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HomeUpcoming Meetings and EventsHerb Weisberg: Finding Semi-Distant Cousins by Mining Your DNA Matches

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Herb Weisberg: Finding Semi-Distant Cousins by Mining Your DNA Matches

When:
Sunday, February 13, 2022, 1:00 PM until 3:30 PM
Additional Info:
Contact(s):
Deborah Long
Category:
Monthly Meetings
Registration is not Required
Payment In Full In Advance Only
Zoom link will be sent one week in advance of the event.
Capacity:
99
Available Slots:
99
No Fee






Herb’s family lived in the Bronx until he was 7, when they moved up to White Plains, NY. He majored in math at Columbia and obtained a Ph.D. in Statistics at Harvard. He then settled in the Boston area and pursued a career there as a biostatistical consultant. Herb has written three books about statistical methods and, more recently, a novel called Data Games. After moving with his wife Nina to Cary in 2018, he became interested in genealogy, mainly to fill a void in knowledge about his father’s family. This has led to a number of surprising discoveries and a deeper understanding of the role that genetic data can play in genealogical research.


 

 

Finding Semi-Distant Cousins by Mining Your DNA Matches

Herb Weisberg, Ph.D.

 

For Ashkenazi Jews, conventional advice about DNA Matches is quite conservative. We are admonished to focus only on strong matches and to attribute most weak matches to measurement error or endogamy. For instance, I have often been advised that a DNA match much less than 100 cM is hardly worth considering, and a shared segment much less than 20 cM may just be endogamy or “noise.” Ironically, however, ignoring modest DNA matches all but precludes finding the most genealogically relevant (third and fourth?) cousins.

After all, such semi-distant cousins are apt to share at most a small amount of DNA distributed in a few short segments, resulting in a Catch-22 situation. So, how can we distinguish the few promising DNA matches from the vast majority whose ancestors are so remote as to be essentially irrelevant? I will start by walking through the basics of DNA matching and triangulated DNA segments. Then, I will suggest a surprisingly simple alternative to conventional DNA triangulation and an associated “data-mining” technique.

This new method can efficiently identify the best DNA matches to pursue. To illustrate this approach, I will share some relevant applications from my own genealogical research.


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