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I know all about how the fossil record shows more human-like species coming about over time, and how modern testing proves we have genetic similarities with other animals.
All that says is we have similar genetic blueprints to animals in the past and present. How do we know these similarities are caused by having a common ancestor?
We don't know, and we never will. Science doesn't work that way. But evolution is the simplest hypothesis that is both falsifiable and consistent with lots of experimental data. Therefore it is the currently accepted scientific theory.
I would agree that genetic similarities between current species does not in itself suggest that they evolved from a common ancestor. But knowing the genome sequence of various species let's you do much more than simply measure pairwise similarity between species. The structure of genetic variation (losses, duplications, inversions, mutations ... ) also fits with evolutionary models. And crucially, genome sequences confirm predictions made by the evolutionary theory --- evolution is not just a post-hoc explanation of data, it has actually been tested many times over, and has never been falsified.
A good example is the sequencing of the human and chimpanzee genomes. We knew long before any genome was sequenced that humans have 23 chromosomes (23 pairs), while chimpanzees have 24. If the theory of evolution is correct and we do have a common ancestor, then that ancestor should have 24 chromosomes, like the chimps do, and it must be that in humans, one chromosome is a fusion of two ancestral chromosomes$^*$. This is a strong, testable prediction: if we don't find this arrangement of chromosomes, then evolution is proven wrong! In the 1980's, the sought fusion chromosome was found using high-resolution cytogenetic banding: it's chromosome 2. Modern genome sequencing later localized the precise fusion point on the human chromosome.
Also, the fossil record provides a timeline, not just a bunch of more or less similar organisms. In general, we don't find old fossils of modern-day animals (including ourselves), but we do find old fossils of species that no longer exist today. And when piecing together the findings, it looks like there is a sequence of gradual changes over time. That's a pretty good clue. What hypothesis might fit this data?
If you are suspicious about the theory of evolution --- which is fine, you should think critically about everything in science! --- then you must look for an alternative hypothesis which is falsifiable and fits the data. (Note that "god made everything" is not a falsifiable hypothesis, since it makes no testable predictions, and therefore not relevant to science.) If you come up with anything, let us know!
$^*$ As pointed out in comments, a priori one could imagine other rearrangements between the ancestor genome and the chimpanzee/human genomes that give the same result, but I believe this is the simplest hypothesis.
It is vanishingly unlikely that when DNA sequences which are thousands and thousands of bases long are identical between two creatures, that those sequences have no common origin.
That's like saying, "Your Honor, I didn't pirate that Justin Bieber track; I generated random 16 bit audio samples which just happen to match!"
That is less likely than the proposition that Melania Trump's speech wasn't plagiarized from Michelle Obama's, which already sits at a laugh-inducingly low probability level.
It's not a question of what we know, but of eliminating vanishingly improbable, time-wasting hypotheses.
Not only do we have the statistical argument that even a vast number of monkeys typing on typewriters are not going to reproduce the text of Macbeth, but there is the observable fact that DNA replicates. We can almost literally see identical pieces of DNA being reproduced from a common origin. The replication from a common origin hypothesis to explain sameness is overwhelmingly probable, whereas independent origin is vastly improbable.
It's not a question of how do we know, but rather what is the most plausible hypothesis from among those that are consistent with our observations.
Knowing is reserved for pure facts, like consistency between logical and mathematical propositions. We can know that some theorem in mathematics proceeds from some axioms. We can also "know" the content of an observation, in some sense. We cannot know whether an explanation for it is true. We can know when an explanation is false and we can gradually repair our understanding by identifying these failing situations and improving the explanations.
A final remark. Just because two creatures have some common DNA doesn't mean that those creatures got that DNA from a common ancestor at the time when they diverged. DNA can be introduced by vectors such as viruses. So that is to say, two creatures that diverged, say, a hundred million years ago could have some common DNA that they acquired more recently, like a million years ago. It is practically certain, though, that this same DNA itself has a common origin.
Unless you regard viruses as your ancestors, then some common strand of DNA is in fact not necessarily evidence of common ancestry per se.
I think one very fundamental fact that you are overlooking, which we pretty much take for granted, is that DNA is the mechanism of inheritance of traits.
What I mean is that my DNA is very similar to my parents' and siblings. It's also similar, but slightly less so, to my grandparents, my cousins, aunts and uncles, etc. The further you go from my known relatives, the less similar our DNA becomes.
This is also true for all living organisms. For everything from bacteria, to plants, to flies, we can look at their DNA, and that of their relatives, and the more closely related (in the familial sense, as in parents, siblings, etc., not in the evolutionary sense, although I'll speak to that in a moment), the more similar the DNA.
So that is demonstrably true for every living thing we can observe today. But what about the past? Well, if this theory of DNA and familial relationship is true, then we can predict that, for instance, my DNA is more similar to the DNA of present-day people in the countries where my ancestors came from than from other countries, even though we have no historical/genealogical evidence of relationship. And this bears out-- a commercial DNA ancestry test shows I share genes with the general populations of the countries my recent ancestors are documented to have immigrated from.
This idea that familial relationship are biological relationship should seem at once obvious but also surprisingly insightful. If I am related to my human cousins because of the mechanism of DNA inheritance and the sharing of a common ancestor (our common grandparents), then it also stands to reason that chimpanzees are literally our familial cousins, very distant, from a common ancestor a long long time ago. Biological relatedness is in fact the same thing as familial relatedness.
If you don't buy that, then you have to explain why it's true that all observable organisms today share familial relatedness through DNA, but for some reason, this isn't true going back into the far past. What changed in the past? When did this start happening?
The only reason we get tripped up in this line of reasoning is because chimpanzees look very different from humans, and we cannot mate. In the past, people thought this was because different species were fundamentally different types of beings. But biological insights, specifically the understanding of DNA, shows that we are all fundamentally the same type of creature, with reproductive incompatibilities developing over time. The tree of life is literally a family tree.
Also, from biochemical understanding, we know generally how long mutations take to arise. Combined with fossil evidence, we can create a pretty good model of how far back divergences arose.
There are some good answers here already but they mostly focus on whether similarities in the DNA sequence of organisms imply common ancestry. This misses something much more fundamental that is very strong evidence for this particular question: we all use essentially the same configuration of machinery to store genetic information and turn it into proteins.
- All organisms on Earth use nucleic acids with a ribose/deoxyribose backbone and not other chemistries like XNA, PNA or other alternatives.
"There is nothing Goldilocks about DNA and RNA," Holliger told Science. "There is no overwhelming functional imperative for genetic systems or biology to be based on these two nucleic acids."
All organisms on Earth use the same nucleobases (G, C, A, T, U), even though alternatives exist that can apparently replicate perfectly well.
All organisms on Earth use the same genetic code for protein synthesis (with a few slight variations) even though the matches between codons and amino acids are probably largely arbitrary.
The Proof Is in the Proteins: Test Supports Universal Common Ancestor for All Life
Earth's first life-form, floating in the proverbial froth of the primordial seas that eventually gave rise to trees, bees and humans, is not just a popular Darwinian conceit but also an essential biological premise that many researchers rely on as part of the foundation of their work.
In the 19th century, Charles Darwin went beyond others, who had proposed that there might be a common ancestor for all mammals or animals, and suggested that there was likely a common ancestor for all life on the planet&mdashplant, animal and bacterial.
A new statistical analysis takes this assumption to the bench and finds that it not only holds water but indeed is overwhelmingly sound.
Was it not already obvious, from the discovery and deciphering of DNA, that all life forms are descended from a single common organism&mdashor at least a basal species? No, says Douglas Theobald, an assistant professor of biochemistry of Brandeis University and author of the new study, detailed in the May 13 issue of Nature. (Scientific American is part of Nature Publishing Group.) In fact, he says, "When I went into it, I really didn't know what the answer would be."
Despite the difficulties of formally testing evolution&mdashespecially back across the eons to the emergence of life itself&mdashTheobald was able to run rigorous statistical analyses on the amino acid sequences in 23 universally conserved proteins across the three major divisions of life (eukaryotes, bacteria and archaea). By plugging these sequences into various relational and evolutionary models, he found that a universal common ancestor is at least 10^2,860 more likely to have produced the modern-day protein sequence variances than even the next most probable scenario (involving multiple separate ancestors).*
"Evolution does well where it can be tested," says David Penny, a professor of theoretical biology at the Institute of Molecular BioSciences at Massey University in New Zealand and co-author of an accompanying editorial. Yet, he notes that evolution can make "testable predictions about the past (especially quantitative ones)" tricky at best. "That Theobald could devise a formal test," he says, "was excellent&hellip. It will probably lead to a jump in what is expected of the formal evaluation of hypotheses, and that would help everybody."
Common ancestor acrimony
The mid-20th-century discoveries about the universality of DNA "really nailed it for people" in terms of establishing in popular&mdashand academic&mdashculture that there was a single universal common ancestor for all known life on Earth, Theobald says. And since then, "it's been widely assumed as true," he notes.
But in the past couple decades, new doubt has emerged in some circles. Microbiologists have gained a better understanding of genetic behavior of simple life forms, which can be much more amorphous than the typical, vertical transfer of genes from one generation to the next. The ability of microbes such as bacteria and viruses to exchange genes laterally among individuals&mdashand even among species&mdashchanges some of the basic structural understanding of the map of evolution. With horizontal gene transfers, genetic signatures can move swiftly between branches, quickly turning a traditional tree into a tangled web. This dynamic "throws doubt on this tree of life model," Theobald says. And "once you throw doubt on that, it kind of throws doubt on common ancestry as well."
With the discovery of archaea as the third major domain of life&mdashin addition to bacteria and eukaryotes&mdashmany microbiologists became more dubious of a single common ancestor across the board.
A test for evolution
Other researchers had put certain sections of life to the test, including a similar 1982 statistical analysis by Penny testing the relation of several vertebrate species. Theobald describes the paper as "cool, but the problem there is that they aren't testing universal ancestry." With advances in genetic analysis and statistical power, however, Theobald saw a way to create a more comprehensive test for all life.
In the course of his research, Theobald had been bumping against a common but "almost intractable evolutionary problem" in molecular biology. Many macromolecules, such as proteins, have similar three-dimensional structures but vastly different genetic sequences. The question that plagued him was: Were these similar structures examples of convergent evolution or evidence of common ancestry?
"All the classic evidence for common ancestry is qualitative and is based on shared similarities," Theobald says. He wanted to figure out whether focusing on those similarities was leading scientists astray.
Most people and even scientists operate under the premise that genetic similarities imply a common relation or ancestor. But as with similarities in physical appearance or structure, these assumptions "can be criticized," Theobald notes. Natural selection has provided numerous examples of convergent physical evolution, such as the prehensile tales of possums and spider monkeys or the long sticky insect-eating tongues of anteaters and armadillos. And with horizontal gene transfer on top of that, similar arguments could be made for genetic sequences.
"I really took a step back and tried to assume as little as possible in doing this analysis," Theobald says. He ran various statistical evolutionary models, including ones that took horizontal gene transfer into consideration and others that did not. And the models that accounted for horizontal gene transfer ended up providing the most statistical support for a universal common ancestor.
Theobald says his most surprising results were "how strongly they support common ancestry." Rather than being disappointed about simply backing up a long-held assumption, he says that at least, "it's always nice to know that we're on the right track."
These findings do not mean that a universal common ancestor establishes the "tree of life" pattern for early evolutionary dynamics. Nor, however, do they infer a "web of life" structure. The tree versus web debate remains "very controversial right now in evolutionary biology," Theobald says, reluctant to pick a side himself.
One of the other big unknowns remaining is just when this universal common ancestor lived and what it might have looked like&mdasha question that will take more than Theobald's statistical models to answer. Theobald also notes that the support for a universal common ancestor does not rule out the idea that life emerged independently more than once. If other, fully distinct lineages did emerge, however, they either went extinct or remain as yet undiscovered.
Research will likely push on into these dusky corners of early evolution, Penny notes, as "scientists are never satisfied." He expects that researchers will try to sort back even earlier, before DNA took over, and assess the early stages of evolution during the RNA days.
On a more foundational level, Penny says, the paper should not put an end to the assessment of ancestral assumptions. Instead it should be a reminder that "we have never thought of all possible hypotheses," he says. "So we should never stop considering some new approach we haven't thought of yet."
*Erratum (5/13/10): This sentence was changed after publication. It originally stated that a universal common ancestor is more than 10 times more likely.
The Institute for Creation Research
Without a doubt, humans, chimpanzees, and other organisms share some very similar features. One explanation for the origin of these features is that they reflect similar designs that serve similar purposes. The common design inference is quite intuitive since components of complicated human-designed systems are all directly analogous to other creature&rsquos features for similar purposes, such as their structural frameworks, pumps, sensors, and data processors.
People willing to hypothesize that God&rsquos supernatural design and creativity caused the great diversity of life on Earth have, for millennia, acknowledged the plausibility of the common-design explanation.
Another approach some people use to explain all phenomena is naturalism, which closes off any appeal to supernatural intelligence or power and rather presupposes that nature&rsquos matter and forces alone are sufficient causes of the origin of the universe and life itself. But naturalism has to appeal to mystical mechanisms since people have never observed anything design and create itself by mechanisms known to have originated purely by nature&rsquos matter and forces.
After all, a heart pumping blood through vessels seems to correspond very well in purpose and design to human-made fluid-pumping systems. Should anyone believe that some purposeless, undetectable mystical intelligence of nature shaped the exquisite details of cardiovascular systems over eons? But a dogmatic commitment to naturalism forces naturalists to construct explanations that are &ldquocounterintuitive&rdquo and &ldquomystifying to the uninitiated,&rdquo according to renowned Harvard geneticist Richard Lewontin. 1
Upcoming issues of Acts & Facts will feature several articles that compare some of those counterintuitive naturalistic explanations to actual discoveries. This comparison will focus attention on the largely suppressed but disappointing track record of naturalism&rsquos dubious notions that have been taught as factual evidence only to later be revealed as total blunders.
For instance, we know that similarity among creatures extends past body parts to their underlying genetics. Decades in advance of current detailed genetic analysis techniques, creationists and evolutionists alike published expectations based on either intelligent design or evolution, respectively. One test of the accuracy of a scientific model is its ability to make accurate predictions of future research results. These published expectations can now be examined in light of new genetic information.
In 1975, prior to any detailed genetic analysis, ICR founder Dr. Henry Morris asserted there would be common underlying design patterns to explain similar structure. He said:
The creative process would have designed similar structures for similar functions and different structures for different functions.&hellipIn the creation model, the same similarities are predicted on the basis of a common purposive designer. 2
Thus, knowing that organisms, per their kind, must have traits to thrive on the same planet but occupy diverse niches, advocates for design-based explanations expected that 1) similar features needed to fulfill similar purposes would be based on similar information, and 2) extreme multistep specified regulation over thousands of details produces unique organisms that may yet have similar overall plans.
Virtually all prominent evolutionists rejected basic common designs, but their rationale differed. Darwin, for theological reasons, doubted &ldquothat it has pleased the Creator to construct all the animals and plants in each great class on a uniform plan&rdquo and derided the concept of underlying common information as &ldquonot a scientific explanation.&rdquo 3
In 1963, Harvard&rsquos leading evolutionary theorist Ernst Mayr predicted that looking for similar DNA between very diverse organisms would be pointless. He claimed that random genetic changes over millions of years explained the differences in creature&rsquos traits and that those many changes would have obliterated genetic similarities.
Much that has been learned about gene physiology makes it evident that the search for homologous genes [similar codes due to common ancestry] is quite futile except in very close relatives. If there is only one efficient solution for a certain functional demand, very different gene complexes will come up with the same solution, no matter how different the pathway by which it is achieved. The saying &ldquoMany roads lead to Rome&rdquo is as true in evolution as in daily affairs. 4
New evolutionary explanations do not explain similarities in organisms whose ancestors supposedly &ldquodiverged&rdquo eons ago. Convergent evolution is a frequently invoked ancillary explanation, as denoted in Mayr&rsquos &ldquoMany roads lead to Rome&rdquo affirmation. For example, how did naturalists explain diverse creatures possessing eyes made up of similar parts? They claimed that similar environments constrained them to &ldquoconverge&rdquo on comparable complex features&mdashindependently at least 40 times&mdashand probably as many as 65 times. 5
This explanation, steeped in evolutionary naturalism, counterintuitively claims that millions of years of genetic tinkering somehow propelled organisms to diverge into increasingly different classes while simultaneously cobbling their traits to converge upon &ldquothe same solution&rdquo to problems.
Creationists, a vocal subgroup of Lewontin&rsquos &ldquouninitiated,&rdquo remained skeptical that similar highly complex structures evolved independently over and over again, but maintained their expectation of finding a similar feature-to-genetic information link.
Evolutionary Predictions Spectacularly Wrong
Landmark discoveries between 1978 and 1984 showed the reality of a common genetic basis prescribing how similar structures could be built across diverse groups of organisms. 6 Genes with regulatory and developmental functions responsible for core basic-design patterns in developing embryos are called Hox genes (a contraction of longer descriptive words, homeotic and homeobox). This astounding finding was so opposite to the evolutionists&rsquo notions that it clearly constitutes a spectacular blunder on their part. Evolutionary developmental biologist Sean Carroll describes the implications of the stunning details:
When the sequence of these homeoboxes were examined in detail, the similarities among species were astounding. Over the 60 amino acids of the homeodomain, some mice and frog proteins were identical to the fly sequences at up to 59 out of 60 positions. Such sequence similarity was just stunning. The evolutionary lines that led to flies and mice diverged more than 500 million years ago, before the famous Cambrian Explosion that gave rise to most animal types. No biologist had even the foggiest notion that such similarities could exist between genes of such different animals. The Hox genes were so important that their sequences had been preserved throughout this enormous span of animal evolution. 7
The discovery that the same sets of genes control the formation and pattern of body regions and body parts with similar functions (but very different designs) in insects, vertebrates, and other animals has forced a complete rethinking of animal history, the origins of structures, and the nature of diversity. Comparative and evolutionary biologists had long assumed that different groups of animals, separated by vast amounts of evolutionary time, were constructed and had evolved by entirely different means. 8
Yet evolutionists remain closed-minded to an explanation of the Hox genes&rsquo origination by a common designer. They need not concede they were greatly mistaken in their predictions, they were merely &ldquostunned&rdquo at the appearance of new, unexpected evidence for evolution (in their reworked, conveniently fluid evolutionary story, that is).
Yet, the only &ldquoevidence&rdquo that Hox genes can be &ldquopreserved throughout this enormous span of animal evolution&rdquo is the belief that life evolved from a common ancestor. All of the stories about convergence get promptly scrapped. Firmly held prior accounts like convergent evolution are run through the magic tunnel of evolutionary belief, and, voila, Hox genes somehow instantly turn into &ldquopreserved&rdquo ancient DNA, which is now used&mdashwith equivalent certainty&mdashas evidence of common ancestry.
Design-Based Expectations Confirmed
Now it is factually confirmed that similar genetic regulatory information is common to many classes of organisms and aids in helping achieve similar function&mdashmany with remarkably similar designs. Sean Carroll again relates the confounding weight of this finding.
It was inescapable. Clusters of Hox genes shaped the development of animals as different as flies and mice, and now we know that includes just about every animal in the kingdom, including humans and elephants. Not even the most ardent advocate of fruit fly research predicted the universal distribution and importance of Hox genes. The implications were stunning. Disparate animals were built using not just the same kinds of tools, but indeed, the very same genes! 9
What about the teaching of 40 independent occurrences of eye evolution? That manifested as another incredible evolutionary blunder and validation of creationists&rsquo design-based expectations. As Carroll candidly continues, &ldquoNatural selection has not forged many eyes completely from scratch there is a common genetic ingredient to making each eye type, as well as to the many types of appendages, hearts, etc.&rdquo 10
Is Common Design More Plausible than Common Ancestry?
Could it be that Hox genes are the &ldquosmoking gun&rdquo of common design expected by supporters of intelligent design for decades? Consider this&mdashif engineers were tasked to investigate for common design in any other area, how would they proceed? They would study various sets of plans and specifications, identify any common features, and verify if there was, in fact, common underlying information. Genetic research has identified this common information across diverse groups of organisms prescribing traits with the same general function. In other areas of research, this fact would be ascribed to common engineering instructions.
Evolutionary theory predicted the complete opposite of common underlying information for similar traits. The fact that it was dogmatically taught as evidence for evolution and later found to be profoundly wrong catalogs it as a spectacular blunder and makes its teaching misguided at best. This repressed prediction-evidence mismatch is connected to ever-changing evolutionary explanations like &ldquoconvergence&rdquo or &ldquoconservation/common ancestry.&rdquo These come across scientifically as a mishmash of improvised, after-the-fact stories aimed at forcing observations into an evolutionary paradigm.
Creationists can say with credibility that in creatures as diverse as bacteria, insects, and humans the same genetic information controls the formation and utilization of many key anatomical or molecular structures observed to be performing broadly similar functions.
Applying organism-focused, design-based analysis to biological phenomena brings great clarity to our understanding of life. A compelling case is made that these are clearly the common designs creationists have been looking for the last 200 years.
How do we tell the difference between convergent, divergent, and parallel evolution?
Because for the most part, all we get to see is a snapshot in the chain, how can we tell if species are related or not?
I'm not sure you're thinking about this the right way. Convergent evolution is where two genetically separate species (meaning, share no 'recent' common ancestors) wind up with very similar phenotypic traits (something we see, such as hair/eye color). An example would be the streamlined shapes of fish and whales/dolphins. They come from very different ancestors (land-based in the case of whales/dolphins), yet look very much alike, because evolution in water strongly favors certain shapes.
Divergent evolution is pretty much evolution we're all familiar with. If there was no divergency, weɽ all look the same (and probably be single-celled organisms and incapable of having this conversation). New species arise 'simply' because enough genetic drift has accumulated that two populations don't interbreed. Once they stop interbreeding, then they usually continue to diverge simply by genetic drift.
Parallel evolution is when two separate lineages wind up following much the same path even though there's no particular pressure (unlike being streamlined in the ocean). Off hand, I can't think of an example right now. It might be something like plants winding up with similar traits even though they've separated genetically for 100's of millions of years.
Any of that address what you're asking about?
Parallel evolution is a very poorly defined term in evolutionary theory, but probably the most sensible way of thinking about it as as a type of convergent evolution in which not only the overall trait is convergent, but the molecular and developmental pathways that produce the trait are also the same. This is most likely to happen in closely related taxa. It's a spectrum running from a hypothetical (though hard to imagine) case where the pathways that produce the trait are the same in every way even though they weren't present in the common ancestor, through all sorts of intermediates where the pathways are partially shared and partially different. It's not clear at what point youɽ stop calling a trait a parallelism altogether.
Common examples of parallelism are the evolution of similar wing patterns in groups of related butterflies or the evolution of grasping tendrils from shoots in several lineages of vining plants.
Two terms that are important for this discussion which I'll introduce right off the bat are "homology" and "homoplasy". Basically, a trait that is shared between two organisms and was also present in their most recent common ancestor are homologous. So homologous traits are the result of common descent, or divergent evolution if you prefer. In contrast, a trait that is present in two organisms but was not present in their common ancestor is homoplastic. So homoplastic traits are the result of convergent evolution, because both lineages evolved the trait independently.
There's a very important point I want to make here though, which is that traits can only truly be described as homologous or homoplastic in relative terms based on the scope of the species we're looking at. Pop quiz: are the two structures shown in this image homologous or homoplastic? The answer is both, depending on the frame of reference. The front limbs of a bird and a bat are homologous in the context of being tetrapod forelimbs, which is clear from the fact that they share the same underlying bone structure (humerus, radius and ulna, etc.). However, they are homoplastic in the context of structures used for flight, because birds and bats are clearly not descended from a recent flying common ancestor, and this is clear in the way that the actual flight surfaces are completely different for both organisms.
The distinction between convergent and parallel evolution is frankly kind of unclear depending on who you ask, but I'll explain the most common way I see these terms used. Convergent evolution describes the phenomenon of two lineages with different ancestral states evolving towards similar solutions to a problem they both face (note: this language is a little misleading since convergence isn't necessarily always adaptive, but for the sake of simplicity I'm going to stick with it). Meanwhile, parallel evolution involves two organisms with similar ancestral states independently evolving towards similar solutions to a problem, sometimes in the exact same way. In practice, what this often means is that parallel evolution involves very similar or even identical changes occurring at the genetic level (i.e., which genes are modified to result in a particular phenotype), while convergent evolution results in superficially similar phenotypes, but with very different underlying genetic changes behind them.
To give a hypothetical example, let's say two bacterial strains both evolve resistance to an antibiotic independently. There are several mechanisms by which this could occur both strains could evolve changes in an enzyme that allowed it to destroy the antibiotic, or perhaps one strain does this while the other evolves the ability to pump the antibiotic out of its cytoplasm to reduce its concentration. In both cases, the two strains show similar phenotypes (antibiotic resistance) that were not present in their common ancestor, so this is a homoplastic trait. However, the former case where both evolved resistance along the same pathway would be considered parallel evolution, while the latter case where they use two unrelated mechanisms to achieve the same effect is convergent evolution.
The answer to how we actually distinguish homologous and homoplastic traits relies on being able to accurately describe the relationship between the organisms of interest through phylogenetics. I won't go into detail here since this comment is already long enough, but creating phylogenetic trees involves the use of both phenotypic and genetic data to model the relationships and evolutionary history of groups of organisms (here's a very brief overview of how trees are actually constructed). To ensure the tree is accurate, we want to use data that is most likely to be homologous, so there is often a lot of consideration over what characters to include and how to score them, especially for phenotypic data (though genetic data also must be carefully selected). By using a large amount of data, most of which is unrelated to the shared traits we're interested in, we can get a good idea of the "true" evolutionary history of a group of organisms, and from there we can predict the ancestral conditions for traits based on which descendant lineages have them. And from there, it's (usually) straightforward to figure out whether two lineages share some feature because their ancestor shared it, or because they acquired it independently.
For example, look at this tree from the Khan academy page. The data as a whole (which, keep in mind, are informed by many different traits) show that species D and E together are more closely related to species C than they are to species B. However, species C does not have whiskers, while species B does. This means that species B and the ancestor of species D and E potentially evolved whiskers independently. (Note: this is actually kind of a bad example since it's just as possible that whiskers evolved in the ancestor of B+C+D+E and were then lost only in C, but it's the best simple figure I could find).
Is gender identity fluid or fixed? What we know about other animals might help inform the debate.Male and female Mandarin ducks.
Before tackling this question, it is necessary to define “sex” and “gender.” Sex refers to biological traits associated with male and female bodies. Sex isn’t a perfect binary, but it is relatively simple compared to gender.
Gender is multifaceted, complex, and a little abstract, and not everyone agrees on exactly what it means. That said, there are a couple of aspects of gender that most experts say are essential. The first is the existence of socially determined roles. Gender roles refer to the range of behaviors that society deems normal or appropriate for people of a particular gender based on their designated sex—the norms that (at least in many Western cultures) cause us to expect men to be assertive and brave, and women to be caring and accommodating, for instance.
It’s common for people to believe that gender roles are natural or innate, ranging from religious claims that they are God-given to the argument made by evolutionary psychologists that they are the biological result of natural selection. On the contrary, while some aspects of gender-correlated behaviors are probably largely genetic in origin (researchers don’t have a great sense of which are and aren’t), most experts agree that many gender-related expectations, such as that girls play princess and boys pretend to be soldiers, are socially determined—that is, we learn them from our culture, often without even being aware of it. This socially learned aspect is as fundamental to gender as the roles themselves.
Another fundamental aspect of gender is an internal sense of gender identity. Most people don’t just act in accordance with the roles associated with their gender identity, they also feel something inside of themselves that tells them what their gender is. For many, this sense of identity aligns with their biological sex (cisgender), but that’s not true for everyone. Plenty of people are biologically male, but they identify as women, or vice versa (transgender). Some individuals have a gender identity that is somewhere in between masculine and feminine, or it’s a mix of both or neither (androgyny). Still others are intersex, having both male and female biological traits just like those who fit on either side of the sex spectrum, intersex people fall across a range of gender identities.
So, two criteria substantiate gender: socially determined roles and an internal sense of identity. Neither of these by itself is enough to fully encompass what gender is, but most experts appear to agree that each is a necessary aspect of gender. Therefore, to assess the common claim that gender is unique to humans, we need to look at how other species fare with respect to these two criteria.
This is a tough endeavor—most of what we know about human gender originated from talking to people, and we usually don’t have the ability to ask other species what they think. Nonetheless (as I’ve written about before on the topic of primate vocal communication), we do have some access to animals’ minds through observing their social behavior. The evidence accrued from numerous studies, while not decisive, shows that gender might, in fact, exist in other species.
First, let’s look at the question of socially determined roles. Plenty of nonhuman species show sex-based differences in behavior. From beetles to gorillas, males of many species are more aggressive than females, and they fight with one another for access to resources and mating opportunities. Males are also often the more flamboyant sex, using showy body parts and behaviors to attract females—for example, take the peacock’s tail, the mockingbird’s elaborate song, or the colorful face of the mandrill (think Rafiki from The Lion King). Females, on the other hand, are in many cases more nurturing of offspring than males after all, by the time an infant is born the female will have already devoted significant time and energy toward forming, laying, and subsequently protecting and incubating her eggs—or, in the case of us mammals, she has gone through an intense process of gestation. The costly nature of reproduction for females limits the number of infants they can have that’s why it generally behooves females to be conservative, expending their time and energy on mating with only the highest-quality males. Being choosy in this way has, over evolutionary time, generally yielded fitter offspring. As a result, females of many species have evolved to be the choosier sex, and their mate choices can direct the course of evolution (an idea that scandalized Victorian England when first proposed by Charles Darwin).
There are exceptions to every rule, of course. Male seahorses get pregnant. Female spotted hyenas dominate males and sport a pseudo-penis (enlarged clitoris) that is capable of erection and can be as much as 90 percent the size of a male’s penis. As matriarchal as spotted hyena society is, it doesn’t quite reach the level of the northern jacana, a wading bird species whose common territory ranges from Panama to Mexico. Female northern jacanas patrol a territory full of males and fight off intruding females the smaller males engage in less territorial behavior than females, instead spending that time caring for a nest full of the resident female’s eggs.
Turning to our closest relatives, chimpanzees and bonobos, we see additional illustrative examples of the natural variation that exists in sex-correlated behavior. Although the two species are 99.6 percent genetically identical (and equidistant from humans), they are quite different. In general, adult male chimpanzees, like males of many species, are aggressive, domineering, and status-seeking. Much of their time is spent either patrolling territorial boundaries to deter or even kill members of other communities, or vying for social power within their own group. Adult females are generally less political and less violent—they have other priorities, like caring for offspring—but they can still influence the state of social affairs by breaking up male fights or leading rival males to reconcile. After all, as is the case in many species, much of what males stand to gain from high status is access to mating opportunities with females.
It’s been said that if chimpanzees are from Mars, then bonobos are from Venus. Bonobo society is generally female-dominated. Unlike female chimpanzees who mostly, though not always, keep their noses out of politics, female bonobos reign by forming male-dominating coalitions. They bond partly through genito-genital rubbing (it is what it sounds like), forming stronger relationships than female chimps typically have with one another. As for male bonobos, they are much less violent on average than male chimps. Unlike with chimpanzees, lethal aggression has never formally been observed in bonobos (though there has been one suspected instance) bonobos are more likely to share food (and maybe sex) with a stranger than to fight.
Some scholars look at the sex differences in behavior described in the above paragraphs as clear examples of nonhuman gender. But none of the evidence I have covered so far proves that behavioral differences between male and female chimpanzees, bonobos, or other nonhuman species are socially determined. Again, gender necessarily entails socially determined roles. Do we have any evidence that chimp and bonobo behaviors are determined socially rather than biologically?
That is the question Michelle Rodrigues, a postdoctoral researcher at the University of Illinois, and Emily Boeving, a doctoral candidate in psychology at Florida International University, set out to answer. They found that there is flexibility in some of the sex roles previously observed in chimpanzees and bonobos—specifically, in grooming. In both chimpanzees and bonobos (as well as in many other primates), grooming serves as a way of strengthening social bonds. In the wild, most of the grooming in both species is male-on-female or vice versa. Where the species differ is that among wild chimpanzees, male-male grooming is generally more common than female-female grooming—an imbalance not seen in bonobos.
Bonobos groom each other at the Columbus Zoo. Image credit: Michelle Rodrigues/Springer Japan KK
Rodrigues and Boeving wondered whether chimps and bonobos living at zoos would show the same grooming patterns. To investigate this, they observed chimpanzees and bonobos at the North Carolina Zoo and Columbus Zoo, respectively, paying special attention to grooming networks. In contrast to data from the wild, zoo-living apes’ grooming seemed to be more related to individuals’ histories and personalities than their sex: Neither species showed the sex-typical grooming patterns displayed by their wild counterparts.
This is solid evidence that certain sex roles are at least partly environmentally determined in these species. But is environmental determination the same as social or cultural determination? Not exactly. Social learning could be responsible for the flexibility we see in chimpanzee and bonobo sex roles. In this hypothetical scenario, wild female chimpanzees groom less than males because growing up, they receive less grooming from other females, and they witness little, if any, female-female grooming. They are socialized in these ways not to spend as much time grooming. In the zoo, then, the “culture” around grooming is atypical, and females are socialized differently. However, an equally plausible (but not mutually exclusive) possibility is that sex-based behavioral differences in the wild are simply the result of individuals finding ways of coping with their environment: Females in the wild have the responsibility of infant care. As a result, they are too busy foraging to spend much time socializing. At the zoo, with humans providing food, females groom more simply because they have the extra time—no social learning of sex roles is required.
Again, these two explanations are not mutually exclusive. Both could play a part. I spoke with Rodrigues about what evidence would be necessary to conclude that chimpanzee or bonobo sex roles were socially determined.
“We would need to see evidence that adults are actively treating male and female infants and juveniles differently, and actively [socializing] them differently,” she said. Rodrigues pointed out that some chimpanzee behavior is suggestive of different treatment of male and female offspring: For example, she noted, “data on young chimpanzees indicates that female chimpanzees spend more time observing their mothers termite-fishing and, in turn, are able to master termite-fishing using their mother’s technique at a younger age.” Researchers aren’t certain whether this is due to active socialization by mothers or an innate preference among female offspring to observe their mothers’ techniques. Even so, this observation is consistent with the idea of social determination of at least some, but probably not all, sex roles in chimps.
The meaning of social construction, biological variation, and their relevance to health disparity
The new drug BiDil has been hailed as a racial pill. 16 It reduced the death rate from congestive heart failure in African Americans 43% compared to those given a placebo. BiDil is a combination of a nitric oxide donor isosorbide dinitrate and the anti-oxidant hydrazaline, which also acts as a vasodilator. Nitric oxide is a gas that plays a role in a variety of neurally mediated events including regulating heart processes, programmed cell death, as an anti-microbial agent, and even assisting penile erection in men. Anti-oxidants protect cells against oxidative damage that result from normal cellular respiration and poisons that accumulate over time. In addition, it has been recently shown that oxidative damage to human cells can be heightened by periods of prolonged stress. 17 The African American Heart Failure Trial (A-HeFT) trial was motivated by studies showing that people self-identified as “black” had lower levels of available nitric oxide and greater amounts of oxidative stress than those self-identified as “white.” 18
Actually, these results do not indicate that BiDil is a “racial” pill. What we know about the mechanism supports that assertion. Nitric oxide is synthesized by individual cells and this is catalyzed by an enzyme known as endothelial nitric oxide synthase (eNOS). Genetic variation at position G894T in this enzyme influences arterial stiffness (after controlling for sex, age, body mass index, insulin, heart rate, and mean arterial pressure). 19 African Americans that had the T allele had less elasticity than those with the G allele. European Americans showed no significant difference between T and G, but the trend was similar. However, they also found that the frequency of T was 0.131 in African Americans v. 0.321 in European Americans, respectively. This of course means, if all other factors were equal, that more “whites” should have less elastic arterioles than “blacks.” If so, BiDil should help whites more than blacks, yet present data do not support this, meaning that other factors must be at play.
Social dominance creates different environmental conditions for the socially constructed races in America. A number of studies exist documenting the relationship of stress to lowered health outcomes. A recent study experimentally demonstrated a mechanism by which emotional stress could actually cause cellular damage. 20 It found psychological stress was significantly associated with oxidative stress, lower activity of the enzyme telomerase, and shorter telomere length. Telomere lengths of women giving care to chronically ill children were significantly shorter than women who gave care to healthy children. Finally, telomere length in chronically stressed groups was proportional to the years of care giving and to the perceived amount of psychological stress.
This result could revolutionize research into chronic illness. Individuals experiencing chronic racialized stress should also show shorter telomere lengths. Racialized stress increases the probability of pre-term and low birth weight deliveries and negatively affects the mental health of pre-school children. 21 BiDil may work for African American patients because they have greater oxidative damage in their cells, due to chronic stress. This would mean that the drug is acting on an environmentally induced difference, not a genetically based one. If the drug were used in Western Africa, where Africans face less racialized stress and a variety of environmental factors differ, we may not observe any “race-specific” effect.
Race, genetics and pseudoscience: an explainer
Human genetics tells us about the similarities and differences between people – in our physical and psychological traits, and in our susceptibility to disorders and diseases – but our DNA can also reveal the broader story of our evolution, ancestry and history. Genetics is a new scientific field, relatively speaking, merely a century old. Over the last two decades, the pace of discovery has accelerated dramatically, with exciting new findings appearing daily. Even for scientists who study this field, it’s difficult to keep up.
Amidst this ongoing surge of new information, there are darker currents. A small number of researchers, mostly well outside of the scientific mainstream, have seized upon some of the new findings and methods in human genetics, and are part of a social-media cottage-industry that disseminates and amplifies low-quality or distorted science, sometimes in the form of scientific papers, sometimes as internet memes – under the guise of euphemisms such as ‘race realism’ or ‘human biodiversity’. Their arguments, which focus on racial groupings and often on the alleged genetically-based intelligence differences between them, have the semblance of science, with technical-seeming tables, graphs, and charts. But they’re misleading in several important ways. The aim of this article is to provide an accessible guide for scientists, journalists, and the general public for understanding, criticising and pushing back against these arguments.
Human population structure is not race
Racial categories, as most people understand them today, have some of their roots in the development of scientific thinking during only the last few centuries. As Europeans explored and colonised the world, thinkers, philosophers and scientists from those countries attempted to apply taxonomic structures to the people that they encountered, and though these attempts were many and varied, they typically reflected sharp geographic boundaries, and obvious physical characteristics, such as pigmentation and basic morphology – that is to say, what people look like. Research in the 20th century found that the crude categorisations used colloquially (black, white, East Asian etc.) were not reflected in actual patterns of genetic variation, meaning that differences and similarities in DNA between people did not perfectly match the traditional racial terms. The conclusion drawn from this observation is that race is therefore a socially constructed system, where we effectively agree on these terms, rather than their existing as essential or objective biological categories.
Some people claim that the exquisitely detailed picture of human variation that we can now obtain by sequencing whole genomes contradicts this. Recent studies, they argue, actually show that the old notions of races as biological categories were basically correct in the first place. As evidence for this they often point to the images produced by analyses in studies that seem to show natural clustering of humans into broadly continental groups based on their DNA. But these claims misinterpret and misrepresent the methods and results of this type of research. Populations do show both genetic and physical differences, but the analyses that are cited as evidence for the concept of race as a biological category actually undermine it.
Even though geography has been an important influence on human evolution, and geographical landmasses broadly align with the folk taxonomies of race, patterns of human genetic variation are much more complex, and reflect the long demographic history of humankind. This begins with our origin as a species – Homo sapiens – in Africa within the last quarter of a million years or so, and is then shaped by our continual mixing and movement throughout the world that began within the last 80,000 years. This history means that the greatest amount of genetic diversity – the oldest splits in the human genealogical ‘tree’ – are found within Africa. If an alien, arriving on Earth with no knowledge of our social history, wished to categorise human ancestry purely on the basis of genetic data, they would find that any consistent scheme must include many distinct groups within Africa that are just as different from each other as Africans are to non-Africans. And they would find it difficult to identify any natural or obvious subdivision of people into groups which accurately partitions human genetic variation due to the constant migrations of people across the world.
Furthermore, there isn’t really a human ‘tree’. Although we use this arboreal metaphor to describe ancestry and evolutionary relationships, the true structure of human ancestry is far more convoluted. Human populations have continued to diverge, expand and interact throughout the last 100,000 years, resulting in a continuously branching and looping ancestral structure: the real history of Homo sapiens is more like an overgrown thicket than a stately branching tree. Much of the population structure that we see today in ancestry testing results dates back only to a few thousand years or less. For example, the majority of European genomes are a mixture of at least three major groups within the last 10,000 years: the early hunter-gatherers who first populated the continent, a second wave of ancestry from the Near East associated with the spread of farming and a third contribution from north Eurasia during the Bronze Age (2000–500 BCE).
Geneticists use a variety of tools to visualise the subtle and complex patterns of genetic variation between people, and to mathematically cluster them together based on relatedness. Such methods are helpful for exploring data, but have also been the source of wider confusion. For example, Principal Component Analysis (PCA) plots often show distinct, colourful clusters of dots that appear to separate groups of people from different parts of the world. In some cases, these clusters even seem to correspond to traditional racial groupings (e.g. ‘Africans’, ‘Europeans’ and ‘Asians’). It is images such as these which are often deployed as genetic evidence for the existence of separate races. But these methods can be misleading in ways which non-experts – and even some specialists – are unaware of. For example, some of the observed genetic clustering is a reflection of the samples that were included in the study and how they were collected, rather than any inherent genetic structure. DNA sample collection typically follows existing cultural, anthropological or political groupings. If samples are collected based on pre-defined groupings, it’s entirely unsurprising that the analyses of these samples will return results that identify such groupings. This does not tell us that such taxonomies are inherent in human biology.
Some ‘human biodiversity’ proponents concede that traditional notions of race are refuted by genetic data, but argue that the complex patterns of ancestry we do find should in effect be regarded as an updated form of ‘race’. However, for geneticists, other biologists and anthropologists who study this complexity, ‘race’ is simply not a useful or accurate term, given its clear and long-established implication of natural subdivisions. Repurposing it to describe human ancestry and genetic structure in general is misleading and disingenuous. The term ‘population’ is used in many contexts within the modern scientific literature to refer to groups of individuals, but it is not merely a more socially acceptable euphemism for race.
It is often suggested that geneticists who emphasise the biological invalidity of race are under the thumb of political correctness, forced to suppress their real opinions in order to maintain their positions in the academy. Such accusations are unfounded and betray a lack of understanding of what motivates science. Discoveries, particularly in biology, have often been challenging or difficult for society to accept, and scientists throughout history are celebrated for establishing them in the face of contemporary objections. Indeed, the biological invalidity of traditional racial categories runs counter to many people’s lived experience, and is in itself a morally neutral conclusion. If the evidence is sound, scientific integrity demands that it is published. The charge that thousands of scientists across the world are covering up a real discovery for fear of personal or wider social consequences is absurd. Furthermore, it is important to distinguish understanding the world around us using science, from the rules, distribution of funds and policies in society. The goal of scientists is to provide that understanding. At the same time, we appreciate that societies determine their principles and policies informed by, but independent of science.
Traits, IQ and genetic diversity
Traits and characteristics vary among individuals within and between different parts of the world, sometimes in ways which are visible, such as with height or pigmentation, and sometimes in other more cryptic ways, such as with disease susceptibility. Understanding how genomes influence traits is a major aspect of genetic research.
There are countless traits one can measure in humans, but none more controversial than those associated with intelligence, such as IQ. ‘Human biodiversity’ proponents tend to fixate on IQ, and one can speculate about why this is and what conclusions they wish to draw however, it should be noted that IQ itself is a valid and measurable trait. Critics often assert that it is an oversimplified metric applied to a far-too-complex set of behaviours, that the cultural-specificity of tests renders them useless, or that IQ tests really only measure how good people are at doing IQ tests. Although an IQ score is far from a perfect measure, it does an excellent job of correlating with, and predicting, many educational, occupational, and health-related outcomes. IQ does not tell us everything that anyone could want to know about human intelligence – but because definitions of “intelligence” vary so widely, no measure could possibly meet that challenge.
‘Human biodiversity’ proponents sometimes assert that alleged differences in the mean value of IQ when measured in different populations – such as the claim that IQ in some sub-Saharan African countries is measurably lower than in European countries – are caused by genetic variation, and thus are inherent. The purported genetic differences involved are usually attributed to recent natural selection and adaptation to different environments or conditions. Often there are associated stories about the causes of this selection, for example that early humans outside Africa faced a more challenging struggle for survival, or that via historical persecution and restriction of professional endeavours, Ashkenazi Jews harbour genes selected for intellectual and financial success.
Such tales, and the claims about the genetic basis for population differences, are not scientifically supported. In reality for most traits, including IQ, it is not only unclear that genetic variation explains differences between populations, it is also unlikely. To understand why requires a bit of background.
It is certainly the case that some traits are the result of local or regional adaptation, corresponding to differences in particular genes. Indeed, one of the reasons for humankind’s success as a global species is local adaptation. The majority of this adaptation is via behaviour and the cultural transmission of successful behaviours, but there are also cases where the adaptation is genetic, that is, small modifications occur within our genomes that enhanced survival in different environments. For example, genetic changes have meant that coastal populations have DNA variants that help them more readily process diets that are rich in oily fish pastoralist farmers all over the world evolved the ability to metabolise milk after weaning, largely through genes that continued to produce a particular enzyme into adulthood that would otherwise be switched off by the age of five. Lighter skin evolved to allow more sunlight, and thus Vitamin D synthesis, into our bodies as we migrated away from the equator. We can see these local adaptations in our DNA. But they only hold for a minority of traits. Most traits have very real genetic and physical differences between individuals, but any group differences do not correspond to traditional race categories such as height, or the susceptibility to type 2 diabetes in an environment with ready access to food.
For traits caused by regional adaptation, contemporary genetic techniques now allow us to see clear evidence for recent selection on new genetic variants or patterns at particular locations in the genome. However, such cases are atypical: most traits have no obvious or localised signal of recent selection. The lack of regional adaptation does not hinder genetic approaches, and all traits (whether under recent adaptive selection or not) can be studied by analysing large numbers of people. The Genome-Wide Association Study (GWAS) is a powerful tool for finding genetic variants associated with all sorts of human traits. GWAS researchers take a group of people with differing values or levels of a trait of interest, and scan their whole genomes to look for specific sections of DNA where their genetic variation correlates with their variation in the trait. For most traits, the GWAS results are complicated. Unlike in more straightforward cases like Sickle Cell Anaemia, where you’d find a big spike of statistical significance in one particular gene (the beta-globin gene, whose variation is the primary cause of the disease), GWAS results typically implicate many thousands of positions in the genome that, in aggregate, build towards the probability of having a disease or some level of a particular trait. And so, for height, or heart disease, or schizophrenia or other complex conditions, we see many small spikes of significance dotted around the genome – so many that we can’t single out individual genes or sections of DNA that sometimes get characterised as “the gene for” that particular outcome. Each of the large number of places across the genome which we associate with a trait contribute a small amount, but collectively the sum of all these effects means that there is in aggregate a substantial genetic influence on how the trait varies between people.
However, GWAS and other similar approaches are affected by population structure, and hence face the same issues of dependence on sampling and confounding with cultural factors mentioned above. Most GWAS approaches have been carried out in populations sampled from across Europe, and have ancestries consistent with this sampling. In many cases though, only certain subsets of people are included in these analyses – for good scientific reasons. For example, samples of “European” populations used in genetic studies often have excluded up to as many as 30% of self-identified Europeans. This is because some individuals introduce hard-to-model complications into the data, forming distinct sub-clusters or complicating the genetic model. For example, Finns and Sardinians are often excluded as they have quite distinct genetic ancestries compared to many other Europeans, as are some people in India, north Africa, Latino/Hispanics, and many individuals with complex ancestries, despite confident self-identification within their ethnic group. Researchers therefore often exclude them from the set of people used in a particular GWAS analyses, on the basis that their unique population histories can invalidate the statistical models used in these techniques.
This, in turn, can confuse people who read the studies and observe distinct and seemingly ‘natural’ population clusters emerge. If they aren’t familiar with the practice of removing these individuals with more complex ancestries (or don’t read the detailed methods, which are often tucked away in elusive supplementary sections of a published paper), they could easily be misled into thinking that the populations in these analyses are much more distinct than they are in reality. The resulting biases are poorly understood, and the terminology involved can be confusing to non-specialists. Furthermore, while it is clear to GWAS researchers that the results of their analyses tend to be specific to the population studied and their predictions cannot be reliably extended to other populations with very different ancestry, this is not widely recognised or understood by non-specialists.
When it comes to a trait as complex as cognitive abilities, there is nothing genetically unusual or special about measures of intelligence such as IQ. Just like other complex traits discussed above (such as height or disease susceptibility) measures of cognitive ability are related to thousands of different genetic variants, each of which may play small but significant roles in brain development and function, or any number of other biological processes that are involved in a person’s cognitive abilities.
IQ scores are heritable: that is, within populations, genetic variation is related to variation in the trait. But a fundamental truism about heritability is that it tells us nothing about differences between groups. Even analyses that have tried to calculate the proportion of the difference between people in different countries for a much more straightforward trait (height) have faced scientific criticisms. Simply put, nobody has yet developed techniques that can bypass the genetic clustering and removal of people that do not fit the statistical model mentioned above, while simultaneously taking into account all the differences in language, income, nutrition, education, environment, and culture that may themselves be the cause of differences in any trait observed between different groups. This applies to any trait you could care to look at – height, specific behaviours, disease susceptibility, intelligence.
Not only that, the genetic knowledge we gain from studying our mainly-European pools of participants becomes highly unreliable when it is applied to those with different ancestries. Although it is a common trope to argue that we will have the answer to the question of the genetic basis of group differences in traits “in the next five years”, or “in the next decade”, the advances in genomics reveal that the question is far more complex than we could have imagined, even just a few years ago. Consequently, anyone who tells you that there’s good evidence on how much genetics explain group differences (rather than individual differences) is fooling you – or fooling themselves.
However, there are some strong hints towards the answer. The genetic variants that are most strongly associated with IQ in Europeans are no more population-specific than any other trait. To put it bluntly, the same genetic variants associated with purportedly higher IQ in Europeans are also present in Africans, and have not emerged, or been obviously selected for, in recent evolutionary history outside Africa. Moreover, since it is a complex trait, the genetic variation related to IQ is broadly distributed across the genome, rather than being clustered around a few spots, as is the nature of the variation responsible for skin pigmentation. These very different patterns for these two traits mean that the genes responsible for determining skin pigmentation cannot be meaningfully associated with the genes currently known to be linked to IQ. These observations alone rule out some of the cruder racial narratives about the genetics of intelligence: it is virtually inconceivable that the primary determinant of racial categories – that is skin colour – is strongly associated with the genetic architecture that relates to intelligence.
Finally, multiple lines of evidence indicate that there are complex environmental effects (as might reasonably be expected) on measures of IQ and educational attainment. Many socioeconomic and cultural factors are entangled with ancestry in the countries where these studies are often performed – particularly in the USA, where structural racism has historically and continues to hugely contribute to economic and social disparities. We cannot use populations in these countries to help answer the question of why IQ scores are claimed to be lower in other countries with entirely different social, economic, and cultural histories, nor to answer the role of genetics for alleged differences in IQ measures between groups inside a country with strong societal differences linked to ancestry (for example, the USA). Thus, confident assertions that current GWAS show us that ‘race’ is associated with cognitive function are simply wrong. It is our contention that any apparent population differences in IQ scores are more easily explained by cultural and environmental factors than they are by genetics.
This argument is bolstered by the observed increase in average IQs over time known as the Flynn Effect. The political scientist James Flynn observed that IQ was rising in test groups on average by around three points per decade from the 1930s onwards. Factors that account for this include improved health, nutrition, standard of living and education, but changes in genes can be ruled out. Because the effect is seen in many places around the globe, and has been observed in just a few years, substantive genetic changes cannot have occurred either within or between generations. If, for example, the Flynn Effect had not occurred in the Netherlands, then the current average IQ there would currently be as it was in the 1950s, that is, around 80. A plausible argument for the putative lower average IQ score in some Sub-Saharan African countries is that the socio-economic factors behind the Flynn Effect have not transpired there. If this is indeed the case, or if other factors explain observed differences in IQ, we believe that explanations relying on genetic differences between populations are fundamentally unsound.
The advent of new tools and an enormous surge in genetics research all over the world has inadvertently revitalised a vocal fringe of race pseudoscience, much of which appeals to our social experience of the people of the world, and the very real, but socially determined races as we describe them colloquially. These novel scientific techniques are complex and sophisticated, and therefore susceptible to misinterpretation and misplaced use. It is incumbent upon scientists to understand and help explain the validity of these tools to other scientists, to journalists and to the wider public. By understanding both our history and contemporary research, we are emboldened by knowing that genetics has only served to undermine its own racist history.
Popular Genetics: How Learning About Our Genes Offers Both Benefits and Limitations
In public health, studying genetics is key to understanding a population's likelihood to contract certain diseases so that steps can be taken to mitigate that risk. On a more individual level, people now have more options than ever for learning about their own genetic makeup and how their genes affect their health, but those options come with benefits and limitations.
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00:00 Mike Boehnke: Public health is about the public's health. We're interested in learning the basis of human health and disease. We're always trying to do what we can to improve human health at a population level. To do that, we need to pay attention to the different determinants of health, and there are lots of determinants of health that aren't genetic but certainly, genetics is part of the picture. Genetics also has the advantage that it's relatively simple. Three billion base pairs is what you get in terms of genetic material, that's finite and it's largely fixed. And so it gives us an entry point to understand human health and disease. It gives us a way to go about studying human health and disease, that in the broader context of all the different things we do in public health in terms of people's behavior, in terms of the environment, that genetic component is a piece of the puzzle, a part of the picture that we need to look at as we're doing our best to improve the public's health.
01:04 Speaker 2: The study of genetics in public health goes much deeper than exploring why someone has blue eyes, or brown hair, or why someone's tall or short. Over the past generation or two, knowledge about DNA has entered the zeitgeist in part due to criminal investigations and celebrity trials. In these cases, the focus is often on the individual and not the larger population. So it may be a little surprising that the building blocks of genetics, DNA, genes and chromosomes passed down to you from your parents, have been an area of focus in public health for just as long.
01:40 S2: Hello and welcome to Population Healthy, a podcast from the University of Michigan School of Public Health. Join us as we dig into important public health topics, stuff that affects the health of all of us at a population level. From the microscopic to the macro-economic, the social to the environmental. From neighborhoods to cities, states to countries, and around the world.
02:13 S2: In public health, studying genetics is key to understanding a population's likelihood to contract certain diseases so that steps can be taken to mitigate that risk. On a more individual level, people now have more options than ever for learning about their own genetic makeup and how their genes affect their health, but there are benefits and limitations to all of this. Today, we'll explore these topics as we talk to three researchers whose work focuses on different areas related to the field of genetics. To get started, here's a quick refresher on our genes and how they work.
02:48 MB: Hi, I'm Mike Boehnke from the Department of Biostatistics at the University of Michigan School of Public Health. So gene's the basic unit of heredity, it's what passes from parents to offspring that codes for the traits, the characteristics that make us human, or for other animals, or plants, or whatever they might be. They determine observable characteristics like eye color, they determine measurable characteristics like your height, and they also predispose to human disease or make a difference in our risk to human disease. In some cases what we call simple Mendelian disorders, where one bad gene or two bad genes can give you a disease.
03:23 MB: In other cases, common diseases, typically, like type 2 diabetes or various forms of cancer, typically it's multiple genes that are involved in the interplay of those genes and interplay with the environment. A gene is actually a sequence of nucleotides within a chromosome. The gene is used as machinery by the cell to be transcribed to RNA, RNA is then translated to proteins, and so basically genes indirectly in that path code for proteins. And since proteins are sort of the building blocks of cells and orchestrate a lot of what goes on in cells through the actions of enzymes, which are proteins, genes are really the information transfer that allows us to build cells, build organisms, build you and I. So, it's a blueprint in the sense that it gives the fundamental genetic information, which is a substantial part of who and what we are, but it's only part of who and what we are. Because what we do, the kind of lifestyle we live, the kind of raising we get from our parents, all the other things that go on as a human grows and develops and becomes whatever she or he might be, the genetics is part of the basis for that but only part. The chromosome is one of the, in the case of humans, 46 entities that carry the genes and a bunch of other stuff.
04:39 MB: It was actually an interesting thing we learned when the genome was sequenced, and actually knew some before that, but as we got more and more information about the human genome, the 46 chromosomes have about 2% of their content that actually codes for genes. There's a fair amount more that's actually responsible for how those genes are expressed, and then there's a fair amount more beyond that we just don't even know what it does.
05:08 S2: Which brings us to some of the exciting frontiers of genetics in public health. Remember, we all have about 20,000 genes. To get a deeper understanding about human health researchers at the University of Michigan School of Public Health are working to understand them and how very specific interventions may prevent or curtail disease.
05:29 MB: When, in about 2006, we carried our first genome-wide association study for type 2 diabetes, a gene called SLC30A8, which is actually a gene that's responsible for transport of zinc to the beta cells of the pancreas. And SLC30A8, and in a particular a single misspelling in that gene, led to about a 10% change in risk for type 2 diabetes. This was one of the first genes that we identified, one of the first 10 that we identified as being important. And that was sort of interesting because it was a misspelling that actually caused a single change in the protein, a change in the amino acid at one particular location, and given that it's something that was specific to the beta cells of the pancreas. I'm not a great diabetes biologist, but even I knew that beta cells of the pancreas are the tissue that produce insulin. And so something that might be important in transport in this really critical tissue, and then possibly being a relevant risk to type 2 diabetes, that seemed pretty exciting.
06:30 MB: Since then, colleagues of ours at the Broad Institute, us helping out, others helping out, looked at a much larger number of people in a much more detailed way, actually sequencing this gene in about 150,000 people, and found that more severe modifications of this gene, what we would think of as mutations, and mutations that didn't just change a single letter of the alphabet, but actually caused the protein product to be truncated and therefore probably not active, that can actually lead to a three-fold decrease in risk to type 2 diabetes. Now, a three-fold decrease is a pretty big deal. I look to try to find the genetic basis of human health and disease, partly because it's just fun and it's an interesting puzzle, but honestly, a major reason for doing it and honestly why the government is happy to fund it and should be happy to fund it is that we might be able to identify possible new therapies. And this is a promising one.
07:23 MB: We've been doing the genetics of type 2 diabetes long enough to know that it's hard. Diabetes is a really complex disease and we know that there's a lot more going on besides genetics. Genetics is only a small part. Behavior, what you eat, what your lifestyle is like in terms of physical activity, those are really important. And so we said, "We wanna work together with other people," because this new technology we had available to this genome-wide association study we thought gave us a chance to be successful. But we knew if we had not just our study, but other studies working together, we'd have a greater chance of being successful. And so we brought together three different groups, ours and two others, who decided that we would share our data right from the very start.
08:04 MB: And when we did, we ended up with a list of nine places in the genome where there were factors predisposing to type 2 diabetes. Without that sharing of information, we would have had maybe one or two, and those basically already known. At the start and at the end, in human genetics, we want to be sharing information, bringing information together for our discoveries, taking the information and broadcasting it as broadly as we reasonably can to other scientists who might be able to take advantage of it.
08:35 MB: We can sequence a human genome in a matter of hours at a cost of about $1,000, in comparison to the Genome Project, where for several years we paid something like a billion dollars to effectively sequence one genome, actually sort of a composite genome. You don't have to be brilliant, when you see that kind of fundamental change in what's possible, to go and do something good with it. I was lucky enough, and my colleagues working together, we were lucky enough to be working at a time when these remarkable transformations happened that enabled the kinds of studies that I knew I wanted to do back in the early '80s, but didn't have a prayer to do for the kinds of diseases we study now.
09:24 S2: So the study of your personal genetics tells us about you, but it also tells us about your history, namely, your ancestors. And if we know more about your ancestors we also know a lot more about patterns in human health. And if we know that, we may be able to project into the future about our risk for disease. Now, unless you are a genetic epidemiologist, the work of this next researcher will be new to you.
09:49 Sebastian Zoellner: I'm Sebastian Zoellner, Professor of Biostatistics in the University of Michigan School of Public Health and Professor of Psychiatry at Michigan Medicine. My work is at the intersection of population genetics and genetic epidemiology. I'm trying to understand how the history of a population affects the patterns we see in modern genetic data and how we can use that history to understand how genetic information affects our risk of diseases.
10:15 SZ: If you want to understand the history based on genetic data, you can obviously go and find genetic data from a 1,000 years ago, 10,000 years ago, you can look at the genetics of the Neanderthal. But you can also just look at present-day genetic data. And the idea here is that if you compare the genetic information between two individuals, that how different they are is a reflection of their past. If you, for example, take the genetic information of two closely related individuals, you'll find it's in some areas very similar, reflecting that they have a common ancestor very recently, say the same grandfather. But if on the other hand, you take two random individuals and you compare their genetic information, you'll see it's reasonably different and that reflects a more distant ancestor.
11:01 SZ: So this way you can tell how far back in time the two individuals have a common ancestor, and that tells you about the history of the population. If we talk about genetic information, remember that only about 1% of the genome actually codes for proteins. 99% of the genome have either regulatory function, or have no function that's particularly beneficial to humans. But all of that carries information about the history, because what we care about is not the functional changes that make us into who we are as beings, but just random changes that accumulated over time. Those are the ones that give us information about the history of a population. Actually there's quite a bit of information available only within the genetic data, so you can compare and you can basically cluster individuals by how similar they are to each other. If you, say, take a sample of Europeans, you get a two-dimensional structure of which populations are close to each other, which populations are far away from each other, that reasonably well recapitulates the European map. So if you turn and twist that, you actually get the European map back, just based on how similar those people are. You need no outside information for that.
12:05 SZ: When modern humans came into the world, they have interacted, interbred with the previous hominids there and they've also kept the DNA that was useful and pretty much rejected the DNA that was useless. So really a nice story there. If you go into Tibet, you'll find that the people there are extremely adapted to living at high heights. Turns out that's a gene they got from the local equivalent of Neanderthal and maintained it because that lived there way longer than them and they had actually the time to adapt.
12:35 SZ: Another cool story is lactose intolerance. It's one of the basic examples that always gets used when we talk about selection because as you probably know the historical state, the ancestral state, is lactose intolerance. Lactose tolerance is something that arises in a population with a lot of dairy production because the gene that is designed to turn off your ability to metabolize lactose breaks. And if you have a lot of cows around, that's good if that gene is broken because suddenly you can use the milk, right? And the interesting thing is that actually happened multiple times in the history of human kind. It happened in Europeans and that's why Europeans are typically not lactose intolerant, but it also happened in East Africa. Same gene, broken in a different position because they were also a cattle-keeping society and so they also had an advantage if they were lactose tolerant.
13:26 SZ: One of the questions that clearly motivates a lot of my research right now is improving the equity in genetic research. For a lot of very valid technical reasons, the first 10 years of genetic research have been heavily focused on only using one population, right? Because homogeneity is useful if you do research. And of course, it was the population where we had the most data, the most money, people of European decent. That has the problem that it increases already existing health inequities. So right now, the research is moving in the direction of using more African American samples, Latino samples. But the problem here is, of course, also the machinery that we use to analyze all of this has developed to analyze the data we have and not the data we're getting, so now we need to think about how can we adapt the existing machinery to analyze these new, diverse samples.
14:21 S2: While one new frontier in genetics is about the diversity of our genetic research, another relates to how a growing number of people may interact or confront their own genetic information. If you've ever received a genetic testing kit as a birthday present you might know what I'm talking about. The prospect of exploring your own genetic makeup is exciting, but the information that comes back can be difficult to understand and perhaps even scary. Scott Roberts, a professor of Health Behavior and Health Education at the University of Michigan School of Public Health, is here to discuss the wide spectrum of ethics and implications of personal and direct-to-consumer genetic testing.
15:00 Scott Roberts: My research interests are focused on how people respond to learning genetic risk information for various diseases. I think what scares some people about genetics and the use of genetic information is based on, I think, a troubled history of how genetics has been used in the past. So everything from Nazi Germany trying to promote this master Aryan race, but even here in the United States, we had a very troubled history. Eugenics was behind a policy of forced sterilization of people who were called feeble-minded and so that actually took place over several decades, into the 1970s even. We've had troubles with genetics research, people know the Tuskegee study, which was actually based on this notion that African Americans were somehow innately biologically different and therefore we needed to see how syphilis occurred in black bodies was the idea. And I think in present day, we see lot of wariness around genetic privacy. Popular culture feeds a lot of this too, I think, in the media, so there's all kinds of dystopian fiction out there based on these nightmare scenarios of scientists run amok.
16:15 SR: And so, I think this combination of factors may help influence some people's wariness about the use of genetic information. In terms of the misuse of genetic technology, some of the modern day concerns are that we might see the use of genetic testing expanding too much before it's ready for prime time. It's very easy now just from a saliva sample even to get even a whole genome sequence. And so I think there's concern that we might go too fast too far in using genetic testing.
16:51 SR: So there are a lot of potential risks and harms of providing people with genetic information that, at least in theory, exists. So one major risk is psychological distress. Genetic information, when it's being provided for example, for maybe risk of a severe, incurable condition like Huntington's disease, can be very distressing. And so, people often stay away from that information, they may choose not to even sign up for genetic testing of that kind in the first place. But I think there's the fear that if people aren't prepared to learn this kind of information, it could be very upsetting to them. There's also a lot of concerns around genetic privacy, fears of genetic discrimination.
17:32 SR: So if genetic risk information for various health conditions gets in the hands of insurers, employers, criminal justice system, there's potential for misuse of that. So I don't think the actual level of genetic discrimination is very high in this country, but understandably, people are still worried about the possibility. So to mitigate these potential risks on the psychological distress side, I think we can pay attention to really good genetic education and counseling. And in fact, there's a whole field of genetic counseling that provides training to clinicians and if it's in a healthcare context, providing this upfront education to prepare people, to make sure that they know what they are gonna receive in terms of information, and to decide, do they even really want this testing at this point in time?
18:21 SR: But even beyond just formal genetic counseling, I think there's a lot of ways we can support genetic education so that people are really giving truly informed consent to genetic tests that they may undergo. I think in terms of these genetic discrimination risks, we do fortunately have some laws on the books that protect against genetic discrimination. So back in 2008, this federal law known as GINA, The Genetic Information Nondiscrimination Act, was passed. And so that prohibits employers or health insurers from using genetic information, for example, in any kind of hiring decisions or insurance decisions about setting of premiums, that kind of thing. I think it's also important though to point out that GINA does not cover life, disability, or long-term care insurance, so some people have suggested we should have a follow-up amendment to that policy to expand that protection.
19:19 SR: Consumer genetic testing refers to this idea that we have many more companies out there developing and offering genetic testing services, both in the context of healthcare, but there's been a lot of growth in this industry known as direct to consumer genetic testing, which can be provided in a variety of ways and for a variety of reasons. There are some companies, like 23andMe, that many people have heard about that offer health-related information. There's other companies that focus on genetic ancestry, there's some that provide genetic testing around fitness and diet. There's different options here. We've seen this really grow in the past decade and so there's a lot of excitement in that area, but then from the public health side, some concerns about do we need to more tightly regulate this growing industry? Are they really offering tests that do what they say they do and that provide true benefit to people? The concerns about companies like 23andMe in terms of the risks involved, this issue of psychological distress as a potential harm, most direct to consumer companies are providing a lot of different genetic information for a lot of different health issues and they're not necessarily getting truly informed consent upfront.
20:36 SR: People are not meeting with any kind of clinician to discuss this testing ahead of time. So there's the fear that people might be blindsided by information they weren't really aware they were going to get. And so, 23andMe for example, includes health risk testing for conditions like Parkinson's, Alzheimer's. So those are pretty serious diseases that people, in theory, could get troubling information that they're not prepared for. On the other hand, I think we've actually done some research basically suggesting that the likelihood of those kinds of reactions is very low, that even though I think in the ethics community those concerns have been raised, we haven't in real practice seen this really happen that frequently. Another concern is that people may take direct to consumer genetic testing results to their primary care physicians for interpretation, but that that's not necessarily the best use of their limited time for interaction with their primary care doc.
21:35 SR: Another potential risk is some companies provide direct to consumer testing and give you access to a raw data file that you can then take and use in other ways if you'd like. And so there's now these third-party testing companies popping up on the web, so people are taking, for example, their raw data from a company like 23andMe, inputting it into this third party site, and there have been some case examples where that third party testing has resulted in people being alarmed they may be at high risk for Alzheimer's or breast cancer, and it turns out that that really wasn't the case, that this unregulated third-party testing was giving them erroneous results.
22:19 SR: So I don't think that's very commonplace, but again, the more you are doing genetic tests for multiple conditions at once, the higher the likelihood there could be some kind of error or false positive that could provide unnecessary concern and distress. I think this is becoming a big issue because some of these companies, they now have a customer bases in the several million and that's just in a relatively short period of time, and so I think we're seeing much more use, access to, awareness of genetic information at the personal level, so I think that's why the time is right for us to really think through, how can we structure a regulatory system? How do we educate the public? How do we think about uses within formal healthcare systems and beyond to really harness this technology in the most effective way?
23:24 S2: Thank you for listening to this episode of Population Healthy from the University of Michigan School of Public Health. We're glad you decided to join us, and hope you learned something that will help you improve your own health or make the world a healthier place. If you enjoyed the show, please subscribe or follow this podcast on iTunes, Apple Podcast, Google Play, Stitcher, Spotify, or wherever you listen to podcasts. Be sure to follow us at UMichSPH on Twitter, Instagram and Facebook so you can share your perspectives on the issues we discuss, learn more from Michigan public health experts, and share episodes of the podcast with your friends on social media. You can also check out the show notes at our website, population-healthy.com for more resources on the topics discussed in this episode. We hope you join us for next week's episode, where we'll dig further into public health topics that affect all of us at a population level.
In This Episode
Richard G. Cornell Distinguished University Professor of Biostatistics, University of Michigan School of Public Health
Michael Boehnke’s research focuses on problems of study design and statistical analysis of human genetic data with a particular emphasis on development and application of statistical methods for human gene mapping. Learn more.
Professor of Biostatistics, University of Michigan School of Public Health
Sebastian Zöllner’s research effort is divided between generating new methods in statistical genetics and analyzing data. The general thrust of his work is problems from human genetics, evolutionary biology and statistical population biology. Learn more.
J. Scott Roberts
Professor of Health Behavior & Health Education, University of Michigan School of Public Health
Scott Roberts’ research interests focus on the process and impact of risk assessment and disclosure for adult-onset disorders, as well as the ethical, legal, and social implications of advances in genomic science and technology. Learn more.
THE HGDP PROJECT AND ITS FUTURE
Nature Reviews Genetics published a short history of HGDP-Centre d'Etude du Polymorphisme Humain (CEPH) (3). The present collection is made of 1064 cultures of B-lymphocytes from 52 populations that were already in existence. All continents except Australia are represented, but inevitably not in the way that was originally desired, with populations spread at fairly regular distances. There is some geographic clustering of the populations because some countries (China, Pakistan, Israel) have been especially active and generous in providing cell lines. The project was made possible by the kindness of research workers who donated cell lines they had collected, and by the fact that CEPH already had all the necessary equipment for growing cell lines, producing DNA, and preparing vials for distribution to laboratories, and made it available for the HGDP. The equipment had been assembled earlier for producing the human linkage maps that used the Utah and a few other pedigrees for linkage studies. Thus, HGDP-CEPH required very little money.
The original idea of the HGDP project was to collect 10,000 cell lines of 25 individuals from each of 400 populations. The Genographic Project is planning to collect 100 individuals from each of 1000 populations but just DNA from saliva and, in a small fraction of cases, blood samples. Unfortunately, it will not generate cell lines. With present technology, the analysis of 1000 individuals is still somewhat demanding for a single laboratory unless it is limited to a gene or to a sample of special markers. There is an inevitable trade-off between the number of individuals and that of markers that will be examined, mostly for economic reasons. However, we are now entering a new era, in which we can become more ambitious, but also need greater capitals.
Cultures of B lymphocytes are still the best material to collect and store. DNA will remain for some time the most important product of genome collections, but cells also offer important avenues of research into epigenetics. Their RNA and proteins can give information on the epigenetic processes, and on epigenetic evolution taking place during the history of the individual, at least for all genes relating to general maintenance and reproduction of the cell, and for specific immunological functions that occur in the B lymphocyte. Therefore, they are also informative on the immunologically significant environment of the populations.
It is important to maintain and extend the present HGDP collection. So far, the HGDP has been developed with minimum investment. There are hopes that it will be expanded, even if governmental support for science has unfortunately shrunk almost everywhere. Clearly, countries where genome studies are more likely to be fruitful in the short term are those for which it is reasonable to invest proportionately more at this stage. It is practically inevitable that the two biggest clusters of populations, one including Europe, North Africa, and west Asia including Pakistan and India, and the other East Asia, will be studied preferentially, given that these two areas have the strongest economic development, and that they also intergrade genetically into each other. But it will also be important to dedicate a reasonable part of the effort to the indigenous parts of the rest of the world—Africa, Oceania, and the Americas—otherwise recent immigrants to the more highly developed part of the world will not have access to the biomedical information available to other residents. Not only is this inequality undesirable but our understanding of human evolution will remain incomplete without adequate consideration of the other continents.