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.

 

 

 

 

 

Leave a Reply

Your email address will not be published. Required fields are marked *