Gedmatch Autosomal DNA Segment Analyzer (ADSA)

Autosomal DNA Segment Analyzer (ADSA)
GEDMATCH Quick Start Guide

ICW means In-Common-With were ever used

To use GEDMATCH with ADSA you must be a Tier 1 GEDMATCH member. That means you must have, at some time, donated at least $10 to GEDMATCH. The GEDMATCH upload process for depends on two Tier 1 tools: Matching Segment Search and Triangulation which you cannot access unless you are a Tier 1 member. And, of course, you must have loaded your raw data to GEDMATCH previously so that it has been tokenized and batch processing is completed.

Some other things to be aware of:

  • Certain fields that are available for Family Tree DNA kits are not presently available for GEDMATCH. These include:

    Match Date

    Predicted Relationship

    Known Relationship

    Relationship Range



    Total Shared cM

    Longest Block cM

    So, this means that using these for sorting, selection, highlighting or display purposes may not have the results you wanted because these fields are empty in a GEDMATCH kit.

  • To manage processing load on GEDMATCH’s servers, only the In-Common-With indicators for your top 400 matches are provided by GEDMATCH, so you will only have ICW bricks in the ADSA report for your longer segments. You can manually determine ICWs for other matches by doing a one-to-many report for one of your matches and comparing their list of matches to yours.

  • Generally, there are a lot more segments in a GEDMATCH ADSA report than for Family Tree DNA. This tends to slow down the responsiveness of your browser when viewing the ADSA report. You may wish to increase the minimum segment size in ADSA to 10 cM(Centimorgans)

  • The GEDMATCH tools that are used to gather the data for DNAgedcom exclude very close relatives (eg. siblings, parents, children) to improve processing performance, so you will not see them as matches on your ADSA report for GEDMATCH kits.

  • The X chromosome matches are not presently included in GEDMATCH kits.

To get started, follow these steps.

  1. If you haven’t already done so, go to and click on “Register”:

  2. Register for a free account at

  3. Logon to with your new username and password:

  4. Prepare to upload your GEDMATCH data to

    You will see a screen with a large, square text input box. Do not enter anything here yet.

  5. Leaving the window above open, create a new browser window or tab and go to the and


    . Click on “Matching Segment Search” in the Tier 1 tools menu near the bottom of the screen:

  6. Enter your kit number and click “No” on the graphic bar (very important!) and click “Submit”:

  7. Now wait for the report to finish – it will probably take a few minutes. When it is complete it will look something like this:

    Select everything on the screen and copy it to the clipboard. In


    you can do this using


    followed by ctrl-c. On a


    you can use command-a and command-c. You may have to wait a little while for the copy to complete. There is a lot of data there to copy. (If you don’t wait long enough, when you paste the information into DNAgedcom you won’t get what you copied. You may see a


    or spinning beach-ball while the copying is going on.


    the copy process doesn’t take more than a minute or two.)

  8. Go to the browser window you have open to Click


    the square box and paste what you copied into it. On


    you can use Ctrl-v or you can use command-v on a Mac.You should see a portion of what you copied like this:

    Click the “Load” button. The load should complete in a few seconds.

  9. Click the Clear button to erase the text-input box again and return to your GEDMATCH browser window. Return to the main GEDMATCH menu again.

  10. Now click on the Triangulation tool.

  11. Enter your GEDMATCH kit number and select the middle radio button (very important!) and click on the “Triangulate” button:

  12. Wait for the report to complete. The Triangulation report may take longer than the Matching Segment Report depending on how many In-Common-With matches you have and the current load on GEDMATCH’s servers. When it finishes there will be 4 rows of asterisks on the screen and the screen will look something like this:

    Once again, select the entire page (ctrl-a or command-a) and copy it to the clipboard (ctrl-c or command-c). Wait for the copy to complete. Then switch back to your DNAgedcom browser window.

  13. Make sure the text-input box in DNAgedcom is empty (use the Clear button if you need to) and then paste the Triangulation report into the box with ctrl-v or command-v. Then click on the Load button.

  14. When the Load process completes the screen will refresh. You can now go to ADSA by selecting the Autosomal Tools menu and the Autosomal DNA Segment Analyzer option on that menu. Or you can go to this link: You will see a screen like this:

  15. Select your kit from the drop-down menu. GEDMATCH kits will start with a letter (A=Ancestry, F=FTDNA, M=23andMe etc.):

  16. Click GET REPORT

  17. If you have Ashkenazi ancestry or are part of an endogamous (interrelated) group you may not be able to generate a report with the default input parameters. Please consult the Tips for People with Ashkenazi Ancestry page before clicking GET REPORT.

For more information about this process, how to interpret your results, or troubleshooting, read the full ADSA manual.


Centimorgans in Genetic Geealogy

Reprinted from the International Society of Genetic Genealogy August 2, 2017. No adjustment was made to this article and is the ISOGG position.


In genetic genealogy, a centiMorgan (cM) or map unit (m.u.) is a unit of recombinant frequency which is used to measure genetic distance. It is often used to imply distance along a chromosome, and takes into account how often recombination occurs in a region. A region with few cMs undergoes relatively less recombination. The number of base pairs to which it corresponds varies widely across the genome (different regions of a chromosome have different propensities towards crossover). One centiMorgan corresponds to about 1 million base pairs in humans on average. The centiMorgan is equal to a 1% chance that a marker at one genetic locus on a chromosome will be separated from a marker at a second locus due to crossing over in a single generation.

The genetic genealogy testing companies 23andMeAncestryDNAFamily Tree DNA and MyHeritage DNA use centiMorgans to denote the size of matching DNA segments in autosomal DNA tests. Segments which share a large number of centiMorgans in common are more likely to be of significance and to indicate a common ancestor within a genealogical timeframe.

The centiMorgan was named in honor of geneticist Thomas Hunt Morgan by his student Alfred Henry Sturtevant. Note that the parent unit of the centiMorgan, the Morgan, is rarely used today.

23andMe and Family Tree DNA both use HapMap to infer their centiMorgans.

centiMorgans vs megabases

CentiMorgans are interpolated numbers that take into consideration each area of a chromosome and its propensity to recombine. This means if two cousins share 40 cM on chromosome 1, and two different cousins share 40 cM on chromosome 5, they both can be predicted to share a certain degree of relationship statistically. Megabases vary slightly in different locations so that in the same scenario, if both sets shared 40 Mb pairs, it would be more difficult to ensure they are of a similar degree of relation without further accounting for location, chromosome and other factors.[1]

Ann Turner provides a useful explanation: “I think of the cM as being a unit of ‘effective’ distance. As an analogy, a mile is a fixed quantity (5280 feet), and so are megabases. But the probability that a person can walk a mile in 20 minutes is more fluid. If the terrain is very rough, the “effective” distance of a literal mile might be more like two miles if you’re trying to arrive at a certain time. We’re more interested in the probability that a segment will be passed on intact than the size of the segment in Mb”.[2]

As the cM is an empirical measure, based on recombination events in a particular dataset of parents and offspring, it can vary somewhat from study to study. This set of maps for each chromosome shows that the general shape of the centiMorgan vs megabase curve is similar for two datasets, but the absolute values are not quite the same:

cm values per chromosome

The following table compares cM values per chromosome at Family Tree DNAGEDmatch, and 23andMeAncestryDNA uses 3475 as the total cM according to the help screen for confidence level in a DNA match. This presumably excludes the X chromosome.

CM chromosome FTDNA&GEDMatch&23andMe.jpg

Probability of crossover

The following chart shows the estimated probability that a segment will be affected by a crossover. The chart does not take into account some variables such as inversions and different recombination rates for males and females.

Crossover probability centiMorgans.png

Converting centiMorgans into percentages

In order to get an approximate percentage of shared DNA from a Family Tree DNA Family Finder test, take all of the segments above 5 cM, add them together and then divide by 68.

The way the calculation works is that your total genome in cMs with the Family Finder test is 6770 cM. A half-identical match (such as a parent/child) is 3385 cM. This number has to be doubled to represent both the maternal and paternal sides giving a total of 6770 cM. Matt Dexter explains: “The reason the number is not 6770 or 6800, but rather 68, is that it saves an additional step doing the math to convert an answer to percent. For example, 3385 / 6770 = .5 then as a second step, .5 times 100 = 50%. Using 68 to start with saves the added math step. So (3385 / 6800) * 100 is the same thing as 3385 / 68, which results in = 50%.”[3]

Human reference genome

The centiMorgan totals per chromosome are based on the Human Reference Genome. 23andMe and Ancestry DNA use Build 37. Family Tree DNA use Build 37 for matching but Build 36 for segment boundaries in the Chromosome Browser. Raw data files are provided in both formats. Build 37 filled in quite a few gaps, and the number of base pairs in each of the chromosomes was longer in Build 37 as compared to Build 36. Consequently the cM totals per chromosome are lower for Family Finder than they are for 23andMe. GedMatch use Build 36, and convert AncestryDNA and 23andMe data from Build 37 to Build 36 for backward compatibility.

The latest version of the Human Reference Genome, Build 38, was released in December 2013. However, none of the companies have as yet adopted Build 38 and there is a “gentleman’s agreement” in place to stick with Build 37 for the present time.

Further reading


DNA Triangulation, What?

Triangulation is a term derived from surveying to describe a method of determining the Y-STR or mitochondrial DNA ancestral haplotype using two or more known data points. The term “Genetic Triangulation” was coined by genetic genealogist Bill Hurst in 2004 Triangulate

Here is a 3-step process for Triangulation: Collect, Arrange, Compare/Group.

  1. Collect all the Match-segments you can. I recommend testing at all three companies (23andMe, FTDNA, and AncestryDNA), and using GEDmatch. But, wherever you test, get all of your segments into a spreadsheet. If you are using more than one company, you need to download, and then arrange, the data in the same format as your spreadsheet. Downloading/arranging is best when starting a new spreadsheet. Downloading avoids typing errors, but direct typing is sometimes easier for updates. I recommend deleting all segments under 7cM – most of them will be IBC/IBS (false segments) anyway, and even the ones which may be IBD are very difficult to confirm as such. You are much better off doing as much Triangulation as you can with segments over 7cM (or use a 10cM threshold if you wish), and then adding smaller segments back in later, if you want to analyze them. NB: Some of your closer Matches will share multiple segments with you – each segment must be entered as a separate row in your spreadsheet. The minimum requirement for a Triangulation with a spreadsheet includes columns for MatchName, Chromosome, SegmentStartLocation, SengmentEndLocation, cMs and TG. Most of us also have columns for SNPs, company, testee, TG, and any other information of interest to you. Perhaps I need a separate blog post about spreadsheets… ;>j
  1. Arrange the segments by sorting the entire spreadsheet (Cntr-A) by Chromosome and Segment StartLocation. This is one sort with two levels – the Chromosome column is the first level. This puts all of your segments in order – from the first one on Chromosome 1 to the last one on Chromosome 23 (for sorting purposes I recommend changing Chromosome X to 23 or 23X so it will sort after 22). This serves the purpose of putting overlapping segments close to each other in the spreadsheet where they are easy to compare.
  1. Compare/Group overlapping segments. All of these segments are shared segments with you. So with segments that overlap each other, you want to know if they match each other at this location. If so this is Triangulation. This comparison is done a little differently at each company, but the goal is the same: two segments either match each other, or they don’t (or there isn’t enough overlapping segment information to determine a match). All the Matches who match each other will form a Triangulated Group, on one chromosome – call this TG A (or any other name you want). Go through the same process with the segments who didn’t match TG A. They will often match each other and will form a second, overlapping TG, on the other chromosome – call this TG B. [Remember you have two of each numbered chromosome.] So to review, and put it all a different way: All of your segments (every row of your spreadsheet) will go into one of 4 categories:
  • – TG A [the first one with segments which match each other]
  • – TG B [the other, overlapping, one with segments which match each other]
  • – IBC/IBS [the segments don’t match either TG A or TG B]
  • – Undetermined [there are not enough segments to form both TG A and TG B                            and/or there isn’t enough overlapping data to determine a match.]
  • NB: None of the segments in TG A should match any of the segments in TG B.
  1. At GEDmatch – the comparisons are easy. Just compare two kit numbers using the one-to-one utility to see if they match each other on the appropriate segment. The ones that do are Triangulated. You may also use the Tier1 Triangulation utility or the Segment utility. I prefer using the one-to-one utility and Chrome.
  1. At 23andMe you have several different utilities:
  • – Family Inheritance: Advanced lets you compare up to 5 Matches at a time. You may also request a spreadsheet of all your shared segments; sort that by chromosome and SegmentStart, and check to see if two of your Matches match each other. The ones that do are Triangulated.
  • – Countries of Ancestry: Sort a Match’s spreadsheet by chromosome and SegmentStart, search for your own name, and highlight the overlapping segments. The Matches on this highlighted list who are also on overlapping segments in your spreadsheet are Triangulated (the CoA spreadsheet confirms the match between two of your Matches)
  1. At FTDNA it’s a little trickier, because they don’t have a utility to compare two of your Matches. So the most positive method is to contact the Matches and ask them to confirm if they match your overlapping Matches, or not. The ones that do are Triangulated. An almost-as-good alternative is to use the InCommonWith utility. Look for the 2-squigley-arrows icon next to a Match’s name, click that, and select In Common With to get a list of your Matches who also match the Match you started with. Compare that list of Matches with the list of list of Matches with overlapping segments in your spreadsheet. Matches on both lists are considered to be Triangulated. Although this is not a foolproof method, it works most of the time. And if you find three or four ICW Matches in the same TG, the odds are much closer to 100%. Remember, every segment in your spreadsheet must go in one TG or the other, or be IBC/IBS, or be undetermined. If a particular Match, in one TG, is critical to your analysis, then try hard to confirm the Triangulation by contacting the Matches.
  1. AncestryDNA has no DNA analysis utilities. You need to convince your Matches to upload their raw data to GEDmatch (for free) or FTDNA (for a fee), and see the paragraphs above.

Comments to improve this blog post are welcomed.

Genetic Genealogy For Beginners – Discovery your Family History Through DNA 101




This course as the first one “Genetic Genealogy For Beginners” is an expansion and goes a little more deeper into the DNA with some additional learning tools. In these lessons rather than chapter we will use Genetic Genealogy, Molecular Genealogy (the field of biology that studies the structure and function at the molecular level and thus employs methods of both molecular biology and genetics. The study of chromosomes and genes expression of an organism.) Sounds intimidating but it will be broken into manageable understandable lessons. There is a test after each lessons to help you gain a solid background before moving to Intermediate and Advance Genetic Genealogy. This will be a four week course and starts May 1 – May 26 2017.

mark you calendar for this course.

Genetic Genealogy For Beginners – Chapter 6




BGA (Admixture) Explained

Source: BGA Admixture

BGA Basics And Science
BGA stands for biogeographical analysis. BGA tests are sometimes callAdmixture Tests. A BGA test basically tries to use your DNA to determine or pinpoint what part of the world your ancestor(s) originated. Using your DNA to show if two people have a common ancestor is valid. DNA contains information such as whether or not two people are related.

However using your DNA to pinpoint where an ancestor was born, lived, or came from, is entirely different.  Here is the idea behind a BGA test.

Suppose we have a population called the Handy Clan. The Handy Clan has 1000 people and is located on a remote island. Now let’s say everyone in the Handy Clan population has a rare DNA marker which we will call -> M. In other words, thefrequency of this DNA marker is 100% because everyone (1000 people) has the DNA marker M. Also, let’s assume that no one outside of the Handy Clan, which is on this remote island, has the DNA marker M.

Now Laurie lives in the US in Oakland, California which is located outside the remote island and outside of the Handy Clan population. Let’s suppose we discover Laurie has this same rare DNA marker M.

Can we say Laurie is from or has ancestry from the Handy Clan population?

Under simple circumstances, yes!!!  We can confidently say that. If no other population in the world has this rare genetic marker M, then we can say yes. Laurie is either from, or has had an ancestor, that originated from the Handy Clan population. That’s what a BGA does. It compares your DNA markers to a studied population. Since all one thousand people have the same DNA marker M, then Laurie must either have been born in that Handy Clan population or Laurie had an ancestor from that population.

However reality is not as simple as that!!!!!  Let’s see a more realistic scenario.

A More Realistic Scenario
Now suppose we have three separate populations, the Handy Clan, Williams Clan, and Henderson Clan. Each population is located in a different part of the world. Each population or clan has 1000 people in it. Every person in each of the populations has the genetic marker M.  In other words, the frequency of the DNA marker M is 100% in each population.

Now we discover again that Laurie, who lives in Oakland, which is outside each population, has the genetic marker M.

Question: Does Laurie has ancestry from the Handy Clan population?

Now things have changed. The question is now harder to answer. The fact that Laurie has a DNA marker M in multiple populations doesn’t necessarily mean Laurie has ancestry from the Handy Clan population.  Laurie could of had an ancestor that lived or was born in any of those populations.

That’s the problem with a BGA DNA test. As we can see, the truth is not so clear cut in tests of this nature. The truth is based on a probability.  Any newly introduced population can change things dramatically. Therefore, when interpreting the results from a BGA or Admixture test, please keep in mind that your results may differ or change tomorrow. Laurie would need a paper trail or some definitive piece of evidence to confirm the inference drawn from the BGA results. The BGA data numbers alone don’t necessarily prove anything.

The reason is that a BGA test is attempting to infer information from DNA that DNA doesn’t define. An ancestor’s original location can be any where. DNA simply doesn’t reflect or store that type of information. From the frequency (or concentration) of those DNA markers in each population, we are making an inference which could be right or wrong. If a child is born in say Atlanta, Georgia, that geographical location and information will not be stored in the child’s DNA.

One of the biggest misconceptions out there, is that a BGA or Admixture Test, can pinpoint the exact tribe or small population someone is from. As one can clearly see, this is not necessarily true. DNA alone simply cannot do this as it’s advertised. This is one of the reasons, the scientific community as a whole has not embraced BGA tests.

Now let’s look at the basic BGA concepts.

BGA Concepts
In BGA terms, the DNA marker M, is called an ancestry informative marker or AIM. Each population is called a reference population. An example of a reference population is the Yoruba. The Yoruba is a West African ethnic group that is studied by population geneticists. Many African-Americans have DNA markers that match to the Yoruba group.

Now that we have the BGA basics, let’s look at the BGA process and engine which is known as PCA.

BGA Process and PCA 
The engine or workhorse of most BGA Analysis is PCA. PCA stands for Principal Component Analysis. PCA is a complex mathematical process that separates a bunch of data into its components. For example, let’s say we have a bag of 100 jelly beans that are of different colors. After separating the jelly beans by color, we see this -> blue=25, red=25, purple=25, and yellow=25. This means that each of the four colors make up 25% (25/100) of the jelly beans. PCA would essentially separate the jelly beans in the exact same way.

The BGA process starts off with about 300,000 AIMs or SNPs. These SNPs are found across the first 44 chromosomes in humans. The SNPs are matched to a number of reference populations. The results are percentages that represent theconcentration of the SNPs in each reference population. The engine running the show is PCA, which runs in the background of an algorithm.

Now let’s look at a few BGA tests.

BGA Tests: Population Finder, Ancestry Painting, McDonald
There are a number of BGA tests out there. Family Tree DNA’s BGA test is Population Finder. 23andME’s is called Ancestry Painting. The Population Finder is a BETA test so it’s a work in progress. Population Finder uses continental groups in addition to reference groups.

Here is an example of PF

Continent (Subcontinent)     Population              Percentage    Margin of Error
Europe (Western European)   French, Orcadian       28.53%            ±0.48%
Africa (West African)             Yoruba, Mandenka     71.47%            ±0.48%

There are four reference populations -> French, Orcadian, Yoruba, Mandenka. This person basically has DNA markers that match those reference populations. It’s likely this person has ancestry from some of those populations, but not necessarily all of them. A paper trail would be needed to confirm ancestry.

Because the Population Finder is a beta test and has limited reference populations (same for 23andME’s Ancestry Painting), many people turn to an Extended BGA Analysis. This is where Dr Doug McDonald comes in.

McDonald’s Extended BGA 
Dr Douglass McDonald is a chemist at University of Illinois in Urbana, Illinois. In fact, he actually created the Population Finder for Family Tree DNA. McDonald has access to more studied reference populations which Family Tree DNA or 23andMe currently doesn’t have. Because of this, you can get a more “fleshed” out or “extended” BGA Analysis.

McDonald gives his results in the form of an email with four graphs. Here are McDonald’s results of my cousin Lonette Lanier’s extended BGA test as shown in quotes below:

Most likely fit is 27.9% (+-  0.1%) Europe (various subcontinents) and 72.1% (+-  0.1%) Africa (all West African).

The following are possible population sets and their fractions, most likely at the top

French= 0.279 Mandenka= 0.721
Hungary= 0.280 Mandenka= 0.720
English= 0.277 Mandenka= 0.723

There is also about 0.4% Native American that is strong and likely real, as well as other little bits on the chromosomes but they are weak and probably unimportant.”

Each line, “French= 0.279 Mandenka= 0.721“, is a population set. There are three population sets. Each population set gives a likely or probable ancestry for my cousin Lonette. Each population set is a combination that gives the best fit for Lonette’s data. It doesn’t mean Lonette necessarily has ancestry from say, the French. But she does have DNA markers that match the French reference population. The multiple population sets are the result of Lonette’s DNA markers that are spread across multiple populations. This is why it’s difficult to pinpoint a person’s ancestral origin to a specific tribe or single population via your DNA alone.

It’s important to always backup DNA evidence with documents or other pieces of evidence to validate a claim. The numbers alone don’t always or neccesarily identify the truth.

Now let’s look at the issues the scientific community has with BGA Testing

Issues With BGA Or Admixture Testing
The scientific community as a whole hasn’t really embraced BGA or Admixture Testing. Using your DNA to establish whether two or people are related via a common ancestor is valid. However using your DNA to locate where your ancestor(s) originated is quite a different task. An ancestor could have been born or lived in any part of the world. More important – DNA simply doesn’t define or contain information such as ancestor’s geographical location or point of origin. That type of information is NOT an attribute of a genetic mutation. Therefore BGA or Admixture tests don’t have a basis in genetics. That’s the scientific community’s main objection to BGA or Admixture tests. The results from a BGA or Admixture test are used to make inferences from observed correlations. A correlation can be dangerous in science because it can lead to an incorrect inference from an observed set of data.

There is a very big difference between a casual relationship (correlation) versus a direct relationship between two variables.

This doesn’t mean BGA tests aren’t valuable. A BGA test can lead one into finding insight into their past. However you must understand that the results from a BGA test aren’t final. The results from a BGA test are tenative and can easily change tomorrow.There are at least three main current hurdles with a BGA Analysis1) Populations can change location and identity. They are not static. What we know about a population’s history is limited and based on what we currently know. Moderns humans have been here for approximately 200,000 years. No one can know the entire history of any population. We can have approximate knowledge, but NOT complete knowledge.2) We simply don’t at this time have a complete set of reference populations to make any final judgment calls as of yet. (I will explain this shortly)3) Different algorithms can produce different results.For example suppose Dr McDonald gives me the following simple BGA results:

Finnish=.100 and Yoruba=.900.

This is based on the fact that the scientific community has studied the Yoruba and Finnish etc. This would lead one to believe that one has a large Yoruba ancestry. The Yoruba ancestry may be true with a paper trail.

Now suppose the scientific community has studied and approved a new reference population, C, in say a few years. Now a rerun of Dr McDonald’s results yields the following:

Population C=.450, Finnish=.100, and Yoruba=.450

Now as you can see, things have changed. My ancestor now could have lived in the Yoruba, or could have lived in the new reference population C. This scenario could happen. As you can see, none of these results are absolute or final in the sense that they can’t change.

In addition, different algorithms can produce different results. An algorithm is simply a method or set of steps to solve a problem. The algorithm is very important. It’s what produces your DNA results. Right now there are a number of tools out there that claim the ability to produce valid BGA results. Each of these tools may run under different algorithms.

For example – I have taken three BGA tests: Ancestry Painting, Population Finder, and McDonald. Each has produced different results. The analysis from 23andME stated I had 7 percent Asian ancestry. Now this could be significant or it could be noise. Neither FTDNA’s Population Finder nor McDonald’s findings gave 7% percent ancestry. The bigger question is which one is correct? Population Finder is a BETA test. So I can assume that it’s findings are approximate. Can the same be said for 23andME’s Ancestry Painting results or Dr McDonald’s BGA findings? The truth is that at this time – it’s impossible to tell which one is correct or is incorrect.

The most important point to take from this tutorial is that a BGA can yield valuable information not necessarily definitive information. Technically, the only factual based information that can be produced from a BGA test is that a person has DNA markers (AIMs) that match a reference population.

Genetic Genealogy for Beginners – Introduction



Genetic Genealogy for beginners was created to describe Genetic Genealogy in a simpler manner. Rather than a scientific approach to explaining genetic genealogy and the method and processes used in DNA testing, a clear and understandable nonscientific educational tool is offered to you in several chapter modules. Right now millions of people worldwide have purchased DNA test kits to explore their family ancestors and people have used perhaps several testing companies with different results and matches. The goal is to clear the fog and provide the tools required to understand genetic genealogy DNA testing. (Going forward I will be using the term DNA interchangeably with genetic genealogy).

There is a real need to educate and to continuously educate and help genealogists (anyone researching their family ancestry), to grasp and understand the beneficial attributes and limitations of DNA testing and methodologies. In this introduction and other chapters to follow, you will develop the tools to understand genetic genealogy (DNA).

After studying the chapters, you will be able to apply DNA specific vernacular and genetic genealogy evidence to explain genealogical questions. I will  introduce types of DNA testing and how the test can be used for genealogy.

Genealogy (jene-ole-je) is a record or table of the descent of a person, family, or group from ancestor or ancestors; a family tree. It is the study or investigation of ancestry and family histories. (   Genetic Genealogy is the use of DNA testing in combination with traditional genealogical and historical records to infer relationships between individuals.  (https://en.wikipedia/wiki/international_Society_of_Genetic_Genealogy) Both type requires meeting the genealogical and genetic genealogical standards if conclusions and evidence can be considered creditable and can be followed by others to reach the same conclusion and or expand upon to work presented. This is how genetic genealogy as any other science evolves over time. It becomes better and better.

Over time this material will change and the chapter modules will be adjusted overtime as new research and methods are introduced.  I would recommend you subscribe to  in order to receive updates to your chapter module materials.

Genetic Genealogy for Beginners Contents

  • Introduction
  • Chapter 1: Basic Genetics
  • Chapter Module 2: Genetic Genealogy Standards
  • Chapter Module 3. Y-DNA Explained
  • Chapter Module 4. MtDNA Explained
  • Chapter Module 5. atDNA Explained
  • Chapter Module 6: BGA (Admixture) Explained


  • Appendix A     Glossary
  • Appendix B    Exercise’s (each chapter module includes and exercise to bring forward your learning experience)
  • Appendix C.    Suggestive Reading List (Offered also at

See chapter 1, next

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