Important update on the Human-chimpanzee DNA fiasco

 Are human populations 99.9% identical?

How a correct finding has been incorrectly interpreted.

Nov 21

https://www.aporiamagazine.com/p/are-human-populations-999-identical

 

Written by Peter Frost. You’ve probably heard that humans and chimpanzees are genetically 98 to 99% the same.  You’ve probably also heard that human populations are 99.9% the same. The second finding  has often been cited, for example by Hillary Clinton. In a speech to high school graduates,  the former First Lady mentioned “genetic research that shows humans are 99.9 percent the same”. The differences in how we look — in our skin color, our eye color, our height — stem from  just one-tenth of 1 percent of our genes. And the differences among us — our cultures, our  religious beliefs, the music we like — it is all so small a distinction in our sea of common humanity. Of course, one tenth of one percent is still a lot. In a post criticizing Clinton’s speech, anthropologist  John Hawks observed that “one-tenth of 1 percent of 3 billion is a heck of a large number —  3 million nucleotide differences between two random genomes” (Hawks, 2007). He added,  “We differ by one-tenth of 1 percent of nucleotides, this is enough to make coding differences   in a large fraction of our genes.” In other words, the 0.1% figure is not the percentage of genes that are different. It’s the percentage  of individual nucleotides that are different. A single gene is a long chain of nucleotides, often  a very long one, and a single nucleotide mutation can significantly alter how the entire gene works.  In theory, then, each and every human gene could work differently from one population to another. Moreover, as Hawks himself showed in a study published the same year, at least 7% of the human  genome has changed over the last 40,000 years — mostly the last 10,000 (Hawks et al., 2007).  This was when our ancestors were spreading over the globe and differentiating into today’s geographic  populations. Those populations cannot all share the  same 7% change. Clearly, 0.1% isn’t the fraction of genes that differ among human populations. The true figure is certainly  larger. Again, each and every gene could differ among human populations by 0.1%, and such a   difference could affect how each and every one functions. Also, genes do not differ solely in  nucleotide sequences. They also differ in the way those sequences are arranged on the chromosomes.  The same sequence may be repeated consecutively or it may be copied and inserted somewhere else.  Such rearrangements can likewise affect how a gene functions. “Structural variations, such as  copy-number variation and deletions, inversions, insertions and duplications, account for much more human genetic variation than single nucleotide diversity” (Wikipedia, 2025). This structural variation became apparent during the first complete sequencing of a human genome: Of the 4.1 million variations between chromosome sets, 3.2 million were SNPs, while nearly  one million were other kinds of variants, such as insertion/deletions (“indels”), copy number variants,  block substitutions, and segmental duplications. While the SNPs outnumbered the non-SNP types of  variants, the non-SNP variants involved a larger portion of the genome. This suggests that human-to-human variation is much greater than previously thought. (Phys.org, 2007; see also Levy et al., 2007) If we return to comparing humans and chimpanzees, we can measure the total genetic difference  between them by looking at what the genes make, i.e., proteins. The two species differ in about 80%  of their proteins — a figure far higher than the 1 to 2% difference in their nucleotide sequences  (Glazko et al., 2005). Even this 80% figure is not the whole story. Some genes regulate how other genes are expressed,  often thousands of others, and thus play a key role in growth and development. These “regulator”  genes are much fewer in number than other genes but far greater in their effects. Plus, they differ  much more between humans and chimpanzees than other genes do. Whereas the two species are  almost identical in the nucleotide sequences of their genes and the amino acid sequences of their  proteins, and relatively similar in the proteins that make up their tissues, they differ radically  in the way their tissues grow and develop, notably the neural tissues of the brain. This was already clear to two researchers, Mary-Claire King and A.C. Wilson, when, half a century ago,  they discovered the startling similarity of nucleotide sequences and amino acid sequences between  humans and chimpanzees: The molecular similarity between chimpanzees and humans is extraordinary because they differ far  more than sibling species in anatomy and way of life. Although humans and chimpanzees are rather  similar in the structure of the thorax and arms, they differ substantially not only in brain size but also  in the anatomy of the pelvis, foot, and jaws, as well as in relative lengths of limbs and digits. Humans  and chimpanzees also differ significantly in many other anatomical respects, to the extent that nearly   every bone in the body of a chimpanzee is readily distinguishable in shape or size from its human  counterpart. Associated with these anatomical differences there are, of course, major differences in  posture, mode of locomotion, methods of procuring food, and means of communication. Because of  these major differences in anatomy and way of life, biologists place the two species not just in separate  genera but in separate families … The contrasts between organismal and molecular evolution indicate that the two processes are to a large  extent independent of one another. Is it possible, therefore, that species diversity results from molecular  changes other than sequence differences in proteins? … According to this hypothesis, small differences  in the time of activation or in the level of activity of a single gene could in principle influence  considerably the systems controlling embryonic development. The organismal differences between   chimpanzees and humans would then result chiefly from genetic changes in a few regulatory systems,  while amino acid substitutions in general would rarely be a key factor in major adaptive shifts. (King & Wilson, 1975, pp. 113–114) Genetic distance between humans and chimpanzees, compared to genetic distances in other taxa.  (King & Wilson, 1975, p. 113) In this context, the two researchers were thinking not only about the human-chimpanzee difference  but also about the differences within our species: [The human-chimpanzee] distance is 25 to 60 times greater than the genetic distance between human  races. In fact, the genetic distance between Caucasian, Black African, and Japanese populations is less  than or equal to that between morphologically and behaviorally identical populations of other species. (King & Wilson, 1975, p. 113) The above paragraph appears in the middle of a discussion about the human-chimpanzee genetic d istance, and its paradoxical smallness. In fact, the two researchers highlight this paradox right after: However, with respect to genetic distances between species, the human-chimpanzee D value is e xtraordinarily small, corresponding to the genetic distance between sibling species of Drosophila or   mammals. Nonsibling species within a genus … generally differ more from each other, by e lectrophoretic criteria, than humans and chimpanzees. The genetic distances among species from  different genera are considerably larger than the human-chimpanzee genetic distance.  (King & Wilson, 1975, p. 113) How should we measure the genetic distance between two human populations? There is no easy answer  because few species resemble our own. Our species is unusual in that it evolved rapidly at the very  time it was splitting up into populations across different environments — not only natural environments  from the equator to the arctic but also an ever-wider range of cultural environments. In fact, this entry  into so many environments largely explains the concurrent rapidity of human genetic evolution.  Natural selection has thus shaped human populations in highly divergent ways (Akbari et al., 2024;  Cochran & Harpending, 2009; Frost, 2023a; Hawks et al., 2007; Kuijpers, et al., 2022;  Libedinsky et al., 2025; Piffer & Kirkegaard, 2024; Rinaldi, 2017). In such a situation, differences in selection contribute much more to genetic diversity between  populations than to genetic diversity within populations. Keep in mind that natural selection causes  a population to diversify only in certain limited cases (e.g., frequency-dependent selection). In most  cases, a population is diversified by stochastic processes of little adaptive consequence, since everybody  is adapting to the same environment and the same selection pressures. We thus return to the same paradox: Fst is relatively low in our species even though human populations  differ much more anatomically than do most sibling species in the animal kingdom. As Charles Darwin  noted, a naturalist would consider some human groups to be “as good species as many to which he had  been in the habit of affixing specific names.” The paradox exists because humans split rapidly to  colonize highly divergent environments, with the result that genetic diversity between populations is   much more consequential than genetic diversity within populations. We are therefore comparing apples  to oranges when we calculate human Fst (Darwin, 1936 [1888], pp. 530-531; King & Wilson, 1975;  Frost, 2023b). What’s more, relatively little of our evolution has been at the level of nucleotide sequences or amino  acid sequences. It has been largely at a higher level — the duplication, rearrangement and regulation of   existing DNA in new ways (Yoo et al., 2025). This point came up in a recent discussion on X: The widely cited Chimpanzee-Human 98-99% DNA similarity figures refer exclusively to nucleotide s equence similarity within alignable genomic regions, which become misleading when portrayed as the t otal amount of DNA shared. While this metric is important, as it highlights the strength of the  evolutionary constraints within the protein-coding and non-coding sequences found in alignable r egions, it ignores the structural and regulatory differences that are key for shaping the phenotypic  differences between Chimpanzees and Humans. When combining these metrics, total   Chimpanzee-Human DNA similarity figures drop to ~84.7% (Origins Unveiled, 2025) Admittedly, I have no idea how the author combined these metrics. I don’t blame Hillary Clinton for drawing the wrong conclusion from the 99.9% estimate, but I’m less  forgiving toward those who have silently gone along with this fallacy while knowing better. Two  decades ago, John Hawks pointed out its flaws in a post criticizing Hillary’s speech. The post remained  on his website until he deleted it in 2021 — when many American academics got the memo that Hillary  had been right all along… on this issue and on any other. “Nice research lab you have there. Pity if anything happened to it.” When academics choose the path of silence, and withhold their objections, they help create a fake  consensus that ultimately brings academia into disrepute. Peter Frost has a PhD in anthropology from Université Laval. His main research interest is the role of sexual selection in shaping highly visible human traits. Find his newsletter here. References Akbari, A., Barton, A.R., Gazal, S., Li, Z., Kariminejad, M., Perry, A., Zeng, Y., Mittnik, A., Patterson,  N., Mah, M., Zhou, X., Price, A.L., Lander, E.S., Pinhasi, R., Rohland, N., Mallick, S., & Reich,  D. (2024). Pervasive findings of directional selection realize the promise of ancient DNA to elucidate  human adaptation. bioRxiv. https://doi.org/10.1101/2024.09.14.613021 Anon. (2007). Finding said to show “race isn’t real” scrapped  http://www.world-science.net/othernews/070904_human-variation.htm Cochran, G. & Harpending, H. (2009). The 10,000 Year Explosion: How Civilization Accelerated  Human Evolution. Basic Books: New York.  https://www.amazon.ca/000-Year-Explosion-Civilization-Accelerated/dp/0465002218 Darwin, C. (1936 [1888]). The Descent of Man and Selection in relation to Sex. reprint of 2nd edition,  The Modern Library, New York: Random House. Frost, P. (2023a). Human evolution didn’t slow down. It accelerated! Peter Frost’s Newsletter, July 12. Frost, P. (2023b). Do human races exist? Peter Frost’s Newsletter. August 15. Glazko, G., Veeramachaneni, V., Nei, M., & Makałowski, W. (2005). Eighty percent of proteins are different between humans and chimpanzees. Gene, 346, 215-219. https://doi.org/10.1016/j.gene.2004.11.003 Hawks, J. (2007). Disagreeing with Hillary Clinton on human genetic differences. John Hawks Weblog https://web.archive.org/web/20210624221131/http://johnhawks.net/weblog/topics/race/ differences/clinton_2007_proportion_differences_speech.html Hawks, J., Wang, E. T., Cochran, G. M., Harpending, H. C., & Moyzis, R. K. (2007). Recent acceleration of human adaptive evolution. Proceedings of the National Academy of Sciences, 104(52), 20753-20758. https://doi.org/10.1073/pnas.0707650104 King, M-C. & Wilson, A.C. (1975). Evolution at two levels in humans and chimpanzees:  Their macromolecules are so alike that regulatory mutations may account for their biological differences. Science, 188, 107-116. https://doi.org/10.1126/science.1090005 Kuijpers, Y., Domínguez-Andrés, J., Bakker, O.B., Gupta, M.K., Grasshoff, M., Xu, C.J., Joosten, L.A.B., Bertranpetit, J., Netea, M.G., & Li, Y. (2022). Evolutionary Trajectories of Complex Traits in European Populations of Modern Humans. Frontiers in Genetics, 13, 833190. https://doi.org/10.3389/fgene.2022.833190 Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, et al. (2007) The diploid genome sequence of an individual human. PLoS Biol, 5(10): e254. https://doi.org/10.1371/journal.pbio.0050254 Libedinsky, I., Wei, Y., de Leeuw, C., Rilling, J. K., Posthuma, D., & van den Heuvel, M. P. (2025). The emergence of genetic variants linked to brain and cognitive traits in human evolution. Cerebral Cortex, 35(8), bhaf127. https://doi.org/10.1093/cercor/bhaf127 Origins Unveiled (2025). Busting the 98% myth: Humans share only ~85% of their DNA with chimpanzees. September 5 https://x.com/OriginsUnveiled/status/1964040999468224774 Phys.org (2007). First individual genome sequence published. Phys.org. September 4. https://phys.org/news/2007-09-individual-genome-sequence-published.html Piffer, D., & Kirkegaard, E.O.W. (2024). Evolutionary Trends of Polygenic Scores in European Populations from the Paleolithic to Modern Times. Twin Research and Human Genetics, 27(1), 30-49. https://doi.org/10.1017/thg.2024.8 Redon, R., Ishikawa, S., Fitch, K. R., Feuk, L., Perry, G. H., Andrews, T. D., ... & Hurles, M. E. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444-454. https://doi.org/10.1038/nature05329 Rinaldi, A. (2017). We’re on a road to nowhere. Culture and adaptation to the environment are driving human evolution, but the destination of this journey is unpredictable. EMBO reports 18: 2094-2100. https://doi.org/10.15252/embr.201745399 Wikipedia (2025). Human genetic variation. https://en.wikipedia.org/wiki/Human_genetic_variation Yoo, D., Rhie, A., Hebbar, P., Antonacci, F., Logsdon, G. A., Solar, S. J., ... & Eichler, E. E. (2025). Complete sequencing of ape genomes. Nature, 1-18. https://doi.org/10.1038/s41586-025-08816-3

 

A New Dimension of coding in DNA greatlyh increases informational content and

 A Fake Headline, and a Real One, About DNA

_{Casey Luskin}

_{https://scienceandculture.com/2025/11/a-fake-headline-and-a-real-one-about-dna/}

 _{displays meaning for formerly thought junk DNA}

_{November 3, 2025}

My Google News feed thought I would be interested in an article on Yahoo News titled “Human DNA detected in 2 billion year old meteorite.” So I checked out the story and it’s fake news. No DNA, much less human DNA, was detected in a meteorite. Instead, we found very simple compounds which everyone already knew are common in meteorites. Here’s what you read in the story after you scroll pass the fake headline: 

NASA’s and Japan’s missions both returned pieces of ancient asteroids to Earth. Inside the asteroids researchers have found carbon, ammonia, salts, and even amino acids, which are the molecules that make up proteins. In January 2025, scientists said OSIRIS-REx’s samples contained 14 of the 20 amino acids used by life on Earth, plus chemical precursors of DNA and RNA.

Great, but No Surprises There

And while it may sound impressive about the 14 of the 20 amino acids used by life, you can almost rest assured that they are a racemic mixture of both right and left handed amino acids and, as Jim Tour recently showed, they won’t be linking up into long polymer chains. 

But if you read the article, there is an interesting admission: 

“Bennu is basically a pantry full of ingredients,” said Dr. Jason Dworkin, NASA’s lead scientist on the OSIRIS-REx mission. “But it wasn’t quite the right conditions to make a cake. On Earth, we have cake, and we don’t know why.”

Did you get that? “Cake,” I believe, is supposed to mean life. So obviously on earth we have cake. And this lead NASA scientist is admitting we don’t know why there is life on earth. So the article might be bluffing about the discovery of human DNA in a meteorite, but at least it goes on to admit we don’t understand how life arose on earth. Very interesting! 

The “Geometric Code”

Meanwhile, I also got an email from a science professor in our network about another news headline titled “Scientists uncover hidden ‘geometric code’ that helps DNA compute and remember.” This seems to be a real story because it’s based upon a study done by scientists at Northwestern University published in the journal Advanced Science, titled “Geometrically Encoded Positioning of Introns, Intergenic Segments, and Exons in the Human Genome.” 

According to the news story, biologists have discovered a “second language” in our DNA that isn’t based upon the precise sequence of bases but rather upon the structural shape of the DNA molecule — as the story puts it “a second language built on geometry rather than chemistry.” Here’s how the finding is described:

Led by biomedical engineer Vadim Backman, the study reveals that DNA’s 3D physical structure holds a “geometric code” — a system that allows cells to compute, remember and adapt.

Essentially, the idea is that the three-dimensional shape of the chromosomes is vital to modulate and control many genomic processes such as gene transcription. Parts of genes which may be distant in one dimension on a chromosome can be brought close together due to the three-dimensional “packing” of chromosomes in the nucleus, creating “functional packing layers of domains” also called “packing domains” (PDs). From the technical paper:

[I]ntrons and intergenic segments are coupled to adjacent exons to generate coherent packing domain volumes … We wish to propose a radical hypothesis — described within this work — that nanoscale packing geometry is encoded in the positioning of exons, introns, and intergenic segments as projections of the functional layers of PDs [packing domains] … 

This idea that the three-dimensional shapes of chromosomes are important for controlling genome function is receiving more and more attention in the literature. Earlier this year I discussed (see here and here) how much of the newly discovered DNA that is different between humans and chimps has been found to be “non-B” DNA — often repetitive DNA — where the number of copies of repeats control the 3D structural shape of chromosomes, and the 3D shape of chromosomes helps regulate genome function. This means that even repetitive DNA is functionally important in shaping chromosome structure, which is very important for creating these “domains” of genome regulation. 

If You’re Catching the Drift…

All of this has direct and negative implications for the idea of “junk DNA” because it suggests that huge amounts of our DNA may be functionally important for controlling chromosomal architecture. 

In fact, the implications of this model for junk DNA were made explicit in a 2019 paper in BioEssays that I reported on which found that the GC content of chromosomes helps define topologically associating domains (TADs). These TADs bring parts of chromosomes spatially near one another in the nucleus in order to regulate things like gene transcription. The paper calls GC-content based code a “genomic code,” and finds it has important implications against the idea of junk DNA: 

[T]he genomic code, which is responsible for the pervasive encoding and molding of primary chromatin domains (LADs and primary TADs, namely the “gene spaces”/“spatial compartments”) resolves the longstanding problems of “non-coding DNA,” “junk DNA,” and “selfish DNA” leading to a new vision of the genome as shaped by DNA sequences.

Now, this new paper in Advance Sciences adds to the body of evidence showing that the 3D shape of chromosomes is vital to creating chromosomal “domains” that interact to produce things like gene transcripts. The fact that they are calling it a “geometric code,” which is “a second language built on geometry rather than chemistry,” shows just how important the three-dimensional architecture of chromosomes is in regulating genome function. 

 

Casey Luskin is a geologist and an attorney with graduate degrees in science and law, giving him expertise in both the scientific and legal dimensions of the debate over evolution. He earned his PhD in Geology from the University of Johannesburg, and BS and MS degrees in Earth Sciences from the University of California, San Diego, where he studied evolution extensively at both the graduate and undergraduate levels. His law degree is from the University of San Diego, where he focused his studies on First Amendment law, education law, and environmental law.

 

A Revolution in Ev0lutionary Theory

 Mutations driving evolution are informed by the genome, not random, study suggests

by University of Haifa

edited by Gaby Clark, reviewed by Robert Egan

 

https://phys.org/news/2025-09-mutations-evolution-genome-random.html

 Editors' notes

MEMDS experimental setup. Credit: Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2424538122

A study published in the Proceedings of the National Academy of Sciences by scientists from Israel and Ghana shows that an evolutionarily significant mutation in the human APOL1 gene arises not randomly but more frequently where it is needed to prevent disease, fundamentally challenging the notion that evolution is driven by random mutations and tying the results to a new theory that, for the first time, offers a new concept for how mutations arise.

Implications for biology, medicine, computer science, and perhaps even our understanding of the origin of life itself, are potentially far reaching.

A random mutation is a genetic change whose chance of arising is unrelated to its usefulness. Only once these supposed accidents arise does natural selection vet them, sorting the beneficial from the harmful. For over a century, scientists have believed that a series of such accidents has built up over time, one by one, to create the diversity and splendor of life around us.

However, it has never been possible to examine directly whether mutations in the DNA originate at random or not. Mutations are rare events relative to the genome's size, and technical limitations have prevented scientists from seeing the genome in enough detail to track individual mutations as they arise naturally.

To overcome this, Prof. Adi Livnat of the University of Haifa, director of the Sagol Lab for Evolution Research, lead author Dr. Daniel Melamed and the team developed a new ultra-accurate detection method and recently applied it to the famous HbS mutation, which protects from malaria but causes sickle-cell anemia in homozygotes.

Results showed that the HbS mutation did not arise at random, but emerged more frequently exactly in the gene and population where it was needed. Now, they report the same nonrandom pattern in a second mutation of evolutionary significance.

The new study examines the de novo origination of a mutation in the human APOL1 gene that protects against a form of trypanosomiasis, a disease that devastated central Africa in historical times and until recently has caused tens of thousands of deaths there per year, while increasing the risk of chronic kidney disease in people with two copies.

If the APOL1 mutation arises by chance, it should arise at a similar rate in all populations, and only then spread under Trypanosoma pressure. However, if it is generated nonrandomly, it may actually arise more frequently where it is useful.

Results supported the nonrandom pattern: the mutation arose much more frequently in sub-Saharan Africans, who have faced generations of endemic disease, compared to Europeans, who have not, and in the precise genomic location where it confers protection.

"The new findings challenge the notion of random mutation fundamentally," said Livnat.

From random mutation to natural simplification

Historically, there have been two basic theories for how evolution happens— random mutation and natural selection, and Lamarckism—the idea that an individual directly senses its environment and somehow changes its genes to fit it. Lamarckism has been unable to explain evolution in general, so biologists have concluded that mutations must be random.

Livnat's new theory moves away from both of these concepts, proposing instead that two inextricable forces underlie evolution. While the well-known external force of natural selection ensures fitness, a previously unrecognized internal force operates inside the organism, putting together genetic information that has accumulated over generations in useful ways.

To illustrate, take fusion mutations, a type of mutation where two previously separate genes fuse to form a new gene. As for all mutations, it has been thought that fusions arise by accident: one day, a gene moves by error to another location and by chance fuses to another gene, once in a great while, leading to a useful adaptation. But Livnat's team has recently shown that genes do not fuse at random.

Instead, genes that have evolved to be used together repeatedly over generations are the ones that are more likely to get fused. Because the genome folds in 3D space, bringing genes that work together to the same place at the same time in the nucleus with their chromatin open, molecular mechanisms fuse these genes rather than others. An interaction involving complex regulatory information that has gradually evolved over generations leads to a mutation that simplifies and "hardwires" it into the genome.

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In the paper, they argue that fusions are a specific example of a more general and extensive internal force that applies across mutation types. Rather than local accidents arising at random locations in the genome disconnected from other genetic information, mutational processes put together multiple meaningful pieces of heritable information in many ways.

Genes that evolved to interact tightly are more likely to be fused; single-letter RNA changes that evolved to occur repeatedly across generations via regulatory phenomena are more likely to be "hardwired" as point mutations into the DNA; genes that evolved to interact in incipient networks, each under its own regulation, are more likely to be invaded by the same transposable element that later becomes a master-switch of the network, streamlining regulation, and so on. Earlier mutations influence the origination of later ones, forming a vast network of influences over evolutionary time.

"Previous studies examined mutation rates as averages across genomic positions, masking the probabilities of individual mutations. But our studies suggest that, at the scale of individual mutations, each mutation has its own probability, and the causes and consequences of mutation are related," says Livnat.

"At each generation, mutations arise based on the information that has accumulated in the genome up to that time point, and those that survive become a part of that internal information."

This vast array of interconnected mutational activity gradually hones in over the generations on mutations relevant to the long-term pressures experienced, leading to long-term directed mutational responses to specific environmental pressures, such as the malaria and Trypanosoma–protective HbS and APOL1 mutations.

New genetic information arises in the first place, they argue, as a consequence of the fact that mutations simplify genetic regulation, hardwiring evolved biological interactions into ready-made units in the genome. This internal force of natural simplification, together with the external force of natural selection, act over evolutionary time like combined forces of parsimony and fit, generating co-optable elements that themselves have an inherent tendency to come together into new, emergent interactions.

"Co-optable elements are generated by simplification under performance pressure, and then engage in emergent interactions—the source of innovation is at the system level," said Livnat. "Understood in the proper timescale, an individual mutation does not arise at random nor does it invent anything in and of itself."

Redefining how evolution works

The potential depth of evolution from this new perspective can be seen by examining other networks. For example, the gene fusion mechanism—where genes repeatedly used together across evolutionary time are more likely to be fused together by mutation—echoes chunking, one of the most basic principles of cognition and learning in the brain, where pieces of information that repeatedly co-occur are eventually chunked into a single unit.

Yet fusions are only one instance of a broader principle: diverse mutational processes respond to accumulated information in the genome, combining it over generations into streamlined instructions. This view recasts mutations not as isolated accidents, but as meaningful events in a larger, long-term process.

More information: Daniel Melamed et al, De novo rates of a Trypanosoma -resistant mutation in two human populations, Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2424538122

Journal information: Proceedings of the National Academy of Sciences 

Provided by University of Haifa 

 

Must reading for understanding the limitations of current ai (addition Aug 13)

 I am not going to try to describe this comprehensive devastating article. Read it for youreselves: https://www.realtimetechpocalypse.com/p/gpt-5-is-by-far-the-best-ai-system

Added August 13: sand this one also

https://www.newyorker.com/culture/open-questions/what-if-ai-doesnt-get-much-better-than-this?utm_source=substack&utm_medium=email

Excellent description of the logic of inferring design

 

How Did the Designer Do It? 

Daniel Witt

https://evolutionnews.org/2025/07/how-did-the-designer-do-it/

n 1852, several years before On the Origin of Species came out, the famed biologist Herbert Spencer defended the theory of evolution and made a broadside attack against the idea of “special creation” in his essay “The Developmental Hypothesis.” He wrote:  

Those who cavalierly reject the Theory of Evolution as not being adequately supported by facts, seem to forget that their own theory is supported by no facts at all… If they have formed a definite conception of the process, let them tell us how a new species is constructed, and how it makes its appearance. Is it thrown down from the clouds? or must we hold to the notion that it struggles up out of the ground? Do its limbs and viscera rush together from all the points of the compass? or must we receive the old Hebrew idea, that God takes clay and moulds a new creature?

This argument has had considerable staying power. Skip ahead to 2015, and we find biochemist Larry Moran asking intelligent design advocate Stephen Meyers some very similar questions: 

Did the gods nudge some of the species toward being arthropods in the first million years but waited until the last few years to create the information required to make chordates and vertebrates? What kind of information did they insert? What did they insert it into? Do we have any evidence of new god-created genes that sprang into existence during this period of time? If so, which ones?

And how old are these gods, anyway? Did the same ones stick around for the entire 10 million years to see if their experiment worked or were there several generations of gods?

Why were the gods so active in the Cambrian? Why didn’t they create all this new information 100 million years earlier or 100 million years later? Have they created any new information since then or did they front-load everything into genome during the Cambrian then turned their attention to some planets in other galaxies? These are all legitimate questions that deserve answers. They’re just like the questions you ask of scientists when you demand detailed evolutionary explanations.

It seems the debate has not progressed much in a century and a half. Clearly, these evolutionary theorists think they have an unanswerable line of attack here. And, admittedly, there is a certain superficial reasonableness to their demands. If we expect evolutionary theorists to explain how unguided evolution created life, shouldn’t design theorists be expected to explain how the designer created life? 

Stonehenge and Smartphones  

But if you mull the argument over, you begin to find it strangely unsatisfying. This may have something to do with the fact that, setting the more controversial subjects aside, there are many cases in which everyone agrees design occurreddespite not knowing the means by which it occurred. The Easter Island heads and Stonehenge are good examples. Scholars might debate exactly how ancient people managed set those massive stones in place without the aid of modern technology, but no one doubts that they did so. So clearly, there are instances where design can be inferred without precise knowledge of the methods of construction.

The analogy becomes even more applicable to the case at hand if we flip it around, and imagine that instead of analyzing artifacts constructed by more primitive societies, we are trying to analyze something more complex and sophisticated than anything we have ever dreamed of making. Imagine, for example, that you are a hunter-gatherer in a stone-age society who is suddenly taken out of the Amazon and brought to New York City. Could you provide even a sketch of the means of construction for a smartphone, a skyscraper, or an airplane? Absolutely not. You wouldn’t even know where to begin. But would that detract from your ability to infer design at work? No, it wouldn’t. 

That is exactly where we stand in regard to biological life. Compared to a living organism, our best technology is like stone spears. We have no notion of how to make even the most basic cell. But we would be foolish to dismiss the possibility of design on that account.

Somewhat perplexingly, perhaps, the same does not seem to hold true for explaining something by natural processes alone. When someone tries to do that, we always expect hard details. If someone insisted that the Easter Island heads could be explained by unguided natural processes, you would certainly expect them to give you a clear explanation of how, or why such improbable shapes were demanded by the laws of nature. And you probably wouldn’t be much moved if they started complaining along the lines of Spencer and Moran that you weren’t being fair because you hadn’t explained how your beloved designers created the heads either: “Did they carve them with iron picks, or stone hammers? Were the carvers men, or women? Or little children, perhaps? Why did they make a few dozen heads, not one or several thousand? And where are they now? Did they quit making heads, or perhaps just move on to other islands…?” 

In a fresh context, it’s obvious that this argument is not worth anything. So all that remains is to determine why the argument is not worth anything. And the answer is simply that it rests on a false equivalence: different types of explanation impose different burdens of evidence on their proponents. There are evidentiary burdens that lie on design theorists alone, and there are evidentiary burdens that lie on Darwinists alone. 

“Will” vs. “Chance and Necessity” 

The important distinction lies in the difference between agents and natural laws. Designers are free agents, by definition, and can do whatever they want. In fact, that’s where the explanatory power of design lies. Natural processes, by contrast, can only do the same predictable things. That is why humans constantly invoke designers to explain things that we would be at a loss to explain by the laws of nature alone — everything from ant hills to cave paintings. But because the explanatory power of free agents is produced by the very thing that makes them hard to predict, it’s no use demanding to know exactly how a free agent went about doing something. Unless you were there, you can’t know. It’s different with natural processes, which are uniform and therefore predictable. 

The question that follows from this is, when are we allowed to invoke a free agent as an explanation? Famously, the principle of parsimony, or “Occam’s razor,” says that you shouldn’t invoke unnecessary entities as part of your explanation. When we invoke an intelligent designer to explain a phenomenon, we are invoking a new entity. Darwinian evolution, to its credit, does not propose any new entities — it tries to explain life using only matter and the observed laws of nature. According to Occam’s razor, however, there is a reason to invoke a new entity: if the entities you already have can’t explain the phenomenon. Of course this is so — otherwise, we would never be able to discover any new entities at all, and would be stuck with only believing in the first thing the first human baby happened to notice. Anyway, that is why intelligent design theorists spend so much time arguing against Darwin’s theory. It’s not that design and evolution are incompatible or that we think disproving evolution automatically proves design; it’s that there’s no need to invoke a new entity such as a designer if the laws of nature that we already know about can explain everything that exists. So to avoid violating Occam’s razor, intelligent design advocates need to show that the laws of nature cannot produce everything that exists. That is the evidentiary burden that lies on design theorists only; evolutionists, by contrast, do not need to somehow demonstrate that intelligent designers are incapable of creating life. 

Conversely, Darwinists argue that there is no need to invoke a designer or any other new entities, because the entities we already know about (matter, chance, and the laws of nature) are a sufficient explanation for every observed phenomenon. So they must be able to show how the phenomena that do not seem like they could be produced by natural processes actually can be produced by natural processes. That is the evidentiary burden that lies on Darwinists alone; design theorists, by contrast, do not need to prove that the proposed designer is capable of producing life. The designer is capable by definition, because the designer was proposed specifically to fill in the explanatory gap left by the insufficiency of matter, chance, and the laws of nature alone.  

A Case in Point: The Discovery of Helium

This may all become clearer if we consider an analogous case. The element helium was first discovered by an astronomer, not a chemist, because its existence was inferred from observations of sunlight before it was actually found on earth. The astronomer, Norman Lockyer, noticed that there was a wavelength of light produced by the sun that was not produced by any element yet discovered. He inferred that there must therefore be an undiscovered element in the sun. This was a perfectly legitimate inference, Occam’s razor notwithstanding, because no already-known entity could produce that wavelength of light. And it made no difference that no one knew exactly how or why different elements produced different wavelengths of light. If any chemist had insisted that there was not a new element in the sun, he or she would have needed to show that you actually can get that wavelength using only the already-known elements. In fact, the English chemist Edward Franklin attempted to do this by putting hydrogen under extreme heat and pressure, but was unsuccessful. 

The same goes for evolution. The evolution of highly complex biological systems has never been observed, and there are certain theoretical arguments that purport to show that it is essentially impossible (statistically speaking) for random variation, natural selection, and the laws of nature alone to produce those systems. So any scientist who wants to argue that the already-known entities in nature could produce all of biology needs to show how this could occur, in spite of the theoretical hurdles. If they can’t do this, then we are perfectly justified in proposing a new entity, such as a designer. 

One final objection must be answered. If we don’t know exactly how the designer crafted life, then how do we even know that a designer is the right explanation? Couldn’t it just as easily be something else? 

Once again, the story of helium is illuminating. Helium and intelligent design are both parsimonious explanations regarding the broad type of cause, yet non-parsimonious regarding the specific entity. Lockyer proposed an element, rather than something else (e.g., a new law of nature), because it was already known that elements can produce light. But he proposed a new element, because it was known that none of the old elements could produce that particular wavelength of light. Likewise, ID theorists propose a designer to explain the specified complexity of life because it is already known that designers (i.e., minds) can produce specified complexity. But we must propose a new designer, because none of the old designers (ourselves, beavers, etc.) are capable of having created the systems in question. 

By now it should be clear why it is the height of silliness to demand proof that the designer could create life, or to expect detailed explanations of how it could be done. If chance and necessity are insufficient to produce life, then we must conclude that something beyond chance and necessity was involved. The proof of that entity’s capability is simply the fact that life exists. 

Photo provided by Daniel Witt.

Daniel Witt

The great lie - 2% difference between human and chimp genes

 Fact Check: New “Complete” Chimp Genome Shows 14.9 Percent Difference from Human Genome

Casey Luskin

May 21, 2025, 6:20 AM

https://evolutionnews.org/2025/05/fact-check-new-complete-chimp-genome-shows-14-9-percent-difference-from-human-genome/

A groundbreaking paper in Nature reports the “Complete sequencing of ape genomes,” including the genomes for chimpanzees, bonobos, gorillas, Bornean orangutans, Sumatran orangutans, and siamangs. I noted this in an article here yesterday, reporting that an evolutionary icon — the famous “1 percent difference” between the human and chimp genomes, touted across the breadth of popular and other scientific writing and teaching — has fallen. The researchers, for whatever reason — I’m not a mind reader — chose to bury that remarkable finding in technical jargon in their Supplementary Data section. Now for more on the scientific details. 

You might be thinking, “Hey, weren’t these genomes sequenced long ago?” The answer is yes but also no. Yes, we had sequenced genomes from these species in the past, but, as the paper explains, “owing to the repetitive nature of ape genomes, complete assemblies have not been achieved. Current references lack sequence resolution of some of the most dynamic genomic regions, including regions corresponding to lineage-specific gene families.” 

Or, as an accompanying explainer article puts it: 

In the past, scientists had deciphered segments of non-human apes’ genomes, but they had never managed to assemble a complete sequence for any species. In the current study, however, [Kateryna] Makova and her collaborators used advanced sequencing techniques and algorithms that allowed them to read long segments of DNA and assemble them into a sequence that stretched from one end of each chromosome to the other, without any gaps. “This has never been done before,” says Makova.

In other words, the complete ape genomes were never fully sequenced. And they used the human genome as a reference sequence, which made the ape genomes look more human-like than they actually were. 

You Don’t Believe Me? 

From the technical paper:

Most previous genome-wide comparative studies of apes have been limited by the mapping of inferior assemblies to a higher quality human genome. Consequently, human reference biases were introduced.

This is consistent with what the National Center for Biotechnology Information said in 2007 about an early draft of the chimp genome:

Contigs were assembled using the human genome as a guide, and are therefore “humanized” in their construction. This is an important distinction, as some sequences, such as insertions, deletions, and gene duplications, may not be accurately represented by the current chimpanzee assembly.

Thus, up until now, most versions of the chimp and other ape genomes were effectively “humanized” because they were “assembled using the human genome as a guide.” This effectively makes them appear more similar to the human genome than they truly are. Can these new drafts of ape genomes help fix the problem? 

Problem Solved?

Another explainer article in Nature seems to suggest that these “complete” drafts of the ape genomes will prove that they are less similar to the human genome than has been claimed:  

Shortly after the first human genome sequence was finalized in 2003, a chimpanzee assembly was released. This was followed by assemblies for other great apes, such as the gorilla, Sumatran orangutan and bonobo, and small apes that are less closely related to humans than are great apes. These genomes offered a valuable opportunity to catalogue the genetic differences that have accumulated during the evolution of apes, including changes that are unique to humans. But, because these initial releases were incomplete drafts, comparisons could be made only between properly resolved portions of the genome. These studies therefore focused only on relatively small differences, and excluded extremely repetitive sequences and large-scale structural differences, such as inversions and duplications of genomic sequences. [Emphasis added.]

That last sentence seems to hint that previous comparisons between human and ape genomes “only focused on relatively small differences” and “excluded” the sections that entail “large-scale structural differences.” The explainer article notes that this study has “fully sequenced the genomes of six living ape species, enabling long-awaited comparisons of hard-to-assemble genomic regions.” Thus, one could expect that these newly “complete” ape genomes would reveal much greater differences compared to the human genome.

The New Ape Genomes and the Human Genome

What’s odd is that as one reads the technical paper, a direct comparison between the ape and the human genomes is hard to find. This passage seems to be as close as it gets:

Overall, sequence comparisons among the complete ape genomes revealed greater divergence than previously estimated (Supplementary Notes III–IV). Indeed, 12.5–27.3% of an ape genome failed to align or was inconsistent with a simple one-to-one alignment, thereby introducing gaps.

What exactly does this mean? Well, first they admit that “sequence comparisons among the complete ape genomes revealed greater divergence than previously estimated.” But the technical Nature paper considers human beings to be an “ape,” so implicit in this statement is their belief that comparing “ape genomes” includes comparisons between human and ape (i.e., non-human hominoid) genomes. So for the rest of this article, I’ll call humans “humans” and I’ll refer to non-human hominoids as “apes,” just like most normal people do.  

Interestingly, two preprints of the paper (v1 and v2) published last year on BioRxiv (which are presumably the versions of the manuscript submitted to Nature initially and after one round of revisions) preface this result with these two sentences:

The oft-quoted statistic of ∼99% sequence identity between chimpanzee and human holds for most of the genome when considering single-nucleotide variants (SNVs). However, comparisons of T2T genomes suggest a much more nuanced estimate.

T2T means examining “telomere to telomere” — i.e., examining the entire chromosomes throughout the genome. These sentences were evidently removed during revisions for the published version in Nature — an interesting editorial decision. So what is the paper saying about the difference between humans and chimpanzees? 

As we’ll see, the statement above — that “sequence comparisons among the complete ape genomes revealed greater divergence than previously estimated” — is true. But it doesn’t reveal the extent of the differences between human and ape genomes that this study uncovered. So let me cut to the chase: 

Look back at those numbers, “12.5–27.3%.” The same numbers show up again buried deep in the Supplemental Data where they compare various ape to human genomes. They’re findable if you know where to look, but should I say “buried” — or “hidden”? From what I can tell, the Supplemental Data reports that the ape genome that is most similar to the human genome is the chimpanzee genome. And it shows a 12.5 percent “gap-divergence” — i.e., difference — from the human genome! And when you look at the “gap divergence” where the human genome is the target and the chimp is the query, the difference is 13.3 percent. Let me be clear: According to this study, the human and chimp genomes aren’t 98.8 percent the same (or 1.2 percent different) — as, for instance, the Smithsonian Institution’s National Museum of Natural History claims (see my “Visitor’s Guide” for more). In fact, they are no more than 87.5 percent similar — i.e., the human and chimp genomes are at least 12.5 percent different if not 13.3 percent different! In fact, the 13.3 percent difference is more relevant since this reflects how similar the whole human assembly is to the chimp genome.

What Exactly is the “Gap Divergence”

Before we go further, I want to explore exactly what the authors mean by “gap divergence” or “gap difference.” The paper defines the “gap divergence” as follows:

Gap divergence is defined as the fraction of positions in the target haplotype that are not aligned to the other haplotype, which could be due to biological processes (e.g., gene loss/gain and insertions/deletions), missing data, or technical problems (e.g., alignment failure due to SVs, repetitive elements, etc.).

So how do they determine the gap divergence? From what I can tell, it’s based upon dividing the target genome within the genome alignment into 1 million base pair (1 Mbp) segments and seeing how many of the bases within each 1 Mbp segment have no aligning base within the query genome that has been aligned to it. If the entire 1 Mbp segment has no alignment by the target genome, it has gap divergence of 100 percent. If 10,000 bp have no alignment, it has gap divergence of 1 percent; if 1,000 bp have no alignment, it has gap divergence of 0.1 percent, etc. According to the study’s results, the mean gap divergence in each 1 Mbp segment of the human genome (as target) aligned to the chimp genome (as query) is 12.5 percent. Thus, 12.5 percent of bases in the human genome have no aligning base in the chimpanzee genome within the whole genome-alignment. 

The figure below — created for illustrative purposes and not from the study — helps show the differences between “SNVs” and “Gaps” between the two genomes:

As you can see, the Gaps represent nucleotides or segments of nucleotides that simply don’t exist in one genome or the other, while the SNVs represent nucleotides that do exist but are different. The two types of differences can be added up to calculate the total difference between the genomes. 

An Upper Estimate

And why is there a range of 12.5 percent to 27.3 percent in the main text? That’s because the upper estimate of the non-alignability between the gorilla genome and human genome is a whopping 27.3 percent. In fact, if we look at the Supplementary Figure III.12, we find the following percentages of “gap-divergence” between various ape genomes when compared to the human genome (non-sex chromosomes):

• Sumatran orangutan (Pongo abelii) vs human: 15.4 percent and 16.5 percent “gap-divergence” (i.e., minimum difference)
• Gorilla (Gorilla gorilla) vs human: 17.9 percent and 27.3 percent “gap-divergence” (i.e., minimum difference)
• Bonobo (Pan paniscus) vs human: 12.5 percent and 14.4 percent “gap-divergence” (i.e., minimum difference)
• Chimpanzee (Pan troglodytes) vs human: 12.5 percent and 13.3 percent “gap-divergence” (i.e., minimum difference)

Do you see how easy it is to summarize that data? These are huge findings for both science and the wider culture, yet the technical Nature paper, and the two Nature explainer articles, failed to clearly bring out these points. They buried them in technical jargon and a lack of clarity deep in the Supplemental Data, and the sentence about “The oft-quoted statistic of ~99% sequence identity” was removed in the revisions of the paper. Nature, I feel confident in assuring you, is not a haphazardly edited journal. These were deliberate choices by someone during the editing process. The lack of clarity is simply incredible.

The Technical Details

Deep in the Supplementary Data we find Figure III.12 which explains the gap divergence between different species:

The caption reads: “Plots show 1 Mbp segments binned by gap divergence for each pairwise alignment,” where a pairwise alignment is an attempt to align two sequences to determine their degree of similarity or difference. Thus, we’re looking at a direct measure of the minimum degree of difference between the two genomes. 

Adding in the Single Nucleotide Variation (SNV)

But there’s another type of variation between genomes also identified in the paper — single nucleotide differences (called “single nucleotide variations” or sometimes “short nucleotide variations” or SNVs). Again, buried in the Supplemental Data we find Figure III.11 which shows the percentage of SNVs between human and various ape genomes reported in this study. Here’s what they found:

• Sumatran Orangutan (Pongo abelii) vs Human: ~3.6 percent different
• Gorilla (Gorilla gorilla) vs Human: 1.9 percent – 2.0 percent different
• Bonobo (Pan paniscus) vs Human: 1.5 percent – 1.6 percent different
• Chimpanzee (Pan troglodytes) vs Human: 1.5 percent – 1.6 percent different

If we add the gap divergence to the SNV differences, we end up with these total degrees of difference between human and ape genomes: 

• Sumatran Orangutan (Pongo abelii) vs Human: ~19 percent – 20.1 percent different
• Gorilla (Gorilla gorilla) vs Human: ~19.8 percent – 29.3 percent different
• Bonobo (Pan paniscus) vs Human: ~14.0 percent – 16.0 percent different
• Chimpanzee (Pan troglodytes) (target) vs. Human: ~14.0 percent different
• Human (target) vs. Chimpanzee (Pan troglodytes): ~14.9 percent different

So now what we’re seeing is that there is about a 14.9 percent total difference between the human genome and the chimp genome. That represents a much greater degree of difference than the often-claimed statistic that we are only 1 percent genetically different from chimpanzees!

Is This the Final Word? 

Undoubtedly more analysis is needed to determine the extent to which nucleotides exhibit “one-to-one exact matches” between the human and chimp genomes, even in the regions that could be more easily aligned. So I suspect that the degree of difference between human and chimp genomes may go up in the future. 

For now, we can safely say that this latest study shows that the human and chimp genomes are at least 14.9 percent different. This means that the human and chimp genomes are at least a full order of magnitude more dissimilar than the public is typically told. 

Of course we’re talking here about the 44 non-sex chromosomes in the human genome. It’s also worth noting that compared to chimps, the human Y chromosome has a whopping 56.6 percent gap divergence (and 3.9 percent SNV divergence), and the human X chromosome has 4.4 percent gap divergence (and 1.1 percent SNV divergence). But that too is all buried in the Supplemental Data. 

These are all groundbreaking findings — and it’s a shame that Nature would not report the data clearly and would make all of this so hard to find — using jargon that most non-experts won’t understand. Why did they do this? It’s important to realize that publishing scientific papers can be a bit like sausage-making: it’s often messy, and the final form that you read usually represents compromise language that all of the authors, reviewers, and editors were willing to publish — and may not represent precisely how every author of a paper feels. So perhaps some authors of this study would have preferred to state the implications more plainly. But we can still ask, Why didn’t Nature state the results clearly and let the chips fall where they may?

I suspect that this radical finding has implications — not just for science, but also for human exceptionalism, for the reliability of heavily marketed talking points, and more — that people will be discussing for a long time. And perhaps for some in the worlds of science and science reporting, especially those who touted the now discredited figure about a mere 1 percent difference from chimps, those conversations may not be welcome.

Photo by Nathan Jacobson, © Discovery Institute (CC BY-SA 4.0)

Casey Luskin

_{Associate Director and Senior Fellow, Center for Science and Culture}

Casey Luskin is a geologist and an attorney with graduate degrees in science and law, giving him expertise in both the scientific and legal dimensions of the debate over evolution. He earned his PhD in Geology from the University of Johannesburg, and BS and MS degrees in Earth Sciences from the University of California, San Diego, where he studied evolution extensively at both the graduate and undergraduate levels. His law degree is from the University of San Diego, where he focused his studies on First Amendment law, education law, and environmental law.