Analyzing DNA Matches
Analyzing DNA Matches
A DNA match list is not a family tree. It is a network of genetic evidence. The real skill is learning how to turn that network into identifiable family branches, research hypotheses, and evidence you can test against your genealogy.
When you first open your DNA match list on MyHeritage, AncestryDNA, FamilyTreeDNA, or another testing service, it is easy to feel overwhelmed. You may see hundreds or thousands of matches, ranging from close relatives to distant connections.
The mistake is to treat the list as something you simply browse. Effective genetic genealogy is different. You start with the strongest evidence, organize matches into networks, investigate their family trees, and then use segment data and documentary research to test your hypotheses.
🧬 The core principle
Do not ask only “Who is this match?”
Ask instead: “Which group does this match belong to, which ancestral branch does that group represent, and what evidence can prove the connection?”
Your DNA Match Analysis Workflow
A reliable analysis becomes much easier when you follow the same sequence every time.
Prioritize Matches by Shared DNA (cM)
The first useful number on a DNA match is usually the amount of DNA you share, expressed in centimorgans (cM). A centimorgan is a unit of recombinant frequency used to measure genetic distance, representing a 1% chance that two markers on a chromosome will be separated by crossing over in a single generation. Shared cM provides an important starting point for estimating which relationships are genetically plausible.
But cM is not a relationship label. The same amount of shared DNA can be consistent with several different relationships because inheritance and recombination are random. The Shared cM Project, a collaborative data collection project created by Blaine Bettinger, therefore presents ranges and possible relationships rather than a single definitive answer. As the ISOGG autosomal DNA statistics page notes, known second cousins on average share about 212.5 cM, but in extreme cases can share as little as 47 cM or as much as 760 cM.
Typical shared DNA by relationship (approximate averages)
Start with your strongest matches
Close and moderate matches generally provide the most useful starting points because their relationships are easier to establish and their family trees can often be connected to yours.
Use cM as a probability clue
Enter the exact shared cM into a relationship calculator rather than accepting a testing company's predicted relationship as fact.
Do not discard distant matches automatically
Smaller matches can become valuable when they belong to a clearly defined cluster or triangulated segment. Their value depends heavily on context.
A match sharing 150 cM does not automatically mean one particular cousin relationship. Multiple relationships can fall within the same shared-cM range. Use the cM value to narrow the possibilities, then combine it with age, shared matches, family trees, geography, and segment evidence.
You can use the DNA Relationship Probability Calculator to explore the possible relationships for a match.
Use the Leeds Method to Cluster Matches
Developed by Dana Leeds, the Leeds Method is one of the most useful manual approaches for organizing autosomal DNA matches. Instead of examining every match independently, you group matches according to their shared-match relationships.
The objective is to reveal groups that may correspond to different grandparental branches or ancestral lines. The exact result depends on your family structure, available matches, endogamy, pedigree collapse, and the range of matches you use.
What a successful Leeds clustering can reveal
How to implement the Leeds Method
- Select a useful range of matches.
A commonly used Leeds range is roughly 90–400 cM. The precise range can be adjusted depending on the test database and your family structure. - Create a working spreadsheet.
Record each match, the shared cM, and the cluster colors or groups you assign. You can use our free genealogy templates to organize the work. - Choose your first match.
Give the match a color and then examine that person's shared matches. - Mark the shared-match network.
Matches who also appear as shared matches with your selected person can be placed in the same color group. - Move to the next unassigned match.
Give that match a new color and repeat the process. - Look for recurring groups.
Once enough matches have been processed, patterns begin to emerge. These groups can then be compared with known maternal and paternal relatives and family trees.
The Leeds Method is often described as producing four grandparent clusters, but real families are not always that simple. Endogamy and pedigree collapse, multiple relationships, limited match data, and uneven inheritance can produce overlapping or incomplete groups.
Treat the clusters as research leads, not automatic proof of a particular grandparent.
Analyze Shared Matches
Once you have identified promising groups, investigate the relationships inside them. Shared Matches — sometimes called In-Common-With (ICW) — show which people appear to be connected through the same genetic network.
Using a known relative as a reference point
Known relative available?
A tested relative from a known side of your family can be an extremely powerful reference point. Matches shared by you and that relative provide evidence that the match belongs somewhere within that side of your family.
No known relative?
You can still build useful networks using the Leeds Method, shared-match analysis, family trees, locations, surnames and other evidence. The process simply requires more corroboration.
If you have hundreds or thousands of matches, manual grouping can become time-consuming. Automated clustering tools can help visualize networks and reveal groups that deserve closer investigation.
See our guide to understanding AutoCluster reports for a visual introduction to automated match clustering.
Map Overlapping DNA Segments
Shared-match analysis tells you that people belong to a connected genetic network. Segment analysis adds another layer: it allows you to examine where on your chromosomes the shared DNA occurs.
A chromosome browser can display overlapping DNA segments between you and your matches. When several people share overlapping DNA with you and the relevant matches also share DNA with one another, this can provide the basis for investigating a triangulated group.
A chromosome browser view: overlapping segments
How triangulation works
This is where DNA analysis can move beyond simply identifying that people are related. Segment evidence can help you organize inherited DNA by ancestral line and test whether different matches may descend from the same ancestral couple.
Explore our DNA Chromosome Browser, read the chromosome browser setup guide, and then continue with our advanced DNA triangulation guide.
Compare Matches With Family Trees
DNA alone rarely gives you the name of an unknown ancestor. The next step is to connect your genetic networks with traditional genealogy.
Once you have a cluster, examine the family trees of several people in that group. Look for recurring surnames, locations, ancestral couples, migration patterns and other genealogical clues.
Several trees, one shared ancestral couple
GEDCOM files can be especially useful when you want to compare trees without manually browsing every online tree. You can use our GEDCOM Tree Viewer to inspect downloaded tree files and search for surnames and ancestral locations.
Move From Clues to Hypotheses
One of the biggest mistakes in genetic genealogy is stopping as soon as one family-tree connection looks plausible.
A stronger approach is to create a hypothesis and then ask whether all available evidence supports it.
Evidence narrows the hypotheses
When you have several known relationships and are trying to determine where an unknown person fits into a pedigree, probability modeling can become useful.
Tools such as WATO (What Are the Odds?) can compare competing pedigree hypotheses using the shared cM values of multiple matches. WATO is a hypothesis-ranking tool, however — a high score does not by itself prove that a hypothesis is correct.
DNA Match Analysis Tools Compared
Different tools answer different questions. The most effective workflow does not depend on one tool — it combines several layers of evidence.
| Method / Tool | Primary Focus | Best Used For | Genetic Voyage |
|---|---|---|---|
| Leeds Method | Manual match clustering | Separating major ancestral branches | Free Templates |
| AutoClustering | Automated match networks | Large match lists and visual grouping | AutoCluster Guide |
| Shared Matches | Match-to-match relationships | Identifying genetic networks | This Guide |
| Chromosome Browser | Physical DNA segments | Segment analysis and triangulation | Chromosome Browser |
| Relationship Calculator | Shared-cM probabilities | Evaluating possible relationships | Calculator |
| WATO / BanyanDNA | Pedigree hypotheses | Complex relationships and unknown ancestry | WATO vs BanyanDNA |
Genetic Voyage DNA Research Tools
Once you understand the workflow, these tools can help you move from individual matches to structured analysis.
Which Method Should You Use?
“I have too many matches.”
Start with shared cM, then use the Leeds Method or automated clustering to organize your match list.
“I don't know which side they're from.”
Use shared matches and, when possible, compare your matches with a known maternal or paternal relative.
“I found the same ancestor in several trees.”
Investigate the genealogy further, then use segment evidence and triangulation where available.
“Several relationships seem possible.”
Use a shared-cM probability tool and compare the alternatives with the rest of your evidence.
DNA evidence becomes harder to interpret when your family has endogamy, pedigree collapse, multiple relationships, or unusually complex ancestry. Standard shared-cM statistics and pedigree models do not fully account for every such situation.
In these cases, use larger matches where possible, analyze the wider network, consider multiple hypotheses, and place greater emphasis on independent genealogical evidence.
Frequently Asked Questions About DNA Match Analysis
How many centimorgans should I use when analyzing DNA matches?
There is no single cM cutoff that works for every research question. Larger matches are generally easier to interpret, while smaller matches can still become useful when they are part of a well-supported cluster or segment analysis.
Can shared cM tell me exactly how I am related to someone?
No. Shared cM can narrow the range of plausible relationships, but the same amount of DNA can occur across multiple relationship types. Combine cM with shared matches, ages, family trees, geography and other evidence.
How do I determine whether a DNA match is maternal or paternal?
The strongest approach is often to compare your match with a known relative from one side of your family. If that is not possible, clustering methods such as the Leeds Method can help organize matches into likely ancestral branches.
Can I use a chromosome browser with AncestryDNA?
AncestryDNA does not provide the same type of native chromosome-browser segment comparison available on some other platforms. Researchers often use raw DNA transfers or other databases when they need segment-level analysis.
Does a triangulated segment prove a specific ancestor?
DNA triangulation identifies a group of matches who share an overlapping DNA segment with one another in the same genomic region. This can support a shared ancestral origin for the segment, but it does not automatically identify a specific common ancestor. Interpret triangulated segments alongside family trees, known relatives, and documentary evidence, while accounting for endogamy and multiple ancestral relationships.
What should I do when several DNA matches point to the same unknown ancestor?
Build the evidence from multiple independent directions: compare shared cM, map the match network, inspect family trees, analyze shared segments where available, and test competing pedigree hypotheses. The goal is not simply to find a plausible answer, but to find an answer that survives independent evidence.
✔ DNA Match Analysis Checklist
- Start with the strongest and most useful matches rather than browsing randomly.
- Record the exact shared cM for important matches.
- Use a relationship calculator to examine plausible relationships.
- Group matches using shared matches, the Leeds Method, or automated clustering.
- Compare clusters with known maternal and paternal relatives whenever possible.
- Examine family trees for recurring ancestral couples, surnames and locations.
- Use segment data and triangulation when available.
- Test competing hypotheses instead of accepting the first plausible tree connection.
- Support the final conclusion with independent genealogical records.
DNA Matches Are the Beginning — Not the Answer
The most powerful DNA research does not come from staring at a match list. It comes from turning thousands of individual matches into structured evidence.
Start with shared cM. Build groups with shared matches and clustering. Follow promising networks into family trees. Move to segment analysis and triangulation when the data allows it. Then test your conclusions against documentary genealogy.
That is how a DNA match changes from a name on a screen into a useful genealogical research lead.
Sources and Further Reading
- ISOGG Wiki: CentiMorgan (cM) — definition and explanation of the centimorgan as a unit of genetic distance.
- ISOGG Wiki: Autosomal DNA Statistics — average and range of shared DNA by relationship.
- DNA Painter: Shared cM Project Tool — interactive tool based on Blaine Bettinger's Shared cM Project data.
- ISOGG Wiki: Leeds Method — definition and description of the manual clustering method developed by Dana Leeds.
- ISOGG Wiki: Triangulation — definition and application of genetic triangulation in autosomal DNA testing.
- ISOGG Wiki: Chromosome Browser — explanation of chromosome browsers for visualizing shared DNA segments.
- ISOGG Wiki: Abbreviations (ICW) — definition of In-Common-With (ICW) and related genetic genealogy terminology.
- ISOGG Wiki: Pedigree Collapse — explanation of pedigree collapse and its effects on genetic relationships.
- NHGRI: Recombination — National Human Genome Research Institute glossary entry on genetic recombination.
- DNA Painter: What Are the Odds? (WATO) — tool for comparing competing pedigree hypotheses using shared cM data.
Ready to Analyze Your Matches?
Start with the right tool for your question, organize your matches, and turn your DNA data into a structured research workflow.
DNA Academy – Quick Answers
What is DNA triangulation and why is it crucial?
Triangulation means finding at least 3 people (you + 2 matches) who all share the same DNA segment on the same chromosome. This proves that segment came from a single common ancestor. Read our advanced guide to triangulation or use our chromosome browser to identify these groups.
How do I analyze my DNA matches to find biological parents?
Start by sorting matches by shared cM (highest first). For adoptees, focus on the top 5–10 matches. Use our Leeds Method Clustering Tool to visually group them into 4 columns, representing your 4 grandparents. Research the strongest column first. Read more in our guide for adoptees.
What is the chromosome browser and how do I use it?
A chromosome browser shows you a visual map of your 23 chromosome pairs. Overlapping colored bars indicate where you share DNA with a specific match. In our tool, you can overlay up to 5 matches at once – if they all overlap on the same spot, you’ve found a triangulated segment linked to a specific ancestor.
How do I handle endogamy (intermarriage) when analyzing DNA matches?
Endogamy (common in Jewish, French-Canadian, and isolated communities) causes matches to share multiple segments from different ancestors, inflating cM values. Use our chromosome browser to focus on single overlapping segments rather than total cM. If multiple matches share the same exact segment, it’s more reliable than high total cM spread across many chromosomes. Read more in our triangulation guide.