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Differences in exam performance between pupils attending
selective and non-selective schools mirror the genetic
differences between them
Emily Smith-Woolley
1
, Jean-Baptiste Pingault
1,2
, Saskia Selzam
1
, Kaili Rimfeld
1
, Eva Krapohl
1
, Sophie von Stumm
3
, Kathryn Asbury
4
,
Philip S. Dale
5
, Toby Young
6
, Rebecca Allen
7
, Yulia Kovas
8,9
and Robert Plomin
1
On average, students attending selective schools outperform their non-selective counterparts in national exams. These differences
are often attributed to value added by the school, as well as factors schools use to select pupils, including ability, achievement and,
in cases where schools charge tuition fees or are located in affluent areas, socioeconomic status. However, the possible role of DNA
differences between students of different schools types has not yet been considered. We used a UK-representative sample of 4814
genotyped students to investigate exam performance at age 16 and genetic differences between students in three school types:
state-funded, non-selective schools (‘non-selective’), state-funded, selective schools (‘grammar’) and private schools, which are
selective (‘private’). We created a genome-wide polygenic score (GPS) derived from a genome-wide association study of years of
education (EduYears). We found substantial mean genetic differences between students of different school types: students in non-
selective schools had lower EduYears GPS compared to those in grammar (d=0.41) and private schools (d=0.37). Three times as
many students in the top EduYears GPS decile went to a selective school compared to the bottom decile. These results were
mirrored in the exam differences between school types. However, once we controlled for factors involved in pupil selection, there
were no significant genetic differences between school types, and the variance in exam scores at age 16 explained by school type
dropped from 7% to <1%. These results show that genetic and exam differences between school types are primarily due to the
heritable characteristics involved in pupil admission.
npj Science of Learning (2018) 3:3 ; doi:10.1038/s41539-018-0019-8
INTRODUCTION
Achievement at the end of full-time compulsory education
represents a major tipping point in life, opening up avenues for
higher education, including university and beyond. Therefore,
understanding the potential predictors of academic achievement
at this juncture is of great importance. One such predictor that has
been hotly debated is school type. In England, when students
transition from primary to secondary school at age 11, they have
the option of attending one of three school types. Ninety-three
percent of children attend state-funded schools, the majority of
which are non-selective
1
(state non-selective). A small proportion
of state-funded schools (163 schools out of 3113 schools in
England) are academically selective ‘grammar’schools. These
schools select their intake based on achievement and ability,
assessed by an entrance exam. The remainder of students
(approximately 7%), are private educated. As well as being fee-
paying, private schools are often also academically selective.
These school types are assumed to set children on different
trajectories, with research linking selective schools (grammar and
private schools) to later success, including higher levels of
academic achievement, acceptance at university, and even higher
earning potential compared to pupils educated in non-selective
schools.
2–4
However, by design, selective schools are able to choose their
student intake based on certain pupil characteristics. This can
include selection on ability or achievement on an entrance test;
both of which have been shown to correlate positively with life
outcomes, including later academic achievement.
5,6
Furthermore,
by virtue of being fee-paying, entrance into private schools is
usually dependent on whether the family can afford it (their
socioeconomic status (SES)), which also correlates with future
outcomes.
7–10
Even for state schools, family SES may play a role in
what school type a student attends, with grammar schools
typically located in more affluent areas and attracting higher SES
students on average.
11
It is, therefore, possible that improved
outcomes for pupils in selective schools do not necessarily reflect
a higher quality of education, but may simply be the consequence
of selection—either active, as in the case of ability or achievement,
or passive, as in the case of family SES.
Given the considerable fees charged by private schools, in
addition to the potential stress of selective school entrance exams,
Received: 18 July 2017 Revised: 17 November 2017 Accepted: 9 February 2018
1
King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London SE5 8AF, UK;
2
Clinical, Educational
& Health Psychology, Division of Psychology & Language Sciences, Faculty of Brain Sciences, University College London, 26 Bedford Way, London WC1H 0DS, UK;
3
London School
of Economics and Political Science, Houghton Street, London WC2A 2AE, UK;
4
Department of Education, Psychology in Education Research Centre, University of York, York YO10
5DD, UK;
5
Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque, NM, USA;
6
New Schools Network, 3 Albert Embankment, London SE1 7SP, UK;
7
Education Datalab, 1st Floor, 11 Tufton Street, London SW1P 3QB, UK;
8
Laboratory for Cognitive Investigations and Behavioural Genetics, Tomsk State University, Lenin Avenue,
36, Tomsk Oblast, Tomsk 634050, Russia and
9
Department of Psychology, Goldsmiths, University of London, 8 Lewisham Way, London SE14 6NW, UK
Correspondence: Emily Smith-Woolley (emily.smith-woolley@kcl.ac.uk)
www.nature.com/npjscilearn
Published in partnership with The University of Queensland
why do families choose these schools? Among the many possible
reasons is superior academic achievement. The finding that pupils
at selective schools outperform their non-selective school counter-
parts in exams has been frequently reported.
2–4,12,13
At age 16,
students in the UK typically take the General Certificate of
Secondary Education (GCSE) exams. The UK Department for
Education shows that 99% of grammar school students obtain top
GCSE grades (A*–C grade) in English and mathematics, compared
to 64% for all state-funded mainstream school students.
14
However, academic achievement at age 16 is positively correlated
with the factors involved in pupil selection, such as prior
achievement, ability and SES.
6,15
Therefore, this raises the
question—are selective schools adding anything over and above
these factors in the prediction of academic achievement?
Several studies have attempted to elucidate the effect of school
type on achievement over and above factors on which schools can
select (for example,
13,16
for a review, see Coe et al.).
3
However,
many of these have not been published in peer-reviewed journals
—for example
2,3,17,18
—and we are not aware of a recent peer-
reviewed study looking at all three school types: state non-
selective, grammar and private schools in the UK. However, the
non-peer-reviewed reports support the conclusion that there are
only small academic advantages to attending a selective school,
after student factors such as achievement, ability and family SES
have been taken into account.
Traditionally, the relationship between the factors involved in
school admission and later achievement have been thought to
operate environmentally. For example, parents with higher SES
may invest more time in their children’s education
19
and can
afford more resources (e.g., more books or private tuition), which
in turn may lead to better opportunities and improved achieve-
ment. However, a less frequently investigated factor influencing
both selection factors, as well as achievement, is genetics. In the
example above, parents with higher SES are not only passing on
educationally relevant environments, but they are also passing on
educationally relevant genes, a concept referred to as gene-
environment correlation (rGE).
A vast literature from quantitative genetics has shown that
genetic factors explain a substantial amount of variance in
selection factors, including ability and achievement.
20–23
Herit-
ability estimates of general cognitive ability (g) from twin studies
range from around 30% in childhood, to 40–50% in adolescence
and approximately 60% in adulthood.
21
Twin studies also show
that much of the relationship between selection factors, such as g,
and later achievement, are substantially influenced by genetics.
22–26
Because twins typically grow up in the same family, the aetiology
of traits such as family SES, which do not vary between twins,
cannot be estimated in this way. However, heritability can be
estimated by genome-wide complex trait analysis (GCTA),
27,28
which uses DNA from unrelated individuals to estimate the
proportion of phenotypic variance explained by hundreds of
thousands of single-nucleotide polymorphisms (SNPs) genotyped
on DNA arrays. This method has also shown that genetics
accounts for a significant amount of individual differences in
family SES,
29,30
as well as gand achievement.
31–33
School type, like SES, does not tend to vary within twin pairs.
However, because GCTA requires large sample sizes, it has so far
not been possible to look at the genetic differences between
students of different school types. However, powerful genome-
wide association (GWA) studies of behavioural traits, which test
associations between specific SNPs and traits are starting to make
this possible. Although individually these SNPs, identified through
GWA studies, are of small effect, by summing their effects
together it is possible to create a genetic score for each individual
in an independent sample, which explains a substantial propor-
tion of the genetic variation.
34–36
These scores, dubbed ‘genome-
wide polygenic scores’(GPS) are a game-changer for genetic
research and have already proved insightful within the area of
educational achievement. For example, a recent study
37
using a
GPS derived from a 2016 GWA study of years of education
(EduYears)
38,39
has shown educational achievement scores at age
16 differ as a function of GPS. There was approximately one
standard deviation difference between those in the highest GPS
septile and those in the lowest; representing almost a whole
school grade difference. Furthermore, while 65% of students in
the highest GPS septile went on to university, only 37% in the
lowest septile progressed to university-level education.
For the first time, we assess differences in a polygenic score for
years of education (EduYears) between students from three school
types: non-selective, grammar and private schools. We predict that
selection involving heritable traits such as achievement, ability
and family SES will be reflected in the genetic differences between
students of different school types. Furthermore, in line with
previous literature, we expect that selection will also create large
achievement differences between students attending the three
school types, which will reduce substantially once controlling for
the selection factors.
RESULTS
Polygenic score differences between school types
Students attending different school types (state non-selective,
grammar and private schools) differed genetically, as shown by
their mean EduYears GPS (see Fig. 1, analysis of variance (ANOVA)
details in Table S1). Non-selective state school students had
significantly lower EduYears GPS scores compared to grammar
school students (t=4.87, p< 0.001) and private school students (t
=7.17, p< 0.001). These differences translate to more than a third
of a standard deviation difference (d=0.41 and 0.37, respectively).
There were no significant mean differences in EduYears GPS scores
between grammar and private school students (t=0.44, p=0.66).
There were also no significant mean differences between state
non-selective schools in varying selectivity areas (see Table S2 and
Supplementary Fig. S1).
Associations between EduYears GPS and selection factors
EduYears GPS was positively correlated with each of the selection
factors (see Supplementary Table S3), explaining 2.1% of the
variance in ability, 5.2% in achievement and 6.6% in family SES.
EduYears GPS was also positively correlated with GCSE, explaining
7.6% of the variance in GCSE scores, similar to previous analysis of
these data.
37
Because selective schools actively select for
achievement and ability and passively select for SES, all of which
correlate with EduYears GPS, we tested whether mean differences
in EduYears GPS remained once controlling for these factors.
We found that, after accounting for the variance explained by
heritable selection factors, there were no significant EduYears GPS
differences between students of the three school types: state non-
selective, grammar and private (see Supplementary Fig. S2 and
Supplementary Table S4). Similar results also emerged when we
looked at differences between state non-selective schools in
varying selectivity areas (see Supplementary Table S5 and
Supplementary Fig. S3), showing small differences in EduYears
between school types.
GCSE differences
Supplementary Table S6 and Fig. 2show unadjusted average
GCSE grades for state non-selective, grammar and private school
students, as well as average GCSE score adjusting separately for
EduYears GPS, family SES, prior ability and prior achievement, and
for all variables together. Unadjusted GCSEs between school types
mirrored unadjusted EduYears GPS results, with large differences
between non-selective and selective schools (see ‘Unadjusted
GCSE’in Fig. 2, details in Supplementary Table S6). Indeed, the
School type, all hype?
E Smith-Woolley et al.
2
npj Science of Learning (2018) 3 Published in partnership with The University of Queensland
1234567890():,;
mean GCSE score of students attending state non-selective
schools was approximately 1 SD below the mean GCSE score of
those attending grammar schools (d=1.05, 95% CIs =0.83–1.28)
and private school students (d=0.92, 95% CIs =0.75–1.09). This
translates to around a whole grade difference between average
GCSE scores for state non-selective school students and selective
school students. There was no difference between grammar and
private school students’average GCSE score (t=1.00, p=0.32).
There were also no significant differences between non-selective
schools in areas that varied in the selectivity of their schools (see
Supplementary Table S7 and Supplementary Fig. S4).
Controlling for selection factors
Controlling for EduYears GPS had a small effect on average GCSE
grades, with the GCSE variance explained by school type dropping
slightly from R
2
=0.07 to 0.06, see Fig. 2, details in Supplementary
Table S6). This relatively small effect is to be expected given that
EduYears GPS accounts for only 8% of the variance in GCSE (see
Supplementary Table S3). Controlling for family SES and prior
ability had a slightly larger effect on GCSE, in line with the GCSE
variance they account for (R² =24% and 27%, respectively). Out of
all of the selection factors, prior achievement had the biggest
impact on GCSE grades between school type, with average GCSE
for grammar schools falling from 10.12 (grade A) to 9.21 (grade B).
After controlling for prior achievement, the variance in GCSE
explained by school type dropped from 7.1 to 1.3%.
Controlling for all of the selection factors and EduYears GPS
together saw a further reduction in average GCSE between school
types, with average GCSE score for grammar (M=9.14; t=2.35, p
< 0.019) and private (M=9.32, t=6.16, p< 0.001) similar to that of
state non-selective school students’average grade (M=8.96).
Although these mean differences between school types remained
significant, they were greatly reduced. Standardised betas
Fig. 2 Plotted means (and 95% confidence intervals) for unadjusted GCSE, GCSE controlling for GPS, GCSE controlling for SES, GCSE
controlling for prior ability, GCSE controlling for prior achievement and GCSE controlling for all variables between three school types: state
non-selective, grammar and private. Note: Details can be found in Supplementary Table S6
Fig. 1 EduYears GPS plotted means (and 95% confidence intervals) between state non-selective, grammar and private school students. Note:
There were significant EduYears GPS mean differences between state non-selective school students and both grammar (t=4.869, p< 0.001; d
=0.413) and private school students (t=7.170, p< 0.001; d=0.372). There was not a significant difference between grammar and private
school students (t=0.436, p=0.659)
School type, all hype?
E Smith-Woolley et al.
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Published in partnership with The University of Queensland npj Science of Learning (2018) 3
indicated that attending a grammar school compared to a non-
selective state school was associated with an increase of just 0.03
of a standard deviation in GCSE, and for private schools, the
increase was 0.07. In addition, no significant differences emerged
between non-selective schools in varying selectivity areas (see
Supplementary Table S7 and Supplementary Fig. S4).
One of our main findings was that after accounting for the
variance explained by the selection factors and EduYears GPS, the
variance in GCSE explained by school type dropped from 7.1% to
only 0.5% (see Supplementary Table S6 for regression results).
DISCUSSION
We report genetic mean differences between students attending
three different types of school: state non-selective, grammar and
private schools. We find that, on average, students in state non-
selective schools have lower polygenic scores for years of
education (EduYears) compared to their peers in selective schools.
Furthermore, following the same pattern of results as EduYears,
there are also substantial mean differences in GCSE performance
between pupils in selective and non-selective school types.
However, almost all of these differences are explained by
heritable, individual-level factors, which schools actively or
passively use in the pupil selection process.
Although finding genetic differences between state non-
selective, grammar and private school students may initially seem
surprising, when we consider the heritable traits that selection is
based on, this difference is less unexpected. Put another way,
students with higher polygenic score for years of education have,
on average, higher cognitive ability, better grades and come from
families with higher SES, and these students are subsequently
more likely to be accepted into selective schools. This results in a
system in which children are intentionally phenotypically selected,
but unintentionally genetically selected.
However, despite finding mean genetic differences between
students of different school types, it should be noted that the
majority of the variation in EduYears GPS occurs within the school
type, not between the school types. For example, a Cohen’sdof
0.41, (the difference between mean EduYears scores for state non-
selective school students and grammar school students), which is
classed as a small-medium effect size, translates to an overlap of
approximately 83% between the two distributions.
40
Nevertheless, finding an association between genotype and
school type suggests that genetic factors are contributing to
variation in educational environments, a concept known as gene-
environment correlation (rGE). This occurs when individuals select,
modify and ‘inherit’their environment, in part based on their
genotype.
20,41
Putting our research within the context of rGE, we
suggest that in addition to students being selected into schools
based on their genetically influenced traits (evocative rGE),
children themselves also actively select educational environments
that correlate with their genotype (active rGE). In the case of high
achieving students, these environments might be challenging or
competitive academic institutions, which grammar and private
schools are often reputed to be. Finally, because we know that the
factors used in school selection are substantially heritable, it is
likely that academically gifted children will come from academi-
cally gifted parents. These parents not only provide the genes but
also the environments to help them progress academically.
As well as having a higher average EduYears polygenic score,
students attending selective schools also achieve better GCSE
results on average.
2,3,12–14,17
There has been some debate in the
literature as to the size of this achievement gap, with studies
accounting for different background characteristics in their
analysis. We find that almost all of the selective school advantage
in GCSE can be explained by family SES, achievement, ability and
EduYears GPS. After controlling for these factors, going to a
grammar vs. a state non-selective school is associated with a mean
GCSE grade increase of just 0.026 of a standard deviation and for
private schools, 0.070 of a standard deviation. Furthermore, the
variance in GCSE that school type explains falls from 7% to <1%.
Controlling for EduYears alone had a fairly small effect on
average GCSE grades between school types. However, this is to be
expected considering that EduYears GPS currently predicts
approximately 8% of the variance in GCSE—15% of the heritability
estimated by the twin design
22
and approximately one-third of
the heritable variance from SNP-based studies of GCSE at age 16.
30
The predictive nature of EduYears is likely to increase with more
powerful GWA studies. For example, there was a threefold
increase in prediction of educational achievement at age 16 from
the 2016 EduYears GPS (based on a GWA study with N=293,723)
as compared to the 2013 EduYears GPS (N=126,559).
37
Although there were only small mean differences between
school types once selection factors and EduYears were controlled
for, this does not mean that other factors are not important for
achievement at age 16. Altogether, these factors do not predict all
of the variance in GCSE (R²=0.69). As shown previously,
achievement is the result of many genetically influenced traits,
including behaviour, personality, home environment and health.
22
Furthermore, by finding a small effect of school type, we are not
saying schools are unimportant, or that teaching does not work.
Without schools, it is hard to imagine a successful education
system that allows children to reach their academic potential.
However, while schools themselves are important for academic
achievement, the type of school appears less so. Educational
achievement is not necessarily the only reason parents opt to
send their children to selective schools. A recent report on private
schools found that these students earned about £200,000 more in
their early career (between ages 26 and 42) as compared to state
school students.
2
However, this report did not distinguish
between non-selective and selective state schools. More research
is needed to see whether differences in university attendance,
career choice and earnings are still predicted by school type once
individual student factors have been accounted for. In addition to
differences in university and career outcomes, it would also be of
interest to identify potential differences between school types in
terms of non-cognitive traits as outcomes, with one survey finding
66% of parents believing that private schools ‘instil a sense of
confidence in pupils’.
2
There are several limitations to our study. First, we recognise
that there is considerable variation in schools within our three
school types—within each of the school types, there will be
examples of exceptional and under-performing schools. In
particular, there is more variance in the state non-selective
schools category as it includes most of the schools. It also includes
a wide variety of other categories, such as schools that are allowed
to select for religion and schools that are allowed to select up to
10% of their pupils for talent in specialist subjects, such as sport,
performing or visual arts, and languages. These schools are not
allowed to select directly on academic grounds. However, there is
some evidence that they do in fact select more able students.
42
Nonetheless, accounting for prior achievement and ability at age
11, before most children enter secondary school, adjusts for this.
Another limitation of the present study is access to school type.
Grammar and private schools are not evenly distributed around the
country. Therefore, in local authority areas where there are no
selective schools, the average GCSE grade of pupils in non-selective
schools may be higher and in areas where there are a greater
number of selective schools, the average GCSE grade of non-
selective schools may be lower. Because there are far fewer
selective schools, this geographical effect may potentially inflate the
average non-selective school GCSE grade. To see whether this had
an impact on GCSE differences, we split the non-selective school
group into three further groups: non-selective schools in selective
areas, partially selective areas and non-selective areas. Once we
controlled for all of the selection factors, we found that there were
School type, all hype?
E Smith-Woolley et al.
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npj Science of Learning (2018) 3 Published in partnership with The University of Queensland
no differences between non-selective schools in areas of varying
selectivity (see Supplementary Table S7 and Supplementary Fig. S4).
Afinal limitation to note is that the GCSE variable we used in
the analysis is a composite of only the three core subjects taken at
age 16—English, science and mathematics. For other subjects,
such as languages, art and social sciences, school type may have a
greater influence. However, because different school types
prioritise different subjects,
43
it is difficult to untangle the effect
of school type on optional rather than core subjects, although this
would be a useful direction for future research.
In the current study, we find genetic differences between
students attending three school types: state non-selective schools,
grammar schools and private schools. We find that selective
school students have higher polygenic scores for years of
education on average compared to students attending non-
selective schools. Furthermore, we find substantial mean differ-
ences in GCSE between school types. However, once student and
family factors have been accounted for, as well as EduYears GPS,
the type of school that a child attends explains less than one
percent of the individual differences in educational achievement
(GCSE mean grade) at age 16.
METHODS
Sample
This study included unrelated individuals from the Twins Early Develop-
ment Study (TEDS). TEDS is a large, representative sample of 16,000 twin
pairs born in England and Wales between 1994–1996 and followed from
birth to the present day.
44
Ethical approval for this study was received from
King’s College London Ethics Committee. Although there has been some
attrition throughout the years, approximately 10,000 twin pairs are still
actively involved in the study and provide rich behavioural and cognitive
data. Importantly, TEDS was and still is a representative sample of England
and Wales, as described in detail elsewhere.
44,45]
In the present study, we
included 4814 unrelated individuals (one twin randomly in a pair) who had
data present for three key variables: genotype data, educational
achievement at age 16 and school type data. This sample included 2597
females (54%) and 2217 males (46%). Of this sample, 2533 individuals also
had data present for the selection factors: ability, achievement and SES,
which included 1427 females (56.3%) and 1106 males (43.7%). For a
breakdown of sample sizes by school type, see Supplementary Table S8.
Written informed consent was given for all participants involved for each
wave of data collection.
Genotyping
For information on how the sample were genotyped and the quality
control process, please see Supplementary Methods S1.
Measures
School type. When TEDS twins were 18, they received a questionnaire that
included a series of questions asking what type of school they attended
when they took exams at age 16—the GCSEs. Respondents were asked to
indicate either ‘Yes’or ‘No’for different school types. We classified all
respondents who reported attending either a state non-selective school as
‘State non-selective’, all those who indicated that they went to a grammar
school as ‘Grammar’and all those indicating that they went to a private
school as ‘Private’. In addition to TEDS data, we also accessed school type
information through the National Pupil Database (NPD; https://www.gov.uk/
government/collections/national-pupil-database). By supplementing TEDS
data with that from NPD, our final school type numbers were: state non-
selective: n=4263, grammar: n=143, private: n=408. We also further split
state non-selective schools into three categories for follow-up analysis: non-
selective schools in fully selective areas (n=331), non-selective schools in
partially selective areas (n=905) and non-selective schools in non-selective
areas (n=3027). For more information on how and why we created these
groupings, including accuracy between data sources and selective area
groupings, please see Supplementary Methods S2.
Educational achievement at age 16. The GCSE is a standardised UK-based
examination administered at the end of compulsory education at age 16
(M=16.31, SD =0.29). Almost all students take the three core subjects:
English, mathematics and science. In addition, students are allowed to
choose a range of other subjects such as geography, history and art. These
subjects were graded from 4 (G, the minimum pass grade) to 11 (A*, the
best possible grade). In the current sample, GCSE results were obtained
from questionnaires sent via mail, in addition to telephone interviews with
twins and their parents. We further supplemented this with data from NPD.
Our analyses focused on the three core subjects: English, mathematics and
science taken by all students. Students taking science GCSE are either
awarded separate GCSEs for physics, chemistry and biology (‘triple
science’) or as one course, which is double weighted (‘double science’),
therefore, we took a mean grade of the science GCSEs. Because English,
mathematics and science grades correlated highly (r=0.70–0.82), we
created a GCSE composite. There were 3920 individuals for whom we had
both self-reported GCSE and NPD data, this composite correlated at r=
0.99 between both data sources, which supported the high accuracy of
TEDS data.
Selection factors
Socioeconomic status. Family SES was measured by taking the arithmetic
mean of five measures: maternal and paternal education (measured on a
scale from 1–8, where 1 =no education and 8 =postgraduate qualifica-
tions), occupation (indexed by the Standard Occupational Classification
(2000) on a scale from 1–9, where 1 =elementary administration and
service occupations and 9 =managers, directors and senior officials) and
maternal age at birth of first child. All measures were standardised to have
a mean of 0 and a SD of 1 and at least three measures were required to
calculate the arithmetic mean.
Achievement tests at age 11. We did not have access to selective school
entrance exams, however, before children transition to secondary school,
they are usually required to take exams, which include English and
mathematics tests. In our sample these tests comprise two English tests
(reading and writing) and three maths tests (calculator and non-calculator
test as well as a mental arithmetic test). Due to the high correlation
between maths and English scores (r=0.67), we created a composite of
these test scores requiring both to be present.
Ability (general cognitive ability, g). To measure general cognitive ability,
participants were asked to complete an online battery of cognitive tests
administered as part of TEDS testing at age 11. These tests included verbal
and non-verbal abilities (M=11.2, SD =0.69). A mean score was derived
from four tests, two verbal tests (the Wechsler Intelligence Scale for
Children (WISC) Vocabulary Multiple-Choice and the WISC General
Knowledge test)
46
and two non-verbal tests (Raven’s Progressive
Matrices
47
and the WISC Picture Completion task).
48
Data availability
For information on data availability, please see the Twins Early Develop-
ment Study data access policy. This can be found at: http://www.teds.ac.
uk/research/collaborators-and-data/teds-data-access-policy.
Analyses
Genome-wide polygenic scores. We calculated polygenic scores that were
based on the summary statistics of the largest GWA study for years of
education (N=293,723 individuals).
39
A GPS is calculated by using
information from GWA study summary statistics about the strength of
association between a genetic variant and a trait, to score individuals’
genotypes in independent samples. For each genotype in the independent
sample, all trait-associated alleles are counted and multiplied by their
effect size (i.e., their strength of association with a trait as reported in GWA
summary statistics). The sum of these weighted and counted alleles forms
a polygenic score for each individual. We used the software PRSice to
create individual GPS. Those SNPs that passed quality control were
clumped for linkage disequilibrium by applying an R
2
=0.1 cutoff within a
250-kb window. It is possible to calculate various GPS based on different
GWA study significance thresholds for genetic variants, with less stringent
p-value thresholds resulting in GPS that include more SNPs. Here, we
calculated GPS for seven p-value thresholds (0.001, 0.05, 0.1, 0.2, 0.3, 0.4,
0.5). We report analyses for the p-value threshold of 0.05 in the main text;
however, the analyses for the other p-value thesholds are reported in
Supplementary Fig. S5. We regressed all GPS on the first ten principal
components and used these standardised residuals in our analyses to
account for population stratification.
School type, all hype?
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Published in partnership with The University of Queensland npj Science of Learning (2018) 3
Mean differences. To estimate differences between the three school
types: state non-selective, grammar and private schools, we used a one-
way ANOVA with planned contrasts. In addition to the three-level school
type analysis, we also conducted follow-up analysis looking at differences
between state non-selective schools in areas with and without grammar
schools: non-selective schools in fully selective areas, non-selective schools
in partially selective areas and non-selective schools in non-selective areas.
As the sample sizes varied between groups, we used adjusted Cohen’sdto
estimate effect size. This test adjusts the calculation of the pooled standard
deviation with weights for the sample sizes.
To test the effect of school type after controlling for selection factors
(SES, prior achievement and prior ability) and EduYears GPS, we conducted
hierarchical linear regression with dummy coding. See Supplementary
Methods S3 for further information on analysis.
All methods were performed in accordance with relevant regulations
and guidelines.
ACKNOWLEDGEMENTS
We gratefully acknowledge the ongoing contribution of the participants in the Twins
Early Development Study (TEDS) and their families. TEDS is supported by a
programme grant to R.P. from the UK Medical Research Council (MR/M021475/1 and
previously G0901245), with additional support from the US National Institutes of
Health (AG046938). The research leading to these results has also received funding
from the European Research Council under the European Union's Seventh Frame-
work Programme (FP7/2007-2013)/ grant agreement n° 602768 and ERC grant
agreement n° 295366. RP is supported by a Medical Research Council Professorship
award (G19/2). S.S. is supported by the MRC/IoPPN Excellence Award and by the EU
Framework Programme 7 (602768). E.K. and K.R. were supported by a Medical
Research Council studentship. S.v.S. is supported by a Jacobs Foundation Research
Fellowship award (2017–2019). Y.K. is supported by the Tomsk State University
competitiveness improvement programme grant 8.1.09.2017. J.B.P. is a fellow of MQ:
Transforming Mental Health (MQ16IP16).
AUTHOR CONTRIBUTIONS
R.P. directs and received funding for the Twins Early Development Study (TEDS). R.P.
and E.S.W. conceived the present study. E.S.W. analysed and interpreted the data with
advice from all co-authors. R.P. supervised the project and interpreted the data. R.P.
and E.S.W. wrote the manuscript with help from all authors (J.B.P., S.S., K.R., E.K., S.v.S.,
K.A., P.S.D., T.Y., R.A., and Y.K.).
ADDITIONAL INFORMATION
Supplementary information accompanies the paper on the npj Science of Learning
website (https://doi.org/10.1038/s41539-018-0019-8).
Competing interests: The authors declare no competing interests.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims
in published maps and institutional affiliations.
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