 1 also. Now he is a ringleader for my Colonial matches.

Other than Jonathan, I cannot pinpoint exact common ancestors for matches in Cluster 1 at this point.

My Largest Matching Clusters

Next, I am going to change my strategy. I will now sort by match on my Cluster List:

I clicked on the cM button until the arrow was pointing down. This gives me the clusters with the largest matches. Hence, the matches that I am likely to know about. The highest matching cluster is #4. #12 is the 2nd highest match. That is because it includes my 1st cousin’s daughter (on my mother’s side). That means that Cluster #12 could be either on my mother’s mother’s side or my mother’s father’s side.

The next Cluster by size is #1 with Jonathan.

Cluster 16 – Nicholson

The next cluster by size goes off my present image, so I will need to ratchet down the image. Cluster 16 has only nine people in it, but I have been in touch with many of them. The known people in this group descend from William Nicholson and Martha Ellis:

Cluster 27 – Clarke

Cluster 27 is important to me. Clarke is my largest brick wall. I will have to go down yet another level for Cluster 27.

I’m starting to use the Key for these higher number clusters.

My Top 23 Clusters by DNA Match Level

Here are my top clusters by match level in a spreadsheet:

This shows that the highest matches are on the paternal sides and on that paternal side, most of the matches are on my Frazer grandparent side.

I can also sort by cluster:

This shows that I am missing Cluster 3 even after looking at my top 23 clusters.

Cluster 3 – Mom’s Side

That makes me curious about Cluster 3. From the match list, I see that the top match is at 27.1 cM. This person has a large private tree, but hasn’t logged in to Ancestry for over a year. This group of matches is a bit of a mystery. I know that this cluster is maternal and probably the Lentz rather than the Rathfelder side as the Rathfelder matches are on the rare side.

Old Cluster and New Cluster

My original AutoCluster was done at conservative default levels and resulted in five clusters.

The old Cluster 1 is found in new Clusters 1 and 4. 2 is now 6 and 27. 3 is now 11. 4 is now 17 and 71. 5 is 19.

AncestryDNA Circles

It occurs to me that it would be helpful to compare clusters to the AncestryDNA circles. Here are my circles:

Nicholson, Ellis and Lentz are maternal and the rest are paternal. Nicholson and Ellis are both in Cluster 16. This points out an error I made on my spreadsheet:

I previously had my Nicholson Cluster as 11 and it should have been 16. My mother’s Lentz circle was emerging and the few matches were either not matching me or too low to be in a cluster.

The Mary Pilling Circle is interesting as this goes back to England. However, those in the circle who are not my 2nd cousins are a match to the circle and not to me.

Descendants of Anthony Snell Circle

I have a similar problem here. There are two people who are not second cousins to me that match me by DNA, but they match at levels below 20 cM. If I check the shared matches of one of these matches, I see that he matches Fred from Cluster 30. That is perhaps a hint as where I may find a common ancestor with Fred. Shared matches with another person in this circle also lead me to a three people who are in Cluster 30.

I believe that the Betsey Luther circle is somewhat redundant. She was the wife of Anthony Snell. Finally, the Churchill Circle. I match second cousins and others in the circle match those second cousins or closer matches. If I run clusters for others in my family, these relationships may be helpful.

This shows that three of my circles are associated with my second cousins in Cluster 4. Shared matches from the Snell circle brought me down to Cluster 30. The two circles for my mother’s side were the husband and wife Nicholson and Ellis. My mother had another Lentz circle but the matches were too low for me. When I look at my mother’s matches, I may find closer matches.

NADs and AutoCluster

NADs are New Ancestor Discoveries. Here are my NADs:

I have no idea who these people are.

The Long NAD

Here are some of the people in the Long NAD:

The orange indicates a match to me. So these are like circles or clusters also. The only difference is that these NADs are pointing to ancestors that I don’t know about. I may not know about them because they may not be my ancestors or the ancestors may be further back than the ones Ancestry is pointing to.

Brenda is in Cluster 7. I didn’t try to identify Cluster 7 above as it wasn’t in the top 23 clusters. This means that I can associate Cluster 7 with my Long NAD. I associate the Long name with Ireland. However, this family was from North and South Carolina. Angela is also in the NAD and in Cluster 7. She also matches Ron who is on my biggest brick wall – the Clarke/Spratt Line. Ron is in Cluster 27. Perhaps that indicates a relationship between these two Clusters. I did find one person who is in Cluster 7 who is not in the Long NAD. I’m not sure why. There are 21 in Cluster 7 and 31 in the Long NAD.

The Weems NAD

John Weems was from Tennessee. I see his connection even less than with Seymore Long. My matches to people in this group, when they do match, are below 20 cM. That means that I don’t have an analogous Cluster to this NAD.

Summary and Conclusions

  • I’m not done playing with AutoCluster yet. There is still more to explore.
  • My original AutoCluster looked at matches between 50 and 250 cM. In this AutoCluster run, I chose limits between 9 cM and 600 cM. The spreadsheet showed matches as low as 9 cM, but the html cluster chart showed matches only down to about 20 cM.
  • As I had so many clusters, I found it useful to look at the clusters with the highest DNA matches. These are the clusters that were, for the most part, easy to identify.
  • I compared the 76 cluster analysis with the 5 cluster analysis I did.
  • AutoCluster does a great job of condensing huge numbers of AncestryDNSA matches and putting those matches into categories.
  • AutoCluster gave me a sense of how many matches I had that were maternal or paternal and from which grandparent side those matches came from.
  • Next, I would like to look at a lower threshold of 25 cM to narrow down the number of clusters that I get.
  • I looked at how AncestryDNA circles related to Clusters.
  • Next I looked at my two NADs. One NAD had an analogous Cluster. The second NAD had matches that were two small and didn’t have an analogous cluster.

 

 

 

Making Sense of My FTDNA AutoClustering with a Leeds Color Analysis

AutoClustering is a new approach to looking at DNA matches. The progamming was created by Evert-Jan Blom. Right now the analysis is working better for FTDNA than it is for AncestryDNA. In a previous Blog, I looked at my 23andMe and FTDNA clusters, but had some trouble identifying many of the clusters. I was hoping that a Leeds Color Analysis would shed some light on my Clusters.

FTDNA AutoClusters

These are the 33 Clusters I came up with at FTDNA. I decided that FTDNA pads their DNA a bit. This padding problem blew up my orange Cluster 1 where there are a ton of matches on my Chromosome 20. These are on my Frazer grandmother’s side.

Here is a summary of some of my AutoClustering that I did previously:

The FTDNA results are in the middle column. It looks like I figured out 6 of the 33 Clusters.

Can the Leeds Color Analysis Help Figure Out More Clusters?

The Leeds Color Analysis also creates clusters, though not as graphically as the AutoCluster method. The good thing about the Leeds method is that it doesn’t rely on a  computer program and it requires some interpretation from the user. These could also be considered negatives.

Here is what I came up with using a Leeds Color Analysis of my FTDNA matches:

  • The first time a name came up as a match I gave them the color over their name.
  • If someone matched someone who matched someone up higher in the Cluster, I noted this on the spreadsheet.
  • I went out as far as FTDNA’s predicted 2nd to 4th cousin matches. This was 88 matches.
  • This represents 21 Clusters. Some are not technically clusters as there is only one person in the cluster. I assume that if I went to lower cM matches, I would get more matches in these one person ‘clusters’.
  • I identified three out of four of my grandparents

Starting with Hartley

In the Leeds Analysis, I used my father’s cousin as the lead Hartley person. He did not show up in the AutoClustering as he was too close a DNA match compared to the thresholds I used. However, the second person in the Blue column is Benjamin. He matches my father’s cousin Jim and becomes the lead person in the AutoClustering. A search for Benjamin in the AutoCluster shows that he is in Cluster #10.

Cluster 10

The problem is that Cluster 10 only has three people in it:

In the Leeds Color Analysis, there were 20 in the Blue column. When I go to my match with Benjamin at FTDNA and choose ICW, I get three people. So that makes sense. This is just one flavor of Hartley. A look at the ancestral names of these matches makes me think that this could be a Colonial SE Massachusetts branch. I’ll call this a Snell/Bradford Line as that covers all my Colonial ancestors:

I could be wrong, but that is my best guess right now. Next, I filtered for Hartley (blue on the Color Analysis) and added a column for the AutoCluster number to keep track of the Cluster number:

The other two people in AutoCluster #10 were not in the Leeds Color Analysis.

Going Down the Blue List

It would seem logical to go down the Blue list and put an AutoCluster numbers in for each person. I find the results interesting:

I would trust the Clusters except for #6 as that shows more than one color. I was a bit surprised that they didn’t all relate to AutoCluster numbers. I’m not sure why that is. Part of the reason is that I went by FTDNA predicted relationship and AutoCluster probably goes by total DNA match in cM.

I plugged these number back into my AutoCluster Summary:

Notice that I had one Hartley in Cluster 4 which I previously had as Frazer. Turns out that was a mistake and she should have been in Cluster 2. It all works out. It turns out I made another mistake and there is no obvious Hartley Cluster 8.

Corrected FTDNA Cluster Summary

I suppose it would be possible to further break down the Hartley into Colonial or non-colonial, but I’ll hold off on that for now. The Hartley List worked well, so I’ll move on to Frazer.

Plugging Leeds Frazer Colors Into AutoCluster

Here is what I get:

Again, I’m unsure why the people at the bottom of the list are not in clusters. The Clusters I found were not shared with other colors, so that was good. Now I feel like I am getting somewhere:

 

These are different flavors of Frazer in green. I also have Clarke who was my Frazer grandmother’s mother from my last look at AutoCluster. Frazer’s married Frazer’s. Frazer’s married McMaster’s who married McMaster’s. It gets complicated.

I now have 13 out of 33 Clusters identified. That is a good start. I have other ideas on how to identify other clusters, but that can wait for now.

Summary and Conclusions

  • I got stuck trying to identify many of my AutoCluster results from FTDNA.
  • Using the Leeds Color Analysis, I was able to put many matches into two major grandparent categories. I was able to cross-reference these matches to the AutoCluster.
  • My next idea is to use chromosomal analysis to identify the clusters. By this, I mean that I will compare the matches to my visual phasing results. This should get the clusters into the correct grandparent area.

 

 

 

 

 

 

 

 

My Mother-In-Law and Her FTDNA AutoClustering

Joan’s Genealogy

I find Joan’s DNA fun to work with. Even though Joan has Canadian background, she has no French Canadian which can muck up the works. I don’t mean to sound prejudice in a DNA sort of way. Joan is 1/4 Newfoundland, 1/4 Daley which is from Nova Scotia and the other half is from Prince Edward Island. Out of Joan’s four grandparents, the Daley side seems to be most obscure. However, the Newfoundland side is problematic due to poor records there. The Church in Harbour Buffet burned down at one point.

  • Ellis and Rayner – PEI
  • Upshall – Newfoundland
  • Daley – Nova Scotia

AutoClustering Joan

For some reason, Joan’s results came through as untitled text files:

I was able to change the first two files to csv files and the last one to an html file and that solved the problem. I chose a range between 12 and 400 cM.

How Many Clusters?

Joan had so many clusters that they ran off the graph:

I’ll say Joan has over 80 clusters. 

This represents about the first 25 of Joan’s clusters. Here is the total at the bottom of the report:

I forgot that FTDNA add small segments to make the matches larger, so I should have had a higher bottom cutoff point.

Joan’s Cluster #1 – Newfoundland

A journey of 1,000 miles starts with one step. Joan’s top match is Ken. I’ve looked at his DNA before and had trouble figuring out where all of his DNA came from. If you look real close, you will see Ken’s grey dots going toward other clusters. Those are other places where he is related to Joan. I mentioned that French Canadians mucked up the works with intermarriage. This would be true of islands also – like Newfoundland and Prince Edward Island.

Joan’s #1 AutoCluster Match: Ken

Ken and Joan both descend from Christopher Dicks born in the 1780’s and his wife Margaret. I have run a DIcks DNA project and I recognize a lot of people in this Cluster.

Joan and Nancy

I didn’t recognize Nancy’s name in the group. Here is her tree:

I don’t get a lot of Upshall leads, so this is interesting. I assume that Nancy also has Dicks ancestry at some point. See, AutoClustering leads to good things.

That was quite easy. Here is the spreadsheet I use to keep track:

Cluster 2: PEI

I recognize some PEI descendants in Cluster 2. I have written about Glenda. She descends from Elllis and Rayner and matches Joan equally on those lines. That means I need to look at other Cluster 2 people and their trees.

Barbara and Lee

Barbara and Lee from Cluster 2 both have McArthur or MacArthur in their trees. That would seem to favor the Ellis side over the Rayner side:

However, I am just matching surnames, I am not matching actual shared ancestors. That would take more work.

Agnes’ Tree

It seems that there a lot of good trees at FTNDA. Agnes matches on the Rayner side.

Agnes’ maternal side has an Edward Rayner. His parent are the Edward John Rayner and Mary Watson in Joan’s tree. Of course, that favors the Rayner side. However, I note that there is an Ellis on Agnes’ Rayner side also.

Jane’s Tree

Here is where I need Ancestry to pull the trees together for me:

Jane has McArthur and Ellis on her paternal side.

I guess I’ll call this cluster Ellis/McArthur for now.

I spent a bit of time on this cluster, but it is Joan’s second largest cluster.

Joan’s Cluster Three People Don’t Look Familiar

Unlike the first two clusters, I don’t recognize these matches. There were four trees for the 13 people in this cluster. I think I’ll skip this one. By the little dots to the left and above this cluster, I would say there is some connection to the previous PEI cluster. It seems like an odd group. At least one tree was from New Zealand and one was from Ireland.

Skipping on to Cluster 4

As I look at the names and trees, it appears that this Cluster is from Newfoundland. I’ll just call this a Newfoundland Cluster:

That also gave me an idea for a name for Cluster 3.

DNAPainter to the Rescue?

I’m getting stuck on these Clusters, so I’ll take a look at what I have already painted for Joan. Here is the key to Joan’s painted Chromsomes:

One problem I see with this is that DNAPainter takes from many places – not just FTDNA.

Melissa in Cluster 34

Melissa has a common ancestor of Ellis/Gorrill with Joan.

I’m not so sure about the other two matches in the group. So I didn’t find a lot by that method.

The Clicking on Trees Method

Next, I’ll just click on trees to see if anything shows up. This resulted in a few general discoveries. I then clicked on the highest cM button to try to overcome FTDNA’s over-counting of their DNA matches.

Here are some of the clusters partly identified:

Summary and Conclusions

  • I had trouble finding specific ancestors for many of these clusters. I think it may be related to FTDNA having higher cM matching than is warranted. This may be partially fixed by raising the lower threshold to 20 cM when running an AutoCluster Report at FTDNA.
  • At Joan’s 2nd great-granparent level, I can identify 16 ancestors. In this analysis, I got 92 clusters. That is too many. 
  • Even though the cluster identification was difficult, it was good to take a fresh look at Joan’s FTDNA through the eyes of AutoClustering. I have at least one new lead to follow up on.
  • Another issue that makes Joan’s cluster identification difficult is that her ancestors come from two islands: PEI and Newfoundland. There was some intermarriage going on there. Joan is also once quarter from Nova Scotia. I’m not aware of intermarriage there, but matches with these relatives are relatively rare (no pun intended). 

AutoClustering My Wife’s Aunt’s Ancestry DNA

My wife’s father had his DNA tested at FTDNA before he passed away. I also had his two sisters’ DNA tested at Ancestry. I’ll use his sister Virginia’s AncestryDNA results for Autoclustering as a stand-in for my later father-in-law Richard.

AutoClustering Virginia

I could have picked either sister, so I picked Virginia for no special reason. Actually, my thought was to pick Lorraine, as she is closer in age to Richard, but I picked Virginia. I chose a low threshold of 12 cM for the AutoClustering.

First the Genealogy

Virginia and siblings have half French Canadian and half Irish DNA. In my experience, the French Canadian DNA tends to take over. This is due to the common ancestry of French Canadians, and many descendants who have tested.

The top part of the tree is Irish and the bottom is French Canadian. I am more interested in the top because there are some missing black arrows. Those are the places where there are missing ancestors. The ancestry is filled in to the level of 2nd great-grandparents. The column on the right represents third cousin, but in many matches this should show as third cousin, once removed.

Looking at Virginia’s AutoCluster

Here is the key for Virginia’s Clusters:

The Key is on the Chart, so there are grey dots representing those that didn’t fit well into the clusters. Cluster 1 is no doubt French Canadian. Between Cluster 18 and 19, the cluster size goes down from three to 2. These numbers do not include Virginia who is in every cluster.

Name That Cluster

The game is to name the clusters. Before I do that, I notice that there are not too many grey dots between the first and second Cluster. I take that to mean that these two groups are not closely related. Perhaps the green Cluster is Irish and the orange is French Canadian.

Identifying Cluster #1

This should be easy as there are so many people. First I go to the list of people below the chart and search for Virginia’s second cousin Fred who is an avid genealogist. He is there in Cluster #1.

Fred’s shared ancestors with Virginia are here:

However, there are 120 members in Cluster #1. Next, I went down the list of people in Cluster #1. The last person I had notes for was Girard. Here is Michel’s Shared Ancestor Hint (SAH) with Virginia:

Michel has 72 people in his tree. The problem with that is that Michel and Virginia could have shared ancestors on other lines. Here is Louis Marie Henri Girard and his wife on Virginia’s tree:

I would say that Louis Girard is a hint as to where the cluster is going. I’ll try another SAH. The next person going up the list has six SAH’s and a large tree. Here is the most likely source of the DNA that is shared between Virginia and this match:

These matches so far tend to be around the bottom of Virginia’s French Canadian Tree:

I’ll try one more. The next SAH has over 1,000 people in his tree and his common ancestor with Virginia is Francoise Gagne:

So far, I would say that these are all ancestors of Elizee Fortin and Rosalie Gagne. It is even possible that I could name this Gagne/Girard if the person with six SAH’s has an ancestor there. It turns out our six-matcher has these ancestors also:

That means I would tend to call this a Gagne/Girard Cluster. I like to get the name as far back as possible to be the most specific name for the cluster.

I’ll look at one more SAH to make sure. Lucie has a good tree, but six SAH’s. For some reason her first hint puts her at 6th cousin once removed to Virginia and her second hint puts her at 6th cousin to Virginia. I’ll choose the 6th cousin which goes to Pierre Girard and Marie Anne Vesina. This ancestral couple is also on the Gagne/Girard Line. This is not a life or death decision, so I’ll go with the Gagne/Girard Label for Cluster #1:

That’s one down and 33 to go. I like to keep track of these clusters in a spreadsheet:

This way I can expand to the right for Richard at FTDNA eventually.

I’m Guessing Cluster #2 Is Irish

However, as I look at my notes nicely displayed by AutoCluster, I see that this cannot be:

This means that the LeFevre side is not as closely matched to the Pouliot side as the Pouliot side matches some other names. This makes sense also.

Name That Cluster #2

It is obvious that Cluster #2 is on the LeFevere side. However, I want to be more specific as in Cluster #1 above. The match at the bottom of the list shows a SAH of Maguerite Anger. The husband is not shown as he is shown as marrying her three times. However, I assume that the husband should be there also:

The husband is Joseph Methot. I am now just showing the line of Virginia’s LeFevre grandfather Joseph Martin as we know that this cluster is along the LeFevre Line. If I were to name this Cluster based on a sample size of one, it would be Methot/Anger. However, I want to be more sure and it is easy to look at these SAH’s by just clicking on a link from the AutoCluster list of matches in Cluster #2.

Going up the Cluster 2 Match List from the bottom, the next SAH is here:

This brings the name of this cluster one generation towards the present:

Based on a sample size of two, I would name this cluster LeFevre/Methot.

I’ll call in Jane for a tie-breaker:

I can see that Jane adds evidence to my previous guess:

Cluster #3 – French Canadian?

By looking at the Cluster Graph above, it appears that the red cluster will be more closely allied to the Pouliot side. There are not as many linked trees for Cluster #3:

Judy has an unlinked tree:

Cousin Fred is not in this Cluster even though he is closely related. This could be a case that he is too closely related to Judy. Judy’s tree shows that she is a second cousin to Virginia on the Pouliot/Fortin Line. This seems to be the best name for this Cluster:

Cluster 4 – Slim Pickings on Trees

Cluster 4 has very few linked trees:

The match names appear to be French Canadian, so that is a hint. The largest tree above is private. From the above three clusters, it appears that I am getting different flavors of French Canadians. Match #3 has a small unlinked tree:

I really don’t want to build out this tree, though I could. I see Gobeil in Virginia’s tree here:

Alexandre is Virginia’s match #6 on Cluster 4. He also has an unlinked tree:

Here is another small tree from Match #8:

Again, I’m not willing to build out his tree. Match #9 had an unlinked tree and Match #10 had a small tree, but they were not helpful. I’ll go with Pouliot/Gobeil for now.

Cut to the Irish Side

This is going slowly, so I’ll start looking for Irish matches. Here is a Leeds color analysis that I did for Virginia about three months ago:

I need to pick out some of these green and blue matches and see where they cluster. The first match is Donna. She matches at 417.5 cM, so this is a case where I set the upper limit too low. The four in a row green Kerivan matches are also all too high for the upper match limit that I picked. Here is part of the tree of the first Butler match that didn’t get filtered out:

The common ancestors are Edward Butler and Mary Crowley. This match is in Cluster 12:

Based on the notes, I can see that I have been tracking three out of four of these matches. I wrote a message to John to see if he has any family history. It may be that he pre-dates the Butler/Crowley connection by one generation.

This Butler/Crowley Cluster is small, but important.

Is There a Kerivan in the House?

The green in the Leeds Color Analysis above stands for Kerivan. Here are some Kerivan descendants in Cluster 11:

I have written about Gaby already as a Kerivan relative. She is Thomas’  Aunt. Here is the tree showing how Virginia and Gaby connect:

Virginia is a second cousin once removed to Gaby and 2nd cousin twice removed to Thomas. Here are the common ancestors on Virginia’s tree:

David: Match #2 in Cluster 11

Here is David’s tree on his maternal side:

I am interested in David’s tree enough that I will build it out a bit. I’m curious to find the common ancestors. I start with David and mark the tree private at Ancestry.  Here is David’s maternal grandmother Joan Kerivan in 1940 Newton, Massachusetts:

Here I use a split screen for working on David’s tree. The tree I am making is on the left and David’s tree is on the right:

I accepted Ancestry’s Joseph Edward Kerivan hint but not his wife as it was different than what David had. It seems like it should be an easy tree. I have the DNA match, the Kerivan name and the right area (Newton, MA).

Here’s David’s great-grandfather in 1910:

Next, Joseph’s birth record comes in handy:

I see on George E Kerivan’s marriage record, that his parents are John Kerivan and Alice. These are the couple that I am looking for. Here is part of my selective tree for David:

Alice is no doubt my wife’s ancestor Alice Rooney.

As an added bonus, I color-coded the Clusters in my summary spreadsheet based on my wife’s Aunt’s grandparents. These are the same colors I used in the Leeds Color Analysis.

The clusters are now taking shape. The magnitude of the French Canadian matches compared to the two Irish clusters is obvious.

Comparison with the Leeds Color Method

Next, I put the cluster names by the appropriate names from the previous Leeds Analysis I did:

I see that one of the people from the Butler Cluster was not in this analysis, so he must have gotten his test results since I did this analysis three months ago. The first green block that doesn’t have an assigned cluster represents Russel. Russel is in Cluster 7.

Cluster 7 – Kerivan?

Here are Virginia’s seven Cluster 7 relatives:

Here is Russell’s tree on his mother’s side:

Time for a Quick Tree for Russell

I found this hint at Ancestry for Thomas Kerivan:

This gets me to where I want to be. Here is my quick tree for Russell:

One might wonder why there is another Cluster for this same couple. It could be that one Cluster is Kerivan and one is Rooney.

Here is Sandra. She has the same mother as Russel, so I could have saved myself some time:

Actually, there is a Rooney in this cluster, so I’ll call this the Rooney/Kerivan Cluster.

There are a few new people in the Rooney/Kerivan Cluster that I should get in touch with.

Cluster 19 – A Butler Cluster on the Outskirts

Here are Brian and Michael:

I associated Brian with the Butler family due to a shared match with Patty. Neither Brian nor Michael have family trees, but it would be worthwhile to follow up with these two.

My guess is that the Cluster 19 Butler predates the Cluster 12 Butler/Crowley families. This is a good place to be as I am trying to pin down a place in Ireland where the Butlers came from.

Where is Patty?

One Butler DNA match I have been tracking is Patty. I couldn’t find her in the AutoCluster. Based on her shared matches at AncestryDNA, I would have expected her to be in Cluster #12. AutoCluster provides a list of names that didn’t match other people. I didn’t see her in that list either.

Summary and Conclusions

  • AutoCluster by Genetic Affairs continues to be a fun and useful tool to use to sort through your DNA matches.
  • The program is similar to the Leeds method but is more useful and takes the guesswork and human error out of the equation for the most part.
  • AutoCluster gives a visual as to where the bulk of the DNA matches are
  • In this Blog AutoCluster highlighted some important new matches. It would be worthwhile to contact these new matches.
  • The list of people in the Ancestry Clusters is especially helpful. I can click on each name and quickly go to their AncestryDNA match and see if they have a SAH or linked or unlinked tree.
  • Even though AutoCluster is one of the best things since sliced bread, it is not perfect. I could not find Patty in the clusters. Also the runs that I get are spotty. It seems to work about half the time for me. I would like to get better results at Ancestry for myself and my mother, but am not able to get results at the thresholds that I want. It may be that these glitches will be fixed as this is such a new tool.