Summary of The Genetic Lottery by Kathryn Paige Harden

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  • Post last modified:July 7, 2024


  • Socio-economic inequalities can be predicted based on the human genome. Educational and financial success and failure, or the propensity to commit crime, can too.
  • The notion of merit is unfair since how successful one can become is genetically determined.
  • Egalitarians fear genetic data because it could be used to justify socio-economic inequalities.
  • But rather than justifying inequalities, genetic data should be used to help those in need to be helped as determined by their genotype.
  • Eugenics is about justifying the current order of the world as natural because determined by genes.
  • Anti-eugenics is about using genetic data to decrease inequalities.
The Genetic Lottery book cover

Summary: 40 min

Book reading time: 6h00

Score: 9/10

Book published in: 2021

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Table of Contents

What The Genetic Lottery: Why DNA Matters for Social Equality Talks About

The Genetic Lottery is a book written by Kathryn Paige Harden, a psychology professor at the University of Texas. It posits that the root of socioeconomic inequalities is genetic, not environmental, and that genetics should be taken seriously when measuring the difference in outcomes between (groups of) people.

Out of the hundred books I’ve summarized, this one has potentially the widest sociopolitical consequences; so big in fact, that I wrote an entire article to summarize them (will be published soon).

It’s also a difficult book. You feel the author tried to make it accessible to everyone but couldn’t quite get rid of her academic vocabulary and notions.

I’ve spent several hours on ChatGPT trying to decipher some of the genetic and statistical concepts that weren’t well-explained or explained at all.

I suppose this is why the book was not more talked about; it’s not an easy read.

I’ll give it a 9/10.

Why not 10?

Because Paige Harden was dishonest; she knows she was dishonest and her readers are smart enough to know that she was dishonest.

The book came out during a time when everyone was getting canceled, and there’s more than enough material in The Genetic Lottery to cancel its author; so she took the lead in making sure this wasn’t going to happen by being dishonest.

This isn’t how a serious scientist should do science.

In the end, she does science justice (very subtly) but still. You can’t consider your audience stupid when writing such a book.

So, it’s a 9.

Go read this book.

Summary of The Genetic Lottery Written by Kathryn Paige Harden

Part I: Taking Genetics Seriously

Chapter 1: Introduction

As human adults, we share with our children and our primate cousins an evolved psychology that is instinctively outraged by unfairness.

Life is unfair.

  • In the US, the richest people live on average 15 years longer than the poorest.
  • Children from low-income families show epigenetic signs of aging as young as 8 years old.
  • Since the 1960s, the wages of the top 0.1% of Americans increased by 400% while the wages of the workforce without a university degree stagnated.

These inequalities leave psychological marks.

However, money is not the only way people are unequal.

image 4
Correlations between rate of college completion, family income, and polygenic score.

If we compare the rate of college completion of the highest quartile and lowest quartile of family income with their respective polygenic index, we get the same result.

Inequality isn’t only a matter of education or money; it’s also a matter of genetics.

Given the heritage of eugenics, it may seem surprising to search for equality in genetics.

But the alternative is likely worse.

The tendency to ignore genetic differences between people has prevented us from moving psychology, education, and other branches of social sciences forward; from better understanding human behavior and improving human life.

The other reason is that hate groups read and use academic papers about genetics and if geneticists don’t talk about it, that these people can fill.

Chapter 2: The Genetic Lottery

Half of first-trimester miscarriages are due to genetic abnormalities.

90% of the variation in vulnerability to autism is due to genetic differences between people.

When you ask people how much variance in a trait is due to genetics, they’re quite good at estimating it.

People’s estimates of how much genetic factors contribute to human
differences (horizontal axis) versus scientific estimates of heritability from twin studies
(vertical axis).

How does this work?

Sperm fertilizing an egg makes a baby. The process of making sperm and eggs is called meiosis.

During meiosis, we remix the DNA that we inherited from our mother and the DNA that we inherited from our father, creating new arrangements of DNA that have never existed before and will never exist again.

image 6
A cell has two copies of 23 chromosomes which are the ones we inherited from our father and our mother. Before making new sperms or eggs, the two copies of chromosomes exchange genetic material to create new cells. In a way, half of your kid will be a mix of your father and mother.

Baby girls are born with two million immature eggs; they will release about 400 of those. Men though, will produce about 525 billion sperm over their lifetime.

For each sperm or egg cell, the meiotic remixing of DNA begins again.

Each pair of parents could produce over 70 trillion genetically unique offspring.

The fact that you inherited your genotype—your unique sequence of DNA —is pure luck (randomness).

You can inherit two variants of the same gene from your parents. Your eggs or sperm may carry one version, or the other, which means your kids will inherit one or the other version, making them different people.

While certain characteristics depend on one gene, others (intelligence, personality, etc) depend on several genes. They’re said to be polygenic.

Polygenic indices (scores) are tools used in genetics to estimate an individual’s predisposition to certain traits or diseases based on the combined influence of multiple genetic variants.

Another way to estimate the likelihood of getting a trait is by using a Galton board. This board lets beads bound over obstacles then place themselves somewhere at the bottom. If you use enough beads, you will get a normal distribution, also called the bell curve.

image 1
Galton board showing a normal distribution.

The bell curve is the curve showing the statistical distribution of human characteristics. As we can see, the masses are the middle and many, while there are fewer people at the tails.

normal distribution
Normal distribution,

If you make 20 embryos with 20 sperm and eggs, how far out into the tail could they go?

Most, like the beads, will fall around the middle. But sometimes, some will fall in the tails.

How further than the average one can go can be expressed using the standard deviation.

image 2
Normal distribution,

If we measure height, and you end up at 1σ, that means you will be taller than 84% of the population.

If you end up at 2σ, then you’ll be taller than 97.6% of the population.

Geneticists usually study genetic variations of children and parents or children and siblings because it makes it much easier to find out what variations are due to the environment and which ones are due to genetics (because they have broadly similar genotypes).

This entanglement of genetic differences between populations with the environmental and cultural differences between them is called “population stratification.”

Chapter 3: Cookbooks and College

In 2013, Science published a study that found three genetic variants associated with educational attainment, which remains controversial today.

While genes don’t “make” you rich or poor, genetics do influence life outcomes.

A gene is a recipe for a protein -> your genome is a large collection of recipes.

Some genes give direct instructions to make the protein, while other segments of DNA are more like annotations. But it’s not “exact” science. Just like one recipe can give different dishes, so, too, can a gene code different proteins.

Who you have become is comparable to a party in a restaurant. The food you eat (the proteins that make you) is cooked according to a recipe (your genes). But the party also depends on the people, the music, the vibe, etc (environment, etc).

As a result, the nature-nurture debate is silly: both are important.

However, small changes to the recipe (switch “salt” with “sugar”) can have asymmetrical impacts. This is the case for Huntington’s disease.

The HTT gene is a recipe for the huntingtin protein, and it contains a segment in which the same bit of DNA is repeated several times. This protein is then snipped up into small sticky pieces that glob together inside a person’s neurons.

Huntington has a simple genetic origin, but intelligence, educational attainment, or one’s capacity to earn money depend on several genes, each of which has a minuscule effect.

In the 2000s, geneticists sought to study the impacts of genes by measuring the correlation between their presence and a certain trait or outcome. That didn’t lead to the discovery of anything meaningful.

So researchers used another method called genome-wide association study (GWAS).

They studied the genetic profiles of hundreds of thousands of people, discarded what they had in common, and focused only on the genes that were unique.

The only problem is that we don’t have time to study hundreds of people individually, so we need to rely on mass measures that aren’t exactly exact.

A GWAS correlates individual elements of the genome with some measurable characteristic of people.

The individual elements most commonly analyzed are the single nucleotide polymorphisms, abbreviated SNPs (pronounced “snips”).

A DNA molecule is made up of two sugary strands, glued together with interlocking pairs of four different nucleotides —guanine (G), cytosine (C), adenine (A), and thymine (T).

A SNP is a genetic difference between people where some people have one nucleotide at a particular spot (locus) in their genome, whereas other people have a different one.

The different versions of the SNP are called alleles. The alleles that are not common are called minor.

A GWAS measures millions of SNPs in thousands of people and correlates each SNP with a phenotype, that is, something that you can measure about a person, like height, body mass index, or years of education.

A GWAS can work even by analyzing a small part of the total pool of genetic variants because the measured SNPs predict variations.

If someone has a “C” in a particular location, you can often reasonably impute that they have a “T” in another location.

Remember that when sperm and egg are created during meiosis, their chromosomes trade genetic material and recombine, leading to genetic diversity. This process is described by Gregor Mendel’s Law of Independent Assortment.

The probability of inheriting a certain version of gene A is independent of the probability of inheriting a certain version of gene B except when genes A and B are close to one another.

These genes are said to be in linkage, leading to their correlation.

We can make four observations:

  1. Educational attainment, defined as the number of years one goes to school, is like a restaurant Yelp rating. Someone with a lot of schoolyears is more likely to be employed just like a restaurant with a higher Yelp rating is likelier to remain open for longer.
  2. The expression of genes is bound to history and culture. We can’t measure women’s educational attainment when they couldn’t really go to university.
  3. GWAS doesn’t take the environment into account so it doesn’t show its influence. It doesn’t show that we understand education at the level of DNA. The correlations that GWAS detects are small, like the correlation between a McDonald’s and a Subway.
  4. GWAS will not tell you how to assemble genes to get X results.

So, why is GWAS valuable?

Before GWAS was available in the early 2000s, scientists assumed that a few genes only were responsible for things like autism or obesity.

If it had been the case, we would have found correlations pretty quickly with GWAS by solely studying a few thousand people. But we did not, so GWAS showed that the genetic reality of these phenomena is more complex.

They are polygenic—associated with thousands upon thousands of SNPs scattered all throughout a person’s genome.

Eg: height. A model calculated that over 100,000 SNPs might each have a small association with how tall you are.

As outcomes become more polygenic, the number of people you need to study, in order to differentiate signal from noise, increases accordingly.

When thousands of genetic variants are involved in a trait, tiny correlations with each one can add up to meaningful differences between people.

After you’ve conducted a GWAS, you have a long list of numbers, one for each SNP that you’ve measured, that represents the strength of the relationship between that SNP and your target phenotype.

You either have 0 relationship, 1, or 2 (we get two copies of every gene, one from the mother and one from the father).

Researchers add up these numbers into a single number (the number of copies of each SNP multiplied by the strength of its relationship with the phenotype, statistically calculated, then all of the numbers are added) that is the polygenic index (or score).

image 9
How scientists can determine the genetic origin of a phenotype.

Now, they must see if this polygenic index has a basis in reality. So they take a new population sample and measure the polygenic index of every individual, based on the blueprint they built in the first study.

If we measure educational attainment, people with low scores should be less likely to graduate and people with high scores should be more likely.

In one of these studies, 11% of people with low scores had graduated college while 55% of people with high scores had.

-> genes matter.

And we can measure how much they matter by calculating R-squared, showing how much variation between people in one thing can be explained by something else.

Eg: variation in weight can be loosely guessed by a variation in height. If, for that relationship, R-squared is 100%, it means people’s body weight is entirely dependent on their height. If R-squared is equal to zero, then it means that height none of the differences in weight are related to height.

In the US, differences in height account for about 20 percent of the variation in how much people weigh.

In samples of White people living in high-income countries, a polygenic index created from the educational attainment GWAS typically captures about 10–15 percent of the variance in outcomes like years of schooling, performance on standardized academic tests, or intelligence test scores.

For example, the R-squared of the tendency of richer people to graduate from college is 11%.

In general, people should lower their expectations of causation and correlation.

Chapter 4: Ancestry and Race

If you go back up your family tree, your ancestors double every generation.

If you go back to 33 generations, you reach 8 billion people which isn’t possible because there weren’t 8 billion people at the time.

So it means that some of your ancestors appear several times.

The question is: how long ago in human history was the most recent common ancestor of all humans? Someone that would be in everyone’s family tree today.

And the answer is: between 1500 BC and 50 AD. If you go back 5000 BC to 2000 BC, everyone alive then who had children is a common ancestor of everyone alive now.

Now. Your genealogical ancestors are not necessarily your genetic ancestors.

Excluding the sex chromosome, you got 22. In making the sperm cell that became you, the chromosomes that your father got from his mother and father traded chunks of genetic material, in order to produce a new DNA sequence that is uniquely yours.

image 6
A cell has two copies of 23 chromosomes which are the ones we inherited from our father and our mother. Before making new sperms or eggs, the two copies of chromosomes exchange genetic material to create new cells. In a way, half of your kid will be a mix of your father and mother.

33 of these recombination events occur every time a genome is transmitted to the next generation.

So, the 22 chromosomes that you inherited can be broken down into 22 + 33 = 55 different chunks (from one parent only), each of which can be traced back to one of your two paternal grandparents.

If you go back to another generation, it will be 22 + 66 = 88. For both parents, it’s 176.

If you go back 42 generations, you get 2,750 chunks that can be traced to two trillion ancestors (2 power 41).

Obviously, you don’t have two trillion ancestors.

Yes, you still have more ancestors than DNA, so the chance that parts of DNA from ancestors of nine generations ago are still in yours is…small.

This helps us make sense of two paradoxical facts:

  • If you go back a few thousand years, everyone’s family tree converges.
  • People who live in different parts of the world are genetically different from one another, and these genetic differences can be very old, much older than a few thousand years.

You only inherited DNA from a few people of all of your genealogical ancestors. And most of them reproduced in geographical proximity to one another. This creates groups that have genetic material in common.

-> genetic variation in humans has structure, which means there are patterns in how your genetic heritage resembles/diverges from the genetics of other people.
-> these patterns reflect both geography and culture.

The patterns of genetic similarities and differences reflect geographical differences. Hence you can group people per genetic similarities based on where they lived, which resembles a lot the idea of races which scientists have replaced with “ancestry”.

And yet, you can’t do race-based politics because while you can distinguish broad patterns if you zoom out, it’s much more complicated to distinguish them by zooming in to the individual scale.

Furthermore, the categorization of races is biased (in the US at least). White people had to have two white parents; half-white half-black were considered black.


  1. Race isn’t so much of a genetic construct as it is cultural, historical, and geographical. Are Italians white? Are there any differences between Irish and Swedish whites?
  2. The definition of race does not correspond to genetic differences in a straightforward way. Some African groups are more genetically different among themselves than Europeans and East Asians, despite that the former look the same while the latter look differently.
  3. Anyone from any race can have ancestors from any geographical location, which makes it hard to group people in races, from a purely genetic standpoint.
  4. Ancestry can be quantified very precisely, leading to a certain diversity not conveyed through the race lens.

Scientists use PCA (principal components analysis) to analyze patterns of genetic similarity between people. They produce a set of ancestry-informative principal components, or “PCs.”

These variables are not a yes or no, but continuous (low, middle, high).

Therefore: ancestry is process-based: it’s about looking at the past. Race is pattern-based: it’s about looking at physical similarities in the present.

Understanding how socially defined racial groups differ in their genetic ancestry helps us see why modern “race science” is actually pseudoscience.

Populations differ genetically due to variants; a rare genetic variant in one population can be common in another.

African populations have the greatest genetic diversity. A genetic variant that may be important in X population doesn’t necessarily have an impact on another one.

Eg: a mutation responsible for over 70 percent of cystic fibrosis cases in European populations is responsible for less than 30 percent of cases in the African populations.

What you discover in one group isn’t expected to apply to another group, and if you study a different group, you might discover different genes.

Polygenic indices for whites cannot be applied to blacks.

The geneticist David Reich said that we needed to prepare society for the upcoming streams of genetic data that will likely reveal that structural race inequalities are as much societal as they are genetic.

However, even if it’s true, that doesn’t mean we can’t do anything about it.

To summarize: genes are great for understanding individual differences, not so great for understanding racial differences.

Chapter 5: A Lottery of Life Chances

GWAS studies found that European children with certain genetic variants did better in school, but does that mean that the genes caused educational attainment? Correlation and causation are two different phenomena.

In 1966, Romania outlawed contraception and abortion amid a demographic crisis. 500,000 babies were given up for adoption.

When the government fell and the orphanage was accessible, those who went to see it discovered immobile and completely silent kids.

A group of US scientists decided to test if the kids could be saved by being provided the caretakers they hadn’t had.

After a few years, they measured the children’s IQs. Those who had grown up in a foster family had an average IQ of 81, while those who had remained in foster care had an average IQ of 73.

Conclusion: there was a difference.

The scientists readily concluded that growing up in a foster family caused higher IQ. But did it?

David Hume defines causality as:

  1. One thing (y) that happens after an initial thing (x)
  2. One thing (y) that doesn’t happen without (x)

David Lewis further defined causality as something that makes a difference from what would have happened without it.

Phrased otherwise, a cause is what would have been if it had not existed in the first place.

Unfortunately, this isn’t something we ever get to see.

Science solves the problem of causal inference by comparing outcomes between people who have experienced X and other people who have experienced Not-X.

But this works only if you can isolate X from everything else – which is impossible.

This is why randomness is important.

If we select people into two groups at random and both groups clearly differ after the experiment, there must be an impact there.

Therefore, a genetic cause is a cause that makes a difference, and the difference it makes must be a difference from what would have happened without it.

Now, it’s not because there is a cause somewhere that we know how the mechanism works.

Second, identifying a cause does not mean that the cause determines the effect, but that it raises the probability that the effect will occur.

Finally, studies must be replicable to have their conclusions validated, and the orphanage study isn’t replicable.

All scientists need to name an effect “cause” is to know that the effect influences the average outcome for a group of people.

This is where we distinguish thin causes from thick causes. Tick causes are things such as having three copies of chromosome 21.

We know that it is the single cause of Down Syndrom.

Chapter 6: Random Assignment by Nature

Sibling studies are great because they will share certain genes and not others and the same environment, so we can study the role of those genes or (their absence)

We can study this with identity by descent.

When the body makes sperm and eggs, it switches chunks of maternal and paternal DNA so the children are never carbon copies of their two parents.

What does that mean?

Your mum’s DNA is made up of 23 pairs of chromosomes, that is, 46 chromosomes in total. 23 come from her father, and 23 come from her mother.

As we said, during the production of eggs, the 23 pairs are going to switch DNA material and recombine, then split.

Now, the egg carries just 23 chromosomes resulting in the recombination of the DNA of your mum’s father and mother.

These 23 chromosomes will combine with your father’s 23 chromosomes to give…you.

Therefore, there’s a 50/50 chance that you inherit the same chromosomes as your siblings.

That’s how a study came to conclude that 80% of the variation in height was due to genetics. Scientists asked if siblings who inherited more different genotypes also showed greater dissimilarity in height.

The answer was yes.

The 80% ratio was obtained from the following formula: how different siblings are in their height because they inherited different genes / how different people are in their height generally.

This ratio is called heritability.

It means that 80% of the differences in height within the population being studied were caused by genetic differences between people.

The other way we can look at it is by comparing fraternal and identical twins.

The first ones have different DNAs, like brothers and sisters; the second are the same. The more different fraternal twins will be on a given trait compared to identical twins, the higher the heritability of that trait.

In 2015, a paper in the journal Nature Genetics summarized fifty years of twin research.

The author chose seven different life domains and looked at how heritable they were.

image 4
Rate of heritability of seven life domains.

As we can see, identical twins always correlate more than fraternal twins.

The greater the distance between the fraternal twin correlation and the identical twin correlation, the greater the heritability.

Anywhere from 25% to 50% of variations in each trait are genetically inherited.

When people inherit different genes, their lives turn out differently.

Now of course, some people will say twin studies should be taken with a pinch of salt because the environment the twins grow up in is also similar.

In the case of years of education, the genes identified in the GWAS together in a polygenic index account for 13% of the variance in educational attainment – not 40%, or 76%.

However, the GWAS doesn’t measure every genetic variant and the sample size, even of 1 million people, is probably not big enough to detect genes with a weak yet non-zero effect.

So while twin studies may overestimate the genetic effects, the GWAS may underestimate it.

The difference between those two is called the missing heritability problem. Real heritability variance is likely in between what GWAS and twin studies detect.

The sibling regression method remains interesting to get a measurement closer to reality.

Or, you can also use a GWAS in a family then build a polygenic index and apply it to the same family.

Three types of within-families studies have been done to investigate the effects of genes on life outcomes.

  1. Sibling comparison studies. The question asked was do siblings who differ in their polygenic index differ in their life outcomes? The answer was yes.
  2. Studies comparing adoptees and non-adoptees. A polygenic index was associated with educational attainment in adoptees but it was weaker than in non-adoptees.
  3. Studies of parent-offspring trios. Parents’ genes and their outcomes are compared to their children’s genes and their outcomes.

When we put together results from fifty years of twin research with results from just a few years of research using measured DNA, the inescapable conclusion is that genetic differences between people cause social inequalities.

Chapter 7: The Mystery of How

While heritability provides information on whether genes cause a phenotype, we don’t know how it works.

If you decide to forbid red-haired people from going to school, it will appear that red-haired genes lead to low IQ.

Such a conclusion is ridiculous.

And yet, this exercise leads to the discussion of three ideas.

  1. Causal chains: genes make hair red and social policies prevent them from attending school.
  2. Levels of analysis: you should not study red-haired’s low literacy rate at the genetic level, but at the societal level.
  3. Alternative possible worlds: the illiteracy of red-haired is not caused by genes but by social policies.

Eugenists assume that the causal chain between genes and social inequality is short and mediated by intelligence and that this social inequality is best understood at the cellular level.

Conceptualizing the links between genetics and social inequality in terms of a short, biological, and universal causal chain saps political will to address inequality.

Ultimately, ideology is the wrong tool to settle this question.

What science knows about it so far is:

  1. The genes relevant for education are active in the brain, not in the hair or skin of people.
  2. The mechanisms linking genes to education start very early in development, before a child is even born.
  3. The genetic effects on educational success are mediated by intelligence as measured by standardized tests…
  4. …as well as by “non-cognitive” skills.
  5. Genetic effects are influenced by the interaction between people and their institutions.

Let’s analyze each of those.

Genes and the Brain

Different cells need to do different things, so they turn different genes on and off which enables scientists to see which genes are used where.

Genes associated with educational excellence are expressed within the brain as neurons. The top genes are involved in neuron communication.

Genes Start Their Effects Very Early in Development

Different genes are active at different points in life. Genes responsible for growth are necessary in infancy but not in adulthood.

Some of the genes associated with educational attainment are expressed when the child is still an embryo, as his brain and nervous system are still being formed.

Genetic effects on intelligence usually emerge at 2 years old.

Genetic Effects Involve Basic Cognitive Abilities

Executive functions (EF) are a set of different tasks children get tested on.

EF is also nearly 100% genetically inherited.

Children who have higher general EF are better at regulating their attention, can stop themselves and can shift from one rule to another.

It also correlates well with academic achievement (0.4 – 0.5).

Twin studies have long found evidence for genetic influences on basic cognitive abilities.

Genetic Effects Involve More than Intelligence

Over the last 20 years, books like Grit or Mindset highlighted non-cognitive skills such as “grit, curiosity, conscientiousness, optimism, and perseverance” as important factors to succeed.

But they did not hold scientific scrutiny.

Indeed, these traits are genetically inherited and can’t just be “developed”. Furthermore, they are not synonymous with performance on standardized tests of cognitive ability or academic achievement.

To study if some non-cognitive skills could influence academic achievement, researchers took two people whose IQ was similar but who differed in their academic achievement.

The genetics of non-cognitive skills related to greater educational attainment were associated with a wide variety of different types of things:

  • Openness to experience
  • Gratification delay
  • Later childbearing
  • Less risk-taking behavior

-> Non-cognitive skills really are skills and many are genetically associated traits and behaviors contributing to going further in school.

However, non-cognitive skills were correlated with a higher risk for schizophrenia, bipolar disorder, anorexia nervosa, and obsessive-compulsive disorder.

-> some non-cognitive skills do help you go further in school but they’re genetically influenced.

Genetic Effects Involve Interactions Among People

Genetic effects on cognitive abilities, openness, and orderliness only get stronger over time.

Why do genetic influences get stronger over time if children get more experienced?

Because interaction with the environment can reinforce the genetic effect.

Intelligence, curiosity, motivation, and self-discipline are not “just there” but unfold over time as a result of the interaction of children and their environment.

Eg: children who were more cognitively advanced also received more cognitive training from their parents. Cognitive stimulation was the only trait associated with parents’ genetics and correlated with educational achievement.

In the US, pupils who take advanced math courses are authorized to apply to STEM programs at university while pupils who do not, are not.

This is an interesting example of genetic (math ability) and environmental selections (like the red-haired example we saw above), leading to genetic stratification in education. From there, we see path dependence.

At the same time, students with low polygenic indices are more likely to drop out of math every year.

In the 1970s, it was becoming clear that genes did have an effect (on) academic achievement, intelligence, income, psychopathology, health, and well-being.

Part II: Taking Equality Seriously

Chapter 8: Alternative Possible Worlds

Since Francis Galton, eugenic thinkers have steadily and successfully engaged in a misinformation campaign, convincing people that the reimagination of society is futile.

Eugenic enthusiasts consider that since genes dictate social conditions, the only way to change society is through genetic editing, not social institutions.

Such a principle is based on a misunderstanding of the relation between genetic causes and environmental interventions.

Genetics can cause stratification in society, and measures to address systematic social forces can be effective at enacting social change.

Eg: genetics cause bad eyesight; glasses come to rectify it.

Genetic determinists believe that glasses are impossible to fabricate. But it’s not. We can build an environment that helps decrease genetically-caused differences between people.

This leads to two questions:

  1. How have social and historical contexts differed in ways that change the relationship between genotype and phenotype?
  2. Looking forward to the question of policy, what do we want the relationship between people’s genetics and their outcomes to look like?

Leveling Down: When the Worst Environments Produce the Most Equal Outcomes

Estonia’s biobank enabled researchers to notice that there was little genetic correlation with educational attainment in the Soviet era, at least compared to after.


Because students did not have the choice to attend school or not.

Expectantly, genes make more of a difference in a meritocratic society with high social mobility than not.

However, the heritability of IQ is lower in poorer societies, so money does play a role.

-> if the social system does not give your genes the opportunity to succeed, they won’t. So, the environment does matter. (Aure’s note: genes don’t need the presence of a good environment to express themselves, but the absence of a negative environment.)

So, we want to give a great environment for everyone to flourish. The risk, of course, is to see genetic inequalities fully expressing themselves, leading to even bigger inequalities among people.

In a society where everyone’s poor, everyone’s equally poor. When you give people the freedom to get rich, some will be richer than others.

Equality versus Equity

As we saw, high heritability is a sign that the environment people evolve in is positive. It can be deemed to be a component of a utopian society, one where environmental differences have been erased to leave only genetic differences.

But why would genetic inequalities be more tolerable than environmental inequalities?

They’re not, which is why the purpose of educational programs is now equity. Equity doesn’t mean that everyone gets the same result or participation trophy; it means that everyone starts on the same ground, as this drawing illustrates.

image 5
Equality VS equity.

The liberal idea of equal treatment … guarantees that the social order will reflect and probably magnify the initial distinctions produced by nature and the past.

Thomas Nagel

While some interventions can level the playing field for everyone, some make it worse.

Eg: taxing tobacco. This mainly discourages smoking those who are the least genetically at risk while increasing the health and economic burden on those who are the most genetically at risk of smoking -> Matthew effect (those who are advantaged will be more advantaged, and those who are disadvantaged will be more disadvantaged).

So, we have three designs of society:

  1. One that levels down to reduce inequalities (communism, everyone’s poor).
  2. One that invests more in those with genetic risks at the expense of those with none (the equity society).
  3. One that levels up the playing field for everyone, thereby advantaging more those who are already advantaged while failing to help the other people (the equality society).

Which, among 2 and 3, is better?

Policy remains blurry in this regard, partly because scientists don’t dare play around with genetics, too scared to find results in favor of eugenics.

Yet blinding ourselves to genetic data doesn’t make genetic differences go away.

Questions about whether the educational system promotes equity (1) and whether it should (2) are sensitive because differences in educational attainment correlate with many other forms of inequality such as wealth, physical health, and psychological well-being.

And while education is one of the rare ways offered to those who are genetically at risk of suffering from inequality later on, this shouldn’t be the case as this penalizes or infers that those without a degree are second-class citizens, or useless to the economy, which isn’t the case (economic determinism).

Furthermore, not everyone will agree on what type of actions to take. Related to options 2 or 3 above, some will argue that society will benefit more by investing resources to help the top talents, while others will prefer investing in helping the bottom reach the middle ground.

The reason why many fear meddling genetics with socio-economic inequalities is that they’re afraid that we find that nothing can be done about them.

But this isn’t the case. The environment, as the eyeglasses example showed, doesn’t have to express those inequalities but can help level them up.

Chapter 9: Using Nature to Understand Nurture

Many scientists have protested against the use of genetic data to solve inequalities because they believe that it’s a waste of time and that we already know what to do to solve them, and that what we lack isn’t knowledge but commitment to social justice.

Others fear the dataification of inequalities.

However, this isn’t incorrect. In regards to intervention in education, the literature reviews have concluded that the large majority of interventions evaluated produced weak or no positive effects compared to usual school practices.

This is the case for most intervention programs, be it drug-taking or other risky behaviors. They impact what students know but not how they behave. Put otherwise, they don’t work.

Why are human problems so difficult to solve?

  1. Human behavior is embedded in complex systems at multiple levels of analysis (from cellular to nations).
  2. The rules that govern societies and behavior vary by local conditions.
  3. Human psychology and behavior involve concepts that are difficult to quantify (eg: happiness).

So, why do we need genetics?

Texas teaches its teenage students that sex outside of marriage leads to psychological trauma, based on studies.

But the studies only showed that those who had sex earlier in life fare worse later in life, not that having sex caused fairing worse in life.

After looking at how twins who had had sex at different ages faired in life, it was concluded that early sexual relationships did not, in fact, have an impact on life’s satisfaction later on but that the genes that caused early sexual relationships and life satisfactions were the same.

And that’s when people agree on what they know.

The word gap was a study that found that children of poor families had heard 30 million words less than children of rich families and that it was why poor children didn’t express themselves as well as rich children.

So philanthropies jumped in to solve the problem of “the word gap”.


  1. The study’s conclusion was shaky, to say the least.
  2. Studies that aimed at replicating the findings couldn’t do it.
  3. Some say it was simply false (poor children didn’t suffer from a word gap).
  4. Genetics are likely at play.

Indeed, children of parents who speak well are likelier to speak well too than children of parents who do not.

So the cause is probably not environmental as much as it is natural.

Phrased otherwise: talking more to poor children won’t help bridge the word gap.

Rather than addressing the problem from a genetic perspective, scientists would rather pretend genetic influences don’t apply to them.

Currently, many quarters of social science still practice a kind of epistemological tacit collusion, in which genetic confounding potentially poses significant problems for inference but investigators do not address it in their own work or raise it in evaluating the work of others.

Jeremy Freese

This has led to thousands of studies being published while being incorrect.

When social scientists routinely fail to integrate genetics into their models of human development, they leave space for a false narrative that portrays the insights of genetics as a Pandora’s box of “forbidden knowledge.

Genetics is important because it helps you understand which type of environment has an impact, and which does not.

Of course, at the cost of admitting that the problem is genetic, and not environmental.

Chapter 10: Personal Responsibility

If genetics is such an influence, how can we hold people accountable?

Genetic association with behavior is interesting but it can be a double-edged sword.

In the case of a trial for violence, pleading poor genomes might make the defendant appear less responsible, and hence non-guilty, but also more dangerous to society as uncontrollable, and so likelier to go to jail.

While genes almost never change the conclusion of a jury, the evidence that they play in how violent someone is, is strong.

Serious behavioral problems beginning in childhood, physical aggression, and emotional callousness are all part of a syndrome of antisocial behavior that is already highly heritable (>80%) in childhood.

There is also emerging genetic data relevant to the likelihood of criminal offending.

Now, the problem with this is that people are quick to blame others differently according to what we blame them for.

Considering which part of responsibility people have in the following knowing that it’s influenced by genes:

  • Obesity
  • Committing a crime
  • Mood

A study made on this found that people are more likely to endorse genetic explanations of prosocial behavior than of antisocial behavior, citing responsibilities for the latter.

So, should we use genetics to decrease the amount of responsibility people are supposed to take for their actions?

There isn’t a clear-cut answer to that question. It is for us to decide, and we decided that while eye color was none of your fault, crime definitely is.

The author does believe that genetics should be taken into account, though.

To what extent genes are taken into account is determined by free will, measured as follows: in the presence of the same environment and genotype, could you have done something different? (Monte Carlo generator).

Your free will determines the extent to which environment and genes play. When it comes to height or hair color, free will has nothing to say.

We can observe what people have a choice over and what they do not by looking at twins. The extent to which twins are different is probably our average free will to us all.

And…variations about twins are small, almost non-existent.

So…while the question “could you have done differently” is impossible to answer, “no” is likely the answer.

When reviewing twin research from this perspective, with an exclusive focus on e2, the nature-nurture debate melts away.

It’s all nature.

Conservatives are less likely than liberals to believe and agree that success is due to luck or genes.

In general, people are likelier to vouch for redistribution when inequalities stem out of luck than not.

But acknowledging the role of genetics is also detrimental to egalitarian values and it reminds people of the idea that some people are more valuable than others.

However, they tend to forget that genotype is itself, the result of luck.

Hence: Genetics is a matter of luck in people’s lives. Appreciating the role of genetic luck in people’s educational and financial success undercuts the blame that is heaped on people for not “achieving” enough and might, in fact, bolster the case for redistributing resources to achieve greater equality.

And the other way around: rejecting genetic influence will lead to increased blame on those who cannot do any better.

Chapter 11: Difference Without Hierarchy

Just as genetic differences between people create differences between them in their likelihood of developing speech problems, so, too, do genetic differences between people create differences between them in their likelihood of being homeless.

While it’s not controversial to say that speech problems can be caused by genes, it is to say that homelessness can too.


Because it justifies inequalities and assumes that some people are more valuable than others. It leads people to think that if your genes make you x, then you should be x.

This is the eugenist ideology, so egalitarians would rather consider that genetic differences simply don’t exist.

The problem with genetic differences is that we directly assume that they lead to a hierarchy. They don’t. People with blue eyes are not superior to people with green eyes.

We should speak about genetic differences between people without automatically putting them in a hierarchy.

But it is difficult, especially with genetically-caused traits like intelligence, equaled with intrinsic human value.

Socially Valued, Not Inherently Valuable

Equating intelligence with worth comes from eugenists who identified human worth with qualities they themselves possessed like intelligence.

Norms, which are averages of large samples, do tell you about where one stands in regards to the general average but it doesn’t tell you why and it doesn’t measure that for which it wasn’t designed (an IQ test doesn’t measure how fast you can run).

When IQ scales were created, scientists realized that some children could do more and better than other children.

It quickly became a test of “primitiveness” in the early 20th century USA, which is problematic.

And yet, IQ tests are too precious to be gotten rid of, since IQ correlates with so many other things (grades, average lifespan, earnings, etc).

Like a measure of a child’s speech impairments, intelligence tests don’t tell you that a person is valuable, but they do tell you about whether a person can do (some) things that are valued.

Good Genes, Bad Genes, Tall Genes, Deaf Genes

Saying you got “lucky” with your genotype means that you inherited positive features, such as height, for example.

But the notion of “good” and “bad” should not be applied to DNA.

Eg: the Deaf community, which by now, has created its own culture in the US.

In the early 2000s, Candace McCullough and Sharon Duchesneau, who were both born deaf, chose a sperm donor who was also deaf so that they could have a deaf child.

The purpose was to signal that they did not consider deafness a handicap, but a simple difference.

Advances in medicine like pre-implantation genetic diagnosis (PGD, embryo selection) help couples test for certain genetic markups of an embryo and choose the one they want while discarding the others.

Most of the time, parents choose for what they consider positive qualities.


A landmark survey of fertility clinics in the US found that a small number of clinics (3%) admitted to using PGD to help parents select embryos for a disease or disability. Similarly, a survey of Deaf parents found that a small minority would consider terminating a pregnancy if a genetic test found that the fetus would be hearing.

Negative selection of traits using PGD is illegal in the UK. The Deaf community was unhappy as the decision considered them to be abnormal.

The example shows how we don’t necessarily have to see genetic differences as negative or positive, but as simple differences.

As John Rawls argues, there’s no just or unjust in the natural distribution of genetic traits; what is unjust is the way institutions use these differences to create a hierarchy.

Like with the Deaf, autistic people might struggle to do daily life but can be still imbued with rare traits valuable to certain companies or organizations.

People are not the problem to be fixed. The problem to be fixed is society’s recalcitrant unwillingness to arrange itself in a way that allows them to participate.

Chapter 12: Anti-Eugenic Science and Policy

The fear of eugenics is that genetics could be used to establish a “hierarchy of human beings, ranked according to intrinsic worth” which will produce “inequalities in the distribution of freedoms, resources, and welfare”.

Assuming we’re not using genetics for eugenics, what should we do with it?

First, we should not pretend it doesn’t exist. Failing to recognize genetics as a force creating inequalities prevents us from engaging with and solving these inequalities, which is exactly what the eugenists want.

Creating a just social order requires anti-eugenics, not gene-blindness.

Anti-eugenics is based on five points:

  1. Stop wasting time, money, talent, and tools that could be used
    to improve people’s lives.
  2. Use genetic information to improve opportunity, not classify
  3. Use genetic information for equity, not exclusion.
  4. Don’t mistake being lucky for being good.
  5. Consider what you would do if you didn’t know who you would

Stop wasting time, money, talent, and tools that could be used to improve people’s lives.

  • Eugenic: Use genetics as a cause for inequalities to justify not intervening.
  • Genome-blind: Ignore genetic differences even if it wastes resources and slows down science.
  • Anti-eugenic: Use genetics as a cause for inequalities to decrease them and to improve people’s lives.

We should stop funding research desperately trying to find environmental influences over highly heritable traits when none exist as it is a waste of time and money.

Use Genetic Information to Improve Opportunity, Not Classify People

  • Eugenic: Classify people into social roles or positions based on their genetics.
  • Genome-blind: Pretend that all people have an equal likelihood of achieving all social roles or positions after taking into account their environment.
  • Anti-eugenic: Use genetic data to maximize the real capabilities of people to achieve social roles and positions.

The geneticist Robert Plomin proposed to grant people jobs and educational positions based on their polygenic scores.

But it is neither feasible (while you can use genetics to predict the average life of a group of people, you can’t do so yet to predict one person’s life) nor ethical (it will lead to considerable exclusion).

Furthermore, a polygenic algorithm will necessarily predict the future based on the past, and can be hence manipulated or flawed because they don’t necessarily resemble one another.

Eg: while educational attainment and parental income are correlated, it doesn’t mean that all kids from low-income families will fail school.

The relationship between family income and college completion is a problem to be solved, an inequality to be closed, not a result to be leveraged to further exclude low-income students.

Plomin and Murray backed up their argument by saying that picking up people based on their DNA was fair and non-racist because nobody could fake their DNA.

The problem is that polygenic indices can pick up on any genes associated with educational outcomes, regardless of what mechanism (genetic or environmental) created that association.

The anti-eugenic use of polygenic indices is to use them to better orientate students at school, for example.

Indeed, the best schools are measured by the average grade of their students -> the best schools don’t have the best teachers, but the best students.

On the other hand, the worst school could have the best teachers, but we don’t know that because the students there aren’t really good.

And since students and teachers are all different, it’s nearly impossible to measure school impacts on students.

Luckily, this would be possible to measure with a polygenic index since they’re a reasonable tool to predict a student’s educational outcome.

The students with high polygenic indices fairing badly in a certain school would indicate that the school isn’t really good.

Use Genetic Information for Equity, Not Exclusion

Like above. Eugenics would use polygenic scores to grant or deny insurance based on health risks.

Anti-eugenic would do the exact opposite.

Don’t Mistake Being Lucky for Being Good

Genes is a lottery. It’s not because your IQ is higher than others that you have more merit, or that you are better.

The anti-eugenic position asserts that no one deserves their success or failure since these are downstream of genes.

There is no measure of so-called “merit” that is somehow free of genetic influence or untethered from biology.

You cannot connect people’s virtue, righteousness, or moral deservingness to their biology. To say that people deserve more because of their genotype is inegalitarian.

Meanwhile, gene-blindness accepts meritocracy without considering the role of genetic luck. It perpetuates the myth that the winners deserve what they have because they “worked harder”.

Recognizing the role of luck is hence the basis for building a fairer society.

That doesn’t mean we should drop selection criteria; we want pilots and doctors to be selected for their ability to do their jobs. We just need to acknowledge that being pilots and doctors doesn’t make them more virtuous.

Consider What You Would Do, If You Didn’t Know Who You Would Be

  • Eugenic: Biologically superior people are entitled to more.
  • Genome-blind: Everyone should have the same regardles of their biology.
  • Anti-eugenic: People with lesser ideal genes should receive more help.

Imagine you need to design a society without knowing who you will be in this society.

Most people would design it as egalitarian as possible. That’s how you should go about in your daily life.

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