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Hi Vince,

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Thank you for the clarification. I do not claim to be an expert in genetic analysis, and I certainly have a large number of gaps to fill in order to claim to understand a lot about the subject that interests us here. My previous comments are mainly based on personnal empirical observations and are therefore certainly not accurate. However, I may also have poorly worded and/or detailed my remarks. So I will try to clarify these (English not being my native language this probably does not help me).

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In my previous post, I did not want to express that these famous micro-array chips could discover new SNPs previously unknown in the Y tree*. I was just wondering if the ??old?? Illumina chip used by FTDNA for these earlier FF tests could cover new Y chromosome positions not covered by the current GSA chip; positions where SNPs have already been identified. For example, the chip used in 2021 at MyHeritage covers, among others, the following SNPs: FT459752 (R-BY160589, rs112197623, 20313517), BY83023 (R-BY66068, rs62608681, 10182366), L199 (R-FGC398, rs781 777817, 1336586) and Z251 (R-ZP61, rs746402982, 8868293). These SNPs do not seem to me to be covered by the GSA chip used by FTDNA. It would therefore possibly be possible that the old Illumina chip could also cover certain SNPs for which no detection was possible with the GSA chip…

If new bigY testers come to form new branches in the Y tree (terminal or not), the new associated SNPs “discovered” would then possibly be possibly covered by the GSA chip, the Illumina chip, or the chip used by MyHeritage, etc.

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now it's incredibly unlikely that a microarray chip could be relied upon to create new branches on the phylogenetic tree without followup testing with Big Y or WGS.

I asked FTDNA a question last December regarding the possible comparison of autosomal test (FF) results with Pvs identified in bigY testers. Their answer :

摆…闭 The short answer is yes, in very rare instances we have found SNPs that have been previously seen in only one other tester in the database. These situations have allowed us to form new branches.

When "I stated" that, "theoretically", it was possible for a microarray type chip to detect "all" possible mutations (there are possibly technical limitations invalidating this, so I in no way guarantee that all mutations such that A to C, A to T, A to G… are really detectable, but probably more than one for the same position of the genome) on the same position, I based myself on an empirical observation concerning the SNP FGC57405 (identified for the first time by Full Genome Corp in 2016, position 11 506 634, mutation A to G). This SNP is only associated, in the current Y haplotree, with the haplogroup R-FGC57423 (excluding PVs possibly... TMRCA ~600 BCE). This haplogroup (and downstream) is only made up of 4 bigY testers to date. I didn't really see why this particular SNP, confidential in a sense, would be voluntarily covered by the GSA chip??? Especially since it does not appear in the file that you linked in your post #7704. However, at the same position 11,506,634 of the Y chromosome we find another SNP identified, namely Z921. Z921 (mutation A to C, identified by Ray Banks in 2011 according to ybrowse.org) is currently only associated with the much older haplogroup E-M132 (TMRCA ~17,420 BCE). It is therefore much more likely that the original target targeted by the GSA microarray chip was Z921 than FGC57405. I therefore concluded that it seemed possible for FTDNA to detect mutations other than the mutation initially sought at a specific position on the Y chromosome (the tagged nucleotides I believe, ).

About the “SNP Map tool”. This is not a part of Discover. This is a tool developed by FTDNA some time ago (accessible in Others, Y-DNA section, from the main page of your FTDNA account). You can make hot spots appear on a world map by selecting up to 6 SNPs, as well as a distance, expressed in km or miles, to take into account between the coordinates provided by each test (geolocation of EKA). If you select a distance to take into account such as 40 000 km/miles, you cover the entire globe. I derived a curl query model allowing to directly return the coordinates (latitude + longitude) of the results. I personally find this tool (although it presents some problems) quite interesting to learn a little more about testers belonging to a haplogroup that interests you, and for which you do not have information via FTDNA groups such as R-U106.

For example, for FGC57405 (28 FF testers have been added to R-FGC57423 by FTDNA in recent months, including 3 Swedes, 1 German, 1 Pole who took an STR test, a Lithuanian, an American, a Russian who is actually of German origin and having also passed a STR test, and 20 testers of unknown origin), I obtain:

• "latitude":48.1800343,"longitude":10.7557069 厂肠丑飞补产尘ü苍肠丑别苍, Bavaria, Germany

• "latitude":58.754921299999992,"longitude":11.894160199999988 H?gs?ter, V?stra G?taland (SE-O), Sweden

• "latitude":58.6502862,"longitude":12.055313100000035 H?gs?ter, V?stra G?taland (SE-O), Sweden

• "latitude":52.7706661,"longitude":22.954561499999954 Bujnowo, Podlaskie Voivodeship, Poland

• "latitude":49.3048,"longitude":25.7049 Terebovlya, Ternopil Oblast, Ukraine

• "latitude":51.5923654,"longitude":45.960803199999987 Saratov, Saratov Oblast, Russia

Cheers,

Ewenn

Ewenn,

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Technically speaking, the microarray chips do not "discover" new Y-SNPs. ?SNPs must be observed BEFORE they are added to the array.

In the early days we didn't always know where on the tree each Y-SNP belonged, but with hundreds of thousands of WGS and NGS samples sequenced now it's incredibly unlikely that a microarray chip could be relied upon to create new branches on the phylogenetic tree without followup testing with Big Y or WGS.

I'm not sure which "SNP Map tool" the rest of your post refers to. ?If you mean the haplogroup migration map (e.g. GlobeTrekker), the FamilyFinder tests are not suited for use in that model because of their limited coverage and the inability to reliably estimate TMRCAs from FamilyFinder-based Y-haplogroups.

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Vince

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Hi folks,

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The exact details of FTDNA's TMRCA calculations are not public. There's supposed to be a White Paper on it, but they haven't got around to writing it yet. Some parts of it are based on the article I wrote following my meeting with them in 2020. If you want all the gory details of that, they're here:
https://www.mdpi.com/2073-4425/12/6/862
I'll try to give a beginner's guide here.

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SNPs are actually the only form of variant that FTDNA tracks, so their non-matching variants list is really the number of SNPs that differ between two people.

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A SNP is simply an error in copying - the accidental replacement of one molecule with another in the genetic sequence. We don't know if this is truly random at the quantum mechanical level, but it is close enough to random that we can treat the formation of SNPs as a random process.

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Many people struggle with the concept of random processes. There are many similar processes in nature. A good example for our purposes is the number of weeds growing in a square inch of garden: if you till a garden bare and wait, weeds will grow and increase in number over time. Similarly, we can take an ancestor's Y chromosome and see how it accumulates SNPs as it is passed down to different lineages.

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If you have an idea how fast weeds grow in your part of the world, you can tell by looking at the garden and the number of weeds in it approximately how long it's been since the soil was tilled. Let's say it normally takes two days for the first weeds to germinate, and you might have an average of seven weeds per inch after a fortnight. Similarly, you can tell the length of time it's been since two people were related (their TMRCA) by looking at the number of SNPs that differ between their Y chromosomes - provided you know how often SNPs form. We know that one SNP happens in every molecule (base pair) of DNA about every 1.25 billion years, on average.

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However, the number of weeds you actually get depends on the size of your garden. The more area your garden has, the more weeds will grow. Similarly, the number of SNPs you can expect depends on the size of your Y chromosome test. This varies slightly from test to test, and with how low a quality of SNP you are prepared to accept. However, a good rule of thumb is that there are about 10 million base pairs in an old BigY-500 test and about 15 million base pairs in a modern BigY-700 test. This means a BigY-500 test gets a new SNP every 125 years or so, and a BigY-700 test gets a new SNP every 83 years or so. The comparison between every pair of tests is slightly different, as no two tests cover exactly the same parts of the Y chromosome.

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Also, if you have a flowerpot that's only a square inch, you might not get seven weeds after a fortnight. You might have only three or you might have twelve, it just depends on how many seeds land in your tiny flowerpot. Similarly, if you are looking at two BigY tests related 290 years ago, you might expect them to have seven non-matching variants between them (290 / 83 * 2 = 7), but they might actually only have three or twelve. It really depends on luck. This means that there's no one-to-one correspondence between the number of non-matching variants that differ between two people and how long ago they are related. All we can do is compare them to the statistical average we expect, and establish some range of time during which the real date is likely to happen (the confidence interval). (The mathematics of how to estsablish confidence intervals is governed by Poisson statistics - a branch of mathematics that was originally devised to deal with another random process: the number of Prussian soldiers kicked to death by their own horses.)

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This gives us an inexact mapping between number of non-matching variants and actual TMRCA, which is why exact TMRCAs are impossible to calculate. How can we improve the accuracy of our TMRCAs? We somehow have to increase the size of our garden. If you take a different square-inch flowerpot, it will have a different number of weeds. If you take many square-inch flowerpots that were planted a fortnight ago, you would find eventually get an average that settles down to our expected seven weeds in each.

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We have two options when it comes to genetic tests. We can either take bigger and better tests - this may be possible in the future, but we are close to running out of Y chromosome to test! Another option is to look at more testers. If you take a man who has two sons, each son's family will accrue an independent set of new SNPs, each of which can provide an estimate of the TMRCA. If you can add in a third son's family, then you have three independent sets of SNPs, and their average number (multiplied by 83) should give you a smaller confidence interval that is closer to the real TMRCA. Adding a fourth son would improve things further, etc., but there is a law of diminishing returns - the difference between 99 and 100 sons is very small!

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Not even the most prolific father has 100 sons, all of whom have descendants today. However, if you run out of sons, then two grandsons from the same son have *almost* independent sets of SNPs, so they are *almost* as good at improving the TMRCA. Conversely, however, testing your brother isn't effective, since you and your brother's sets of SNPs aren't independent of each other - you will share most or all of them. This is the reason I would not suggest encouraging brothers or close cousins to take a BigY test if you have already done so, unless there is a very specific reason for doing so. Normally, there's no benefit from testing anyone closer than about fourth cousins and, if you do want to test additional family to improve your TMRCA, then test the most distant cousin you can find, as their SNPs will be the most independent of yours.

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Hopefully this gives you some insight into TMRCAs and how they can be improved. I should point out that FTDNA's TMRCAs also use Y-STRs as well as Y-SNPs, though these obviously aren't reported in your non-matching variants lists. It's possible to also include other larger mutations in DNA (e.g. MNPs, insertions, deletions, etc.), but these are unreliably reported by BigY in many cases. My paper sets out the basic principles behind how each of these can be used.

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Best wishes,

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Iain.

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Shane,

The correct formula is for TMRCA is [(NMV/2) x years-per-NMV], but I'd suggest avoiding using this method and directly lookup the TMRCA estimate using FTDNA's "Discover" tool.

Their TMRCA estimates account for many factors that the simple rule of thumb measures can't account for. Plus FTDNA give you 95% and 99% confidence intervals in addition to the central estimate.

Regardless of the number NMVs, you are more closely related to people within your terminal haplogroup than to people outside of it. ?If you're in a paragroup it's a little different.

Vince