Use Match Notes from ThruLines to Identify Clusters

A Segment-ology TIDBIT

When you add ThruLine info to a Match’s Notes box, it can be very helpful. When this ThruLine info is the initial entry in the Notes box, it is then visible when viewed in a list of Shared Matches. This makes it easy to scan down a list of Shared Matches and quickly determine if there is a clear thread – a Cluster – there. This is confirming evidence that we are on the right track. And I add a line in my Notes to the effect that X number of other Matches all agree on the same line of my Ancestry.

Type ThruLine info into Match Notes to find Clusters of Matches.

 

[22AM] Segment-ology: Use Match Notes from ThruLines to identify Clusters TIDBIT by Jim Bartlett 20190729

ThruLines Has X-ray Vision

A Segment-ology TIDBIT

AncestryDNA’s new ThruLines automatically finds Common Ancestor(s) – out to 5xGreat grandparents.

AND, because AncestryDNA can also see the Private Trees, it can find Common Ancestors you cannot see – it’s like ThruLines has X-ray vision! If your Match’s Private Tree is searchable, it will even show you the path to the Common Ancestor (otherwise it will show Private for each generation back to the Common Ancestor). This is a very powerful feature. Many of us complain about all the Private Trees.

Well… now ThruLines looks right into them for us and determines our Common Ancestor(s). ThruLines works to find these Private Matches even when they have small Trees, but have an Ancestor who is also in your Tree as a descendant of one of your Ancestors (see previous post about adding children and grandchildren). If you think you know how a Private Match might be related to you (say, by finding several Shared Matches, all on one of your lines), try adding some more descendants in that part of your Tree and wait a day or two. This also helps in developing larger groups of Shared Matches and identifying more Matches in Clusters.

Use ThruLines to find Common Ancestors with Matches who have Private Trees!

 

[22AI] Segment-ology: ThruLines Has Xray Vision TIDBIT by Jim Bartlett 20190729

Help ThruLines by Adding Children and Grandchildren

A Segment-ology TIDBIT

Many of us complain about the small Trees of our Matches. Well, with a little help from you, ThruLines can still find Common Ancestor(s).

Your important task:

Add the children of your Ancestors to your Tree. Actually, add the grandchildren of your Ancestors, too! ThruLines will connect the dots – it will match an Ancestor in the small tree of your Match to a grandchild in your Tree and show you the Common Ancestor you both have. ThruLines will draw the picture from you to the Common Ancestor and from your Match to his/her known ancestor, who matches a grandchild in your tree and then extend the Match’s ancestry back to the Common Ancestor, too.

I’ve actually watched this work. I was working in ThruLines for one of my Ancestors with a group of Matches, and I entered the children and grandchild of the Ancestor into my Tree (which I had originally set up just with Ancestors). The green “EVALUATE” tag disappeared for Match Ancestors I had entered, and often Private Ancestors turned into named Ancestors. In some cases, almost immediately, new ThruLine Matches appeared – in other cases new Matches showed up in a day or two.

Help ThruLines do the work by adding descendants of your Ancestors to your Tree!

 

[22AH] Segment-ology: Help ThruLines by Adding Children and Grandchildren TIDBIT by Jim Bartlett 20190729

ThruLines Finds Your Common Ancestors

A Segment-ology TIDBIT

ThruLines Finds Your Common Ancestors

AncestryDNA’s new feature, ThruLines, replaces the Circles* – sort of. TLs are in, Circles are out. Like Circles did before, ThruLines does the genealogy work for you and finds your Common Ancestor(s) with a Match. Currently it does this out to your 5xGreat grandparents (at the 6th cousin level). Circles were formed out the 7xG grandparents (the 8th cousin level). Let’s hope AncestryDNA will extend ThruLines out that far too. In any case ThruLines shows your line and your Match’s line back to the Common Ancestor. (more on this important feature in another post).

You have two important tasks to make ThruLines as effective as possible:

  1. Build your Tree out to as many 5xG grandparents as you can. (While you’re at it, extend your Tree to 7xG grandparents, where you can, in the hopes that AncestryDNA will use them in the future.)
  2. Use only the basic standard names for ancestors, dates and places. Enter John JONES, not John, the Immigrant, JONES or Capt John JONES or Reverend John JONES – just John JONES. A computer will be comparing your names to the names entered by others. Your best bet for a match is when you both use the same names. TIP: if you want to note nicknames, use: aka Sally rather than: “Sally” – I’ve heard the quotes often throws the computer off. TIP: I’ve also heard that surnames in CAPS is often better. The same standardization is true for dates – the best is 4 Jul 1776 format, which is a good standard for genealogy. Don’t use “after 1775, but sometime maybe before 1777” – the computer has a hard time with that and sometimes doesn’t make a match. If you say Virginia and your Match says Colonial Virginia or British Colony, the computer may or may not connect the dots.

To get ThruLines to do the work for you, build out your Tree and use standard nomenclature.

* Edited to correct an error: replaced “Shared Ancestry Hints” and “SAH” with “Circles”

[22AG] Segment-ology: ThruLines Finds Your Common Ancestors TIDBIT by Jim Bartlett 20190729, Edit 20190730

 

Advanced Genetic Genealogy Book

Advanced Genetic Genealogy Book

I should let you all know, in case you missed it, there is a new book on DNA which just came out. There are several beginner and intermediate books on DNA for genealogy already available. But to my knowledge this is the only one so far focused on advanced topics. There are 14 chapters, each by a different author with lots of genetic genealogy experience. I wrote Chapter 1: Lessons Learned from Triangulating a Genome. The Editor, Debbie Parker Wayne, and I were at the FamilyTreeDNA Genetic Genealogy Conference in Houston, TX on 23 March when we found out the book was available on Amazon. So Debbie made an impromptu announcement with the proof copy she had…

Pictured: me, Debbie (Editor & Chapter 7), and Pattie Hobbs (Chapter 10)

Here is a picture of the front and back of the book:

Edited 4/6/2019 to add list of Chapters and Authors:

  1. Lessons Learned from Triangulating a Genome, Jim Bartlett, PE
  2. Visual Phasing Methodology and Techniques, Blaine T. Bettinger, JD, PhD
  3. X-DNA Techniques and Limitations, Kathryn J. Johnston, MD
  4. Y-DNA Analysis for a Family Study, James M. Owston, EdD
  5. Unknown and Misattributed Parentage Research, Melissa A. Johnson, CG
  6. The Challenge of Endogamy and Pedigree Collapse, Kimberly T. Powell
  7. Parker Study: Combining atDNA & Y-DNA, Debbie Parker Wayne, CG, CGL
  8. Would You Like Your Data Raw or Cooked? Ann Turner, MD
  9. Drowning in DNA? The Genealogical Proof Standard Tosses a Lifeline, Karen Stanbary, CG
  10. Correlating Documentary and DNA Evidence to Identify an Unknown Ancestor, Patricia Lee Hobbs, CG
  11. Writing about, Documenting, and Publishing DNA Test Results, Thomas W. Jones, PhD, CG, CGL, FASG, FUGA, FNGS
  12. Ethical Underpinnings of Genetic Genealogy , Judy G. Russell, JD, CG, CGL
  13. Uncovering Family Secrets: The Human Side of DNA Testing, Michael D. Lacopo, DVM
  14. The Promise and Limitations of Genetic Genealogy, Debbie Kennett, MCG

Glossary

Recommended Reading

Index

 

 

[99B] Segment-ology: Advanced Genetic Genealogy Book by Jim Bartlett 20190406

Clustering Programs

A Segment-ology TIDBIT

A number of folks have asked me about the different Clustering Programs, so I thought I’d post some information to get you started.

Clustering analyzes your InCommonWith (ICW) Matches at a company, and groups Matches who are ICW each other the most. Each Match in a Cluster will be ICW with most (but usually not all) of the other Matches in the Cluster. With Cluster groups of 4 or more Matches, they tend to group on a specific Ancestor, which would impute the same Ancestor to every Match in the Cluster. NB: this is not a guarantee, but it appears to work almost all the time.

Clustering Programs:

Leeds Method by Dana Leeds (free)

https://www.danaleeds.com/ see the Video and updated methods

This began as a color coding method of grouping close Matches at AncestryDNA into four columns, one for each grandparent. It has been expanded.

Genetic Affairs by Evert-Jon “EJ” Blom (several spreadsheets free, then a small fee)

http://www.geneticaffairs.com/ Register first, then log in

– automates the retrieval of new genetic Matches from 23andMe, FTDNA and AncestryDNA to a periodic email; and the AutoCluster tool will cluster close/large Matches

DNAGedcom Client by Rob Worthen ($5/mo fee; $50/yr)

Register here to start: https://www.dnagedcom.com/

– log onto your DNA company, and download Match and ICW files

– use Collins” Leeds Methos 3D to run cluster report

Shared Clustering by Jonathan Brecher (free)

https://github.com/jonathanbrecher/sharedclustering/wiki/Quick-start

– installs program on your computer

-currently need to download Match and ICW files at DNAGedcom Client

MyHeritage – offers a free report by Genetic Affairs!

GEDmatch – offering a Genetic Affairs type report soon! Under Tier 1 ($10/mo fee)

My recommendations include:

– Use a large threshold (80cM to 200cM) first to get the hang of it. This will only include your closest cousins.

– If offered, use an upper threshold of 1000cM or so, to cull out parents, siblings, children, aunt/uncle – they only appear in one Cluster anyway, and don’t really add any value in most cases.

– Reducing the threshold will increase the number of Clusters, and those Clusters will tend to form on more distant Ancestors.

NB: Some additional Clustering Programs and ideas may show up in the comments below. I’ve used all of the programs above. I have also continued to do D I Y Clustering, outlined in a different Segment-ology blog post.

[22AF] Segment-ology: Clustering Programs TIDBIT by Jim Bartlett 20190404

Clusters Link to TGs and an Apology

A Segment-ology TIDBIT

Are Clusters based on Common Ancestors (CAs) or Triangulated Groups (TGs)? I said CAs, and Jonathan Brecher said TGs. I now think Jonathan has the best answer. My apologies for doubting his conclusions.

My point was that with a few, large, close, Clusters, each Cluster must be formed on a CA, and include many TGs. A Match Clustering which results in 4 or 8 or 16 Clusters (which they tend to do) are clearly formed on 4 grandparent, 8 Great grandparents or 16 2xGreat grandparents – and this is born out with the Leeds Method and other experience. These large Clusters must each include many TGs – and my experience bears this out.

However…  There’s often an “however.” As the Clustering thresholds are decreased, the number of Clusters formed are increased. In my recent example I had 156 Clusters using a 20cM threshold. Note that one fourth of my Ancestry is from 1850’s immigrants, with very few Matches (and all of them were close cousins in one Cluster). I should have had about 208 Clusters. And 161 Matches did not cluster. This gets us pretty close to 256 6xGreat Grandparents 8 generations back. And a number of my 156 Clusters appear to link with only one TG [Note this is AncestryDNA data, most of it without TGs].

I am now reviewing my AncestryDNA Matches and trying to assign Cluster IDs to each one, by looking at the info I have in the Notes box for each Match and reviewing all their Shared Matches (and their Notes – easily viewed with MEDBetterDNA). In most cases, where the Match shares a single segment with me, I’m tending to identify a single Cluster. And when I have TG information, it’s tending to be one TG.

So, I’m going to eat some crow, and apologize to Jonathan Brecher. I now think he was on the right track, and that we should try to link Clusters to TGs (specific DNA segments). After all, each TG is from a specific Ancestral line. Of course, at AncestryDNA (without segment data), we’d still Cluster mainly on CAs. However, with a comparison between AncestryDNA Clusters and Clusters with other companies (with segments and TGs), we should be able to find a correlation between our AncestryDNA CA Clusters and TGs. Through this correlation, we could “impute” TGs to AncestryDNA Clusters.

So – thank you, Jonathan Brecher – for Clustering for several of us, for your comprehensive analysis and for your insight!

If anyone has been Clustering around CAs, that is still OK. Think of your Cluster CAs as potentially having multiple TGs – particularly the closer CAs (4C-6C range). And as you run Clustering with smaller thresholds, and find more Clusters, you’ll find your former Clusters subdividing into smaller Clusters, which smaller Clusters would tend to match up with one TG – Walking the Clusters Back!

 

[22AE] Segment-ology: Clusters Link to TGs and an Apology TIDBIT by Jim Bartlett 20190222