Predicting educational achievement from DNA

Abstract

A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N=127,000), explained 2% of the variance in total years of education (EduYears). In a follow-up study (N=329,000), a new EduYears GPS explains up to 4%. Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16. Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social and behavioral sciences.Molecular Psychiatry advance online publication, 19 July 2016; doi:10.1038/mp.2016.107.

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ORIGINAL ARTICLE
Predicting educational achievement from DNA
S Selzam
1
, E Krapohl
1
, S von Stumm
2
,PFO’Reilly
1
, K Rimfeld
1
, Y Kovas
1,2,3
, PS Dale
4
,JJLee
5
and R Plomin
1
A genome-wide polygenic score (GPS), derived from a 2013 genome-wide association study (N= 127,000), explained 2% of
the variance in total years of education (EduYears). In a follow-up study (N= 329,000), a new EduYears GPS explains up to 4%.
Here, we tested the association between this latest EduYears GPS and educational achievement scores at ages 7, 12 and 16 in an
independent sample of 5825 UK individuals. We found that EduYears GPS explained greater amounts of variance in educational
achievement over time, up to 9% at age 16, accounting for 15% of the heritable variance. This is the strongest GPS prediction to
date for quantitative behavioral traits. Individuals in the highest and lowest GPS septiles differed by a whole school grade at age 16.
Furthermore, EduYears GPS was associated with general cognitive ability (~3.5%) and family socioeconomic status (~7%). There was
no evidence of an interaction between EduYears GPS and family socioeconomic status on educational achievement or on general
cognitive ability. These results are a harbinger of future widespread use of GPS to predict genetic risk and resilience in the social
and behavioral sciences.
Molecular Psychiatry advance online publication, 19 July 2016; doi:10.1038/mp.2016.107
INTRODUCTION
Identifying the genetic variants responsible for the ubiquitous
heritability of behavioral dimensions and disorders is transforming
genetic research in the social and behavioral sciences by making it
possible to predict genetic strengths and weaknesses of
individuals from DNA alone.
1
Over the past decade, genome-
wide association (GWA) research across the life sciences has
revealed that there are almost no genetic variants with large
effects on complex traits and common disorders.
2
This consistent
finding implies that the heritability of behavioral traits is due to
many genetic variants of small effect. GWA studies of behavioral
traits began to be successful as their sample sizes increased
sufficiently to detect associations of very small effect size between
single-nucleotide polymorphisms (SNPs) and outcome.
3
Although
the largest effect sizes of the associations between SNPs and
behavioral traits are very small, it is possible to aggregate
the effects of thousands of SNP associations, ranked by effect
size, into a SNP genotypic score for a particular trait.
4–6
Here, we
refer to this SNP genotypic score as a genome-wide polygenic
score (GPS).
7
Although many different labels have been ascribed
to polygenic scores that usually include the word risk, we prefer
GPS. It highlights the genome-wide nature of these polygenic
scores and encompasses positive as well as negative effects
implied by the normal distribution of polygenic scores.
4
The largest GWA analysis of a behaviorally relevant trait so far
was performed on years of education, which is a proxy for
educational achievement and to a lesser extent for learning
ability.
8
Information about the years spent in education is available
in many GWA samples because it is a demographic descriptor. In
2013, a GWA analysis of EduYears based on 126,559 individuals
was published.
9
The corresponding GPS accounted for 2–3%
of the variance in years of education in independent samples.
9,10
The latest GWA on years of education published in 2016 included
329,000 individuals.
8
A revised GPS based on this new GWA
almost doubled the effect size, with EduYears GPS explaining 3.9%
of the variance in years of education in an independent sample.
8
EduYears GPS has also been associated with other phenotypes,
most notably, measured educational achievement. In a Dutch
study, the 2013 EduYears GPS accounted for around 2% of the
variance in educational achievement in a sample of about 1000
children tested at age 12.
11
A UK-based longitudinal study of 4500
participants reported significant associations between the 2013
EduYears GPS and educational achievement at 7, 11 and 16;
12
however, the authors did not report the phenotypic variance
explained by EduYears GPS. In a subsample of the present study
of ~ 3000 individuals, we previously found that the 2013 EduYears
GPS accounted for about 2% of the variance in educational
achievement at age 16.
13
The present study evaluates the extent to which a GPS
constructed on the basis of the published summary statistics of
the 2016 GWA analysis of years of education in adulthood predicts
educational achievement assessed during the school years, which
we have shown to be about 60% heritable estimated by the twin
design.
14,15
Using effect size estimates from the 2016 EduYears
GWA analysis, we calculated a GPS for each individual in a sample
of 5825 unrelated UK students for whom we had educational
achievement scores at ages 7, 12 and 16 based on UK-wide
assessments of the national curriculum.
As mentioned, the 2016 EduYears GPS is based on a GWA
sample almost three times as large as the 2013 GWA (329,000
versus 127,000), and as a result, the amount of variance that
EduYears GPS accounted for in the discovery sample doubled
(~4 versus 2%). Accordingly, here we tested the extent to which
the 2016 EduYears GPS accounts for more variance in educational
1
King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, London, UK;
2
Department of Psychology,
Goldsmiths University of London, London, UK;
3
Laboratory for Cognitive Investigations and Behavioural Genetics, Tomsk State University, Tomsk, Russia;
4
Department of Speech
and Hearing Sciences, University of New Mexico, Albuquerque, NM, USA and
5
Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA.
Correspondence: Professor R Plomin, King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, 16
DeCrespigny Park, London SE5 8AF, UK.
E-mail: robert.plomin@kcl.ac.uk
Received 1 April 2016; revised 10 May 2016; accepted 23 May 2016
Molecular Psychiatry (2016) 00, 1–6
www.nature.com/mp

achievement than the 2013 EduYears GPS. In addition, we
addressed two specific questions about the role of EduYears GPS
for educational achievement.
First, we tested the extent to which the 2016 EduYears GPS is
associated with general cognitive ability (g, aka intelligence) and
with family socioeconomic status (SES), both of which pheno-
typically correlate with educational achievement ~ 0.40–0.50.
16
Using summary statistics derived from GWA analyses, a study
applying the LD score regression method
17
identified very high
genetic correlations between years of education and childhood IQ
(rg = 0.73).
18
In a subsample of ~ 3000 individuals from the current
study, the 2013 EduYears GPS accounted for ~ 2% of the variability
in gat age 16.
19
We also reported that this GPS explained ~ 2.5%
of the variance in family SES, which refers to the SES of the
children’s parents.
13
In the present study, we predicted that the
2016 EduYears GPS would yield stronger associations with gand
family SES than previously found for the 2013 EduYears GPS. In
addition, we tested whether the 2016 EduYears GPS is significantly
associated with educational achievement independent of gand
family SES.
Second, we tested the hypothesis that SES moderates genetic
influences on educational achievement and g, as predicted by
previous studies that observed decreased heritability estimates in
low compared with high SES families.
20
This genotype–environ-
ment interaction hypothesis leads to the prediction that EduYears
GPS is more strongly associated with educational achievement
and gin high compared with low-SES families. In addition, we
tested whether this genotype–environment interaction increased
from childhood through adolescence as family SES should have a
progressively stronger effect on these aspects of children’s lives
if the genotype–environment interaction hypothesis is correct.
MATERIALS AND METHODS
Participants
This study included unrelated individuals from the multivariate long-
itudinal Twins Early Development Study that recruited almost 17,000 twin
pairs born in England and Wales between 1994 and 1996.
21
The sample
is representative of British families in ethnicity, family SES and parental
occupation.
21
The genotyped subsample is representative of UK census
data at first contact (Supplementary Table S1). The Institute of Psychiatry,
Psychology and Neuroscience ethics committee (05.Q0706/228) granted
project approval and parental consent was obtained prior to data
collection.
DNA for 3497 individuals was extracted from saliva samples and
hybridized to HumanOmniExpressExome-8v1.2 genotyping arrays at the
MRC SGDP Centre Molecular Genetics Laboratories. The raw image data
from the array were normalized, pre-processed, and filtered in GenomeS-
tudio according to Illumina Exome Chip SOP v1.4. (http://confluence.brc.
iop.kcl.ac.uk:8090/display/PUB/Production+Version%3A+Illumina+Exome
+Chip+SOP+v1.4). In addition, prior to genotype calling, 869 multi-
mapping SNPs and 353 samples with call rate o0.95 were removed.
The ZCALL program
22
was used to augment the genotype calling for
samples and SNPs that passed the initial QC.
DNA from an additional 3665 samples genotyped earlier in the project
was extracted from buccal cheek swabs and genotyped at Affymetrix
(Santa Clara, CA, USA). Samples were successfully hybridized to Affymetrix-
GeneChip 6.0 SNP genotyping arrays (http://www.affymetrix.com/support/
technical/datasheets/genomewide_snp6_datasheet.pdf) using experimental
protocols recommended by the manufacturer (Affymetrix). The raw image
data from the arrays were normalized and pre-processed at the Wellcome
Trust Sanger Institute (Hinxton, UK) for genotyping as part of the Wellcome
Trust Case Control Consortium 2 (https://www.wtccc.org.uk/ccc2/) according
to the manufacturer’s guidelines (http://www.affymetrix.com/support/down-
loads/manuals/genomewidesnp6_manual.pdf). Genotypes for the Affymetrix
arrays were called using CHIAMO (https://mathgen.stats.ox.ac.uk/genetics_-
software/chiamo/chiamo.html).
After initial quality control and genotype calling, the same quality
control was performed on the samples genotyped on the Illumina and
Affymetrix arrays separately using PLINK,
23
R
24
and VCFtools.
25
Samples
were removed from subsequent analyses on the basis of call rate (o0.99),
suspected non-European ancestry, heterozygosity, array signal intensity
(44 s.d. from the mean) and relatedness. SNPs were excluded if the
minor allele frequency was o0.05%, if more than 1% of genotype
data were missing or if the Hardy Weinberg P-value was lower than 10
−5
.
Non-autosomal markers and indels were removed. Association between
the SNP and the array, batch or plate on which samples were genotyped
was calculated; SNPs with an effect P-value less than 10
−3
were excluded.
A total sample of 5825 samples, with 2698 individuals genotyped on
Illumina and 3127 individuals genotyped on Affymetrix, remained after
quality control.
Genotypes from the two arrays were separately imputed using the
Haplotype Reference Consortium
26
and Minimac3 1.0.13
27,28
available on
the Michigan Imputation Server (https://imputationserver.sph.umich.edu) as
reference data. A series of quality checks were performed before merging
data from the two arrays imputation (e.g. array effects, allele frequencies
by imputation quality). For the present analyses, we limited our analyses to
variants genotyped or imputed at info 40.95 on both arrays, and with
Hardy Weinberg Equilibrium test P-value 410
−5
. After stringent pruning to
remove markers in high linkage disequilibrium (R
2
40.1) and excluding
high linkage disequilibrium genomic regions so as to ensure that only
genome-wide effects were detected, we performed Principal Component
Analysis on a subset of 40, 745 autosomal SNPs that remained after
applying our quality control criteria, and that overlapped between the two
genotyping arrays. To control for population stratification, we regressed
the GPS on the first 10 principal components and used the residuals in all
subsequent analyses.
Measures
National Curriculum levels age 7 and 12. English and mathematics
National Curriculum levels were collected from teachers when the twins
were aged 7 (M = 7.2, s.d. = 0.27) and 12 (M = 11.4, s.d. = 0.66). National
Curriculum data and genotypes were available for 4047 children at age 7
and 2950 at age 12. The assessments are based on a rubric aligned with
the UK National Curriculum, which is the standardized core academic
curriculum formulated by the National Foundation for Educational
Research (NFER) and the Qualifications and Curriculum Authority (QCA)
(NFER: http://www.nfer.ac.uk/index.cfm; QCA: http://www.qca.org.uk). After
receiving parental consent, teachers were contacted directly via mail.
Teacher ratings assessed two main abilities: English (including ‘speaking
and listening’,‘reading’and ‘writing’) and mathematics (including ‘using
and applying mathematics’,‘numbers’and ‘shapes, space and measures’).
At age 7 and 12, teachers rated National Curriculum levels on a 5-point
and 9-point scale, respectively, with higher scores representing greater
ability. Mathematics and English abilities correlated 0.74 and 0.81 at age 7
and 12, respectively. Therefore, we created overall academic achievement
mean scores by calculating the standardized mean for the English and
mathematics scores for both ages.
General Certificate of Secondary Education measures age 16. The General
Certificate of Secondary Education (GCSE) is a standardized UK-based
examination taken at the end of compulsory education at age 16. In
addition to the compulsory core subjects of English, mathematics and
science, students can choose from a variety of subjects such as physical
education, music, geography, modern foreign languages, and information
and communication technology.
GCSE results were obtained by questionnaires sent via mail and by
telephone interviews of parents and twins themselves. The grades were
coded to range from 4 (G; the minimum pass grade) to 11 (A*; the best
possible grade). The GCSE score used in this study represents the mean of
the compulsory core subjects mathematics and English (if both English
language and English literature were taken, a mean grade for English was
derived). The two subjects correlated 0.70. We included only mathematics
and English grades in the composite score to improve comparability
between the educational achievement measures at the different ages. Self-
reported GCSE grades of Twins Early Development Study participants show
high accuracy, correlating 0.98 English and 0.99 for mathematics grades
with data obtained for a subsample from the National Pupil database (NPD:
https://www.gov.uk/government/collections/national-pupil-database).
14
Data for subject grades and genotypes were available for 4301 twins
(mean age = 16.62, s.d. = 0.32).
General cognitive ability (g). To measure general cognitive ability, the
twins were assessed on various tests including verbal and non-verbal
abilities at age 7, 12 and 16. A mean score composite was derived from
Predicting educational achievement from DNA
S Selzam et al
2
Molecular Psychiatry (2016), 1 –6

four tests (’Conceptual Grouping’,
29
‘Similarities’,
30
‘Vocabulary’,
30
‘Picture
Completion’
30
) at age 7; three tests (‘Raven’s Progressive Matrices’,
31
‘General Knowledge’
32
‘Picture Completion’
30
) at age 12; and two tests
(’Raven’s Progressive Matrices’and ’Mill Hill Vocabulary test’) at age 16.
Behavioral and genotypic data were available for 3559 individuals at age 7
(M = 7.17, s.d. = 0.29); 3349 individuals at age 12 (M = 11.46, s.d. = 0.64) and
1743 individuals at age 16 (M = 16.52, s.d. = 0.30). General cognitive ability
measures at the different ages correlated on average 0.48. For simplicity
we created a general cognitive ability mean composite based on data
available at ages 7, 12 and 16. Only participants with data from at least two
ages were included (N= 2228), and mean imputation was performed on
those with a missing third measure. We also report results related to
general cognitive ability measured at each age individually in
Supplementary Table S6.
Family SES. A composite of several factors such as parental education and
occupation is considered to reflect SES better than any single factor.
33
Data
from 4958 genotyped individuals were available for family SES. This
measure represents maternal age at birth of eldest child, the mean score of
maternal and paternal highest education level, as well as the respondent’s
(mother or father) occupation, administered by the Standard Occupational
Classification 2000 (Office for National Statistics, 2000) at child age 2, which
was the first age of contact.
Small but significant mean differences between girls and boys were
found for educational achievement at all ages (Supplementary Table S2).
Small age effects were found for educational achievement within each of
the three ages (Supplementary Table S2). Therefore, all measures with the
exception of SES and EduYears GPS were recalculated as standardized
residuals corrected for gender and age. To account for a slight negative
skew in educational achievement tests at age 7 and 16 and a slight positive
skew at age 12, measures were quantile normalized.
34
Statistical analyses
Genome-wide polygenic scores. We computed GPS for 5825 unrelated
individuals using β-weights and P-values from summary statistics obtained
by GWA analysis. Summary statistics were derived from the 2016 GWA
study on years of education
8
with a sample size of 328,918 individuals. It
should be noted that the summary statistics we used are slightly different
to those of the 2016 EduYears study:
8
here 23andMe data are excluded due
to legal restrictions, and an initial release of the UK Biobank data are
included (see Supplementary Table S3 for cohort details). GPS based on
these modified summary statistics correlated highly (r= 0.86) with the
published GPS
8
when both GPS were constructed using the Health and
Retirement Study as target sample. Quality-controlled SNPs were clumped
for linkage disequilibrium in PRSice,
35
using R
2
= 0.1 cutoff within a 250-kb
window. In toal, 108,737 SNPs remained after linkage disequilibrium
clumping. We used PRSice
35
to calculate polygenic scores. Firstly, PRSice
calculated GPS for each individual in our sample by summing the trait-
associated SNPs that are weighted by their effect size derived from GWA
analysis. PRSice then performed a regression analysis to test for association
between GPS and each of our outcomes (educational achievement at 7, 12,
16, SES and g). This is repeated for GPS calculated at a large number of
P-value thresholds, ranging from 0.001 to 1 (increments of 0.001) in the
GWA results, under the high-resolution scoring option in PRSice. Through
this high-resolution scoring we identified the ‘best-fit’GPS for all measures
(Supplementary Table S4), which were used throughout our analyses for
each respective trait. The ‘best-fit’GPS is identified as that which gives the
smallest P-value for association with outcome among all the regression
tests performed on the GPS (see Supplementary Figures S4). Given the
multiple testing involved in high-resolution scoring we use an association
significance threshold of P= 0.001, as recommended in Euesden et al.
35
For our GPS analyses, we have more than 80% power to explain 0.2% of
the phenotypic variance (see Supplementary Methods S1 for details). To
test interactions between different levels of EduYears GPS and family SES,
we have more than 80% power to detect a small interaction effect of
η
2
= 0.02 (given α= 0.05; N= 600; number of groups = 4).
We performed regression analyses with EduYears GPS as a predictor of
educational achievement at ages 7, 12 and 16, as well as of gand family
SES. To test for potential differences between correlations between
EduYears GPS and educational achievement at the different ages, we
performed Fisher’sr-to-ztransformations. We also used multiple regression
to test whether associations between EduYears GPS and educational
achievement remain after controlling for family SES and g. We also tested
for mean differences in educational achievement between the extreme
septiles of EduYears GPS at each age using analyses of variance. Finally,
interaction effects between EduYears GPS and SES on educational
achievement and on gwere analyzed using multiple regression models
that included each main effect and the interaction effect term.
RESULTS
Polygenic score analyses
As illustrated in Figure 1, EduYears GPS accounted for a significant
proportion of variance in educational achievement at all ages,
increasing from age 7 (R
2
= 0.028,Po0.001) to age 12 (R
2
= 0.046,
Po0.001) to age 16 (R
2
= 0.091, Po0.001). Betas indicated that an
increase of one standard deviation in EduYears GPS resulted in a
z-standardized mean educational achievement score increase of
0.17, 0.21 and 0.30 at age 7, 12 and 16, respectively. The increase
in association between EduYears GPS and educational achieve-
ment between age 7 and age 16 was significant, as was the
association between age 12 and age 16, but not between age 7
and 12 (Supplementary Table S5).
EduYears GPS was also associated with g(R
2
= 0.036,Po0.001)
and family SES (R
2
= 0.073,Po0.001) (Figure 1). Additionally,
EduYears GPS significantly predicted gat ages 7, 12 and 16
(Supplementary Table S6); these associations were not statistically
different. Because educational achievement, g, and family SES are
intercorrelated phenotypically (Supplementary Table S6), we
tested the effect of EduYears GPS on educational achievement
independent of gand SES by including gand SES into a regression
model before entering EduYears GPS. After adjusting the P-value
threshold for multiple testing (see the Materials and methods
section), EduYears GPS remained a significant predictor of
educational achievement at age 16 after accounting for gand
SES, although the effect size was reduced to 1.2% of the variance
explained (Supplementary Table S7).
Extreme group differences
Figure 2 shows the z-standardized mean educational achievement
scores by EduYears GPS septiles. At all ages, individuals scoring in
the highest EduYears GPS septile performed on average
Figure 1. Variance explained (R
2
) and standard error of EduYears GPS
predicting: EA 7 =educational achievement age 7; EA 12 =educa-
tional achievement age 12; EA 16 =educational achievement age 16;
g=general cognitive ability; SES =family socioeconomic status; in
this analysis and all subsequent analyses, the unique ‘best-fit’GPS
was used for each respective trait; see the Materials and methods
section for details. GPS, genome-wide polygenic score.
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S Selzam et al
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Molecular Psychiatry (2016), 1 –6

significantly and substantially better at school than those scoring
in the lowest GPS septile (Supplementary Table S8). By age 16,
there was almost a standard deviation difference in educational
achievement between the lowest and highest GPS groups, which
represents a whole school grade difference. Similar results were
obtained for EduYears GPS extreme quintiles rather than septiles
(Supplementary Table S9 and Supplementary Figure S1).
Using Monte Carlo integration,
36
we calculated a substantial
non-overlap of 38% between educational achievement distri-
butions at age 16 for the lowest and highest GPS septiles
(Supplementary Figure S2).
Genotype–environment interaction effects
The genetic influence of EduYears GPS on educational achieve-
ment at age 16 and on gwas not greater in high SES than in low-
SES families, as would be predicted by the genotype–environment
interaction hypothesis described earlier. As illustrated in Figure 3a,
at age 16 the difference between low and high GPS groups
was similar for low-SES and high-SES groups, despite the higher
mean educational achievement of the high-SES group. We
also did not find G × E interaction for general cognitive ability
(Figure 3b), and educational achievement at ages 7 and 12
(Supplementary Figure S3). Hierarchical multiple regression
analyses that tested for G × E interaction using continuous
data yielded no significant interactions between EduYears
GPS and SES as they relate to educational achievement at ages
7, 12 and 16 (Supplementary Table S10) or as they relate to g
(Supplementary Table S11).
DISCUSSION
Our results show that DNA can be used to predict educational
achievement, especially at the end of the compulsory school
years. Although the 2016 EduYears GPS accounted for ~ 4% of
the variance in the GWA target trait of years of education in
independent samples, we found that the 2016 EduYears GPS
accounted for 9% of the variance in educational achievement at
age 16, tripling the effect size from previous reports
13
based on
the 2013 EduYears GPS.
9
The predictive power of EduYears GPS can
be seen especially at the extremes of the distribution of GPS
scores, suggesting that it is possible to identify individuals early in
life at genetic risk and resilience, moving us closer to the
possibility of early intervention and personalized learning.
37
We have previously reported a heritability estimate of 60% for
educational achievement at age 16 using a sample from which the
Figure 2. Standardized means and standard errors for educational achievement at age 7, 12 and 16 by genome-wide polygenic score (GPS)
septile. EduYears GPS was rescor ed as septiles (1 =lowest, 7 =highest).
Figure 3. (a) Standardized educational achievement mean scores at age 16 by EduYears GPS and family SES for individuals scoring in the
highest and lowest 20% of the distribution of EduYears GPS. There was no evidence for an interaction effect (F(1,605) =1.29, P=0.18); (b)
general cognitive ability mean scores by EduYears GPS and family SES for individuals scoring in the highest and lowest 20% of the distribution
of EduYears GPS. No interaction effect was found (F(1,327) =1.06, P=0.30). GPS, genome-wide polygenic score; SES, socioeconomic status.
Predicting educational achievement from DNA
S Selzam et al
4
Molecular Psychiatry (2016), 1 –6

present sample was drawn.
14
The present study demonstrated
that EduYears GPS predicts 9% of the total variance in educational
achievement, thus accounting for only 15% of the heritability
estimated by the twin design. However, unlike twin study
estimates of heritability, GPS is derived from GWA studies, which
are limited to additive effects of the common variants employed
on SNP arrays. For this reason, SNP-based estimates of heritability,
which have these same limitations, represent the current upper
limit for GPS prediction. For educational achievement, SNP-based
estimates of heritability are about 30%,
13
and EduYears GPS
explains almost one-third of the heritable variance from SNP-
based studies at age 16.
We believe that the substantial increase in heritability explained
by the 2016 EduYears GPS represents a turning point in the social
and behavioral sciences because it makes it possible to predict
educational achievement for individuals directly from their DNA.
Although other variables account for more of the variance of
educational achievement, DNA has a unique predictive status in
that inherited DNA sequence variation does not change from the
single cell with which life begins. For this reason, unlike the case
with many other predictors, the correlation between EduYears GPS
and educational attainment cannot feasibly be interpreted in
terms of reverse causation. That is, the correlation between
EduYears GPS and educational achievement cannot be caused by
the effect of educational achievement on inherited DNA sequence
variation. In contrast, although gpredicts much more of the
variance of educational achievement at age 16 (29% in our study),
this correlation could be confounded by factors related to both
educational achievement and g, such as social and family risk
factors. Similarly, educational achievement at age 7 predicts 35%
of the variance of educational achievement at age 16 but this
correlation could also be due to other factors, including
genetics,
14
that affect educational achievement at both ages.
Moreover, educational achievement and gcannot be assessed at
earlier stages of development. Family SES, which also predicts
substantial variance of educational achievement at age 16 (21% in
our study), can be assessed early but this correlation is also likely
to be partly caused by other factors, including genetics,
13
that
affect both family SES and educational achievement. Although
family SES can be assessed early, it can change over time, whereas
DNA variations within individuals are stable across the lifespan.
Moreover, family SES is a family-wide index not specificto
individual children in a family.
EduYears GPS predicts educational achievement independently
of gand family SES only at age 16, which may be due to the
associations between g, educational achievement, family SES and
EduYears GPS. It is possible that family SES and gare earlier in the
chain of the causal pathway from genetic variants to educational
achievement, which may explain the attenuated relationship
between EduYears GPS and educational achievement at age 7 and
12 after controlling for these variables. Our findings suggest
pleiotropic effects of EduYears GPS on educational achievement, g,
and family SES, which are in line with previous reports that
describe the genetic overlap between educational achievement, g,
and family SES.
12,13,38
However, the threefold increase in predic-
tion of educational achievement at age 16 from the 2016 EduYears
GPS as compared with the 2013 EduYears GPS (~3% vs 9%) was
not mirrored in the prediction of g(~2% vs ~ 3.5%). The finding
that EduYears GPS accounts for more variance in educational
achievement than in gis likely due to the fact that educational
achievement is influenced by gas well as many other factors that
are under genetic influence.
14
Variance explained by the 2016 EduYears GPS in family SES also
increased almost threefold compared with previous results with
the 2013 EduYears GPS in the a subsample of the current study
(~2.5% vs ~ 7%).
13
Explaining ~ 7% in family SES by EduYears GPS
is impressive for two reasons. First, the children’s genotypes are
only an approximation of their parents’genotypes; the effect of
EduYears GPS on SES should be even stronger for the parents’own
GPS. Second, our findings account for a third of the SNP-based
heritability estimate for family SES (~20%),
39
which, as noted
earlier, represents the upper limit for GWA and GPS studies. With
that, our results demonstrate that family SES is genetically
influenced and that its genetic effects are also partly shared with
educational achievement.
When interpreting the current results, three caveats should be
considered. First, the finding that the predictive validity of
EduYears GPS increases across the school years may be due to
increasing approximation of our measures to the EduYears GWA
target trait of years of education. That is, our measure of
educational achievement at age 16 is a standardized examination
taken at the end of compulsory education that strongly influences
whether pupils go on to higher education. Alternatively, it is also
possible that GCSE results are more reliable measures than
national curriculum teacher ratings, which might contribute to the
difference in variance explained in these variables by EduYears
GPS. Second, as we measured family SES in a traditional way by
including parental education, this could have increased the
association of the SES composite with EduYears GPS. Although
parental education and occupation are related, future studies
should investigate if the relationship between EduYears GPS
and SES varies as a function of different SES indicators. Third, our
finding that EduYears accounts for 9% of the variance of
educational achievement at age 16 needs to be tested for
generalization in other samples and beyond the UK.
The finding that individuals’polygenic scores for years of
education predict educational achievement entails no necessary
policy implications. However, our findings corroborate that
individual differences in educational achievement are partly due
to DNA differences between children and are not solely created
by environmental forces. By creating a dialogue between
scientists and policymakers, the introduction of polygenic scores
may soon become a useful tool for early prediction and
prevention of educational problems and for personalized learning.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGMENTS
We gratefully acknowledge the ongoing contribution of the participants in the
Twins Early Development Study (TEDS) and their families. TEDS is supported by a
program grant to RP from the UK Medical Research Council (MR/M021475/1
and previously G0901245), with additional support from the US National Institutes of
Health (HD044454; HD059215). SS is supported by the MRC/IoPPN Excellence
Award and by the EU Framework Programme 7 (602768). EK and KR are supported by
a Medical Research Council studentship. RP is supported by a Medical Research
Council Advanced Investigator award (295366). The funders had no role in study
design, data collection and analysis, decision to publish or preparation of the
manuscript.
AUTHOR CONTRIBUTIONS
RP directs and received funding for the Twins Early Development Study
(TEDS). RP and SS conceived the present study. SS analyzed and interpreted the
data. RP supervised the project and interpreted the data. RP and SS wrote
the manuscript with help from EK, SvS, PFO, KR, YK, PSD and JJL.
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