Machine Learning Prediction of ADHD Severity: Association and Linkage to ADGRL3 , DRD4 , and SNAP25

Abstract

Objective
To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes are associated with and predict ADHD severity in families from a Caribbean community.

Method
ADHD severity was derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build predictive models of ADHD severity based on demographic and genetic data.

Results
Individuals with ADHD exhibited two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains.

Conclusion
This study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework with potential clinical use.

Full text

https://doi.org/10.1177/10870547211015426
Journal of Attention Disorders
1 –19
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DOI: 10.1177/10870547211015426
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Article
Introduction
ADHD is a neurodevelopmental disorder characterized by a
complex pattern of persistent clinical symptoms of inatten-
tion, hyperactivity, and impulsivity, in association with the
development of chronic functional impairment (Association,
2002a, 2002b, 2013). ADHD affects 8% to 18% of children
and adolescents, and ~1% to 7% of adults worldwide
(Cornejo et al., 2005; Faraone & Biederman, 2005; Pineda
et al., 1999; Polanczyk et al., 2014), and persists into adult-
hood in 40% to 50% of cases (Thapar et al., 1999, 2016).
ADHD is a major risk factor for the appearance of disrup-
tive (externalizing) disorders, including conduct disorder
(CD), oppositional defiant disorder (ODD), and substance
use disorder (SUD) (Molina et al., 2007; Sibley et al., 2011).
Children with ADHD have a higher predisposition to poor
educational achievement, low-income, underemployment,
legal problems, and impaired social relationships (Molina
et al., 2007; Sibley et al., 2011).
A diagnosis of ADHD requires that a specific set of cri-
teria outlined in the Diagnostic Statistical Manual for
Mental Disorders (DSM) are met (Association, 2002a,
2002b, 2013). According to the number of symptoms exhib-
ited by each subject in different environments, and in coher-
ence with a detailed cognitive clinical evaluation and
comparison between previous and current symptoms, each
subject is assorted to ADHD subtypes (Acosta López et al.,
2013; Acosta et al., 2011; Luo et al., 2019; Posner et al.,
2020). These symptoms relate to the presence or absence of
18 items (operational criteria of the DSM), which are regis-
tered using a binary scale (0: absence; 1: presence), and
grouped by domain (inattention symptoms: items 1–9;
hyperactivity/impulsivity symptoms: items 10–18). These
symptoms provide a differential categorical diagnosis into
four subtypes: ADHD of predominantly inattentive type,
1015426JADXXX10.1177/10870547211015426Journal of Attention DisordersCervantes-Henríquez et al.
research-article2021
1Universidad Simón Bolívar, Barranquilla, Colombia
2Universidad del Norte, Barranquilla, Colombia
3National Institutes of Health, Bethesda, MD, USA
4Universidad de Antioquia, Medellín, Colombia
5Universidad del Atlántico, Barranquilla, Colombia
*These authors contributed equally to this work.
Corresponding Authors:
Martha L. Cervantes-Henríquez, Grupo de Neurociencias del Caribe,
Unidad de Neurociencias Cognitivas, Universidad Simón Bolívar, Calle 54
# 59 -189, Sede 1, Bloque C, Barranquilla, Colombia.
Email: cervantesmh@unisimonbolivar.edu.co
Jorge I. Vélez, Universidad del Norte, Km 5 via Puerto Colombia,
Building K, Room 7-3K, Barranquilla, 081007, Colombia.
Email: jvelezv@uninorte.edu.co
Machine Learning Prediction of ADHD
Severity: Association and Linkage to
ADGRL3, DRD4, and SNAP25
Martha L. Cervantes-Henríquez1,2*, Johan E. Acosta-López1, Ariel F. Martinez3,
Mauricio Arcos-Burgos4*, Pedro J. Puentes-Rozo1,5, and Jorge I. Vélez2*
Abstract
Objective: To investigate whether single nucleotide polymorphisms (SNPs) in the ADGRL3, DRD4, and SNAP25 genes
are associated with and predict ADHD severity in families from a Caribbean community. Method: ADHD severity was
derived using latent class cluster analysis of DSM-IV symptomatology. Family-based association tests were conducted to
detect associations between SNPs and ADHD severity latent phenotypes. Machine learning algorithms were used to build
predictive models of ADHD severity based on demographic and genetic data. Results: Individuals with ADHD exhibited
two seemingly independent latent class severity configurations. SNPs harbored in DRD4, SNAP25, and ADGRL3 showed
evidence of linkage and association to symptoms severity and a potential pleiotropic effect on distinct domains of ADHD
severity. Predictive models discriminate severe from non-severe ADHD in specific symptom domains. Conclusion: This
study supports the role of DRD4, SNAP25, and ADGRL3 genes in outlining ADHD severity, and a new prediction framework
with potential clinical use. (J. of Att. Dis. XXXX; XX(X) XX-XX)
Keywords
ADHD severity, machine learning, LPHN3, SNAP25, DRD4.

2 Journal of Attention Disorders 00(0)
ADHD of predominantly hyperactive/impulsive type,
ADHD of combined type, and ADHD unaffected
(Fernandez-Jaen et al., 2018; Schachar et al., 2000).
However, this dichotomous classification approach to
ADHD diagnosis (0: unaffected; 1: affected) excludes the
existence of intermediate spectrum ADHD classes (Cuthbert
& Insel, 2010; Insel, 2014; Larsson et al., 2012) and inexo-
rably defines a minimum-required number of symptoms to
classify individuals (Acosta et al., 2004, 2011; Arcos-
Burgos & Muenke, 2004; Acosta et al., 2011; Cuthbert &
Insel, 2010; Larsson et al., 2012; Lilienfeld & Treadway,
2016).
The use of machine learning (ML) algorithms and multi-
variate analyzes allows for the identification and classifica-
tion of individuals with subtle differences in symptoms.
This is achieved by constructing symptom profiles (i.e.,
indicators) to predict ADHD diagnosis and eventually
resolve ADHD severity (Casey et al., 2014; Insel, 2014;
Kautzky et al., 2020; Tenev et al., 2014; Yasumura et al.,
2017). In addition, these ADHD severity indicators allow
for the construction of more precise and intelligent diagnos-
tic schemes to characterize individuals within extreme
forms of the disorder, either moderate or severe, which con-
fers higher power and resolution in identifying the causes
underpinning ADHD (Tandon et al., 2016).
Recent ADHD studies suggest the presence of subtle but
significantly independent subgroups within ADHD classi-
cal subtypes, mainly characterized by a differential severity
of symptoms and the presence/absence of externalizing
symptoms (i.e., CD and ODD) (Acosta et al., 2004; Andrews
et al., 2009). This system leads to a complex diagnosis
based on severity and persistence (Acosta et al., 2016; Jain
et al., 2011). There is also evidence that ADHD severity is
influenced to some degree by gender, psychiatric comorbid-
ity, family environment, conduct symptoms, and poor anger
management (Ramy et al., 2018). The identification of these
intermediate and extremely hidden classes is important, as
individuals with severe ADHD forms are associated with
poor academic achievement, moderate to poor pharmaco-
logical treatment response (Kotte et al., 2013; Owens &
Jackson, 2017), development of externalizing symptoms
and comorbidities that persist across the life span (Korsgaard
et al., 2016; Lee et al., 2020).
Longitudinal family- and twin-based genetic studies
estimate ADHD heritability at ~75% (Curran et al., 2001;
Faraone & Biederman, 2005), strongly linking genetic fac-
tors to the etiology of ADHD and its comorbidities (Acosta
et al., 2004, 2006; Arcos-Burgos, Castellanos, Pineda, et al.,
2004; Arcos-Burgos, Jain, et al., 2010; Jain et al., 2011;
Puentes-Rozo et al., 2019). Genetic studies have identified
genetic variation in DRD4, DRD5, DAT1, SNAP-25, FGF1,
HTR1B, 5-HTTLPR, SLC6A3, TTC12, NCAM1, and
ADGRL3 to be associated with ADHD and/or ADHD sever-
ity (Acosta et al., 2016, 2011; Arcos-Burgos, Jain, et al.,
2010; Arcos-Burgos & Muenke, 2010; Bruxel et al., 2020;
Faraone & Mick, 2010; Franke et al., 2010; Gizer et al.,
2009; Jain et al., 2011; Kotte et al., 2013; Martinez et al.,
2016; Mastronardi et al., 2016). We showed that variants in
ADGRL3 (also known as LPHN3) interact with gene vari-
ants harbored at chromosome 11q (in a region spanning the
NCAM1-TTC12-ANKK1-DRD2 genes) to dramatically
increase ADHD risk and severity in young children (Acosta
et al., 2016, 2011; Arcos-Burgos, Jain, et al., 2010; Bruxel
et al., 2020; Jain et al., 2011). Most of these associations
have been replicated in samples of Caucasian, Asian, and
Latino descent, but remain to be tested in populations with
a predominantly African ancestry.
In this report, we assess the genetics of ADHD severity
using analytical methods of linkage and association on a
cohort of 113 nuclear families ascertained from a Caribbean
community (Cervantes-Henriquez et al., 2018; Pineda et al.,
2016; Puentes-Rozo et al., 2019). This community has one
of the largest African genetic components in the Caribbean,
Central America and South America (Barragán-Duarte,
2007). We hypothesize that a new phenotypic construct of
ADHD severity based on ML algorithms may reduce intrin-
sic biases associated with its clinical heterogeneity to
enhance the performance of ADHD diagnostic tools. This
approach has potential applications in precision and person-
alized medicine in understudied populations.
Patients and Methods
Patients
During the last 12 years, we prospectively recruited and
clinically characterized 408 individuals (175 [43%] females
and 233 [57%] males; 236 [58%] affected with ADHD)
belonging to 120 nuclear families with at least one child
affected with ADHD (proband) whose members were born
in Barranquilla, Colombia, and its metropolitan area.
Because of the African Diaspora across the Americas dur-
ing the last five centuries, this community exhibits a strong
genetic admixture among aboriginal Amerindian communi-
ties, Spaniards, and Africans (Barragán-Duarte, 2007;
Puentes-Rozo et al., 2019). Admixed populations from
South America and the Caribbean are often grouped into a
single racial construct by the American census, which fails
to grasp the distribution of genetic variation among these
populations in health disparity studies (Tishkoff & Kidd,
2004). This is important as individuals of African ancestry
suffer from a disproportionate burden of morbidity and dis-
ability associated with common chronic diseases (Ezzati
et al., 2008).
The demographic, ascertainment, clinical, and genotyp-
ing data collection methods are reported elsewhere
(Cervantes-Henriquez et al., 2018; Pineda et al., 2016;
Puentes-Rozo et al., 2019). Briefly, 120 nuclear families

Cervantes-Henríquez et al. 3
comprising 408 individuals, ascertained from probands
affected by ADHD, participated in our clinical and genetic
studies of ADHD. Families of size three (n=74; 65.4%),
four (n=33; 29.2%), five (n=4; 3.5%), and six (n=2; 1.8%)
were present. Participant ages ranged between 6 and 60
years (average age=26.6±15.4 years), and 233 (57.1%)
were affected with ADHD. A total of 246 (60.3%) were
adults (aged 18 or older) of whom 97 (39%) were affected
with ADHD (14.5% females) and 149 unaffected (35.7%
females). In children and adolescents (ages 6–18, n = 162),
136 (84%) individuals were affected with ADHD (includ-
ing 34 [25%] females) and 26 (16%) were unaffected (15
[57.6%] females). No children or adults were treated with
medication for ADHD at initial assessment (Cervantes-
Henriquez et al., 2018; Pineda et al., 2016; Puentes-Rozo
et al., 2019). ADHD diagnosis was assessed in all individu-
als using the structured Diagnostic Interview for Children
and Adults (DICA) version IV (Reich, 2000). The DICA-IV
considers the A criterion of the DSM-IV and uses a system-
atic approach to collect clinical information about the
ADHD symptoms exhibited by an individual, and uses a
binary classification (0 = absent; 1 = present) and has been
extensively used by our group and others in genetic studies
of ADHD (Acosta et al., 2008, 2011; Arcos-Burgos,
Castellanos, et al., 2004; Arcos-Burgos, Jain, et al., 2010;
Palacio et al., 2004). Parents or guardians were adminis-
tered the Spanish version of the DICA-IV interview for par-
ents (DICA-IV-P). Parents and teachers of school-age
children also provided behavior rating scales. Adult partici-
pants completed the modified DICA-IV-P disruptive behav-
ior module to retrospectively collect information about the
beginning, severity and duration of their current behaviors
and conduct (Palacio et al., 2004).
In this study, we included 113 out of the 120 nuclear
families from the original cohort; seven families were
excluded because genotyping data were not available
(Puentes-Rozo et al., 2019). The average family size was
3.4 ± 0.65 (74 trios, 33 families with four members, four
families with five members, and two families with six
members). Adult participants signed a written informed
consent and individuals under 18 years of age were signed
for by their parents or legal guardians. Ethics approval was
obtained from the Ethics Committee of Universidad Simón
Bolívar at Barranquilla, Colombia (approval # 00032,
October 13, 2011).
Definition of Severity
Severity of symptoms was defined with unsupervised
machine learning (ML) algorithms aimed at identifying
latent subgroups of individuals with distinctive symptom
profiles based on clinical data. Individuals were classified
as “severe” or “not severe” based on the number of clinical
symptoms that are likely to occur more often within specific
clinical profiles (Acosta et al., 2011). ADHD symptom data
was collected during the clinical assessment stage, where 11
schools were visited (seven of medium socio-economic
stratum). Meetings were held with teachers of children aged
between 6 and 11 years old to explain the objective of the
study. Teachers were asked to identify children about whom
they had concerns that might affect their academic perfor-
mance and/or behavior in the school environment. Screening
for ADHD symptoms was performed using the teachers’
version of a brief questionnaire (checklist) based on the
DSM-IV A criterion questions. This questionnaire has pre-
viously been standardized for Colombian children and ado-
lescents. Possible ADHD cases for further investigation
were selected based on a standard score T = 50 ± 9. Thus,
children who scored T ≥ 60 were suspected of having
ADHD per Colombian norms.
Clinical profiles were derived with Latent Class Cluster
Analysis (LCCA) (Vermunt & Magidson, 2002), as imple-
mented in Latent GOLD 4.0 (Statistical Innovations,
Belmont, MA, USA), using clinical symptoms as defined
by the DSM-IV (DSM-IV, 2002) diagnostic criteria and the
DICA interview. LCCA is an unsupervised ML algorithm
widely used to identify subgroups of individuals from
mixed data in which records of variables of different nature
are available. Symptoms were registered using a binary sys-
tem that assessed the presence or absence of 20 clinical
symptoms from a symptom-based questionnaire completed
by all participants (0: absence; 1: presence), which were
further used as indicators in all LCCA models. For children,
questionnaires were answered by the parents or legal guard-
ians. We explored LCCA models, with up to 10 subgroups
for all symptoms (questions 1–20) and those specific to the
inattention (questions 1–10), hyperactivity (questions 11–
16), and impulsivity (questions 17–20) domains separately.
In these models, demographic information such as sex and
age group (children: 4–11 years; adolescents: 12–17 years;
adults >17 years) (Arcos-Burgos et al., 2010) were used as
covariates. Because of the family-based structure of our
data, LCA models were adjusted to account for the non-
independence of individuals within families. The number of
clusters was selected using a likelihood ratio test (LRT) and
their statistical significance was assessed via parametric
bootstrap with B = 500 replicates. Further, individuals are
assigned to each cluster based on the highest posterior prob-
ability of belonging to each cluster according to their symp-
toms’ profile.
To classify individuals as “severe” or “not severe” (i.e.,
the severity phenotype) according to the DSM-IV criteria,
we derived the clinical profile associated with the clusters
identified by LCCA. Individuals within a particular LCCA-
derived cluster were defined as “severe” if the number of
questions with probability of occurrence above 50% was
higher than half of the total number of domain-specific
questions. For example, in the inattention domain, which

4 Journal of Attention Disorders 00(0)
assessed 10 clinical symptoms, a particular cluster (and
therefore all individuals belonging to it) will be classified as
“severe” if at least six questions have an occurrence proba-
bility greater than 50%. From that point on, individuals with
severe symptoms were labeled as “cases” or as “controls”
otherwise.
Demographic Characterization
Measures of central tendency and dispersion were employed
to summarize continuous variables. Frequencies and pro-
portions were estimated for categorical variables. Potential
confounders were controlled with logistic regression, and
odds ratios (OR) with 95% confidence intervals (CI) were
used to summarize results. Unless otherwise stated, statisti-
cal analyzes and plotting were performed in R version 3.6.2
(R Core Team, 2019). The false discovery rate (FDR) was
used to control type I error derived from multiple testing
(Benjamini & Hochberg, 1995; Vélez et al., 2014).
Hierarchical clustering (HC) was used to evaluate group-
ing of symptoms severity. This method, in contrast with the
Spearman correlation coefficient, uses an agglomerative
algorithm that joins the most similar component features,
and sequentially joins the next most similar with the first
two converted to a new combined unit (Venables & Ripley,
2002). Clusters were generated via complete linkage; their
uncertainty was assessed using approximately unbiased
(AU) and bootstrap probability (BP) values as implemented
in the R pvclust (Suzuki et al., 2019) package. The AU
p-values and the BP values were computed by multiscale
and by normal bootstrap resampling, respectively, with the
former being a better approximation to unbiased p-values.
For this analysis, a total of 10,000 bootstrap samples were
generated. Thus, AU p-values > 95% strongly support the
existence of a cluster structure.
Genetic Analysis
DNA extraction and genotyping. DNA extraction and geno-
typing were performed as described elsewhere (Puentes-
Rozo et al., 2019). Briefly, genomic DNA was isolated
from blood samples using the MasterPure® DNA Purifica-
tion Kit (Epicentre Biotechnologies, Chicago, IL, USA)
according to the manufacturer’s protocol. DNA concentra-
tions were measured using a NanoDrop™ 2000 spectropho-
tometer (Thermo Fisher Scientific, Waltham, MA, USA).
Genotyping was performed at the University of Arizona
Genetics Core using the multiplex Sequenom® Technology
on Agena Bioscience’s MassARRAY® MALDI-TOF
instrument.
Family-based association analysis. Genetic association and
linkage analyzes were performed using the generalized fam-
ily-based association test (FBAT) model, which provides a
unified framework for the transmission disequilibrium test
(TDT) (Laird et al., 2000; Spielman et al., 1993). FBAT
accounts for different genetic models, family-based ascer-
tainment designs, complex phenotypes (diseases) architec-
ture, missing parents, and different subtypes of null
hypotheses, (Laird et al., 2000) while being minimally
affected by non-causal SNPs (Brookes et al., 2006). We used
the FBAT model as implemented in the PBAT module of
SNP Variation Suite (SVS) 8.8.3 (Golden Helix, Inc., Boze-
man, MT, USA). Briefly, several genetic models of inheri-
tance were explored (additive, dominant, recessive, and
heterozygous advantage). Haplotype tests for selected com-
binations of phenotypes and markers were applied because
PBAT automatically controls for both the type I error rate
generated by multiple comparisons (Rabinowitz & Laird,
2000) and the problem of genetic stratification (Benjamini &
Hochberg, 1995; Laird et al., 2000; Lange et al., 2004;
Lunetta et al., 2000; Vélez et al., 2014). Another advantage of
the FBAT design is that low genotype call rates in probands
can be compensated by imputation from parental genotypes,
and Mendelian inconsistencies are generally removed from
analyzes (Lange & Laird, 2002a, 2002b). As age and sex are
known to impact ADHD susceptibility (Mowlem et al., 2018;
Oerbeck et al., 2019; Ramtekkar et al., 2010; Skogli et al.,
2013), both variables were included as covariates under the
hypothesis of no linkage and no association. As a result,
inclusion of these covariates substantially improved FBAT
statistical power (Lange & Laird, 2002a, 2002b).
Predictive Genomics Models of Symptoms
Severity
We used ML algorithms to construct predictive models of
symptom severity for different symptom domains (i.e.,
global, inattention, hyperactivity, and impulsivity). The set
of predictors consisted of demographic variables, and
genetic markers. Several ML methods were explored,
including Logistic Regression, Classification and Regression
Tree (CART) (Breiman et al., 1984), Random Forest (RF)
(Breiman, 2001; Satterfield et al., 1974), Support Vector
Machine (SVM) (Cortes & Vapnik, 1995; Salazar et al.,
2012), and Tree Boosting (XGBoost) (Chen & Guestrin,
2016; Chen et al., 2020). The full list of ML algorithms is
provided in Table S1 of the Supplementary Material.
Construction, validation, and testing of these predictive
models were performed using the methods implemented in
the R caret (Kunh, 2020) package. Initially, models were
constructed and tuned with 70% of the data (training data)
using a 10-fold cross-validation procedure with five repeti-
tions. In the construction stage, models were tuned with the
training data set to identify the best combination of param-
eters for each ML algorithm that maximized the model’s
accuracy for predicting symptom severity in a particular
domain. Finally, models were validated using the remaining

Cervantes-Henríquez et al. 5
30% of the data set (testing data) and a measure of balanced
accuracy was derived. This measure was calculated as the
percentage of individuals correctly classified. On average,
the training and testing data sets were composed of 241 and
115 individuals, respectively. Models were assessed using
the Receiver Operating Characteristic (ROC) (Metz, 1978)
curve and the area under the ROC curve (AUC) as perfor-
mance measures. In addition, the sensitivity (Se), specificity
(Sp), correct classification rate (accuracy), positive predic-
tive value (PPV), negative predictive value (NPV), false
discovery rate (FDR), and false positive rate (FPR) perfor-
mance measures, were also calculated.
Results
Severity of Symptoms
Of the 386 patients studied, 224 (58%) were affected by
ADHD. The distribution by sex and age of the 386 individu-
als was as follows: 218 (56.5%) males and 168 (43.5%)
females; 120 (31.1%) were children (6–11 years), 34 (8.8%)
adolescents (12–17 years), and 232 (60.1%) adults (>17
years). We identified six significantly different latent class
clusters using all items of the DSM-IV criteria, three clus-
ters within the inattention domain, three clusters within the
hyperactivity domain, and two clusters within the impulsiv-
ity domain (Table 1 and Figure 1). Fit statistics for LCCA
models, the average posterior probabilities for each cluster
and the distribution of the total number of symptoms by
LCCA-derived cluster are presented in the Supplementary
Material. We identified that the number of DSM-IV symp-
toms differ by LCCA-derived clusters (Figure 1S;
Supplementary Material).
Following our approach, individuals assorted to clusters
2, 3, and 6 based on the full set of items globally exhibit
severe symptoms (n = 167, 43.4%; Figure 1a); individuals
classified in clusters 2 and 3 based on the inattention items
exhibit severe symptoms in this domain (n = 222, 57.5%;
Figure 1b); individuals assorted in cluster 3, based on the
hyperactivity items, were classified as severely affected in
this domain (n = 90, 23.3%; Figure 1c); and individuals
assorted in cluster 2 based on impulsivity items were classi-
fied as severely affected in this domain (n = 142, 36.8%;
Figure 1d). Regardless of the domain, severely affected
clusters are predominantly constituted by male children
with a positive ADHD diagnosis (Table 1). Overall, indi-
viduals exhibiting a complex severity phenotype show
higher number of DSM-IV symptoms (Figure 2S;
Supplementary Material). Further analyzes of age within
LCCA-derived (Figure 3S; Supplementary Material) and
severity phenotype (Figure 4S; Supplementary Material)
indicate that children, adolescents and young adults (age <
25) are severely affected, while the group of not severe indi-
viduals is mostly constituted by adults (age > 25).
Logistic Regression analyzes revealed that individuals
with ADHD are 8.9 times more likely to display a severe
phenotype based on global items (95% CI = 4.9–16.6). As
expected, children and adolescents exhibited the highest
risk of developing global-based severe symptoms (odds
ratio [OR] = 7.3, 95% CI = 4.1–13.4; OR = 4.9, 95% CI
= 2.1–11.7, respectively). Interestingly, 19 (11.4%) indi-
viduals identified as severely affected (Figure 1a) were not
diagnosed with ADHD (six children, two adolescents, and
11 adults; Table S4 and Figure 5S; Supplementary Material).
In the inattention domain, individuals with a positive
ADHD diagnosis were 14.9 times more likely to exhibit
severe inattention than controls, regardless of sex (95% CI
= 8.6–26.5). In addition, children were more likely to have
severe inattention than adults (OR = 2.1, 95% CI = 1.1–
4.1), while adolescents were equally likely (OR = 1.8, 95%
CI = 0.7-4.7). Of the individuals with severe inattention
symptoms (Figure 1b), 35 (15.8%) were not diagnosed with
ADHD (six children, two adolescents, and 27 adults; Table
S4 and Figure 5S; Supplementary Material).
In the hyperactivity domain, individuals with a positive
ADHD diagnosis were 6.9 times more likely to have severe
hyperactivity symptoms than adults, regardless of sex (95%
CI = 3.2–17.7) (Figure 2c). In contrast, adolescents and
adults were equally likely to suffer from severe hyperactiv-
ity symptoms (OR = 1.4, 95% CI = 0.53–3.8). Of individu-
als exhibiting severe inattention symptoms (Figure 1c), 7
(7.7%) were not diagnosed with ADHD (two children, one
adolescent, and four adults; Table S4 and Figure 5S;
Supplementary Material).
In the impulsivity domain, individuals with a positive
ADHD diagnosis were 4.8 times more likely to have severe
inattention symptoms (95% CI = 2.7–8.7). In addition,
children (OR = 4.8, 95% CI = 2.7–8.7), and adolescents
(OR = 2.5, 95% CI = 1.1–5.7) were more likely than adults
to be severely affected in this domain. Of all individuals
with severe impulsivity (Figure 1d), 20 (13.9%) were not
diagnosed with ADHD (four children, four adolescents, and
12 adults; Table S4 and Figure 5S; Supplementary Material).
Differential Clinical Signature on Severity
Hierarchical clustering of the LCCA-derived severity phe-
notypes revealed a differential clinical signature in our
cohort (Figure 2). Our data suggests that individuals diag-
nosed with ADHD manifest severity of symptoms in two
seemingly independent configurations. The first configura-
tion involves a combination of global severity symptoms,
while the second configuration involves symptom severity
ascribed exclusively to the hyperactivity-impulsivity
domain (Figure 2; left). Bootstrap validation suggests that
these two configurations are statistically distinct (strongly
supported by the data) and differ from that manifested by
individuals without an ADHD diagnosis (Figure 2; right).

6
Table 1. Demographic and Clinical Characteristics of Clusters Derived using Latent Class Cluster Analysis Based on DSM-IV Symptomatology.
DSM-IV domain Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
Statistic (df)pGlobal n (%) n (%) n (%) n (%) n (%) n (%)
129 (33.4) 72 (18.7) 57 (14.8) 51 (13.2) 39 (10.1) 38 (9.8)
Number of symptoms (mean ± SD) 1.5 ± 1.5 12.2 ± 2.4 18.4 ± 1.5 6.5 ± 2.2 7.2 ± 1.7 13 ± 2 814.3 (5, 376)a<.000001b
ADHD (no/yes) 111 (86)/18 (14) 9 (12.5)/63 (87.5) 5 (8.8)/52 (91.2) 23 (45.1)/28 (54.9) 9 (23.1)/30 (76.9) 5 (13.2)/33 (86.8) 173.24 (5)c<.000001
Sex (F/M) 70 (54.3)/59 (45.7) 25 (34.7)/47 (65.3) 13 (22.8)/44 (77.2) 28 (54.9)/23 (45.1) 15 (38.5)/24 (61.5) 17 (44.7)/21 (55.3) 21.39 (5)c<.001
Age 153.9 (10)c<.000001
Children 15 (11.6) 42 (58.3) 40 (70.2) 2 (3.9) 8 (20.5) 13 (34.2)
Teens 11 (8.5) 2 (2.8) 6 (10.5) 1 (2) 2 (5.1) 13 (34.2)
Adults 103 (79.8) 28 (38.9) 11 (19.3) 48 (94.1) 29 (74.4) 12 (31.6)
Inattention
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
Statistic (df)pn (%) n (%) n (%) n (%) n (%) n (%)
164 (42.5) 134 (34.7) 88 (22.8)
Number of symptoms (mean ± SD) 0.9 ± 1.1 5.7 ± 1.5 9.6 ± 0.7 – – – 1789.8 (2, 379)a<.00001b
ADHD (no/yes) 127 (77.4)/37 (22.6) 24 (17.9)/110 (82.1) 11 (12.5)/77 (87.5) – – – 147.9 (2)c<.00001
Sex (F/M) 85 (51.8)/79 (48.2) 55 (41)/79 (59) 28 (31.8)/60 (68.2) – – – 9.8 (2)c.00729
Age 69.1 (4)c<.00001
Children 22 (13.4) 49 (36.6) 49 (55.7) – – –
Teens 12 (7.3) 8 (6) 15 (17) – – –
Adults 130 (79.3) 77 (57.5) 24 (27.3) – – –
Hyperactivity
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
Statistic (df)pn (%) n (%) n (%) n (%) n (%) n (%)
188 (48.7) 108 (28) 90 (23.3)
Number of symptoms (mean ± SD) 2.5 ± 3.1 5.3 ± 3 7.8 ± 2.6 – – – 127.97 (2, 379)a<.00001b
ADHD (no/yes) 126 (67)/62 (33) 29 (26.9)/79 (73.1) 7 (7.8)/83 (92.2) – – – 101.8 (2)c<.00001
Sex (F/M) 102 (54.3)/86 (45.7) 42 (38.9)/66 (61.1) 24 (26.7)/66 (73.3) – – – 20.2 (2)c<.00001
Age 73.9 (4)c<.00001
Children 27 (14.4) 35 (32.4) 58 (64.4) – – –
Teens 17 (9) 11 (10.2) 7 (7.8) – – –
Adults 144 (76.6) 62 (57.4) 25 (27.8) – – –
Impulsivity
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6
Statistic (df)pn (%) n (%) n (%) n (%) n (%) n (%)
164 (42.5) 134 (34.7)
Number of symptoms (mean ± SD) 0.6 ± 0.8 2.9 ± 0.9 – – – – 726.580 (1, 380)a<.00001b
ADHD (no/yes) 143 (58.6)/101 (41.4) 19 (13.4)/123 (86.6) – – – – 73.539 (1)c<.00001
Sex (F/M) 121 (49.6)/123 (50.4) 47 (33.1)/95 (66.9) – – – – 9.2721 (1)c.002327
Age 90.282 (2)c<.00001
Children 36 (14.8) 84 (59.2) – – – –
Teens 20 (8.2) 15 (10.6) – – – –
Adults 188 (77) 43 (30.3) – – – –
Note. Categories with the higher frequency are shown in bold. See Figure 1 for information. df = degrees of freedom; p = p-value; SD = standard deviation.
aF statistic with (m, n) degrees of freedom.
bCorrected for ADHD diagnosis, age and gender.
c-based test.

Cervantes-Henríquez et al. 7
Figure 1. Profile plots derived using latent class cluster analysis applied to ADHD symptoms derived from DSM-IV criteria. Results
are shown for: (a) all items (Q1–Q20), (b) inattention items (Q1–Q10), (c) hyperactivity items (Q11–Q16) and (d) impulsivity items
(Q17–Q20). The number of individuals per cluster is also shown. Individuals exhibiting a severe phenotype correspond to clusters 2, 3
and 6 in (a), clusters 2 and 3 in (b), cluster 3 in (c), and cluster 2 in (d).
Q1: difficulty to do homework; Q2: concentration problems; Q3: difficulty to listening directly; Q4: insecurity when instructed; Q5: disorganized; Q6:
hates to do homework; Q7: loses objects; Q8: gets distracted by anything; Q9: forgets daily activities; Q10: gets tired, goes from one activity to the
next without finishing; Q11: writhes in his position, moves hands and feet; Q12: difficulty sitting; Q13: frequently running, climbing and jumping; Q14:
has trouble doing something quietly; Q15: does not get tired; continuously moving; Q16: frequently speaks and speaks successively; Q17: answers
questions before they finish asking them; Q18: difficulty waiting for turns; Q19: interrupts conversation and/or games of other children; Q20: performs
dangerous activities.
Both the impulsivity and hyperactivity severity of symp-
toms were different between individuals with ADHD and
controls, with no support for a particular clustering pattern
of severe symptoms in the control group (Figure 2; right).
Markers Conferring Susceptibility to Severe
Symptoms
Table 2 shows the main results of the FBAT analysis. We
found significant linkage and association of either global
or domain-specific severity with markers DRD4-rs916457,
SNAP25-rs362990, ADGRL3-rs2122642, and
ADGRL3-rs10001410. In particular, DRD4-rs916457 and
SNAP25-rs362990 were associated with both global and
inattention symptom severity; ADGRL3-rs2122642,
ADGRL3-rs10001410, and DRD4-rs916457 were found to
be associated with severity of hyperactivity symptoms;
and DRD4-rs916457, ADGRL3-rs2122642, and
SNAP25-rs362990 were associated with the severity of
impulsivity symptoms under different genetic models of
inheritance (Table 2).
Predictive Genomics Framework for Severity of
Symptoms
Figure 3 shows the accuracy of the ML algorithms used to
predict ADHD severity. We found that the SVM with poly-
nomial kernel (svmPoly), linear discriminant analysis (lda),
gradient boosting machine (gbm), and CART (rpart2) ML
algorithms provide the highest accuracy for predicting
global, inattention, hyperactive and impulsivity severity,
respectively (Figure 3a).
ROC analysis indicates that, in all cases, the AUC pro-
vides a moderate ability of the predictive models to discrim-
inate severe from non-severe individuals based on
demographic and genetic data, with accuracy values

8 Journal of Attention Disorders 00(0)
ranging between ~70% and 82% for the derived models
(Table 3 and Figure 3b). These models also provide com-
petitive Se, Sp, PPV, NPV, FDR, and FPR values, which
make them a suitable alternative to diagnose ADHD symp-
tom severity in the clinical setting (Table 3).
Analyzes of variable importance indicate that, overall,
age, sex, and severity-associated SNPs are important pre-
dictors of derived latent class clusters (Figure 3c). Among
those SNPs (Table 2), marker SNAP25-rs362990 is simi-
larly ranked in terms of variable importance for predicting
global, inattention, and hyperactivity symptom severity,
and ranked as least for predicting impulsivity severity
(Figure 3c). Interestingly, ADGRL3-rs10001410 also pro-
duces a similar effect, although to a lesser extent, for pre-
dicting inattention and hyperactivity severity, while marker
ADGRL3-rs2122642 is the third most important predictor
of hyperactivity severity, and the fourth most important to
predict impulsivity severity (Figure 3c). On the other hand,
marker DRD4-rs916457 was identified as the most impor-
tant genetic predictor of impulsivity severity, and ranked
last for predicting the severity of the remaining symptoms
(Figure 3c).
Discussion
ADHD and related behaviors fit better a classification system
based on a continuous spectrum of symptom severity, rather
than in a discrete category with a dichotomous affection status
(i.e., affected and unaffected) (Asherson & Trzaskowski,
2015). Indeed, the severity of ADHD symptoms is
significantly correlated with neuropsychological functioning
(Rajendran et al., 2013) and represents a major risk factor for
major depression disorder and ODD (Tandon et al., 2016).
These aspects might represent important factors to define
ADHD natural history and its implications for the clinical
practice (Acosta et al., 2011; Brown et al., 2017; Fletcher &
Wolfe, 2009; Shaw et al., 2012). Thus, proper and early iden-
tification of individuals at risk for severe ADHD may facili-
tate assessment, diagnosis, treatment, and follow-up
(prediction, prevention and intervention) (Kotte et al., 2013;
Owens & Jackson, 2017). Altogether, these findings highlight
the importance of assessing the severity of symptoms, espe-
cially at young ages, for the definition and selection of appro-
priate therapeutic strategies to reduce ADHD burden.
In this study, a phenotypic classification system based on
the severity of ADHD symptoms was derived from the
identification of latent classes using unsupervised ML algo-
rithms. We argue that this severity phenotype, in conjunc-
tion with ML algorithms, is suitable for and should be
preferred when dissecting causes and building predictive
frameworks, as it reduces global heterogeneity and enhances
more precise, unique and personalized demographic, cul-
tural, clinical, and biological features(Acosta et al., 2008,
2011; Jain et al., 2007; Larsson et al., 2012; Owens &
Jackson, 2017; Pineda et al., 2016). By using different ML,
we were able to build a diagnostic tool for ADHD severity
with the ultimate goal of identifying, based on training data
and a cross-validation strategy, which of them had the best
prediction accuracy. These ML algorithms (Table S1;
Supplementary Material) are particularly useful to construct
Figure 2. Correlation-based dendrograms for the severity of global, inattention, hyperactivity and impulsivity symptoms by ADHD
diagnosis. Approximately unbiased (AU) and bootstrap probability (BP) values are shown in red and green, respectively. AU p-values
greater than 95% suggest that the clustering structure is strongly supported by the data. Node numbers appear in gray. The vertical
dotted line represents the optimal number of clusters for the ADHD group. We identified two different co-occurrence configurations
in ADHD affected individuals (cases), and only one mechanism in unaffected individuals (controls). However, this latter mechanism
was not supported by the data.

Cervantes-Henríquez et al. 9
predictive models when the response variable (i.e., outcome
of interest) is of binary. Considering that individuals are
classified as “severe” or “not severe” based on the number
of symptoms with probability of occurrence above 50%, the
choice of such ML algorithms was justified. In the near
future, those ML best-performing algorithms could easily
be implemented in an intelligent information system that
facilitates the identification of individuals with one or more
ADHD severity patterns.
Our findings suggest the presence of one or more inde-
pendent groups subtly integrated as part of the ADHD phe-
notype that correlate with ADHD symptoms and functional
impairment (Kupper et al., 2012; Theule et al., 2013) ADHD
severity, and parenting problems (Anastopoulos et al., 1992;
Faraone & Larsson, 2019; Graziano et al., 2011; Palacios-
Cruz et al., 2014; Rajendran et al., 2013). In line with previ-
ous studies correlating ADHD diagnosis with long-term
outcome and poorer neuropsychological functioning (Helfer
et al., 2019; Owens & Jackson, 2017; Rajendran et al.,
2013), we found that a positive ADHD diagnosis is a risk
factor for symptoms severity either globally or domain-spe-
cific. We also identified two seemingly independent symp-
toms severity configurations in individuals with ADHD that
differ from those observed in unaffected individuals (Figure
2). Altogether, these findings suggest that symptoms sever-
ity is distinct in individuals with ADHD, but may also have
important implications for those without a positive ADHD
diagnosis. This could be partially explained by the fact that
some individuals are below a well-accepted threshold for
ADHD diagnosis (Larsson et al., 2012).
Previously, we identified that SNPs in the ADGLR3,
DRD4 and SNAP25 and FGF1 genes confer susceptibility
to ADHD in the same families evaluated in this study
(Puentes-Rozo et al., 2019). Here, we explored the associa-
tion between those SNPs and ADHD symptom severity. We
found that markers DRD4-rs916457, SNAP25-rs362990,
ADGRL3-rs2122642, and ADGRL3-rs10001410 are in link-
age and association with the severity of either global or
domain-specific symptoms under different genetic models
of inheritance (Table 2). To the best of our knowledge, this
Table 2. Results of the FBAT Analysis Applied to Symptoms Severity by Domain.
Chr Marker Gene Positiona
FBAT results
Allele
Cohort PFBAT (NIF)
Frequency Additive Dominant Recessive HA
Global
11 rs916457 DRD4 637,014 T 0.050 0.0097 (27) 0.0078 (27)
20 rs362990 SNAP25 10,295,573 A 0.906 0.0495 (55)
Inattention
11 rs916457 DRD4 637,014 T 0.050 0.0067 (27) 0.0053 (27) 0.0339 (27)
C 0.950 0.0067 (27) 0.0053 (27) 0.0339 (27)
4 rs10001410 ADGRL3 61,608,511 C 0.673 0.0067 (66) 0.0265 (54)
A 0.327 0.0067 (66) 0.0265 (54)
20 rs362990 SNAP25 10,295,573 A 0.906 0.0290 (55) 0.0290 (55) 0.0290 (55)
T 0.094 0.0290 (55) 0.0290 (55) 0.0290 (55)
Hyperactivity
4 rs2122642 ADGRL3 61,832,546 T 0.256 0.0004 (55) 0.0010 (45) 0.0339 (55)
C 0.744 0.0004 (55) 0.0010 (45) 0.0339 (55)
4 rs10001410 ADGRL3 61,608,511 C 0.673 0.0183 (66)
A 0.327 0.0183 (66)
11 rs916457 DRD4 637,014 C 0.950 0.0164 (27) 0.0269 (27)
T 0.050 0.0164 (27) 0.0269 (27)
Impulsivity
11 rs916457 DRD4 637,014 C 0.950 0.0043 (27) 0.0059 (27) 0.0253 (27)
T 0.050 0.0043 (27) 0.0059 (27) 0.0253 (27)
4 rs2122642 ADGRL3 61,832,546 T 0.256 0.0060 (55) 0.0221 (45)
C 0.744 0.0060 (55) 0.0221 (45)
20 rs362990 SNAP25 10,295,573 A 0.906 0.0112 (55) 0.0141 (55) 0.0236 (55)
T 0.094 0.0112 (55) 0.0141 (55) 0.0236 (55)
Note. For interpretation purposes, positive p-values indicate susceptibility. Chr=chromosome; HA = heterozygous advantage; NIF = number of infor-
mative families; FBAT = family-based association test.
aUCSC GRCh37/hg19 coordinates.

10 Journal of Attention Disorders 00(0)
is the first report describing an association between genetic
variants within these genes and the severity of ADHD
symptoms in a population of predominantly African
ancestry.
Marker DRD4-rs916457 was previously associated to
ADHD susceptibility in the families evaluated in this study
(Puentes-Rozo et al., 2019) and in families from the Paisa
genetic isolate from Antioquia, Colombia (Arcos-Burgos,
Castellanos, Konecki, et al., 2004). Here, this variant was
found to be associated with the severity of global, hyperac-
tivity and impulsivity symptoms (Table 2), and an impor-
tant predictor of symptom severity in our predictive
genomics framework (Table 3 and Figure 3). The role of the
DRD4 in ADHD etiology has been studied extensively
(DiMaio et al., 2003). Variants in this gene are an important
predictor of preschool aggression and a moderator of family
environmental effects, stressful life events, and inattention
severity among adults with ADHD, as well as parental and
youth ADHD outcomes (Farbiash et al., 2014; Martel et al.,
2011; Nikolas & Momany, 2017; Sanchez-Mora et al.,
2015). Furthermore, DRD4 variants have been associated
with ADHD symptom severity (Tabatabaei et al., 2017;
Tahir et al., 2000; Tovo-Rodrigues et al., 2013) and play a
significant role in the default mode, executive control and
sensorimotor networks in children with ADHD (Qian et al.,
2018). Altogether, these findings support the role of DRD4
not only in the etiology of ADHD, but also in the prediction
of ADHD symptom severity. These results contribute to our
understanding of the genetic basis of ADHD severity in this
understudied population.
SNAP25 has been extensively implicated in the etiology
of ADHD (Barr et al., 2000; Brophy et al., 2002; Feng et al.,
2005; Galvez et al., 2014; Gizer et al., 2009; Herken et al.,
2014; Kim et al., 2007). It plays an important role in the
synaptic function of specific neuronal systems. SNAP25 is
an essential component of the soluble N-ethylmaleimide-
sensitive factor attachment protein receptor (SNARE) com-
plex, which is involved in the exocytotic release of
neurotransmitters during synaptic transmission (Antonucci
et al., 2016). More recently, SNAP25 variants were reported
to be associated with working memory (Wang et al., 2018)
and neuropsychological performance in individuals with
Figure 3. Performance measures for ML algorithms used to construct predictive genomics models for ADHD symptom severity:
(a) Balanced accuracy based on the 10-fold cross validation (CV) procedure. Segments represent 95% confidence intervals. The best
performance was achieved by the Support Vector Machine (SVM) with polynomial kernel (svmPoly), linear discriminant analysis (lda),
gradient boosting machine (gbm) and Classification and Regression Tree (rpart2) ML algorithms when predicting global, inattention,
hyperactivity and impulsivity severity, respectively, (b) ROC curves for the 10-fold CV procedure and the training and testing data
sets. The 10-fold CV procedure was performed using five repetitions for each fold. The training and testing data sets data sets
consisted of 70% (n = 271) and 30% (n = 115) of the data, respectively, and (c) variable importance for the predictive models derived
in our cohort. A brief description of the ML algorithms is provided in the Supplementary Material.

Cervantes-Henríquez et al. 11
ADHD (Kim et al., 2017) Evidence also suggests that
SNAP25 is differentially expressed in the prefrontal cortex
of animal models of ADHD and of children with ADHD
when compared to controls (Li et al., 2009). Having previ-
ously identified that marker SNAP25-rs362990 conferred
susceptibility to ADHD (Puentes-Rozo et al., 2019) and
finding that this same variant is associated with the severity
of inattention and impulsivity symptoms (Table 2) expands
our understating of the role of SNAP25 in ADHD etiology.
Variable importance analyzes revealed that this marker is an
important predictor of severity latent phenotypes when
included in the ML-based predictive framework (Figure 3
and Table 2). The fact that a SNAP25 variant is associated
with impulsivity symptoms severity in our study supports
the findings by Nemeth et al. (2013), who reported an asso-
ciation between impulsivity and a polymorphic microRNA
binding site on the SNAP25 3’UTR region. Furthermore,
considering that our sample exhibits ADHD predominantly
of inattentive and impulsivity component (Cervantes-
Henriquez et al., 2018; Pineda et al., 2016; Puentes-Rozo
et al., 2019), our findings support the role of genetics in
influencing the severity of these symptoms, at least in indi-
viduals from this Caribbean population.
Variant ADGRL3-rs2122642 was previously associated
with increased ADHD susceptibility in our cohort and is
harbored in a susceptibility haplotype formed by markers
rs1565902, rs10001410, and rs2122642 (OR = 1.74,
Ppermuted = 0.021) (Puentes-Rozo et al., 2019). Variants in
ADGRL3, a member of the latrophilin family of adhesion G
protein-coupled receptors (Martinez et al., 2011; Moreno-
Salinas et al., 2019), increase the risk of developing ADHD,
externalizing disorders, including CD, ODD, and SUD, and
Table 3. Performance Measures for ML-Based Predictive Models of ADHD Severity Based on Demographic and Genetic Data.
Measure Procedureb
Severity
Global Inattention Hyperactivity Impulsivity
Confusion
matrixa
10-Fold CV 84/30/32/124 116/41/39/74 21/10/42/197 63/29/38/141
Training 85/32/29/125 119/37/38/77 22/41/9/199 64/37/29/142
Testing 34/16/11/54 51/15/22/27 10/17/8/80 31/11/14/58
Se10-Fold CV 0.724 0.748 0.333 0.624
Training 0.746 0.758 0.710 0.688
Testing 0.756 0.699 0.556 0.689
Sp10-Fold CV 0.805 0.643 0.952 0.829
Training 0.796 0.675 0.829 0.793
Testing 0.771 0.643 0.825 0.841
PPV 10-Fold CV 0.737 0.739 0.677 0.685
Training 0.726 0.763 0.349 0.634
Testing 0.680 0.773 0.370 0.738
NPV 10-Fold CV 0.795 0.655 0.824 0.788
Training 0.812 0.670 0.957 0.830
Testing 0.831 0.551 0.909 0.806
FDR 10-Fold CV 0.263 0.261 0.323 0.315
Training 0.274 0.237 0.651 0.366
Testing 0.320 0.227 0.630 0.262
FPR 10-Fold CV 0.195 0.357 0.048 0.171
Training 0.204 0.325 0.171 0.207
Testing 0.229 0.357 0.175 0.159
Accuracy 10-Fold CV 0.770 0.704 0.807 0.753
Training 0.775 0.723 0.815 0.757
Testing 0.765 0.678 0.783 0.781
AUC 10-Fold CV 0.765 0.696 0.643 0.727
Training 0.771 0.717 0.769 0.741
Testing 0.763 0.671 0.690 0.765
CV = cross-validation; Se = sensitivity; Sp = specificity; PPV = positive predictive value; NPV = negative predictive value; FDR = false discovery rate;
FPR = false positives rate; AUC = area under the ROC curve.
aThe confusion matrix is formatted as i/j/k/l, where i is the number of severe individuals correctly classified, j is the number of severe individuals classi-
fied as non-severe, k corresponds to non-severe individuals classified as severe, and l to the number of non-severe individuals correctly classified.
bThe 10-fold CV procedure was performed using 5 repetitions for each fold based on 70% of the data (training data set; n = 271 on average). Testing
was performed using the remaining 30% (testing data set; n = 115 on average). See methods for more information.

12 Journal of Attention Disorders 00(0)
adverse long-term outcomes (Acosta et al., 2016; Arcos-
Burgos et al., 2010, 2012, 2019; Jain et al., 2007, 2011).
Finding that ADGRL3-rs2122642 confers susceptibility to
more severe hyperactivity and impulsivity symptoms and
that ADGRL3-rs10001410 is associated with an increased
severity of global symptoms (Table 2 and Figure 3) high-
lights the importance of ADGRL3 as a pivotal gene in
ADHD. Although this effect has previously been shown in
the Paisa genetic isolate (Jain et al., 2007), this is the first
time it is reported for a community with a predominantly
African genetic background.
Despite our encouraging results and the possibility of
rapidly applying our findings in the clinical setting, some
limitations need to be acknowledged. First, it is frequent in
this culture to find unmedicated ADHD children.
Consequently, neither children nor adults included in this
study were medicated because they were untreated inciden-
tal cases. Although it may be possible to observe the same
medication pattern in other cultures, this is not necessarily
the norm and imposes some restrictions when extrapolating
our results to the general population. In the same vein, a
second limitation is the representativeness of the sample
across different age groups due to the family-based design
utilized for recruiting the sample. In our case, 94/386 indi-
viduals were children, 34/386 adolescents and 232/386
adults, and age ranged between 6 and 60 years, which indi-
cates that individuals are not uniformly distributed across
age groups. Thirdly, the use of the same assessment meth-
ods across age groups. We clinically assessed all individuals
in our genetic studies of ADHD using a multimodal
approach (Cervantes-Henriquez et al., 2018; Palacio et al.,
2004; Pineda et al., 2016). However, we are aware that
using the same assessment methods across age groups may
not be ideal. Thus, future studies may use different or com-
plementary assessment strategies across age groups, espe-
cially in order groups, as some participants may have
difficulties with episodic memory and hence may not be
able to assess ADHD symptoms retrospectively. A fourth
limitation is the use of related individuals in the LCCA clus-
ter analysis. Even though clustering methods designed for
related individuals exist, LCCA methods for dissecting hid-
den structures was initially proposed for unrelated individu-
als. While we used each individual's family membership to
reduce the impact of individuals' relatedness in the resulting
clusters derived by LCCA, the use of other clustering meth-
ods for identifying complex structures in data with related
individuals is yet to be explored. Finally, our sample is
comprised of nuclear families characterized by a predomi-
nantly African ancestry, which may represent both a limita-
tion and a strength of this study. It would be interesting to
compare the severity patterns in our cohort and those
reported in other populations around the world.
In summary, our study supports the role of the DRD4,
SNAP25, and ADGRL3 genes in the etiology of ADHD
severity in a Caribbean community of predominantly
African ancestry. The identification of variants within these
genes previously reported to confer susceptibility to ADHD
sheds light into the genetic basis of ADHD severity, espe-
cially in this understudied population. We argue that future
genetic studies of ADHD may greatly benefit from our find-
ings. In fact, such studies could use the severity of symp-
toms to elucidate how individuals with symptoms in
different domains—inattention, hyperactivity, impulsiv-
ity—perform neuropsychological tasks (i.e., measures of
working memory, cognitive function, and executive func-
tion) (Jimenez-Figueroa et al., 2017; Pineda-Alhucema
et al., 2018; Suarez et al., 2020), and to determine the con-
tribution to ADHD severity of genetic variants that confer
susceptibility in Caucasian populations (Demontis et al.,
2019). Although these candidate variants have not been rep-
licated in genome-wide analyzes, their utility as risk vari-
ants for ADHD needs further investigation. In addition,
these complex severity phenotypes may be used to identify
at-risk populations using ML approaches (Table 3 and
Figure 3) or genetic markers already identified to confer
susceptibility to ADHD via GWAS. In the latter case, ML
methods (i.e., clustering and dimensionality reduction tech-
niques) may be applied to identify individuals with severe
and subtle forms of ADHD based on clinical information.
Further, such ML-derived phenotypes may be used as the
trait of interest in future genetic association studies either
by evaluating the partial contribution of each genetic marker
or by calculating polygenic risk scores (Du Rietz et al.,
2018). Ultimately, the combination of clinical data, genetic
association analyzes and ML techniques may contribute to
elucidate the molecular basis of ADHD symptomatology
utilizing a multi-omics approach, and the development of
predictive tools with potential clinical application (Tenev
et al., 2014; Vahid et al., 2019).
Acknowledgments
We express our highest sentiment of appreciation to all families
enrolled in this study.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
study was financed by COLCIENCIAS, project “Fenotipos
Complejos y Endofenotipos del Trastorno por Déficit de Atención
e Hiperactividad y su Asociación con Genes Mayores y de
Susceptibilidad”, grant 1253-5453-1644, contract RC 384-2011,
awarded to Grupo de Neurociencias del Caribe, Universidad
Simón Bolívar, Barranquilla. M.L.C.H received a PhD scholarship

Cervantes-Henríquez et al. 13
from Universidad Simón Bolívar, Barranquilla; M.L.C.H, J.E.A.L
and J.I.V were partially supported by research grant FOFICO
32101 PE0031 from Universidad del Norte. M.L.C.H is a doctoral
student at Universidad del Norte; some of this work is to be pre-
sented in partial fulfilment of the requirements for the PhD degree.
ORCID iDs
Johan E. Acosta-López https://orcid.org/0000-0002-7330-6356
Jorge I. Vélez https://orcid.org/0000-0002-3146-7899
Supplemental Material
Supplemental material for this article is available online.
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Author Biographies
Martha L. Cervantes-Henríquez is a psychologist and MSc in
Genetics from Universidad Simón Bolívar, Barranquilla, member
of the Grupo de Neurociencias del Caribe and Research Professor
at Psychology Department from the same institution. PhD student
in Biomedical Sciences at Universidad del Norte, Barranquilla,
Colombia. Her current research includes the genetics of ADHD,
and the development and application of bioinformatics tools to
underpin the genetic causes of this neuropsychiatric disorder.
Johan E. Acosta-López is a psychologist, MSc in
Neuropsychology and PhD student in Neuropsychology and
Cognitive Sciences at Universidad Maimonides in Buenos Aires,
Argentina. Currently, he is the Coordinator of the Cognitive
Neurosciences Unit and Research Professor at the Psychology
Department of Universidad Simón Bolívar, Barranquilla. He has a
vast experience in clinical neuropsychology and is currently con-
ducting research studies on the neuropsychology profiles and
genetic components of mild cognitive impairment, substance use
disorders and ADHD.
Ariel F. Martinez is a research biologist at the Medical Genetics
Branch, National Human Genome Research Institute, National
Institutes of Health (Bethesda, MD) with 10 years of experience
working on human developmental diseases, both Mendelian and
complex. Dr. Martinez earned his Ph.D. in Biochemistry and
Systems Biology from the George Washington University in
Washington, D.C., United States. He is an Active Candidate of the
American Board of Medical Genetics and Genomics in the spe-
cialty of Clinical Molecular Genetics. His current work involves
genetic studies of holoprosencephaly (HPE) and ADHD, and
genetic testing of patients with HPE and Muenke syndrome. His
research is focused on understanding the molecular mechanisms
underlying disease pathogenicity.
Mauricio Arcos-Burgos is a full professor at the Department of
Psychiatry, Institute of Medical Research, Universidad de
Antioquia, Medellín, Colombia. He received his MD degree from
Universidad del Cauca, MSc from University of Antioquia, a PhD
in Genetics from Universidad de Chile and a PhD in Clinical

Cervantes-Henríquez et al. 19
Genetics from the NIH-Johns Hopkins University Partnership
Program. He has a vast experience in clinical and basic research,
management, administration, teaching, publishing, consulting,
and supervision in human genetics, clinical genetics, genetic epi-
demiology, population genetics, and evolution. His current
research aims at identifying genetic variations, biomarkers, prog-
nostic assays, and eventual personalised treatment options for dis-
eases that have a genetic background, such as rheumatologic dis-
eases, ADHD, Alzheimer's disease, cleft lip with or without cleft
palate, major depressive disorder, and obesity.
Pedro J. Puentes-Rozo is a psychologist with a specialisation in
Clinical Neuropsychology, a MSc in Neuropsychology and a PhD
in Psychology. He is a Research Professor at Universidad Simón
Bolívar, Barranquilla, and Universidad del Atlántico, Barranquilla,
and the Director of the Grupo de Neurociencias del Caribe in both
institutions. His research interests include Cognitive Neuroscience
and genetics of ADHD.
Jorge I. Vélez is an assistant professor at the Department of
Industrial Engineering of Universidad del Norte, Barranquilla. He
earned a BSc in Industrial Engineering and a MSc in Statistics
from the National University of Colombia at Medellín, and a PhD
in Medical Sciences (Genomics and Precision Medicine) from the
Australian National University in Canberra, Australia. He is inter-
ested in the development and application of Machine Learning,
bioinformatic and statistical genetic models/tools to lead to a bet-
ter understanding of the aetiology of complex human diseases as
well as the identification of key genetic factors that confer suscep-
tibility to ADHD, Alzheimer’s disease and Cancer.