ABOUT THIS EPISODE
How can DNA testing, artificial intelligence, and genealogical bioinformatics uncover family secrets hidden for generations? In this episode of Dis A Fi Mi History Podcast, host Wendy Aris is joined by Arun Konanur, a genetic genealogist and creator of patented genealogical bioinformatics tools, to explore how advanced DNA analysis is transforming family history research.
Discover how Correlated Multiphasic Analysis (CMA) combines DNA evidence, data mining, and pattern recognition to solve complex genealogical mysteries that traditional research alone cannot. Arun shares the fascinating case of tracing a nineteenth-century Jones family, demonstrating how innovative analytical methods can identify distant relatives, overcome brick walls, and navigate the challenges of endogamous populations.
The conversation also examines the ethical responsibilities of DNA genealogy, including informed consent, privacy, and balancing genetic evidence with archival records, historical documents, and oral history. Looking ahead, Arun discusses the future of genealogy and how artificial intelligence (AI) and bioinformatics are creating new opportunities for researchers to uncover ancestral connections with greater accuracy.
Whether you're interested in genetic genealogy, family history, DNA testing, AI in genealogy, or Caribbean ancestry, this episode provides practical insights into the cutting-edge tools shaping the future of genealogical research.
Learn more about Arun's work at DiscoveringNewAncestors.com and through the Genealogical Bioinformatics Facebook community.
BIO:
Arun Konanur is a professional genetic genealogist, bioinformatics researcher, and founder of Discovering New Ancestors, a genealogy research company based in London, Ontario. His work combines DNA analysis, artificial intelligence, data mining, and computational genealogy to solve complex family history mysteries that traditional documentary research alone cannot resolve.
Before entering the field of genealogy, Arun studied classical music composition in New York City and spent more than twenty years in information technology and business consulting. His passion for problem-solving ultimately led him to genealogy, where he developed innovative methods for interpreting DNA evidence and uncovering previously unknown ancestral connections.
Arun is the creator of several patented and patent-pending genealogical bioinformatics processes, including Correlated Multiphasic Analysis (CMA), AASK (Axiomatic Ancestral Stratification by Kinship), Shared Match Differentiation (SMD), and Reverse-CMA. These tools help genealogists analyze distant DNA matches, identify unknown ancestors, address endogamy, and break through long-standing genealogical brick walls using advanced computational techniques.
A sought-after international speaker, Arun has presented to genealogical organizations and conferences across North America on topics including DNA analysis, Genealogical Bioinformatics, artificial intelligence, and innovative research methodologies. His work continues to advance the field of genetic genealogy by making sophisticated analytical tools more accessible to researchers and family historians.
On Dis A Fi Mi History Podcast, Arun discusses how DNA testing, Genealogical Bioinformatics, and AI are transforming family history research. He explains how his Correlated Multiphasic Analysis method combines genetic evidence with archival records to solve complex genealogical puzzles, helping researchers uncover hidden family connections and better understand their ancestral past.
Links:
Website: http://discoveringnewancestors.com/about/about/
FaceBook: https://www.facebook.com/DNAncestors
Instagram: https://www.instagram.com/getyourDNAinfo/
🎧 Subscribe to Dis A Fi Mi History Podcast for conversations exploring Caribbean history, genealogy, archives, DNA, and the hidden stories that connect families across generations.
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SHOW NOTES 🔗
TRANSCRIPT 🔗
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Wagwan, everybody. Welcome to the Dis Afimi History podcast,
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where we'll be speaking about history and as well family history and how history
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relates in terms of Caribbean people,
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for the present as well as in the past and how in the past, what that does and
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brings forth for what we are going through at present and what we can learn
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from our history, from our family and take that moving forward.
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So I do hope you enjoy the podcast, and if you like it, please ensure to subscribe,
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like, and review. Thank you.
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I just want to thank you so much for coming on to the podcast and to talk about
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genetic bioinformatics.
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So for listeners who may be new to this field, can you just introduce yourself
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and explain the work you do through discovering new ancestors?
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Certainly, and thank you for having me. Great. I'm a genetic genealogist,
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although I've also certainly done a lot of conventional genealogy research in
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researching my own family.
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Most of the client work I would do for people involves genetics.
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In addition to doing research with the conventional tools that,
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many people use, I've also spent the past five, six years developing my own
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tools and methods in this field, which I refer to as genealogical bioinformatics.
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And I do this through my business, Discovering New Ancestors.
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To date, I have filed five patents for processes relevant to bioinformatics,
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and I have a sixth process in development.
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And I found these tools are very useful in addressing longstanding problems
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that genetic genealogy has been unable to talk about through conventional methods,
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as well as assisting me in my own personal research.
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Many of these processes were motivated by.
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Problems I had in my own research, which I needed to address for my own work.
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No, thank you so much. And again, what is exactly genetic bioinformatics?
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And, you know, how would you be able to explain it to someone that,
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you know, for everyday genealogists that they can be able to understand?
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Certainly, certainly. Genealogical bioinformatics basically takes everything
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we know about genetic genealogy.
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And to that, it adds elements of what we call information engineering,
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which are basically different ways of looking at a familiar problem,
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looking at these problems in ways that a computer might be able to more formally
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understand them in terms of using elements of mathematics
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and logic to take information and things that we almost intuitively understand.
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As a genealogist, but looking at them in a way that a computer or these days
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an AI system might be better able to understand also.
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And so we take everything we know about genetic genealogy, and none of that's
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wrong, but we take those methods and we marry them up with ways of looking at
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information, certain things that we would call pattern recognition,
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something called data mining, basically ways of looking at huge amounts of data,
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and trying to make sense of them.
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And, you know, what first drew you to this area of research and what made you
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decide to become such a strong advocate and public voice for this?
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Well, I found that when researching my own family tree and ancestry,
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there were questions I had and brick walls, which weren't,
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which were basically, again, I guess, brick walls, things that couldn't be solved
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immediately and easily.
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And yet I knew that there had to be an answer to some of these questions.
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In some cases, I had ancestors for whom I didn't have any really good documentation
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and they didn't appear in other people's family trees and there weren't really
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any good conclusive records about them,
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in terms of who their parents or cousins were.
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But yet I knew that, okay, if I've tested and my cousins have tested,
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I must have matches which connect me through this person.
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I just don't know who they are or where they are.
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And by thinking about that, I began to try to work out some methods that would
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allow me to find those people and isolate those people and at least be able to sort of look.
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At my whole set of DNA matches and say, well, these ones I know and these ones
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I don't know, and these ones here, these have to be from my mystery ancestor. And,
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What I found was that genetic genealogy was very good at working with close
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matches and even DNA matches that are of a medium and to medium close range.
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But then when you began to work with really distant matches,
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I found that I needed to find ways to work with a lot of people's data,
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my data, my cousin's data, and really try to sort of make sense of it
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in a way that went beyond what one person could do.
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I needed to sort of bring a computer in to start working with all of these points of data.
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And that's when I really began to sort of say, OK, well, if I'm going to do
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this, I need to sit down and really think about what I'm doing.
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And I need to I'm going to need to build some tools of my own which can take
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these bits of information and make sense out of them. And that's really where
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the tools began to evolve.
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The first tool I filed a patent for was in the fall of 2021.
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And then things grew from there. And how does, I guess, genetic bioinformatics
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differ from traditional genealogical methods?
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Good question. Basically, traditionally with genetic genealogy,
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we look at how much DNA you would share with someone.
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And there are fairly well-established guidelines for what you would share with
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a child or a parent or an aunt or an uncle or a cousin or a second cousin.
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And we take that information, and then we also work with things that we understand
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as genealogists, but we don't necessarily lend themselves to hard numbers.
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For instance, if I talk to you about your maternal cousin, you know intuitively
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who they would match. They would match people on your mother's side,
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and who they wouldn't match. They wouldn't match people on your father's side so much.
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We look at things like that, and we express those things in mathematical terms,
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terms which a computer system can better understand.
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And the reason we do that is because we're not dealing just with our close matches,
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but if a person takes a DNA test, they often have thousands of matches.
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They'll have 20,000, they'll have 40,000 matches.
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If they come from a community, which might be an island population or something
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that has a group of people that has a dogomy, then you might have 60,
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80, 100,000 DNA matches.
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And that's really much too much information for a person to deal with on their own.
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And so by treating not just looking at a single point of information,
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but rather looking at the relationships between matches and points,
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we begin to go beyond just what genetic genealogy can do and look at,
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the whole, the totality of your DNA matches as basically a unit of information.
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And that if we take that unit of information and we compare it to other people's
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results, we can then devise tests
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that can tell us more things about our matches based on how our matches.
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Respond to those tests.
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And so we begin to, we can sort of read information and connect the dots that way.
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Okay. And then for, I guess, for people researching their own family history,
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what kinds of questions can this approach help answer that conventional records sometimes cannot?
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Well, one thing to consider is that when we look at, when we test our DNA.
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It's a given that we'll have DNA matches connected through both of our mother and father.
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And we'll have DNA matches slightly more distant. That'll be cousins that connect
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to one or any of our four grandparents.
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And then second cousins will be connected to us through any of our great grandparents.
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But when you move beyond that, what we find is that we don't necessarily have
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matches that connect us with each of our great-great-grandparents.
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Some of that DNA may have washed out, or we may have inherited some DNA from
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a second-great-grandparent, and our third cousins may have inherited other parts of their DNA.
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And so even though we're biologically related, we don't match in DNA to quite
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the way we would expect to.
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And what bioinformatics can do is basically allow us to map out where our DNA
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matches lie, where we have received DNA and where we haven't.
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So that rather than choosing a certain ancestral line and saying,
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oh, this is what I want to find.
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Instead, we can ask the question, what DNA do I have and where does that DNA lead me?
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It might not be where we often expect.
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It may not be the line we're most interested in.
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Often what people will do is have a family name, and often it's a father's name
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and the father's father, and people want to research that.
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And sometimes that's great because they have inherited that DNA,
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but other times they may have inherited DNA from the father's mother's family.
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And these bits of DNA, again, it's not even obvious where they come from until
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we begin to look at the big picture and correlate what we have with what our
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cousins have and begin to see that, oh.
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Whatever questions we want to answer, this is where we have to look.
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And it's not necessarily where we thought we were going to look.
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Yeah, exactly. And, you know, for some genealogists, do they have to have a
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scientific background to kind of incorporate bioinformatics into their own research?
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Well, the promising thing about these tools is that.
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Basically the ideas at their heart are scientific ideas, but the tools I've
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built will allow you to do the work and have the tool do a lot of the processing and lifting.
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What you would still have to do at this point is you would have to harvest or
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make a downloadable copy of
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the full set of your matches and the full sets of your cousin's matches.
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And that's a lot of work, but it's still something that's doable on with the
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computer if you are inclined to do that.
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Ultimately, what people, really the way these tools will benefit the most people
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is if the methods are actually eventually built into the back-end reporting
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of a DNA testing provider so that really you can ask the questions and the database
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will give you the answers, but you don't need to do any of the science any more
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than, let's say right now, when you take a test, you log in and you read your
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matches, but you don't have to do any science to do that. You simply,
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read what your answers are in terms of who do I match, who do I not match,
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and all the hard work is done in the cloud.
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And that's ultimately where these tools will do their best work for the most greatest people.
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The thing I'm doing right now often is telling people that these tools exist
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so that there's an awareness.
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When people often are aware that they have a problem, they don't necessarily
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know what can be done about it.
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And so to try and sort of educate people and inform them that this is what's
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next, this is what can be done, you need to just sort of make them aware that it's even possible.
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No, that's good to know. And in terms of with, I guess, with the role of DNA,
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how does that play within genealogical bioinformatics?
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And how do you kind of balance the genetic evidence with archival and,
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of course, documentary records that you would obtain?
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Well, the DNA evidence is basically our sort of what I would describe as our source materials.
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We start with that, and then your DNA matches, your raw DNA data are the raw
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materials that go into the system.
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There is, of course, no such thing as genetic proof. DNA won't prove anything.
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It's only a form of evidence similar to archival records.
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And so all of these things have to be balanced out to make sense with each other.
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The DNA evidence, at least, we're able to read and interpret.
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And with either conventional genetic genealogy or with bioinformatics,
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we can interpret that information and get suggestions and answers.
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But then still, we need to do the research with that evidence to make sense of things.
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So this is really sort of allowing us to get more evidence and to often as not,
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really see what's, what information is already there in our DNA matches,
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and then to try to reconcile that with.
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With what makes sense, what is actually reality. Yeah.
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And I think you mentioned it in a previous answer where, you know,
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there are certain communities or populations such as other diasporic or enslaved
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dissented communities where this work can be especially meaningful for them.
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Yes, absolutely. Some of basically, as I mentioned, one of the things that I
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find very useful about bioinformatics is that it can answer questions or address
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longstanding problems that genetic genealogy has.
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And one of those problems that some of my processes are better at dealing with
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is something we call endogamy,
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which basically, again, often happens in historically isolated or marginalized
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communities where people have either been forced because of geography, let's say, to
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marry within a certain group, again, if you were all on a certain island, or again, socially,
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often enslaved people or people at the margins of society were obliged,
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you know, didn't have much control over who they.
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Could be involved with.
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And so, again, those are situations where,
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people, even if they take a DNA test, will find that the results don't often
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make sense because they share more DNA with a closed group of people than we would expect them to.
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And often is not historically what we've tried to do within genetic genealogy
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on its own, was to either work with the results as best we can,
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or to look for results where we have a match, which has a large number of segments to it.
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Or basically disregard small matches, especially in endogamy situations,
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because those matches tend to be less reliable.
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And what bioinformatics can do is it can actually,
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create an individualized profile for each of us based on who we match and who
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we don't match and where our genetic pileups may exist so that, in fact, even,
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DNA matches from endogamous situations can be made to more closely resemble
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what we expect to receive in a DNA test.
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And so, on a very individual, personalized basis, our DNA results can make sense,
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or at least we can understand them better through these tools.
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Absolutely. And, you know, what would be some of the common misconceptions that
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people have about, of course, DNA and then genealogy? research?
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Well, a common misconception, which I mentioned briefly, was that DNA can prove things.
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In fact, DNA is a type of evidence, and what we do with that evidence really constitutes our proof.
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In some cases, the proof may be obvious in terms of, you know,
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is this person my parent or not?
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In other cases, however, especially
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as we get more distant with our DNA matches, we really do have to,
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look at what's logical and what's possible and eliminate what isn't possible
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in order to find something resembling the truth.
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But there's still a lot of research and a lot of interpretation there.
00:16:57.381 --> 00:17:06.080
Often people have the misconception that genetic testing can tell us about matches
00:17:06.080 --> 00:17:12.027
that occurred hundreds and hundreds of years ago, and that's not always possible. But.
00:17:12.992 --> 00:17:18.931
Again, the more we work with these advanced tools, the better we can interpret
00:17:18.931 --> 00:17:20.771
what information we do have.
00:17:21.656 --> 00:17:26.428
And so, you know, DNA doesn't have all the answers. And I think that's often a misconception.
00:17:26.794 --> 00:17:32.651
But the more people we test and the more we look at our own results carefully,
00:17:32.976 --> 00:17:34.731
the more information we can find.
00:17:35.031 --> 00:17:39.401
So there are always possibilities, I like to say. Yeah, absolutely.
00:17:39.751 --> 00:17:43.601
Absolutely. And would you be able to share, I guess, a personal story or a discovery
00:17:43.601 --> 00:17:49.173
from your own journey with genealogical bioinformatics that deeply impacted you?
00:17:49.730 --> 00:17:57.327
Certainly, certainly. I mean, one of the tools, the first tool I built, which was in 2021,
00:17:57.988 --> 00:18:05.607
It is a process which I call CMA, which in the long term stands for Correlated Multiphasic Analysis.
00:18:06.008 --> 00:18:11.513
And what it basically does is it allows you to take your DNA matches and the
00:18:11.513 --> 00:18:14.419
DNA matches of your known family members.
00:18:15.487 --> 00:18:20.493
And it allows you to work with these matches and the relationships of you and
00:18:20.493 --> 00:18:26.543
your cousins to find more distant, basically to make sense of more distant matches
00:18:26.543 --> 00:18:27.742
than you might otherwise do.
00:18:28.177 --> 00:18:34.353
One of the problems with a small DNA match is that it may not be,
00:18:36.173 --> 00:18:40.973
a match which is generally, well, excuse me, a match which is generally connected
00:18:40.973 --> 00:18:45.653
to us through a distant ancestor, but it may be something that just occurs to
00:18:45.653 --> 00:18:48.963
us because our ancestors came from a particular place.
00:18:49.833 --> 00:18:54.617
And so everyone who comes from a certain island has this segment of DNA,
00:18:54.936 --> 00:19:00.788
or everyone who comes from Scotland or India or somewhere else has this particular type of match.
00:19:01.275 --> 00:19:06.773
And by correlating our matches with our cousins and other known family members,
00:19:07.092 --> 00:19:12.043
we're able to eliminate what we would call false matches and look at matches
00:19:12.043 --> 00:19:13.306
that have greater significance.
00:19:13.706 --> 00:19:18.733
So in my particular case, something I was interested in researching is my great,
00:19:18.733 --> 00:19:22.010
great, great grandmother. I didn't know much about her family.
00:19:22.306 --> 00:19:26.890
She was British and her family name was Jones, which was terribly common.
00:19:27.801 --> 00:19:32.690
And I wanted to learn more. I knew that there had to be, intuitively,
00:19:32.690 --> 00:19:38.378
I felt there had to be some answers to be found that didn't exist in the documents I was seeing.
00:19:39.116 --> 00:19:45.760
And because the surname was so common, it would be easy to find answers that
00:19:45.760 --> 00:19:49.901
without really being able to test or prove as to whether they were really correct.
00:19:50.759 --> 00:19:54.689
But I wanted to find more information about this ancestor's family.
00:19:55.148 --> 00:20:01.660
And what I did is I used my tool to basically look at my DNA matches and the
00:20:01.660 --> 00:20:08.130
DNA matches of about 20 other cousins and found that basically if I put all
00:20:08.130 --> 00:20:09.916
of those data points together,
00:20:10.537 --> 00:20:19.424
I could look and see what DNA matches were shared most by my cousins and myself.
00:20:19.836 --> 00:20:25.088
And some of those matches were people known to me and, and those things made, made perfect sense.
00:20:25.559 --> 00:20:29.771
But if I went down the list of who matched me and my cousins the most,
00:20:30.258 --> 00:20:36.733
I eventually came to one person who matched about 14 of my 20 cousins.
00:20:37.594 --> 00:20:42.522
But yet was a very small match and not someone whose connection was in any way obvious to me.
00:20:43.854 --> 00:20:48.854
I took that person and, you know, sort of messaged them and looked to see who
00:20:48.854 --> 00:20:55.054
they might be more closely related to and found the first cousin and a son of
00:20:55.054 --> 00:20:56.418
theirs who had also tested.
00:20:56.761 --> 00:21:00.994
But nothing about that was really very helpful either because I could see their
00:21:00.994 --> 00:21:04.458
immediate family, but I really couldn't see how they connected to my family.
00:21:05.374 --> 00:21:09.037
What I ended up doing was building another tool,
00:21:09.639 --> 00:21:14.674
that allowed me to take the full set of my mystery person's matches,
00:21:15.274 --> 00:21:21.444
and compare it back to my cousin's matches and my own and generate a small list
00:21:21.444 --> 00:21:27.279
of people who matched my family, but matched the mystery person more closely.
00:21:27.808 --> 00:21:33.124
And that list of six or eight people, I was then able to work up and build up
00:21:33.124 --> 00:21:36.805
their family trees through conventional genealogy research.
00:21:37.316 --> 00:21:41.794
And what I found is that they were all descended from a man named Christopher
00:21:41.794 --> 00:21:45.699
Bowman and his wife, whose family name was also Jones.
00:21:46.163 --> 00:21:52.184
And so here now was a family who basically I ended up finding 30 of them all
00:21:52.184 --> 00:21:57.544
together who had tested, who were all descended from the wife of Mr.
00:21:57.544 --> 00:22:00.840
Bowman, whose wife's name was Jones.
00:22:01.188 --> 00:22:06.523
And indeed, my great-great-great-grandmother, whose name was Susanna Jones,
00:22:06.750 --> 00:22:08.176
married a man who was a baker.
00:22:08.553 --> 00:22:13.122
And sure enough, this other woman, whose name was Christiana Jones.
00:22:14.034 --> 00:22:15.698
Her husband was also a baker.
00:22:16.133 --> 00:22:20.844
And in fact, she was born in London, England around the same time I would expect
00:22:20.844 --> 00:22:24.540
a a younger sister of my great-great-great-grandmother to have been born.
00:22:24.896 --> 00:22:30.544
And so now, even though I had this very common Jones family, I also now had,
00:22:31.296 --> 00:22:36.207
my great-great-great-grandmother, and a woman who I very strongly believe would be her sister.
00:22:37.115 --> 00:22:42.495
And this was all sort of there in the DNA matches, but I was able to find this
00:22:42.495 --> 00:22:49.615
just by using these tools and these techniques, which could at least lead me in the right direction.
00:22:51.155 --> 00:22:54.169
Ultimately, you still have to do a lot of conventional work and interpretation.
00:22:54.772 --> 00:23:00.962
But whereas myself and each of my cousins might have had 40,000 DNA matches,
00:23:01.327 --> 00:23:04.404
And so you're looking at 800,000 points of data.
00:23:04.793 --> 00:23:08.745
These tools were at least able to tell me, look at the family trees of these
00:23:08.745 --> 00:23:15.431
eight people who have tested out of 800,000 and build up their trees to see what line they share.
00:23:15.704 --> 00:23:18.136
And this is the ancestor you want to look at.
00:23:18.624 --> 00:23:24.695
The same tool also led me to another family group, which appears to share matches
00:23:24.695 --> 00:23:28.545
with both of the other groups I've talked about, but is probably descended from
00:23:28.545 --> 00:23:31.173
a third person from the Jones family.
00:23:31.627 --> 00:23:36.085
And so what this, of course, allows me to do is that rather than just looking
00:23:36.085 --> 00:23:40.007
for a Susanna Jones, who could be any number of people in England,
00:23:40.419 --> 00:23:44.053
I'm now looking for a Susanna Jones, who has a sister, Christiana,
00:23:44.482 --> 00:23:46.659
and very likely another descendant.
00:23:47.031 --> 00:23:51.575
And this allows us at least to take the DNA evidence and say,
00:23:51.575 --> 00:23:56.475
well, if anything exists, it needs to be documented with these names.
00:23:56.621 --> 00:23:59.680
And now we can bring in conventional research.
00:24:00.946 --> 00:24:04.826
And work with that. So that was something that was very exciting to be able
00:24:04.826 --> 00:24:11.889
to see that these very distant matches, again, we're looking at people who were born in the late 1700s,
00:24:12.561 --> 00:24:17.266
that their descendants have tested, but that because the matches are so small
00:24:17.266 --> 00:24:20.266
and so distant, you really need good evidence.
00:24:20.266 --> 00:24:26.830
You need to be able to say that this person matches, again, 12 of my 20 cousins
00:24:27.091 --> 00:24:32.095
in order to really say this is a significant small match and not just an accident.
00:24:32.432 --> 00:24:36.553
And then by working it from there, we begin to make sense of things.
00:24:37.116 --> 00:24:39.986
Well, that is just so fascinating because I know if it was just looked up by
00:24:39.986 --> 00:24:42.370
traditional, that would take probably years to even...
00:24:43.200 --> 00:24:46.553
Kind of weave that all together. But the fact that you were able to use those
00:24:46.553 --> 00:24:49.863
tools is just amazing. And then the other question is, I guess,
00:24:50.456 --> 00:24:55.783
what ethical considerations should researchers keep in mind when working with
00:24:55.783 --> 00:24:58.188
genetic information and family histories?
00:24:58.792 --> 00:25:03.083
Certainly. You know, a lot of the same concerns that would normally apply with,
00:25:03.543 --> 00:25:09.913
regular genetic genealogy apply here, that you want to be able to basically
00:25:09.913 --> 00:25:13.666
ethically have the consent of the people whose DNA matches you're working with.
00:25:14.183 --> 00:25:19.063
When I'm working with my own family members' matches, they understand that I'm
00:25:19.063 --> 00:25:23.743
going to information and download it to my computer and,
00:25:24.363 --> 00:25:29.300
work with it in my tools to find new connections and new bits of information.
00:25:29.736 --> 00:25:34.828
If I'm reaching out to people, as I did with my mystery match descended from,
00:25:35.333 --> 00:25:39.313
my great-great-great-grandmother's sister, then I explain to them that this
00:25:39.313 --> 00:25:41.404
is why, this is what I'm asking for.
00:25:41.660 --> 00:25:44.231
I'd like you to share your DNA matches with me.
00:25:44.609 --> 00:25:48.033
I'm going to download them and put them into my tool. And again,
00:25:48.033 --> 00:25:53.123
it all comes down to informed consent that people are going to give us their
00:25:53.123 --> 00:25:56.241
information, share their personal data with us.
00:25:56.589 --> 00:26:01.925
We want to be upfront with what it is we're taking, what it is we're doing,
00:26:02.169 --> 00:26:04.155
and what it is we may find.
00:26:04.980 --> 00:26:09.951
Ultimately, it's also helpful to ask, let's say in the case of someone who you're
00:26:09.951 --> 00:26:15.574
asking to take a DNA test, it's also very useful to ask them, are they,
00:26:16.108 --> 00:26:19.986
taking the test just to help you and they don't really want to know what you find?
00:26:20.328 --> 00:26:26.050
Or are they interested in knowing what you discover based on their information?
00:26:26.375 --> 00:26:31.021
Some people are happy to help, but they really don't want to know what it is
00:26:31.021 --> 00:26:32.533
that you're looking about.
00:26:32.875 --> 00:26:35.671
And other people are genuinely curious and would like to know.
00:26:35.671 --> 00:26:41.841
So it's good to ask them in advance so that there's no ambiguity,
00:26:41.841 --> 00:26:49.091
no problems later if you should discover something that wasn't necessarily what you expected to find.
00:26:50.191 --> 00:26:54.531
Yeah, just making sure that everything is open and on the table before anything
00:26:54.531 --> 00:27:00.781
gets started. So how do you see the future of genealogical bioinformatics evolving
00:27:00.955 --> 00:27:03.109
over the next decade? Sure.
00:27:03.815 --> 00:27:10.288
Basically, what I see, what I anticipate happening is that right now,
00:27:10.416 --> 00:27:14.746
our DNA testing providers have huge amounts of information,
00:27:15.402 --> 00:27:25.253
and our genetic genealogy tools basically allow us to usefully access the closest
00:27:25.253 --> 00:27:28.585
3% to 5% of our DNA matches.
00:27:29.044 --> 00:27:33.241
So, and most tools, again, if we're talking about the units of DNA matching,
00:27:33.241 --> 00:27:38.241
we talk about something called a centimorgan, and our best matches are going
00:27:38.241 --> 00:27:40.279
to be 40 centimorgans or greater.
00:27:40.703 --> 00:27:44.971
And those tend to be the top 3% to 5% of our DNA test matches,
00:27:44.971 --> 00:27:52.840
which means that 95% of our DNA matches aren't going to be very useful to most conventional tools.
00:27:53.380 --> 00:27:58.429
And what I anticipate happening is these bioinformatic tools.
00:27:59.208 --> 00:28:03.934
Can be used to basically can be used in conjunction with,
00:28:04.494 --> 00:28:07.594
and again, I hesitate to just put the word out there, but it can be used with
00:28:07.594 --> 00:28:10.235
artificial intelligence systems so that,
00:28:10.916 --> 00:28:18.104
our raw data, our DNA matches, our DNA sequences themselves can be inputs to
00:28:18.104 --> 00:28:21.442
systems that can then look for patterns, look for answers,
00:28:22.088 --> 00:28:24.560
look for correspondences. so that.
00:28:26.814 --> 00:28:31.784
We have questions and we can bring those questions to these tools,
00:28:31.784 --> 00:28:37.054
to these what we call agents or artificial intelligence agents to begin to do
00:28:37.054 --> 00:28:40.520
the work for us and work with our information.
00:28:41.002 --> 00:28:45.954
These agents can also guide us as to who we want to test or where we want to
00:28:45.954 --> 00:28:49.065
test or what information would be helpful or missing.
00:28:49.337 --> 00:28:55.054
And this is what I anticipate the future will bring for us basically the ability
00:28:55.054 --> 00:29:00.986
to use large amounts of data and for us to basically begin with questions,
00:29:01.591 --> 00:29:07.974
and have the computer systems assist us with our answers based on just looking
00:29:07.974 --> 00:29:11.294
through piles and piles of information that,
00:29:12.494 --> 00:29:17.164
could take us years to go through and that these computer systems will be able
00:29:17.164 --> 00:29:22.614
to both help us find answers to questions we have and also guide us as to where
00:29:22.614 --> 00:29:25.344
to look and instruct us really,
00:29:25.884 --> 00:29:28.614
what answers are there in the data we have.
00:29:28.614 --> 00:29:32.601
That if we have questions about our paternal lines,
00:29:33.591 --> 00:29:38.952
It may be that the DNA patterns and data actually don't have those answers.
00:29:38.952 --> 00:29:43.892
And in fact, the agents, our AI agents can then tell us, really,
00:29:43.892 --> 00:29:48.772
you should be looking at these maternal lines for your solutions because that's
00:29:48.772 --> 00:29:50.114
the information that we have.
00:29:51.442 --> 00:29:56.000
And ultimately, then at least we're using our time and our research efforts productively.
00:29:56.656 --> 00:30:01.802
Absolutely. And I guess for listeners who are curious but are intimidated by
00:30:01.802 --> 00:30:07.151
the silence, what would do you say to encourage them to explore this video?
00:30:08.409 --> 00:30:14.741
I would say basically there are right now on my website, Discovering New Ancestors,
00:30:15.095 --> 00:30:22.188
I have a bioinformatics page which provides descriptions of the tools that I've authored so far.
00:30:22.641 --> 00:30:27.663
And the basic descriptions describe what the tools can do.
00:30:28.278 --> 00:30:34.882
Let's say one tool may be able to give you a whole group of DNA matches who
00:30:34.882 --> 00:30:36.987
are connected to you by a certain ancestor.
00:30:37.672 --> 00:30:42.862
Another tool can do the kind of mystery match work that I described that I did
00:30:42.862 --> 00:30:44.207
with my great-great-great-grandmother.
00:30:44.671 --> 00:30:49.897
Another tool can take the matches of full sets of DNA matches of three people,
00:30:50.571 --> 00:30:53.787
and tell you whether they share a common ancestor or whether they don't.
00:30:54.756 --> 00:31:00.522
And these sorts of tools address specific problems. And if you basically read
00:31:00.522 --> 00:31:03.562
the description of one of these tools and said to yourself, well,
00:31:03.562 --> 00:31:07.962
this is a problem I have, then you can go a little deeper,
00:31:08.762 --> 00:31:13.362
and read more about, well, what do I need to do to use this tool and try to
00:31:13.362 --> 00:31:14.685
work for things from there.
00:31:15.630 --> 00:31:20.361
Also, at least knowing that these tools exist, even if you don't feel capable
00:31:20.361 --> 00:31:21.720
of doing the work yourself,
00:31:22.156 --> 00:31:27.321
at least you know that there's a tool which perhaps another researcher,
00:31:27.741 --> 00:31:31.163
or if you have a cousin who is, you know, very skilled at these sorts of things,
00:31:31.496 --> 00:31:36.362
at least you know that there is something to look at which might be able to solve the problem.
00:31:36.786 --> 00:31:40.490
Because again, not, I always like to sort of put it this way,
00:31:40.856 --> 00:31:44.745
you know, not everyone wants to do their own taxes. Yeah.
00:31:45.601 --> 00:31:47.131
But at least, you know, we know
00:31:47.131 --> 00:31:51.136
where the forms are and what we have to do and when it has to be done.
00:31:51.612 --> 00:31:56.141
And so maybe there's someone there who can help us. And these sorts of problems
00:31:56.141 --> 00:31:59.111
in family research are kind of similar.
00:31:59.111 --> 00:32:06.601
Not everyone is going to feel up to and able to really dig into all the little
00:32:06.941 --> 00:32:10.110
minutiae and problems that occur in family research.
00:32:10.360 --> 00:32:15.265
But at least knowing that the tool is there and that you have this information
00:32:15.654 --> 00:32:19.710
may be helpful in finding solutions with help from others.
00:32:20.268 --> 00:32:24.731
Absolutely. And what do you hope people ultimately gain, not just scientifically,
00:32:24.731 --> 00:32:28.823
but personally from engaging with genealogical bioinformatics?
00:32:29.467 --> 00:32:34.279
Well, my hope is, again, similar to what genetic genealogy has brought us,
00:32:34.563 --> 00:32:39.368
which has been basically answers and connections and information,
00:32:39.693 --> 00:32:43.901
either confirming what we already knew or providing us with insights.
00:32:44.818 --> 00:32:46.908
Into connections we weren't aware of,
00:32:47.757 --> 00:32:53.351
that really kind of bring us all a little closer to understanding our own heritage
00:32:53.351 --> 00:32:55.025
and our own place in the world.
00:32:56.029 --> 00:33:02.572
These are just new tools or enhanced tools for doing what researchers have been
00:33:02.572 --> 00:33:10.270
doing and wanting to do all of which is to make sense of difficult questions and to really sort of,
00:33:10.909 --> 00:33:14.232
bring information, to make sense of information,
00:33:15.032 --> 00:33:20.792
to bring, in this case, information science to bear on the biology that we've
00:33:20.792 --> 00:33:25.342
already been using in genetic genealogy, which itself is just bringing more
00:33:25.342 --> 00:33:30.022
evidence to what researchers have already been working on for decades.
00:33:30.625 --> 00:33:34.427
Yeah. And, you know, Arun, as we come to a close with this episode,
00:33:34.654 --> 00:33:37.942
where can listeners learn more about your work and, of course,
00:33:37.942 --> 00:33:39.629
continue exploring this field?
00:33:40.273 --> 00:33:44.459
Well, I have a website, which is discoveringnewancestors.com.
00:33:45.389 --> 00:33:51.044
There's also a Facebook group I started, Genealogical Bioinformatics,
00:33:51.398 --> 00:33:57.033
which I started basically as a way of sort of getting people online able to
00:33:57.033 --> 00:33:59.689
discuss different issues and problems.
00:34:00.182 --> 00:34:04.041
There's, I think, a hundred or maybe so members of the group now,
00:34:04.384 --> 00:34:11.121
and it's not terribly active, but it's something that I hope to engage with more in the future.
00:34:11.579 --> 00:34:18.903
Something I recently began is a project to document and basically prepare educational
00:34:18.903 --> 00:34:20.738
materials about the subject.
00:34:21.110 --> 00:34:26.993
I was awarded a research grant by the American Society of Genealogists to prepare
00:34:26.993 --> 00:34:30.750
educational materials on genealogical bioinformatics.
00:34:30.982 --> 00:34:37.223
And so I hope to preview some of those materials in the Facebook group to get,
00:34:37.863 --> 00:34:40.124
feedback from interested people there also.
00:34:40.565 --> 00:34:44.463
So I would certainly start with my website, DiscoveringNewAncestors.com,
00:34:44.783 --> 00:34:48.422
and there are links to the Facebook group and other things there.
00:34:49.096 --> 00:34:52.823
Perfect. Thank you so much. I'll make sure to put that in my show notes so people
00:34:52.823 --> 00:34:55.308
can connect with that. But I just wanted to thank you so much,
00:34:55.592 --> 00:35:00.033
Arun, for coming on to the podcast, talking about this, and so people can be
00:35:00.033 --> 00:35:01.983
able to explore. So thank you so much.
00:35:02.308 --> 00:35:05.194
Oh, thank you for having me. It's been my pleasure. No, no problem.
00:35:06.796 --> 00:35:11.973
Hope you enjoyed this episode and if you did please make sure to like follow
00:35:11.973 --> 00:35:18.377
subscribe and write a review for the episode wherever you listen to your podcast thank you.