For reference, here are all posts for the Shared cM Project:
- Most up-to-date post: “Version 4.0! March 2020 Update to the Shared cM Project!” (March 27, 2020)
For reference, here are all posts for the Shared cM Project:
Interested in learning about the unique inheritance of the X chromosome through the use of some cool visual charts? Look no further:
And be sure to check out Debbie Parker Wayne’s great charts at “X-DNA Inheritance Charts“!
The Shared cM Project (ScP) is a collaborative data collection and analysis project created to understand the ranges of shared cM associated with various known relationships. The ScP has been very successful, with more than 60,000 submissions from amazing genealogists like YOU! To add your data, the Submission Portal is HERE. I am always collecting data, and hopefully the next update will have more than 100,000 submissions!
The full PDF for Version 4.0 of the Shared cM Project is here and it is ESSENTIAL that you read the full PDF for all the details from the project: The Shared cM Project Version 4.0 (March 2020).
Today, the most recent version of the ScP, Version 4.0, goes live. I’ve taken nearly 60,000 submissions and analyzed the data for almost 50 different relationships. For each relationship the 100s or 1000s of submissions were analyzed to remove outliers, to provide minimum, maximum, average, and standard deviation values, and to generate a histogram for the distribution of the submissions. Here are some of the other differences between this new Version 4.0 and the previous version (click to enlarge):
[WARNING: I discuss or imply violent acts by ancestors in this post, read at your own risk].
We’ve all heard it. Some of us have even made it. A joke or implication about an affair or dalliance that conceived a child, often referring to the milkman or a neighbor. It’s usually directed to the biological mother, always ignoring or downplaying any act by the biological father, and is always consensual. The audience (whether in a Facebook forum or at a talk/seminar/webinar), seemingly always primed for the joke, laughs and the speaker moves on.
It’s time for this joke or implication, whether blatant or implied, to die the ignoble death it deserves.
A few years ago during a lecture, I make a flippant remark about a misattributed parentage conception. It may have been as simple as raising my eyebrows at a key moment, or even a simple pause that implied meaning, I don’t remember. After the talk, an audience member came up and called me out for being flippant about misattributed parentage conceptions. And the audience member was right, I had been flippant. I was wrong.
In this blog post we will briefly review an extreme Grandparent/Grandchild relationship, where a grandchild appears to share just 9% of her DNA with a paternal grandmother rather than the expected 25%. All information is anonymized.
I’m a little afraid to post this article about an extreme outlier scenario. There is a danger that it could support misinterpretation rather than foster critical thinking. If you have a possible outlier scenario, be sure to try to disprove that it is an outlier situation, rather than simply proceeding as if is an outlier. Avoid confirmation bias!
This is the third post on my blog specifically examining outliers in confirmed relationships:
This was discussed in a previous article about outliers, but it bears repeating.
The annual RootsTech convention at Salt Lake City in February has become a showcase for new tests and tools offered by the DNA testing companies. The biggest winner of all, of course, is the consumer!
Both AncestryDNA and MyHeritage announced major new developments at this year’s RootsTech. For example, AncestryDNA announced “MyTreeTags,” “New & Improved DNA Matches,” and “ThruLines,” three different tools currently in beta. The first two require an opt-in, while everyone is currently eligible for ThruLines. Meanwhile, MyHeritage announced “Theory of Family Relativity” and “AutoCluster,” both of which are currently available to members of their DNA database.
There is a LOT to digest with these tools, including knowing how to use them and understanding their limitations. To help you understand what the tools are and how to use them, I’ve created two new YouTube videos:
Sheryl, a member of the Genetic Genealogy Tips & Techniques group (which just broke 50,000 members!) recently commented on a thread about shared DNA outliers about a situation within her own family. I thought it would be a great opportunity to discuss outliers and how to deal with them. Sheryl kindly agreed!
For background, we examined an outlier situation once before on this blog, where second cousins once removed (2C1R) did not share DNA (see “Analyzing a Lack of Sharing in 2C1R Relationship“).
Sheryl indicated that she and her mother Grace appeared to be outliers with Sally, their first cousin (1C) and first cousin once removed (1C1R), respectively. Grace shared 482 cM with her 1C Sally, and Sheryl shared 215 cM with her 1C1R Sally. Not surprisingly, Grace and Sheryl share an expected amount for mother/daughter:
It is canon that you received your mtDNA from your mother, who received it from her mother, who received it from her mother, back through time to Mitochondrial Eve. But could that canon be wrong?
Probably not. And even if some paternal mtDNA were to “leak” into and survive in the embryo, it would happen so rarely that it could only affect things like the timing to Mitochondrial Eve and population studies, NOT genealogical research.
[New] Research from PNAS
In new research from the journal PNAS published today (“Biparental Inheritance of Mitochondrial DNA in Humans“), which is unfortunately behind a paywall, researchers identified paternally-inherited mitochondrial DNA in 17 individuals spanning three unrelated families. What is missed from the media coverage, however, is that these families were identified because member(s) were presenting with conditions that made the researchers suspect a mitochondrial disorder.
Links to Other Blog Posts:
Artifact testing promises to be an interesting component of the future DNA evidence and genealogy. If we can obtain and reliably identify DNA from deceased ancestors, relatives, or other individuals, we might be able to enrich our genealogical research.
For years I’ve been telling people that there is an enormous untapped market for artifact testing, and that they should hold on to their artifacts because a company will arise to offer this service. I typically follow that up by telling them NOT to literally “hold on to their artifacts” because I don’t want them to contaminate them! But seriously, there are many thousands, potentially millions, of artifacts that could possess DNA from long-dead individuals.
The genealogical community has a serious issue we need to talk about.
We are amassing one of the largest collections of genealogical information ever created, in the form of DNA match data. As of October 2018, approximately 20 million people have taken autosomal DNA (atDNA) tests, and that number continues to grow rapidly. DNA evidence is being added as an additional record to support existing genealogical conclusion, being used to generate new hypotheses, and helping break down decades-old brick walls.
However, since many genetic matches are either unwilling or unable to respond to communication or provide permission for public use of the genetic data, much of the massive database is potentially locked behind privacy walls such that the information can’t be utilized in scholarship and can’t be publicly shared. Indeed, Standard #8 of the Genetic Genealogy Standards (PDF) mandates the following:
Many DNA test takers have a wealth of genetic relatives! For example, I have more than 50,000 different genetic cousins across all the genealogy DNA testing companies. Although many regions of the world do not yet have 1,000’s of genetic cousins in their match lists, they will in the future as DNA testing grows increasingly popular and the testing companies target other countries.
Unfortunately, the testing companies have not provided users with the tools necessary to organize these matches. Indeed, clustering and organization of genetic cousins is a huge component of the future of DNA evidence. Clustering of our matches allows us to identify information that is not visible or apparent when the matches are unorganized.
This is where the DNA Match Labeling extension for Chrome comes in! I worked with a programmer to build this extension.
AncestryDNA today (12 September 2018) released updated ethnicity estimates for all customers. Everyone in the AncestryDNA database will see some change in their estimate.
This update represents one of the most significant refinements of AncestryDNA’s ethnicity estimates. Both the reference populations and the ethnicity algorithm underwent significant development.
The size and makeup of the reference populations grew substantially, from ~3,000 reference samples to ~16,000 reference samples (many provided by test takers that consented to participating in AncestryDNA research). The update adds 17 new regions to the ethnicity analysis (from 363 to 380). Many more are needed in areas such as Asia and Africa, of course, but this is a great addition. As well, many regions were redefined or their names were changed to more accurately reflect the region.