Forensic genetic genealogy (FGG) is being revolutionized by tools that streamline the analysis of forensic genetic matches, which in turn saves investigators time and effort, while still promoting accuracy and successful outcomes. A few months ago, we introduced automatic clustering to the Multi-Dimensional Forensic Intelligence (MDFI) platform, enabling investigators to group genetic matches into clusters based on total shared centimorgans (cM). This powerful feature organizes matches into distinct groups that often correspond to branches of a family tree, making it easier to prioritize connections and better navigate the complexities of FGG.

Now, we are excited to introduce a new feature: clustering using triangulated segments. This enhancement brings a new level of clustering analysis, allowing investigators to tackle cases involving imperfect DNA profiles or complex familial structures such as endogamy. In this post, we’ll explain how triangulated segment clustering works, compare it to traditional shared cM clustering, and explore how these two approaches complement each other in solving FGG cases.

Clustering with Shared cM

Shared cM clustering groups genetic matches on the basis of total shared DNA. Matches that share similar total cM values with the unknown individual are grouped together, providing a high-level view of relationships.

This method is particularly useful for:

  • Broad overviews of potential relationships, especially for closer relatives.
  • Efficient prioritization of matches based on how much DNA they share.

However, shared cM clustering has limitations, particularly in cases involving incomplete or low-quality DNA profiles. Forensic profiles often come from degraded DNA, making the calculated total cM data less reliable. Additionally, in endogamous populations where individuals are related through multiple lines, shared cM alone may not clearly identify distinct branches of the family tree.

Clustering with Triangulated Segments

Triangulated segment clustering is a more granular approach to grouping genetic matches. Unlike shared cM clustering, which relies on the total amount of DNA shared between individuals, triangulated clustering identifies matches that share overlapping segments of DNA with both the unknown individual and other individuals. This method provides data everyone in a cluster that shares a common ancestor, making it an invaluable tool for FGG.

To understand triangulation, imagine it as a three-way confirmation of a relationship. If Person A shares a segment of DNA with Person B and also shares that same segment with Person C—and if Persons B and C share that segment with each other—it’s highly likely that all three individuals inherited that DNA from a shared ancestor. This process is called triangulation because it forms a “triangle” of genetic connections, validating the shared DNA as originating from a specific branch of the family tree.

In triangulated segment clustering, genetic matches are grouped on the basis of sharing the same DNA segments, and in MDFI the user can fine tune the threshold for minimum segment size, in which to enable clustering.

Triangulation is particularly valuable because it filters out matches that may appear related based on total shared cM but do not share a specific segment(s) of DNA. These matches could result from coincidental similarities or distant endogamous relationships rather than a direct familial connection. By focusing on shared DNA segments, triangulated clustering isolates true relationships, providing a clearer and more accurate picture of genetic connections.

For example, in forensic investigations involving degraded DNA, total shared cM values may be unreliable due to incomplete or partial profiles. Triangulated clustering can overcome this limitation by homing in on overlapping segments, which are more likely to provide insight even in challenging samples. Similarly, in cases involving endogamous populations—where individuals may share DNA through multiple ancestral lines—triangulated clustering helps disentangle the web of connections, pinpointing the specific branch of the family tree where a match is most relevant.

Key Advantages:

  1. Enhanced Accuracy: By identifying shared segments, triangulated clustering avoids false connections that can occur when matches share DNA with the unknown individual but not with each other.
  2. Utility with Imperfect Profiles: Degraded DNA often results in incomplete profiles. Triangulated clustering can make sense of these profiles by focusing on specific segments, rather than relying on total shared cM, which may be misleading.
  3. Navigating Endogamy: In endogamous populations, shared cM clustering can result in overlapping clusters that are hard to disentangle. Triangulated clustering identifies true familial branches by focusing on segment overlap, helping investigators isolate relevant connections.

Additional features to assist in Forensic Genetic Genealogy 

The addition of triangulated segment clustering to MDFI represents another step forward in FGG. The real power of MDFI’s clustering tools comes from combining these two approaches. Shared cM clustering provides a broad initial overview, quickly highlighting the most promising genetic matches. Triangulated clustering refines the analysis, validating relationships and uncovering specific branches of the family tree. By leveraging both clustering methods, investigators can approach even the most challenging cases with greater confidence. Triangulated clustering provides the precision needed to handle incomplete profiles and complex family structures, ensuring that every possible lead is explored.

With our new clustering tools, we also now feature biogeographical ancestry and surname insights directly into each cluster. Previously, we launched in-line biogeographical ancestry for individual match lists, providing investigators with immediate context about the geographic origins of each match. Now, this feature extends to clusters, where the biogeographical ancestry of each individual is reported within the group. This provides valuable clues about the shared ancestral region or population associated with a specific branch of the family tree, helping investigators identify patterns that can guide genealogical research. In addition to biogeographical ancestry, we now report the most common surnames found within each cluster. This feature can provide another layer of investigative insight, especially when combined with Y-DNA testing or surname analysis.  

Explore the New Features Today

Triangulated segment clustering and all the other features described in this post are available right now on the MDFI platform. Start using these tools today to investigate cases more effectively, faster, and to unlock the full potential of FGG.

Do you have ideas or requests for tools that would streamline or accelerate identifications with forensic genetic genealogy? Reach out and tell us what you want to see in future updates to our platform.

Let’s work together to unlock answers and bring justice to those who need it most. Get started here.

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