DNA of WAB, Pt 2: Needles in Haystacks

Part 1 introduced the Genetic Affairs program which organizes the various ancestors by creating clusters of DNA shared with WAB. As mentioned, any shared match above 30 cM suggests a likely genealogical match. By the end of the article, clusters 20, 21 and 22 remained unidentified, and there were a series of nine results with > 30 cM that have only one match (making them a lonely cluster of one). Now comes the slow process of examining each of these twelve clusters one-by-one. The hope is to understand each one, and identify its place in the tree, with emphasis on uncovering the Nicklos subgroup, consisting of Ernest Nicholas, Charlotte Zieryacks, James McDowell of Scotland (not to be confused with James McDowell of Pennsylvania in the Woods line) and Isabella McLeod.

The Cryptocluster and the Pseudocluster
Despite all these efforts some clusters remain unidentified. These clusters contain a lot of shared DNA and robust trees, but with no connection found to WAB. I am going to coin the term “cryptocluster” because of the way these clusters hold their secrets. Cryptoclusters might be legitimate or they might be fake earning them my derisive term “pseudocluster”. I don’t like pseudoclusters because they do not lead to a common ancestor, yet they suck up a lot of research time. Currently, I can think of five reasons why a cluster remains a mystery: 

• Potential Causes of Legit Cryptoclusters
  • I am wrong – The result points to an erroneous or unknown branch with my tree, possibly due to a NPE (see below)
  • They are wrong – Other members of the cluster may be presenting inaccurate information
  • Everyone is wrong – The underlying genealogical record is faulty.
  • Endogamy – Marriage of somewhat related individuals (see below)
• Potential Causes of Pseudoclusters
  • A false positive – The result in an individual match is wrong due to random variation
  • Pile up – The result of a group of matches is wrong due to a systemic issue in the DNA (see below)
  • A faulty computer algorithm – Genetic Affairs or Ancestry has created a cluster in error

The Nonpaternity Event (NPE)
Sometimes a cryptocluster points to a completely unexpected direction: the surprise DNA result. Genealogists have dubbed these discoveries as nonpaternity events (NPE’s). It’s such a nondescript term. I have heard many NPEs better described as “shenanigans”. Because they occur for a variety of reasons (adoption, out-of-wedlock births, etc), the surprise unmasks a messy side to DNA testing. Without getting into an ethical argument of uncovering these family secrets, it should be noted that it can be very difficult to prove the results with certainty because the supporting genealogy evidence has been swept under the family rug. WAB’s DNA has uncovered a few NPE’s, all over 100 years old. Fortunately, the major branches of the tree has survived intact (so far). If the amount of shared DNA is high enough, I can usually figure what likely happened. However, if the event happened too long ago, then the amount of shared DNA will get too low to unravel the story. The surprise result will remain a cryptocluster.

I have identified a special subset of cryptocluster. The sequence NNNN in cluster 13 of the Genetic Affairs chart points a guy named Elijah Fuller Knapp. Who is that?, or as a Louisiana Cajun might ask, whodat? While he could be a NPE, other possibilities exist. For the time being, cluster 13 is a whodat cryptocluster.

Endogamy
Think of endogamy as “cousin love”. Not the first cousin stereotype. Instead, consider second cousins, third cousins, second half cousins, etc. Let us say you live in a small, isolated community and you want to marry someone who shares your interests, values and culture. That special someone could also easily share a recent common ancestor. The effect on DNA results can be profound. You can see it in the cluster results in two significant ways. First, you will see high levels of shared matches in endogamous results, since shared DNA comes from multiples sources. A 30 cM match from a endogamous branch appears to be more closely related than it is. Stated another way, endogamy acts as a shared match magnifier. Second, you will see a whole bunch of the gray dots in Genetic Affairs since there are multiple common ancestors in a given cluster. Look no further than my father’s side if you want an extreme example of endogamy.  Clusters 1 to 21 map the DNA of my paternal grandfather, born of Ashkenasi Jewish parents, a classic endogamous population. You have to look hard to find cluster 22 representing the DNA of my paternal grandmother. “Cluster Bomb” would seem the appropriate term.

Since WAB’s grandparents arrived from such different geographic locales, much of WAB’s tree would seem devoid of endogamy. Or is it? Take a look at all the gray inside the boxes of the Woods, Baker and Perkins subgroups.

Now, let us revisit the ancestryDNA odds table where it states “the likelihood of a single recent common ancestor” is 50% for a 16-30 cM match. Normally, we think that these odds apply to a shared match of one person to another. Pile up suggests that these odds are not necessarily random. Instead, shared match errors may be correlated (the same error happens repeatedly in a given group). As a result, the confidence score could apply to a whole cluster, or part of a cluster. I have tried to use 20 cM as a cutoff to avoid pile up or any other cause of a pseudocluster, but it may not be good enough. If the error is so correlated, then it might be that 50% of entire clusters are pseudoclusters when Genetic Affairs is run at the lower 20 cM threshold.

What does a pile up look like? Rick Marshall, writing in the Genetic Affairs User Group, used chromosome maps to identify a pile up region in his DNA. He then showed the results on Genetic Affairs. I have seen similar results in my cluster diagrams as illustrated below. Whereas we can think of endogamy as a shared match magnifier, pile up acts as a shared match parasite. Here are the notable characteristics:

1. The cluster is really big compared to the clusters around it. DNA is piling up.
2. The cluster may contain a few legitimate matches in the upper left corner representing the legit host cluster. Those matches have the highest amount of shared DNA. The rest of the cluster is the parasitic pile up DNA latching onto the host.
3. You wouldn’t find any common ancestors, because none exist.
4. Because it has no matches, the pile up region has few grays dots.

Many of the comments above are observational with a dash of pure science to give me cover. More observations and testing will be necessary to understand the cluster charts at lower thresholds. You have to be flexible in this analysis since new tools, techniques and people get added all the time. Recently, a new single match has surfaced. As you will see, single match 10 has proven to be extremely important.

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