Ewan Birney, Jennifer Raff, Adam Rutherford, Aylwyn Scally
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.
Conclusion
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.
Ewan Birney
European Molecular Biology Laboratory, European Bioinformatics Institute
Jennifer Raff
Department of Anthropology, University of Kansas.
Adam Rutherford
Genetics, Evolution & Environment, University College London
Aylwyn Scally
Department of Genetics, University of Cambridge