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All human sub-populations are not equal. That depends on how big your sub-population is. If your population is 1, then the difference between two populations,

All human sub-populations are not equal. That depends on how big your sub-population is. If your population is 1, then the difference between two populations, unless they happen to be maternal twins, will be HUGE. Statistically speaking. But as your population size grows the difference between populations begin to shrink. Statistics again. The reason for that is simple, humans are among a few species that happen to have very little genetic diversity. Approximately 80 thousands years ago, something (bad) happened and the totality of humans on earth was reduced to a terrifyingly small number. We don't know exactly how small but it could have been as little as a few hundred individuals. And of those only 4 or 5 (I forget) matrilineal lines survive today. Besides humans, cheetahs are one of the other species that is similarity genetically pure. Not that genetic purity is a good thing, quite the opposite, it makes us susceptible to extinction by disease. We could be wiped out by something like a more easily transmissible HIV. Even a single tribe of chimps has more genetic diversity then all of humanity. And that's why looking at the genetic difference of human populations is not very insightful. Take one of the most famous reproductively isolated human sub-populations - the Ashkenazi. DNA suggested that about 40% of the current Ashkenazi population is descended matrilineally from just four women. As is common in cases of the founder effect: http://en.wikipedia.org/wiki/Founder_effect http://en.wikipedia.org/wiki/Founder_effect A few genes that are very rare in a population, can be common in the population resulting from the few founders, if even just one of the founders had the rare genes. And the Ashkenazi appear to have a much higher incidence of genes responsible for neural diseases but perhaps also increased intelligence. So what happens if you pull a "Trading Places" trick, but with two babies instead of Eddie Murphy and Dan Aykroyd? Nothing, remember humans sadly have a very shallow gene pool, so even an Inuit and an Ashkenazi could have more in common genetically with each other then the Ashkenazi with most other Ashkenazi. And that's why talking about DNA in human populations, is like talking about the gold plating in Monster audio cables. The people who do, aren't very smart about the subject they're ranting about.

defen 18y ago
Mitochondrial and Y-chromosomal DNA are not good ways of determining population bottlenecks, since they are inherited exclusively from the mother and father, respectively. The "Two individuals from one race are more different than two random people" myth is a pernicious one that needs to die. The short answer is that you need to look at correlations in DNA, especially of regions under selection. Imagine a single human's DNA as a point in 4-billion dimensional space (where each dimension can take on the value A,C,G, or T). If you do a principal component analysis on the DNA of large numbers of people, you will see clusters, and these clusters correspond to geographic ancestry. http://www.nature.com/ejhg/journal/v16/n12/abs/ejhg2008210a.html http://www.nature.com/ejhg/journal/v16/n12/abs/ejhg2008210a.... "A major East–West gradient from Russian (Moscow) samples to Spanish samples was identified as the first principal component (PC) of the genetic diversity. The second PC identified a North–South gradient from Norway and Sweden to Romania and Spain." In picture form, from that paper in case you don't have access to Nature: http://www.gnxp.com/blog/2008/11/another-genetic-map-of-europe.php http://www.gnxp.com/blog/2008/11/another-genetic-map-of-euro... Now, please tell me again with a straight face that I don't know what I'm "ranting" about, and that DNA in humans is irrelevant.
- biohacker42 18y ago
  I get paid to do PCA analysis for the biotech industry. Except that PCA is the least I do, PCA is what you learn in a bioinformatics 101 class, there are MUCH better tools then PCA. If two things are noticeably different you don't need PCA, it's redundant. You use PCA, when you're plumbing a huge data set, where things are mostly indistinguishable and you're desperate for ways to separate things out. That's why PCA analysis is so convenient for human DNA, because we're so similar. Look at this image: http://scienceblogs.com/gnxp/genmapeuropeGermany.jpg http://scienceblogs.com/gnxp/genmapeuropeGermany.jpg Are you telling me that the Germans from the blue part of Dresden bordering Munich and the Germans from the green part of Munich bordering Dresden are super duper different? That picture sure makes it looks like Hans living in the exurbs of Munich and Frans from the exurbs of Dresden are two different species. It looks that way if you don't have a fucking clue about what PCA is and how it works and what DNA is and how it works. And two individuals from the same race are more different then two large groups of people from the opposite end of the earth, is not a myth. It is a fact. It is a statistical fact, the math doesn't care what you think and your grasp of stats and genomics is not nearly as tight as you imagine it to be. No one imagines DNA as 4-billion dimension space, you're full shit. And if you start looking at what ever regions you're fantasizing about, and apply a bunch more conditions, you can prove anything you want. That's not science it's charlatanry. In conclusion, you do not know what you're ranting about, so stop embarrassing yourself in front of biotech engineers on the internet.
  - defen 18y ago
    I intentionally simplified because I didn't know you were a biotech engineer. Also I'm impressed that you were able to divine how much I know about statistics from my few short posts here. So you're telling me that if I do PCA on the covariance matrix for many loci, I'm not going to be able to correlate DNA with geographic ancestry? How do you explain this: http://www.sciencemag.org/cgi/content/abstract/298/5602/2381 http://www.sciencemag.org/cgi/content/abstract/298/5602/2381 I will grant you one point - "Within-population differences among individuals account for 93 to 95% of genetic variation; differences among major groups constitute only 3 to 5%." However, if you read the paper you'll find that they were able to identify clustering along geographic lines, and could place individuals with a discrepancy rate of 0.14%.

defen 18y ago

Mitochondrial and Y-chromosomal DNA are not good ways of determining population bottlenecks, since they are inherited exclusively from the mother and father, respectively. The "Two individuals from one race are more different than two random people" myth is a pernicious one that needs to die. The short answer is that you need to look at correlations in DNA, especially of regions under selection. Imagine a single human's DNA as a point in 4-billion dimensional space (where each dimension can take on the value A,C,G, or T). If you do a principal component analysis on the DNA of large numbers of people, you will see clusters, and these clusters correspond to geographic ancestry. http://www.nature.com/ejhg/journal/v16/n12/abs/ejhg2008210a.html http://www.nature.com/ejhg/journal/v16/n12/abs/ejhg2008210a.... "A major East–West gradient from Russian (Moscow) samples to Spanish samples was identified as the first principal component (PC) of the genetic diversity. The second PC identified a North–South gradient from Norway and Sweden to Romania and Spain." In picture form, from that paper in case you don't have access to Nature: http://www.gnxp.com/blog/2008/11/another-genetic-map-of-europe.php http://www.gnxp.com/blog/2008/11/another-genetic-map-of-euro... Now, please tell me again with a straight face that I don't know what I'm "ranting" about, and that DNA in humans is irrelevant.

biohacker42 18y ago
I get paid to do PCA analysis for the biotech industry. Except that PCA is the least I do, PCA is what you learn in a bioinformatics 101 class, there are MUCH better tools then PCA. If two things are noticeably different you don't need PCA, it's redundant. You use PCA, when you're plumbing a huge data set, where things are mostly indistinguishable and you're desperate for ways to separate things out. That's why PCA analysis is so convenient for human DNA, because we're so similar. Look at this image: http://scienceblogs.com/gnxp/genmapeuropeGermany.jpg http://scienceblogs.com/gnxp/genmapeuropeGermany.jpg Are you telling me that the Germans from the blue part of Dresden bordering Munich and the Germans from the green part of Munich bordering Dresden are super duper different? That picture sure makes it looks like Hans living in the exurbs of Munich and Frans from the exurbs of Dresden are two different species. It looks that way if you don't have a fucking clue about what PCA is and how it works and what DNA is and how it works. And two individuals from the same race are more different then two large groups of people from the opposite end of the earth, is not a myth. It is a fact. It is a statistical fact, the math doesn't care what you think and your grasp of stats and genomics is not nearly as tight as you imagine it to be. No one imagines DNA as 4-billion dimension space, you're full shit. And if you start looking at what ever regions you're fantasizing about, and apply a bunch more conditions, you can prove anything you want. That's not science it's charlatanry. In conclusion, you do not know what you're ranting about, so stop embarrassing yourself in front of biotech engineers on the internet.
- defen 18y ago
  I intentionally simplified because I didn't know you were a biotech engineer. Also I'm impressed that you were able to divine how much I know about statistics from my few short posts here. So you're telling me that if I do PCA on the covariance matrix for many loci, I'm not going to be able to correlate DNA with geographic ancestry? How do you explain this: http://www.sciencemag.org/cgi/content/abstract/298/5602/2381 http://www.sciencemag.org/cgi/content/abstract/298/5602/2381 I will grant you one point - "Within-population differences among individuals account for 93 to 95% of genetic variation; differences among major groups constitute only 3 to 5%." However, if you read the paper you'll find that they were able to identify clustering along geographic lines, and could place individuals with a discrepancy rate of 0.14%.