Leeds Method Clustering Tool: Classic 4-Column DNA Match Organizer

1. What is the Leeds Method in Genetic Genealogy?

The Leeds Method, created by genetic genealogist Dana Leeds in 2018, is one of the most effective manual sorting techniques for organizing autosomal DNA matches. When you receive autosomal DNA test results from platforms like AncestryDNA, MyHeritage, 23andMe, or FamilyTreeDNA, you are often presented with thousands of cousin matches listed purely by shared centimorgans (cM). Without a structured system, identifying which matches belong to which ancestral line can be overwhelming.

The core objective of the Leeds Method is to group 2nd and 3rd cousin matches (typically sharing between 90 cM and 400 cM) into four distinct color-coded columns. Because you inherit 25% of your DNA from each of your four biological grandparents, these four columns naturally align with your four grandparental branches (Maternal Grandfather, Maternal Grandmother, Paternal Grandfather, and Paternal Grandmother).

By organizing matches based on who they share in common (In-Common-With / ICW data) rather than relying on paper family trees, the Leeds Method allows genealogists, adoptees, and researchers investigating unknown parentage to isolate biological branches even when no family tree documentation exists.

2. Tool Comparison: Classic 4-Column Leeds vs. Leeds Auto Clustering

Genetic Voyage provides two specialized clustering tools. Understanding how they differ ensures you select the right tool for your specific research problem:

3. Step-by-Step Instructions: How to Use the Classic Leeds Tool

To successfully organize your DNA matches into grandparental clusters, follow these five steps:

1. Gather Your Match Data: Open your testing database (AncestryDNA, MyHeritage, 23andMe, or FamilyTreeDNA). Filter or identify matches sharing between 90 cM and 400 cM.
2. Add Matches to the Tool: Under "1. Your matches", enter the match's name and total shared cM, then click Add match. The tool automatically sorts matches from highest shared cM to lowest.
3. Set Your Exclusion Threshold: Under "2. Settings", verify the exclusion threshold (default: 1500 cM). Matches above this threshold (parents, siblings, aunts/uncles, grandparents) share DNA across multiple lines and are automatically set aside into the "Excluded" list to prevent clutter.
4. Fill in Shared-Match Connections (ICW): In "3. Shared-match grid", look at each match pair. Check the "Shared Matches" or "In-Common-With" (ICW) list on your testing company website. If Match A and Match B appear on each other's shared-match list, tick the checkbox at their intersection in the grid. Ticking one box automatically mirrors the connection across the diagonal.
5. Generate & Name Your Clusters: Click Run Leeds Method clustering. The tool generates your classic 4-column cluster table. Once you recognize known relatives or surnames in a column, type a custom label into the cluster legend (e.g. "Paternal Grandfather - Smith") to update the column heading automatically.

4. How to Interpret Your Cluster Results

When the clustering algorithm finishes, matches are presented in a clean spreadsheet layout. Here is how to interpret the visual markers and notes:

• Color-Coded Dots: A colored dot in a column indicates that the match belongs to that cluster network. In an ideal 4-grandparent result, you will see four distinct vertical columns of colored dots with minimal cross-over.
• Cluster Labels: The top legend allows you to rename "Cluster 1", "Cluster 2", etc., to your confirmed ancestral lines once you identify anchor relatives in those groups.
• Unclustered — Possible New Line: Displayed when a match has no ICW connections to any established cluster. This often occurs with distant matches (near 90 cM), matches from a branch with few tested relatives, or half-cousin lines.
• Overlaps X Lines: Displayed when a match shares ICW connections with members of multiple columns (e.g. Cluster 1 and Cluster 3). This indicates cross-branch relatedness caused by pedigree collapse, endogamy, double first cousins, or relatives related on both your mother's and father's sides.

5. Mathematical Methodology & Algorithm Architecture

The Classic Leeds Method Clustering Tool executes a top-down greedy assignment algorithm directly within JavaScript. Below is a formal breakdown of the computational steps:

Step 1: Match Filtering & Sorting

Let $M = \{m_1, m_2, \dots, m_n\}$ represent the set of entered matches, where each match $m_i$ has a shared centimorgan value $c_i$. The tool filters out matches exceeding the exclusion threshold $T$ (default $T = 1500\text{ cM}$):

$$\text{Pool} = \{ m_i \in M \mid c_i \le T \}$$ $$\text{Excluded} = \{ m_i \in M \mid c_i > T \}$$

The active pool is then sorted in descending order of shared cM so that stronger matches are evaluated first: $c_1 \ge c_2 \ge \dots \ge c_k$.

Step 2: Top-Down Greedy Cluster Assignment

Let $C = \{C_1, C_2, \dots, C_p\}$ represent the list of active clusters, where each cluster $C_j$ is a set of match IDs. The algorithm iterates through each match $m_i \in \text{Pool}$ from highest to lowest cM:

1. It checks connectivity with existing clusters. For each active cluster $C_j$, match $m_i$ is connected to $C_j$ if there exists at least one member $u \in C_j$ such that $\text{icwHas}(m_i, u) = \text{true}$.
2. If $m_i$ connects to one or more existing clusters, it is added to all matching clusters: $C_j \leftarrow C_j \cup \{m_i\}$, and recorded in the assignment lookup $\text{assignment}[m_i] = \{j \mid m_i \text{ connects to } C_j\}$.
3. If $m_i$ connects to no existing clusters and $|C| < \text{maxClusters}$ (default 4), a brand new cluster is instantiated: $C_{\text{new}} = \{m_i\}$, and added to $C$.
4. If $m_i$ connects to no existing clusters and $|C| = \text{maxClusters}$, $m_i$ remains unassigned ($\text{assignment}[m_i] = \emptyset$) and is flagged as Unclustered.

Step 3: Privacy & Computational Efficiency

The time complexity of the clustering algorithm is $O(K \cdot N)$, where $K$ is the number of active clusters ($K \le 8$) and $N$ is the number of pooled matches. Because calculation occurs entirely in memory within the client browser, processing 50+ matches takes less than 5 milliseconds with zero external server calls.

6. Practical Research Workflows & Case Studies

7. Methodological Assumptions & Scientific Limitations

While the Leeds Method is a cornerstone of genetic genealogy, researchers must understand its underlying scientific boundaries:

• ICW Shared Matches vs. Chromosome Triangulation: In-Common-With (ICW) data indicates that two matches share DNA with you and with each other. However, ICW does not guarantee that all three individuals share the exact same physical DNA segment on the same chromosome (Identical by Descent, IBD). True segment verification requires a chromosome browser tool.
• Pedigree Collapse & Endogamy: In populations with high endogamy (e.g. Ashkenazi Jewish, Acadian, or island populations) or intermarriage between branches, individuals inherit shared DNA from multiple ancestral lines. This causes match clusters to merge together or causes matches to appear in 3 or 4 columns simultaneously.
• Uneven Genetic Recombination: Due to random genetic recombination, you do not inherit equal 25% portions from your four grandparents. One grandparent line may yield numerous 2nd cousin matches while another yields zero matches in the 90–400 cM range.
• Database Limitations: The tool relies entirely on the matches available in your testing company's database. If relatives from one grandparent's line have not tested, that cluster column will remain empty.

8. Frequently Asked Questions (FAQs)

9. Verified Scientific & Methodological References

• Leeds, Dana. (2018). The Leeds Method. Official documentation on 90–400 cM four-column sorting workflows.
• Bettinger, Blaine. (2020). The Shared cM Project v4.0. DNA Painter crowd-sourced empirical dataset of observed centimorgan sharing ranges.
• ISOGG Wiki. (2026). Leeds Method & Autosomal DNA Statistics. International Society of Genetic Genealogy definitions of ICW networks and cousin detection probabilities.
• Donnelly, Peter. (1983). The Probability that Related Individuals Share Some Section of Genome Identical by Descent. Theoretical Population Biology, 23(1), 34–63. Mathematical basis of IBD segment distribution.

Explore Related Genetic Genealogy Tools & Guides

• Leeds Auto Clustering Tool (2D Matrix): Generate AutoCluster grids with up to 12 clusters and segment triangulation markers.
• DNA Relationship Probability Calculator: Estimate relationship probabilities from total shared cM, segment count, and largest segment.
• DNA Chromosome Browser: Plot and compare shared DNA segments across chromosomes 1–22.
• DNA Phasing Guide: Learn how to separate maternal and paternal chromosomes for cleaner segment analysis.
• Free Genealogy Excel Templates: Download pre-formatted spreadsheets for Leeds Method match tracking.