When I first started using DNA for genealogy, I was overwhelmed by match lists and centimorgans. Fast forward a few years, and I’m now using advanced techniques like network clustering and DNA coverage estimates to solve centuries-old family mysteries. Today, I want to share how these powerful methods helped me prove that Harrison Johnson (born c1813) was the son of Uriah Johnson.

Beyond the Basic Match List

If you’ve tested your DNA, you’re familiar with the long list of matches that testing companies provide. While these lists are valuable, they only show your direct relationship to each match. To really unlock DNA’s potential, I needed to understand how my matches related to each other.

This is where network analysis came into play for my Harrison Johnson research.

Creating a Network Graph with Gephi

One of the most powerful tools in my DNA analysis was Gephi, a network visualization software that helped me identify genetic communities. Here’s how it worked:

I gathered DNA match data for my extended family using DNAGedcom
Imported this data into Gephi, which created a visual network of connections
The software automatically identified clusters of related individuals

What emerged was fascinating—four distinct subclusters representing descendants of different children of Uriah Johnson. My matches from Harrison’s line clustered together, as did matches from Billington’s, Mary Ann’s, Tilmon’s, and Manerva’s lines.

Tech Tip: While Gephi has a learning curve, the visual insights it provides are worth the effort. There are several good tutorials online to help you get started.

These clusters formed not because I told the software who was related, but purely based on shared DNA patterns. Seeing my paper trail confirmed through this objective genetic clustering was incredibly validating!

Understanding DNA Coverage

Another concept that transformed my research was DNA coverage. This refers to how much of an ancestor’s genome we can potentially “recover” by testing multiple descendants.

Since autosomal DNA gets diluted with each generation (we inherit roughly 50% from each parent), a single descendant will have only a small fraction of a distant ancestor’s DNA. But by testing multiple cousins who descend through different children of our target ancestor, we can recover a much larger percentage.

In my case, I analyzed 25 DNA tests (including my own, siblings, cousins, and identified matches) to achieve:

36.7% coverage of Uriah Johnson’s genome
45% coverage of Harrison Johnson’s genome
12.1% coverage of Billington Johnson’s genome
12.1% coverage of Tilmon Johnson’s genome
6.2% coverage of Mary Ann Johnson’s genome
4.6% coverage of Manerva Johnson’s genome

To put this in perspective, I personally only inherited about 1.56% of Uriah’s DNA directly. By using this wider testing strategy, I was able to recover over one-third of his genetic material—enough to make confident relationship assessments.

Looking for Segment Triangulation

While network clustering showed that my matches grouped as expected, I also wanted to see if we shared the same DNA segments—a concept called segment triangulation.

I found that my DNA matched with Billington1 (a descendant of Billington Johnson) on chromosomes 1, 7, 13, and 15. Meanwhile, Mary Ann2 (a descendant of Mary Ann Johnson) and I matched on chromosomes 5, 8, and 22.

These distinct matching patterns make sense—I wouldn’t expect to match both cousins on the same segments since I likely inherited different portions of Uriah’s DNA than each of them did. This pattern actually strengthens the case that our shared DNA comes from a common ancestor (Uriah) rather than from coincidental matches.

Checking the Numbers Against Known Relationships

To ensure my conclusions were scientifically sound, I compared the amount of shared DNA (measured in centimorgans or cM) with expected ranges for our theoretical relationships.

For instance:

I shared 15 cM with Abington1, a 5th cousin once removed
I shared 30 cM with Billington1, a 4th cousin once removed
I shared 26 cM with Mary Ann2, a 5th cousin
I shared 18 cM with Manerva1, a 5th cousin once removed

All of these values fall within the expected ranges according to “The Shared cM Project,” a valuable tool created by genetic genealogist Blaine Bettinger. Some shared slightly more DNA than average, but this is normal due to the random nature of DNA inheritance.

When DNA and Documents Tell the Same Story

What makes this case so compelling is how perfectly the DNA evidence aligns with my documentary research. The genetic relationships confirm exactly what the census records, migration patterns, and naming practices had suggested—that Harrison Johnson was indeed the son of Uriah Johnson.

This is the power of combining traditional genealogical methods with modern DNA analysis. When both approaches lead to the same conclusion independently, the proof becomes exceptionally strong.

How You Can Apply These Techniques

Ready to use these advanced methods in your own research? Here’s how to get started:

Test multiple family members from different branches of your family tree
Upload your raw DNA data to multiple sites to find more matches
Learn to use clustering tools like Gephi, DNA Painter, or Genetic Affairs
Calculate your DNA coverage for key ancestors using online tools
Look for patterns across multiple matches rather than focusing on individual relationships

DNA analysis can seem intimidating at first, but I’ve found that taking it step by step and building on each success makes it manageable and incredibly rewarding.

This post is adapted from one of my professional genealogical research reports. The blog draft was prepared with AI assistance from Claude (Anthropic) and reviewed and approved by me prior to publication.