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Title
Novel Loci Associated With Attention-Deficit/Hyperactivity Disorder Are Revealed by
Leveraging Polygenic Overlap With Educational Attainment.
Permalink
https://escholarship.org/uc/item/7153w9nx
Journal
Journal of the American Academy of Child and Adolescent Psychiatry, 57(2)
ISSN
0890-8567
Authors
Shadrin, Alexey A
Smeland, Olav B
Zayats, Tetyana
et al.
Publication Date
2018-02-01
DOI
10.1016/j.jaac.2017.11.013
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California
NEW RESEARCH
Novel Loci Associated With Attention-Deficit/
Hyperactivity Disorder Are Revealed by Leveraging
Polygenic Overlap With Educational Attainment
Alexey A. Shadrin, PhD, Olav B. Smeland, MD, PhD, Tetyana Zayats, MD, PhD,
Andrew J. Schork, PhD, Oleksandr Frei, PhD, Francesco Bettella, PhD, Aree Witoelar, PhD,
Wen Li, PhD, Jon A. Eriksen, PhD, Florian Krull, PhD, Srdjan Djurovic, MD, PhD,
Stephen V. Faraone, MD, PhD, Ted Reichborn-Kjennerud, MD, PhD, Wesley K. Thompson, PhD,
Stefan Johansson, MD, PhD, Jan Haavik, MD, PhD, Anders M. Dale, PhD,
Yunpeng Wang, PhD, Ole A. Andreassen, MD, PhD
Objective: Attention-deficit/hyperactivity disorder (ADHD) is a common and highly heritable psychiatric condition. By exploiting the reported
relationship between ADHD and educational attainment (EA), we aimed to improve discovery of ADHD-associated genetic variants and to investigate
genetic overlap between these phenotypes.
Method: A conditional/conjunctional false discovery rate (condFDR/conjFDR) method was applied to genome-wide association study (GWAS) data
on ADHD (2,064 trios, 896 cases, and 2,455 controls) and EA (n ¼ 328; 917) to identify ADHD-associated loci and loci overlapping between
ADHD and EA. Identified single nucleotide polymorphisms (SNPs) were tested for association in an independent population-based study of ADHD
symptoms (n ¼ 17; 666). Genetic correlation between ADHD and EA was estimated using LD score regression and Pearson correlation.
Results: At levels of condFDR < 0:01 and conjFDR < 0:05; we identified 5 ADHD-associated loci, 3 of these being shared between ADHD and
EA. None of these loci had been identified in the primary ADHD GWAS, demonstrating the increased power provided by the condFDR/conjFDR
analysis. Leading SNPs for 4 of 5 identified regions are in introns of protein coding genes (KDM4A, MEF2C, PINK1, RUNX1T1), whereas the
remaining one is an intergenic SNP on chromosome 2 at 2p24. Consistent direction of effects in the independent study of ADHD symptoms was
shown for 4 of 5 identified loci. A polygenic overlap between ADHD and EA was supported by significant genetic correlation (r
g
¼0:403,
p ¼ 7:90 10
8
) and >10-fold mutual enrichment of SNPs associated with both traits.
Conclusion: We identified 5 novel loci associated with ADHD and provided evidence for a shared genetic basis between ADHD and EA. These
findings could aid understanding of the genetic risk architecture of ADHD and its relation to EA.
Key words: attention-deficit/hyperactivity disorder, educational attainment, conditional/conjunctional false discovery rate, genetic overlap
J Am Acad Child Adolesc Psychiatry 2018;57(2):86–95.
ttention-deficit/hyperactivity disorder (ADHD) is a common
neurodevelopmental condition, caused by interplay of genetic
and environmental risk factors. Its prevalence is estimated to
be 5% in school-aged children and 2.50% in adults.
1
The heritability of
ADHD is one of the highest reported among psychiatric disorders in
epidemiological studies, estimated at 0.70 to 0.80.
1
However, it has been
difficult to identify genetic risk variants that account for the high herita-
bility of ADHD, resulting in a relatively modest single nucleotide poly-
morphism (SNP)Lbased heritability, currently estimated at 0.28.
2
This
may be in part explained by its complex phenotypic structure (heteroge-
neous clinical features, developmental course and outcome, high rate of
comorbid symptoms and disorders
3
) and genetic architecture with a highly
polygenic etiology, with both common and rare variants contributing small
effects.
4
Moreover, large sample sizes are needed for reliable detection of
such effects. The relatively small samples of existing ADHD genetic
studies, as compared to those available for other psychiatric disorders,
5,6
present an additional challenge. Up to now, no published genome-wide
association studies (GWASs) have been able to detect genome-wide sig-
nificant association (p < 5:00 10
8
) for ADHD.
It is well established that complex traits of ten have a polygenic
structure with shared genetic backg round .
7,8
Recently, a conditional/
conjunctional false discovery rate (condFDR /conjFDR; see Table 1 for
explanation of terminology) method was developed
9
to exploit over-
lapping association across GWASs and thereby boost association signals
in GWAS of one phenotype by combining it with genome-wide asso-
ciation data of another phenotype (condFDR) or to enable detection of
speci fic genetic loci shared between 2 phenotypes (conjFDR). If genetic
overl ap between 2 phenotypes exists, the method offers increased sta-
tisti cal power compared to conventional multiple hypoth eses testing
approaches.
10,11
This method was successfully applied to discover novel
associations and to detect shared genetic variants in various complex
disorders, including neurological
12,13
and psychiatric
9
diseases.
A
86 www.jaacap.org Journal of the American Academy of Child & Adolescent Psychiatry
Volume 57 / Number 2 / February 2018
ADHD is consistently associated with lower levels of EA
1,14
: the
percentage of US adolescents not completing high school is 5%, whereas
it is approximately 35% for adolescents diagnosed with ADHD.
15
There
are several ways in which ADHD may relate to lower EA, which are not
mutually exclusive. First, the clinical and cognitive symptoms of ADHD
(e.g., attention deficits) may directly perturb EA. Second, ADHD has a
number of common comorbidities, including learning disabilities,
16
mood disorders,
16
and disruptive behavior,
16
which are associated with
lower EA. Another possibility is that ADHD and EA share causative
factors. Recent findings demonstrate negative genetic correlation between
ADHD and EA (r
g
¼0:305, SE ¼ 0:141, p ¼ 3:00 10
2
)
17
,
suggesting that genetic variants conferring risk for ADHD may
contribute to lower EA in the general population. Thus, we can
hypothesize that ADHD and EA may have a shared genetic basis and
may amplify association signal by combining these phenotypes in the
condFDR/conjFDR method.
In contrast to ADHD, where the currently published largest
GWASs contain fewer than 4,000 cases,
18,19
the latest GWAS on EA
contains more than 300,000 individuals, uncovering multiple genome-
wide significantly associated loci.
20
Combining this EA GWAS with
moderately powered GWAS of ADHD
18
in the condFDR/conjFDR
approach, we aimed here to identify novel loci associated with ADHD as
well as loci shared between ADHD and EA. The latter may provide
insights into the molecular genetic mechanisms jointly influencing
ADHD and EA and inform their biological underpinnings. Applying
novel statistical methods, we also tested whether the observed phenotypic
correlation between ADHD and EA implies a genetic correlation be-
tween these traits. In addition, for the identified ADHD-associated
variants, we assessed consistency of effect directions in an independent
population-based study of ADHD symptoms and performed in silico
analyses of their functional effects (eQTL, expression quantitative loci).
METHOD
Participant Samples
We used ADHD data from the Psychiatric Genomics Consortium
(PGC).
18
The data set contains information from 2,064 trios, 896 in-
dividuals with ADHD, and 2,455 controls. EA data were obtained from
the Social Science Genetic Association Consortium (SSGAC),
20
where
EA was measured as the number of years of schooling completed that was
harmonized between different educational systems. For our analyses, we
used summary statistics generated by the meta-analysis of all discovery
and replication cohorts, except the 23andMe sample (64 datasets with
total n ¼ 328; 917).
Top association signals identified in our analyses were examined in
the summary statistics from an independent GWAS of ADHD
symptoms performed by EArly Genetics and Lifec ourse Epidemiology
(EAGLE) consortium.
21
Unlike the PGC case-contr ol ADHD GWAS,
EAGLE GWAS represents a meta-analysis of 9 population-based pe-
diatric cohorts containing information on 17,666 children under the
age of 13 years with measures of ADHD symptom scores.
Detailed description of data used for analysis and data preprocessing
steps is given in Supplement 1, available online.
Statistical Analyses
To assess genetic overlap between ADHD and EA and thus warrant
subsequent condFDR/conjFDR analysis, we generated conditional QQ
plots and fold-enrichment plots in both directions: conditioning ADHD
on EA and vice versa.
9
To explore the nature of the polygenic overlap and
to test the hypothesis that the investigated phenotypes correlate geneti-
cally, we calculated Pearson correlations between association z scores of
ADHD and EA SNPs within nested subset (strata) of SNPs with
increasing significance of p values in either ADHD or EA (formal defi-
nition of SNP stratum is given in Supplement 1, available online). To
further support this hypothesis, we estimated genetic correlation between
ADHD and EA using LD score regression.
8
Details of these analyses are
described in Supplement 1, available online.
To identify specific loci associated with ADHD, we applied the
condFDR method described previously.
9
The condFDR method takes
summary statistics that reflect genetic association of a phenotype of interest
(primary) together with those of an auxiliary (conditional) phenotype and
estimates a posterior probability that an SNP is null (has no association) in
the primary phenotype, given that p values of the SNP in both the primary
and conditional phenotypes are lower than observed p values. Thus, the
condFDR method increases the power to discover loci associated with a
primary phenotype by leveraging associations with a secondary phenotype.
It does so by re-ranking SNPs compared to nominal p valuebased
ranking.
9
In contrast, ranking SNPs based on unconditional FDR (e.g.,
using Benjamini–Hochberg or Benjamini–Yekutieli procedure) does not
change their order (compared to nominal p values).
Although both conditional QQ plots and genetic correlation based
on the LD score regression can be useful to get a general idea of whether
TABLE 1 Glossary of Core Terms Used in the Study
GWAS (genome-wide association study): a study that performs genome-wide scans of common genetic variants aiming to identify variants associated
with the trait.
LD (linkage disequilibrium): the statistical correlation between alleles at 2 loci.
FDR (false discovery rate): a posterior probability that an identified association is false.
condFDR (conditional FDR): the method that uses association p values of 2 traits (primary and conditional) to estimate a posterior probability that a
variant has no association in a primary trait, given that p values of the variant in both the primary and conditional traits are lower than their observed
p values.
conjFDR (conjunctional FDR): an extension of condFDR method that estimates a posterior probability that a variant has no association for either
phenotype or both at the same time, given that the p values for both phenotypes are lower than the observed p values.
QQ (QuantileLQuantile) plot: a visual tool used to compare 2 probability distributions (e.g., observed vs. expected) by plotting their quantiles against
each other.
Conditional QQ plot: a QQ plot comparing probability distributions of association p values in primary trait for different strata of variants (e.g., variants
with different levels of significance in conditional trait).
eQTL (expression quantitative trait loci): genetic variants that affect gene expression levels.
Journal of the American Academy of Child & Adolescent Psychiatry www.jaacap.org 87
Volume 57 / Number 2 / February 2018
NOVEL LOCI ASSOCIATED WITH ADHD
2 traits have a significant genetic overlap, they are unable to find specific
susceptibility loci shared by the traits. The conjFDR approach is an
extension of condFDR allowing the identification of specific loci asso-
ciated with both traits.
9
The conjFDR is defined as the maximum of the
2 condFDR values (taking one phenotype as primary and another as
conditional and vice versa) for a specific SNP. Thus, the conjFDR
approach estimates a posterior probability that an SNP is null for either
phenotype or both at the same time, given that the p values for both
phenotypes are lower than the observed p values. The method, therefore,
uncovers loci associated with both phenotypes simultaneously.
To avoid inflation of the results due to LD dependency in fold-
enrichment and QQ plots as well as in condFDR/conjFDR analyses, we
randomly pruned all SNPs across 500 iterations. For each iteration, all but
one random SNP in each LD-independent region (clump of SNPs in
strong LD, r
2
> 0:2) were removed, and finally the results were averaged
across all iterations. LD (r
2
values) was estimated based on the 1000 Ge-
nomes Project phase 3 European subpopulation data using PLINK.
22
As for meta-analyses based on multiple data sources, the quality of our
condFDR/conjFDR analysis will depend on the robustness of the primary
data. More details about condFDR and conjFDR methods can be found in
Supplement 1, available online, and in the original publication.
9
Evaluation of the Detected ADHD Loci in an
Independent Study of ADHD Symptoms
We used genetic data on association of ADHD symptoms obtained from
the EAGLE consortium to test whether our results could be supported by
data from the independent sample. For this purpose, we examined
whether effects of the most significant SNPs in the loci identified by
condFDR/conjFDR analyses were consistent between PGC ADHD and
EAGLE data sets.
In Silico Identification of Allele-Specific Effects of
Significant SNPs on Transcription
Identifying and investigating genetic variants that might affect gene
expression (expression quantitative trait loci or eQTLs) may shed light on
how associated variants may contribute to biological mechanisms un-
derlying a phenotype. eQTLs vary significantly both between different
tissues and over time.
23
Existing GWASs on ADHD and EA clearly
demonstrate remarkable enrichment of association signals in genomic
regions implicated in the regulation of gene expression in the brain.
18,20
Hence, we focused on eQTL analysis of genes expressed in brain tissues.
Significant associations identified with condFDR and conjFDR analyses
were queried for known eQTLs using the GTEx portal (http://gtexportal.
org) and the Braineac database (http://www.braineac.org). The latter
database contains information on cis-eQTLs for 10 brain regions: cere-
bellar cortex, frontal cortex, hippocampus, medulla (specifically inferior
olivary nucleus), occipital cortex (specifically primary visual cortex), pu-
tamen, substantia nigra, thalamus, temporal cortex, and intralobular
white matter. In addition, we checked age-dependent variations of
expression in genes containing identified significant SNPs using the
Human Brain Transcriptome database (http://hbatlas.org).
24
RESULTS
Evaluation of Genetic Overlap and Correlation
In the absence of genetic overlap between 2 traits, it is expected that p values
for association with 1 trait are independent of the p values for association
with the other. However, conditional QQ plots in Figure 1 clearly
demonstrate an increasing degree of leftward deflection for strata of more
significant SNPs. This is observed both when conditioning ADHD on EA
(Figure 1A) and vice versa (Figure 1B), suggesting substantial cross-trait
polygenic enrichment. Enrichment of association signals for 1 trait
among those of another is also clearly visible in the fold-enrichment plots,
with more than 10-fold enrichment of SNPs from the strictest stratum
(p
conditional trait
< 1:00 10
3
) for both traits (Figure S1, available online).
In addition, association z scores of ADHD and EA demonstrate increasing
negative correlation in more strictly defined strata of SNPs, both when
strata are defined based on ADHD p values (Figure 1C) and on EA
p values (Figure 1D). Moreover, LD score regression analysis also
showed significant negative genetic correlation (r
g
¼0:403,
SE ¼ 0:075, p ¼ 7:90 10
8
) between these phenotypes.
Identification of ADHD-Assoc iated Loci and Loci Shar ed
Between ADHD and EA
Using the condFDR/conjFDR method, we identified 5 LD-independent
regions significantly associated with ADHD (condFDR < 0:01,
conjFDR < 0:05), 3 of which were also identified as shared between
ADHD and EA. From each of these regions, a single SNP with the lowest
condFDR/conjFDR value (strongest association signal) was selected to
represent their loci. These SNPs are presented in Table 2. Manhattan
plots resulting from condFDR and conjFDR analyses are presented in
Figures 2 and 3, respectively. Four of 5 identified most significant SNPs
revealed the opposite directions of effect in ADHD and EA.
Identified Loci and Related Genes
Two loci (represented in Table 2 by variants rs618678 and rs412458)
were identified both in condFDR and conjFDR analyses.
rs618678 represents the strongest signal in the conjFDR analysis
(conjFDR ¼ 3:82 10
3
) and the second strongest in the condFDR
analysis (condFDR ¼ 3:77 10
3
). This SNP is an intronic variant
within KDM4A on chromosome 1p34.2 (Figure 4B).
25
Figure 4B and
Figure S2B (available online) show the genetic context of rs618678,
indicating, respectively, the conjFDR and condFDR values of adjacent
SNPs. It is worth noting that in our analysis, rs618678 tags a broad
region of association. As can be seen in Figure 4B, multiple significant
SNPs in strong LD ( r
2
> 0:60) with rs618678 were detected in this
region, spanning more than 200,000 base pairs (bp). Besides KDM4A,
the region also contains PTPRF (located in 1p34.2, upstream of
KDM4A) and ST3GAL3 (1p34.1, directly downstream KDM4A)
genes. The latter was also identified in the eQTL analysis (discussed
below). Another significant signal identified in both condFDR
(condFDR ¼ 7:34 10
3
) and conjFDR (conjFDR ¼ 2:11 10
2
)
analyses is represented by rs412458, an intronic variant within MEF2C on
chromosome 5q14.3 (Figure S2A, D, available online).
Two loci were identified by condFDR, but not conjFDR. The
strongest signal was detected at rs4324303 (condFDR ¼ 2:17 10
3
),
that is, in the intergenic region on chromosome 2p24 (Figure 4A). Multiple
significant variants tagged by rs4477079 (condFDR ¼ 4:37 10
3
)
were also identified on chromosome 8 within RUNX1T1 (Figure S2C,
available online).
Finally, conjFDR analysis identified a shared variant
(conjFDR ¼ 4:48 10
2
)atPINK1 (rs17414302, intronic, 1p36.12)
(Figure S2E, available online). There were no LD-linked SNPs in the
direct vicinity and only 25 SNPs in LD (r
2
> 0:20) with this variant,
residing upstream of PINK1, at about 100,000 bp.
None of the SNPs identified either in condFDR or conjFDR
reached genome-wide significance in previously published GWAS
of ADHD.
18
Rs618678 reached genome-wide significance in EA
(p ¼ 1:05 10
10
).
20
Rs412458, which was identifi ed by both
condFDR and conjFDR, was not reported as genome-wide significant
88 www.jaacap.org Journal of the American Academy of Child & Adolescent Psychiatry
Volume 57 / Number 2 / February 2018
SHADRIN et al.
by the published EA GWAS (p ¼ 3:73 10
6
),butitisinLD
(r
2
¼ 0:35) with rs588282, which did reach genome-wide significance
in that study (previously reported p ¼ 1:69 10
10
). Other loci
identified in our analyses were below the genome-wide significance
threshold in EA. It is also worth noting that the unconditional FDR
values for all identified SNPs were above 0.01 and 0. 05 in condFDR and
conjFDR analysis, respectively.
Evaluation of the Detected ADHD Loci in an
Independent Study of ADHD Symptoms
To assess the robustness of our results, we examined the loci identified in
either the condFDR or conjFDR analyses (Table 2) in the association
summary statistics from the independent GWAS of ADHD symptoms
conducted by the EAGLE consortium.
21
Four of 5 loci (represented by
SNPs rs17414302, rs412458, rs618678, rs4324303) have the same di-
rection of effect in the PGC and EAGLE GWASs, whereas the last locus
(represented by rs4477079 SNP) has an opposite direction of effect in
these GWASs. These results are presented in Table S1 (available online).
In Silico Identification of Allele-Speci fic Effects on
Transcription
According to Human Brain Transcriptome data,
24
all 6 implicated genes
(Table 2, genes in the region) have a pronounced expression in different
brain regions during the whole life cycle (Figure S3, available online).
Therefore, alterations in the expression level of these genes (where the
detected SNPs are located) may affect a broad variety of processes over an
extended period. We scanned the Braineac database to check whether
SNPs identified in either the condFDR or conjFDR analyses are
FIGURE 1 Conditional QQ plots and correlation plots.
Note: Conditional QQ plots (A, B) demonstrate relation between expected (x axis) and observed (y axis) significance of markers in the primary trait when markers are strat-
ified by their p values in the conditional trait. A sequence of 4 nested strata is presented: all single nucleotide polymorphisms (SNPs) (i.e., p values of the conditional trait
1:00), p
conditional trait
<:1, p
conditional trait
<:01; and p
conditional trait
<:001. (A) attention-deficit/hyperactivity disorder (ADHD) conditioned on educational attain-
ment (EA). (B) EA conditioned on ADHD. (C, D) Correlation plots show Pearson’s correlation coefficients between association z scores of ADHD and EA for the nested strata
of SNPs (as introduced in the conditional QQ plots) averaged over 500 iterations of random pruning. Solid black lines indicate standard deviations. (C) SNP strata are
defined by the p values of markers in educational attainment (ADHD|EA). (D) SNP strata are defined by the p values of markers in ADHD (EA|ADHD).
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NOVEL LOCI ASSOCIATED WITH ADHD