Chromosome Matrix App (CMA)

Overview

CMA is a chromosome-focused analysis tool that organizes DNA matches based on shared DNA segments on specific chromosomes. Unlike other clustering tools that rely primarily on ICW (In Common With) relationships, CMA uses actual chromosome segment data. This makes it useful for chromosome mapping, segment-specific analysis, and identifying segment groups.

CMA supports both database kits and CSV file imports. FTDNA, 23andMe, MyHeritage, GEDmatch, and A*DNA are supported.

Prerequisites

For Database Kits

Data	Required?	Purpose
Matches	Required	List of DNA matches
ICW (In Common With)	Required	Shared match relationships
Chromosome Segments	Required	Shared DNA segments — what makes CMA unique
Ancestors / Trees	Optional	Ancestor information overlay

Important: Unlike CLM and Shared Clustering, CMA requires chromosome segment data. Kits without chromosome data will not produce meaningful CMA results.

For File-Based Kits

When using CSV file imports, the following files are required:

Match File — Required
ICW File — Required
Chromo File — Required

Supported services for file import: FTDNA and GEDmatch.

CMA Settings

Setting	Purpose
Kit Filter	Prefilter kits
DNA Kit	Select target kit
Match / ICW / Chromo files	CSV file source (for file-based kits)
cM Range	Select by total cM (default: 10–1500)
Min SNPs	Minimum SNP count per segment (default: 500)
Chromosome	Which chromosome to analyze (All, 1–22, or X)
Include Non-ICW Segments	Include segments from non-ICW matches
Include Ancestors	Show ancestor info
Open HTML When Done	Auto-open HTML output
Tag Include / Exclude	Filter by GEDCOM tags

Setup Details

cM Range

Default: 10 to 1500 cM. This is wider than CLM because CMA works at the segment level, where smaller matches can be meaningful when backed by actual chromosome data.

Min SNPs

Default: 500. SNP count is a quality indicator for segment reliability.

Higher values (700, 1000) filter out small, unreliable segments and produce cleaner results.
Lower values include more segments but add noise from potentially false matches.

Chromosome

Options: All (default), 1–22, or X.

Selecting a specific chromosome is useful when investigating a known segment or region.
The X chromosome has unique inheritance patterns that can be useful for narrowing ancestral lines, since it follows a predictable path through generations.

Include Non-ICW Segments

Default: Off. When enabled, includes segments from matches that are not ICW with each other. Use cautiously — these segments may represent different ancestral lines.

Include Ancestors

Default: Off. When enabled, includes ancestor information in the output when tree data is available in the database.

Tag Management

Same as CLM: use Exclude and Include toggles to filter matches by their GEDCOM tags.

Running CMA

Select your DNA Kit from the dropdown (or browse for CSV files).
Adjust the cM Range, Min SNPs, and Chromosome settings.
Click Run CMA.
The HTML output opens automatically when complete (if enabled).

Reading the Output

The HTML output shows a chromosome matrix where rows and columns represent matches. Each cell indicates shared segments, including the chromosome number, start and end positions, cM value, and SNP count.

The output provides two perspectives:

By match — View all chromosomes shared with a single match across the genome.
By segment — View all matches sharing a specific segment region on a chromosome.

Important Notes

Clusters are not based on triangulation. Overlapping segments on the same chromosome do not prove a shared ancestor.
Half-identical regions (HIR) may be on different copies of a chromosome (maternal vs. paternal).
Small segments are more likely to be identical by state (IBS) rather than identical by descent (IBD). Use the Min SNPs filter to reduce false positives.
The X chromosome has unique inheritance patterns — it can be especially useful for narrowing ancestral lines.

Tips

Start with “All” chromosomes to get a broad picture, then narrow to specific chromosomes.
Increase Min SNPs for cleaner, more reliable results.
Use a specific chromosome when investigating a known segment or match group.
Combine CMA with CLM or WIC for ICW-based clustering alongside segment analysis.
Focus on segments above 10 cM for the most reliable results.