On Race, Genetics, and Pseudoscience

The biological race concept emerges from a particular
history

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.

Describing race as a social construct does not undermine its
existence, nor its importance; it merely points out that there is no
fundamental biological basis for race.

Human population structure is not race

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 older notions of races as biological categories (some dating back
to the 18^th century) 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.

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.

Traits, IQ, and genetic diversity

‘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.

(Most genome-wide association studies
for detecting variants associated with complex traits such as IQ, known as
GWAS) 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.

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.