Identical Twins are more Similar in IQ than Personality

Identical, but Not Quite: Why IQ Marches in Step while Personality Wanders Off

It is a familiar observation: identical twins often present with astonishing similarities, not only in appearance but also in mannerism and speech. Friends and even parents may struggle to tell them apart in the early years.

Yet as they mature, the illusion of interchangeability fades. One twin excels in mathematics, the other in literature; one becomes a public speaker, the other prefers a solitary vocation. What begins in near-perfect alignment gently diverges. But how far does the divergence go, and why?

The Data

Behavioural genetics offers an empirical framework to think about these questions. Here are the observed correlations for identical (monozygotic, MZ) twins reared together:

Trait	MZ Twin Correlation (r)
Height	0.90 – 0.95
IQ (Full-Scale)	0.85 – 0.88
Big Five Personality Traits	0.40 – 0.60

The conclusion is immediate: intelligence is more tightly correlated between identical twins than personality traits. Height, as a proxy for straightforward genetic architecture, sits at the top. Personality, despite its deep integration with brain function, reveals a lower correspondence. This is not liberal ideology, merely observation.

1. Measurement Reliability

IQ tests are among the most robust tools in psychometrics. Instruments such as the WAIS-IV report test–retest reliabilities upwards of 0.95. In contrast, personality inventories, while well-validated, typically hover around 0.80–0.85 for domain scores. This psychometric limitation alone reduces the maximum possible observable correlation.

2. Neurobiological Substrates

General intelligence is strongly associated with the fronto-parietal control network. A broadly connected and globally efficient set of hubs in the brain supports reasoning, problem-solving, and learning. Minor improvements in network integrity can shift the overall performance upwards across all subdomains of IQ.

Personality traits, in contrast, arise from a mosaic of partially independent systems. Dopaminergic tone influences Extraversion and Openness; serotonergic regulation is linked to Neuroticism and Agreeableness. The anatomical correlates are more diffuse and variable. A tweak in one circuit may have little bearing on others. The architecture is simply less centralised.

3. Genetic Additivity versus Interaction

IQ is highly additive in its genetic structure. Genome-wide association studies identify thousands of single nucleotide polymorphisms (SNPs) that contribute small, independent effects. The result is a stable, predictable transmission across generations and high resemblance in MZ twins.

Personality is less cooperative. Dominance effects, epistasis, and gene–environment interactions dominate the landscape. These introduce a degree of stochasticity—twins may inherit the same genes, but the way those genes interact during development can vary.

4. Developmental Contingency and Environmental Divergence

Identical twins share a genome but not a placenta. Even intrauterine environments can differ subtly in nutrient distribution or hormonal exposure. Postnatal environments are even more idiosyncratic: peer groups, teachers, illnesses, and accidents all impose small but cumulative effects on the formation of personality. The “non-shared environment” typically explains 40–50% of variance in personality traits, compared to perhaps 20% in intelligence.

5. The Surface Illusion

In early childhood, identical twins are often treated as interchangeable. The clothing, names, routines—sometimes even the expectations—are aligned. This creates an illusion of profound sameness. However, deeper measurement reveals divergence beneath the symmetry.

Conclusion

From a neurobiological and psychometric perspective, it is no surprise that IQ shows greater concordance between identical twins than personality. Intelligence reflects centralised brain infrastructure and a dominantly additive genetic script. Personality, by contrast, emerges from a web of interacting systems, developmental contingencies, and environmental nuance.

The genome loads the dice for both, but it throws more heavily in the case of IQ. Personality is the shadow cast by chance, history and context over a shared biological template.

"Why Are Handsome Men Such Jerks?"

Jordan Ellenberg writes:

"You may have noticed that, among the men in your dating pool, the handsome ones tend not to be nice, and the nice ones tend not to be handsome.

"Is that because having a symmetrical face makes you cruel? Does it mean that being nice to people makes you ugly? Well, it could be. But it doesn’t have to be. ...

"The handsomest men in your triangle, over on the far right [the green line], run the gamut of personalities, from kindest to (almost) cruelest. On average, they are about as nice as the average person in the whole population, which, let’s face it, is not that nice. ..

The ugly guys you like, though—they make up a tiny corner of the triangle [the red line], and they are pretty darn nice. They have to be, or they wouldn’t be visible to you at all.

The negative correlation between looks and personality in your dating pool is absolutely real. But the relation isn’t causal. "

You can see that the "niceness" distribution of the "uglies" is totally bunched up at nice while the "niceness" distribution of the "handsomes" is not dissimilar to the overall population norm.

It follows that the average of the most handsome people [centre of green line] is going to be meaner than the average of the ugly ones [centre of red line].

Niceness suddenly negatively correlates with handsomeness, whereas in the overall population - by hypothesis - there is no correlation at all.

Notice you could equally ask, "Why are the nicest ones so ugly?" - and then the green and red lines are interchanged and horizontal.

This is called Berkson's paradox and is a general feature of populations where a selection is made jointly on two weakly-correlated or uncorrelated variables. The selection process itself can induce correlations where none existed previously, or even reverse previous correlations.

The effect is particularly pernicious in academic admissions (and also medical trials).

---

[h/t: Razib Khan].

Heritability, correlation and prediction

We're told that intelligence is 60-80% heritable, and that personality is 40-60% heritable. In some hand-wavy way, we know that heritability captures the nature side of the nature-nurture contribution to traits.

But what does heritability really mean? It's a rather slippery concept. We'll get there by stages.

1. The contribution of genes to a phenotype

Let's take height as our running example (pretty much the same heritability as intelligence). Let's take a person with height P (P stands for phenotype - the measured trait). P is measured in inches away from the population mean height.

How did a person get to be that height? Nature and nurture, right?

We assume that the alleles the person got from their father contributes X_father inches of height, X_mother counts the inches they received from their mother's alleles they inherited, and then there is a nurture - or environmental - term E inches. So their total height,

P = X_father + X_mother + E.

Note these are genetic additive effects: each additional allele is plausibly assumed to make its independent contribution into raising or lowering X a fraction. Dominance and epistatic effects are neglected in this simplified conceptual model (in a polygenic trait, they tend not to be large).

Since we're measuring deviations from the mean, the average values across the population of X_father, X_mother and E must all be zero. And so, therefore, must be the average value of P.

So without loss of generality, we assume X_father, X_mother and E are normally distributed random variables with mean zero and variances as follows:

Var(X_father) = V_additive/2    -- each parent provides half the additive genetic 'input'

Var(X_mother) = V_additive/2   -- each parent provides half the additive genetic 'input'

Var(E) = V_environment.

So what is Var(P), the variance of height as we observe it in the population?

Var(P) = Var(X_father) + Var(X_mother) + Var(E) +

        2Cov(X_father, X_mother) + 2Cov(X_father, E) + 2Cov(X_mother, E).

Messy, but if we assume X_father, X_mother and E are independent, their covariances are zero, so

Var(P) = Var(X_father) + Var(X_mother) + Var(E),

V_phenotype  = V_additive + V_environment

The fraction of the population phenotypic variation due to genetic, additive effects is then simply

h² = V_additive/V_phenotype = V_additive/(V_additive + V_environment).

This is the definition of heritability, h².

So if h² is 0.5, then 50% of the variance in the phenotype is genetic in origin (additive-genetic, that is) and 50% is environmental (everything else).

Note that the more you reduce environmental variance, for example making sure that everyone's well-fed, properly educated and not knocked about, the more genetic differences predominate .. and heritability goes up. Not what the SJWs really want to hear!

---

2. Correlations

What is the correlation, ρ, between a parent and child for height?

If we have two random variables, A and B, the correlation between them is defined as follows:

ρ =  Cov(A,B)/√(Var(A) * Var(B)).

This is the standard definition.

In the case of one parent and their offspring, under some simplifying assumptions,

Cov(parent,offspring) = V_additive/2

- this takes a few lines to work out, setting most of the X_father, X_mother and E cross-terms to zero. It reflects the 50% of genetic material they have in common.

More obviously,

Var(parent) = Var(offspring) = V_phenotype,

So using the formula for ρ above,

ρ = (V_additive/2) / V_phenotype = h²/2.

This shows that heritability is not the same as the correlation between a child and one of its parents.

In general, the correlation, ρ, on a trait between relatives is equal to the coefficient of relatedness times the heritability, ie ρ = rh².

---

3. Predictions

If we know the height of both the parents, what's our best prediction of the height of their offspring? In our mind, we draw the best-fit regression line on the scatter-plot of parental-midpoint and offspring heights measured across the population.

If we centre the graph-axes at the mean values of the two populations (parental mid-point heights and offspring heights) then the regression line goes through the origin, with slope β. Then the equation of the regression line takes this simple form:

predicted-offspring-height = β * parental-midpoint-height

with both heights measured as inches in deviation from the respective means.

How do we compute β?

In this special case it turns out that β equals the heritability, so β  = h². *

This should remind you of the Breeder's Equation.

---

Example: suppose the heritability of height is 0.673 and we know that one parent is 3 inches above the population mean while the other parent is 1 inch above the mean, what's the predicted (expected) height deviation from the mean for their child?

Answer: predicted-offspring-height = β * (3 + 1)/2 = 2h² = 1.35 inches.

Yes, the child has regressed towards the mean.

---

This is problem 6.3 (p. 149) from 'Population Genetics: a concise guide' by John H. Gillespie, from which all the material above has been summarised.

---

* In general, β = ρ * (σ_y/σ_x) where x is the independent variable.

Offspring IQ vs parental midpoint IQ

We had Alex and Adrian with us over the Easter and fell to talking .. as you do .. about the correlation between parental IQ and the intelligence of their offspring. I was trying to recall this post, from Steve Hsu, from which I reproduce the key material below.

There are three key ideas in Steve's post:

• You start from the parental midpoint IQ (the average of father and mother)
• To get the mean, or expected value of the offspring IQ multiply by h²  (Breeder's Equation)
• The distribution of offspring IQ has a tightened standard deviation, 12 rather than 15 IQ points.

---

"Assuming parental midpoint of n SD above the population average, the kids' IQ will be normally distributed about a mean which is around +.6n with residual SD of about 12 points. (The .6 could actually be anywhere in the range (.5, .7), but the SD doesn't vary much from choice of empirical inputs.)

"So, e.g., for n = 4 (parental midpoint of 160 -- very smart parents!), the mean for the kids would be 136 with only a few percent chance of any kid to surpass 160 (requires +2 SD fluctuation). For n = 3 (parental midpoint of 145) the mean for the kids would be 127 and the probability of exceeding 145 less than 10 percent.

"No wonder so many physicist's kids end up as doctors and lawyers. Regression indeed! ;-)
 ...
"Assuming bivariate normality (and it appears that IQ has been successfully scaled to produce this), the offspring density function is normal with mean n*h² and variance 1-(1/2)(1+ρ)h², where ρ is the correlation between mates attributable to assortative mating and h² is the narrow-sense heritability. *

"I put h² between .5 and .7. Bouchard and McGue found a median correlation between husband and wife of .33 in their review many years back, but not all of that may be attributable to assortative mating. So anything in (.20, .25) may be a reasonable guesstimate for ρ.
...

"Note: Some people are confused that the value of h² = narrow sense (additive) heritability is not higher than (.5 - .7). You may have seen *broad sense* heritability H² estimated at values as large as .8 or .9 (e.g., from twin studies). But H² includes genetic sources of variation such as dominance and epistasis (interactions between genes, which violate additivity). Because children are not clones of their parents (they only get half of their genes from each parent, and in a random fashion), the correlation between midparent IQ and offspring IQ is not as large as the correlation between the IQs of identical twins."

* Putting in the numbers: σ = 15 √(1 - 0.5 * 1.225 * 0.6) = 12.
___

The other discussion we had was how to estimate the IQ of my parents, now both dead.

Neither ever took an IQ test as far as we know. We have my mother's DNA with 23andMe so eventually with full genome sequencing we may expect to read this off (when the research ...).

We don't have my father's DNA although we might forensically get it one day via his belongings.

Still, all is not lost. Life is an IQ test and we have the biographies. We can also do some reverse correlations from the children, (my brother, sister and myself). IQ test data is not available here either, but we can still make biographical estimates.

You're similar to your spouse in so many ways ...

You already knew that spouses were correlated for intelligence.

R Plomin and I J Deary have this to say:

" ... Assortative mating is greater for intelligence (spouse correlations ~0.40) than for other behavioural traits such as personality and psychopathology (~0.10) or physical traits such as height and weight (~0.20)."

Here's what a correlation of 0.4 looks like (top right).

Marginal Revolution gets excited about this result though:

"Nordsletten and colleagues find an amazing amount of assortative mating within psychiatric disorders.

"Spouse tetrachoric correlations are greater than 0.40 for attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and schizophrenia.

"The next highest spouse correlation emerged for substance abuse (range, 0.36-0.39).

"Assortative mating was significant but far less substantial for other disorders, such as affective disorders (range, 0.14-0.19)."

They conclude:

"Beyond genetics and genomics, assortative mating matters because it means that the person closest to an individual with a psychiatric disorder is also likely to have psychiatric problems, which could exacerbate problems for both spouses and their offspring."

The ubiquity of assortative mating - who knew?

Success in life: is it coz i is nice?

Thought for the day.

"We assessed the association and underlying genetic and environmental influences among intelligence (IQ) and personality in adolescent and young adult twins.

Data on intelligence were obtained from psychometric IQ tests and personality was assessed with the short form of the NEO five factor inventory (NEO-FFI).

IQ and personality data were available for 646 twins. There were an additional 1307 twins with NEOFFI data, and 535 with IQ data. Multivariate genetic structural equation modeling was carried out.

Significant positive phenotypic correlations with IQ were seen for agreeableness (r = 0.21) and openness to experience (r = 0.32). A negative correlation emerged for neuroticism and IQ (r = -0.10).

 Genetic factors explained (nearly) all of the covariance between personality traits and IQ.

Genetic correlations were 0.3–0.4 between IQ and agreeableness and openness. The genetic correlation between IQ and neuroticism was around -0.18. Thus, personality and IQ did not appear to be independent dimensions, and low neuroticism, high agreeableness and high scores on openness all contributed to higher IQ scores."

All that stuff you were told, that personality was independent from intelligence, was so much guff. Smarter people tend to be nicer.

Self-control is also positively associated with intelligence. The famous marshmallow test purported to show that those with self-control got ahead in life. Maybe so, but wait .. they were also smarter.

---

By the way, before you complain that you know people who are nice but dim, or cite The Donald as a counterexample .. which part of the following scatter diagram are you struggling with?

Correlation here is 0.3 

Hint: think Intelligence (IQ) on the horizontal axis and a psychological trait such as Agreeableness on the vertical axis.