Autism spectrum disorder diagnosis in adults: phenotype and genotype findings from a clinically derived cohort

Abstract

Background
The past decade has seen the development of services for adults presenting with symptoms of autism spectrum disorder (ASD) in the UK. Compared with children, little is known about the phenotypic and genetic characteristics of these patients.

Aims
This e-cohort study aimed to examine the phenotypic and genetic characteristics of a clinically presenting sample of adults diagnosed with ASD by specialist services.

Method
Individuals diagnosed with ASD as adults were recruited by the National Centre for Mental Health and completed self-report questionnaires, interviews and provided DNA; 105 eligible individuals were matched to 76 healthy controls. We investigated demographics, social history and comorbid psychiatric and physical disorders. Samples were genotyped, copy number variants (CNVs) were called and polygenic risk scores were calculated.

Results
Of individuals with ASD, 89.5% had at least one comorbid psychiatric diagnosis, with depression (62.9%) and anxiety (55.2%) being the most common. The ASD group experienced more neurological comorbidities than controls, particularly migraine headache. They were less likely to have married or be in work, and had more alcohol-related problems. There was a significantly higher load of autism common genetic variants in the adult ASD group compared with controls, but there was no difference in the rate of rare CNVs.

Conclusions
This study provides important information about psychiatric comorbidity in adult ASD, which may inform clinical practice and patient counselling. It also suggests that the polygenic load of common ASD-associated variants may be important in conferring risk within the non-intellectually disabled population of adults with ASD.

Declaration of interest
None.

Full text

Autism spectrum disorder diagnosis in adults:
phenotype and genotype findings from a clinically
derived cohort
Jack F. G. Underwood, Kimberley M. Kendall, Jennifer Berrett, Catrin Lewis, Richard Anney,
Marianne B. M. van den Bree and Jeremy Hall
Background
The past decade has seen the development of services for adults
presenting with symptoms of autism spectrum disorder (ASD) in
the UK. Compared with children, little is known about the
phenotypic and genetic characteristics of these patients.
Aims
This e-cohort study aimed to examine the phenotypic and gen-
etic characteristics of a clinically presenting sample of adults
diagnosed with ASD by specialist services.
Method
Individuals diagnosed with ASD as adults were recruited by the
National Centre for Mental Health and completed self-report
questionnaires, interviews and provided DNA; 105 eligible indi-
viduals were matched to 76 healthy controls. We investigated
demographics, social history and comorbid psychiatric and
physical disorders. Samples were genotyped, copy number
variants (CNVs) were called and polygenic risk scores were
calculated.
Results
Of individuals with ASD, 89.5% had at least one comorbid psy-
chiatric diagnosis, with depression (62.9%) and anxiety (55.2%)
being the most common. The ASD group experienced more
neurological comorbidities than controls, particularly migraine
headache. They were less likely to have married or be in work,
and had more alcohol-related problems. There was a signifi-
cantly higher load of autism common genetic variants in the
adult ASD group compared with controls, but there was no dif-
ference in the rate of rare CNVs.
Conclusions
This study provides important information about psychiatric
comorbidity in adult ASD, which may inform clinical practice and
patient counselling. It also suggests that the polygenic load of
common ASD-associated variants may be important in confer-
ring risk within the non-intellectually disabled population of
adults with ASD.
Declaration of interest
None.
Keywords
Autistic spectrum disorders; genetics; social functioning.
Copyright and usage
© The Royal College of Psychiatrists 2019. This is an Open Access
article, distributed under the terms of the Creative Commons
Attribution licence (http://creativecommons.org/licenses/by/
4.0/), which permits unrestricted re-use, distribution, and
reproduction in any medium, provided the original work is
properly cited.
Autism spectrum disorder (ASD) is a group of neurodevelopmental
disorders characterised by persistent difficulties in social interaction
and communication, as well as restricted interests, stereotypic beha-
viours and resistance to change.
1
Epidemiological studies report a
prevalence of ASD in the general population of around 1%, with a
male:female ratio of approximately 3:1.
2–5
The majority of studies
of ASD have been carried out in paediatric populations and have
included individuals with intellectual disability. The limited studies
in individuals diagnosed with ASD as adults have shown that
adults with ASD experience significant disadvantage in employment,
social relationships and quality of life.
6,7
ASD has also been
associated with increased lifetime psychiatric comorbidity.
3,8–10
Research has particularly focussed on anxiety and depression,
where a meta-analysis by Hollocks et al demonstrated lifetime rate
estimates of 42% and 37%, respectively.
11
This analysis found con-
siderable heterogeneity between the studies, and no studies examin-
ing comorbidity in non-clinical samples of adults with ASD.
11
There
has been very little research reporting lifetime outcomes for indivi-
duals diagnosed with ASD as adults. This information has the poten-
tial to inform specialist service development and tailor clinical care.
Advancing knowledge in this area is important because since the
Autism Act 2009 and recommendations in the National Institute
for Health and Care Excellence Guidelines of 2012, many adult
ASD diagnostic services have been set up across the UK, although
provision remains sporadic.
12–14
Furthermore, although some adult
services now offer genetic testing for individuals diagnosed with
ASD, little is known about the genetic characteristics of adults pre-
senting with ASD. Studies of predominantly paediatric ASD popula-
tions have shown a substantial contribution of both rare copy
number variants (CNVs) and polygenic burden of common variants
to risk for ASD. Rare CNVs are reported to occur in 10–15% of child-
hood ASD cases, which has encouraged formal genetic testing in
childhood ASD.
15,16
However, it is not known whether similar
rates are seen in individuals presenting to adult diagnostic services.
In this study we examine the demographic, social, psychiatric and
physical health characteristics of a cohort of individuals presenting
with ASD in adulthood compared with a healthy control population
from the same source databank. We also report the rates of neurode-
velopmental CNVs and polygenic burden of common variants asso-
ciated with ASD in this sample.
Method
Sample
Data was obtained from the National Centre for Mental Health
(NCMH, https://www.ncmh.info), a Welsh Government-funded
Research Centre that investigates neurodevelopmental, adult
and neurodegenerative psychiatric disorders across the lifespan.
Participants were recruited using a variety of systematic approaches
in primary, secondary and tertiary healthcare services, including the
identification of potential participants by clinical care teams,
The British Journal of Psychiatry (2019)
Page 1 of 7. doi: 10.1192/bjp.2019.30
1

screening of clinical notes and the use of disease registers. The
majority of participants in our sample were recruited via specialist
diagnostic services. Non-systematic recruitment approaches
included advertising in local media, placing posters and leaflets in
National Health Service waiting areas, liaising with voluntary orga-
nisations and contacting individuals enrolled in previous studies
within the Institute of Psychological Medicine and Clinical
Neurosciences, Cardiff, UK. To allow comparisons, the cohort
includes control participants who self-report no experiences of
any mental health disorder. All adult participants included in this
study provided written informed consent for recruitment into the
NCMH. Trained research assistants administered a brief standar-
dised interview assessment to consenting participants to ascertain
details related to the participant’s personal and family history of
mental health experiences, including any comorbidity, past and
current medication use, sociodemographic information including
employment and education, physical health diagnoses and any sub-
stance misuse. A sample of venous blood or saliva was taken for
genetic and other analyses. Participants were given a pack of stan-
dardised self-report questionnaires to complete and return to the
research team by post after the initial assessment. Confirmation
and further information regarding the primary diagnosis of ASD
was obtained from clinical records where appropriate consent had
been obtained to do so.
To date, 10 870 individuals have been recruited to the NCMH. At
the point of initial search in June 2016, the database included approxi-
mately 6600 participants, of whom 172 held a primary self-report
diagnosis of Asperger syndrome, ASD or autism and no self-report
comorbid intellectual disability, and were potentially eligible for
inclusion. On case-note review, 67 out of 172 participants were
excluded, predominantly because of loss of contact with the
NCMH to confirm diagnosis (n= 37; full details in Supplementary
Material, available at https://doi.org/10.1192/bjp.2019.30). The
remaining 105 individuals were all confirmed to have an ASD diag-
nosis consistent with ICD-10criteria by case-note review, with no evi-
dence of recorded intellectual disability and with a first diagnosis of
ASD made by secondary care clinicians assessing in a diagnostic
role when the participant was over 18 years of age.
1
Seventy-six con-
trols were randomly selected from a derived cohortmatched pairwise
on age (within 5 years and over 18 years), ethnicity and gender from
the NCMH database. Control participants were selected from indivi-
duals in the NCMH database with no current or previous self-
reported difficulties with mental health and no psychotropic medica-
tion usage. A favourable ethical opinion was received from the Wales
Research Ethics Committee 2 on 25 November 2016 for the NCMH,
and through internal NCMH applications for this study.
Measures
Demographic information
Demographic data was collected by interview and questionnaire.
Biological offspring was recorded as a binary yes/no variable and
with free-text number and biological age of children, recorded in
101 of the 105 participants with ASD and all controls. Lifetime
marriage and cohabitation was recorded as a binary yes/no variable,
recorded in 97 ofthe participants and all 76 controls. Current profes-
sion responses were multiple-choice skill level (see Supplementary
Material for categorisation), reduced to ‘currently in work’/‘currently
not in work’for our analysis and recorded for 97 participants with
ASD and 73 controls.
Comorbidities
Physical health comorbidities were established with a multiple-
choice list of 22 common physical diagnoses selected for collection
at the inception of the NCMH (Supplementary Material), reduced
to clinical system categories for comparison. Lifetime mental
health comorbidities were established with a multiple-choice
list of 37 common psychiatric diagnoses, as reported in the
Supplementary Material. By definition the control group had no
psychiatric comorbidity for comparative analysis, thus preventing
comparative analysis of mental health comorbid rates. Fifty-nine
participants with ASD and 53 control participants completed the
Beck Depression Inventory (BDI),
17
providing information on
current mood. Information on lifetime psychotropic medication
usage was collected by medication class and provided by between
93 and 95 participants with ASD, dependent upon the question
response rate.
Substance use
Individuals reported on problems encountered in their lifetime
through substance use in financial, medical, relationship and occu-
pational domains. For analysis purposes these were reduced to
binary yes/no categories. Fifty-eight participants with ASD and 60
out of 76 control participants responded to alcohol problem ques-
tions. Regular smoking was reported as a binary yes/no variable,
as was regular cannabinoid use, with complete data available for
79 participants with ASD and all controls. Regular cannabinoid
use was reported as a binary yes/no variable and completed by 31
participants with ASD and 25 control participants. Usage of other
street drugs was recorded as an initial binary yes/no variable and,
if positive, through a multiple-choice question incorporating
common UK street drugs. Eighty participants with ASD and all con-
trols responded to the initial question, with 13 participants with
ASD and 13 controls giving further answers.
Phenotype statistical analysis
Statistical analysis was performed with IBM SPSS Statistics version
23 for Windows.
18
Prevalence of comorbid psychiatric disorders
was analysed through descriptive statistics for mean, s.d., variance
and range. No comparative statistics of comorbid psychiatric diag-
nosis and associated medication usage was possible as by definition
our control population was unaffected. Prevalence of sociodemo-
graphic, physical health comorbidity and family history was initially
graphed. For normally distributed dependent variables, analysis was
performed by χ²-test followed by a binomial logistic regression with
ASD diagnosis (yes/no), with age and gender entered as covariates.
Where dependent variables were non-normal, a non-parametric
Mann–Whitney U-test was used, with Fisher’s exact test for
expected cell counts fewer than five.
Genotyping
DNA was obtained from blood and saliva samples, and genotyping
was carried out at the Medical Research Council Centre for
Neuropsychiatric Genetics and Genomics (Hadyn Ellis
Laboratory, Cardiff University, Wales). Samples were available
from 90 individuals with ASD and 60 control participants.
Individuals were genotyped on two versions of the Illumina
Infinium PsychArray: the Infinium PsychArray 24v1-2 (34 indivi-
duals with ASD, 59 controls) and the Infinium PsychArray with
custom content (IPMCN PsychChip; 56 individuals with ASD,
1 control).
CNV calling
CNVs were called using PennCNV run through a custom Galaxy
pipeline.
19,20
Individual samples were excluded if they had ≥30
CNVs, a waviness factor >0.03 or <−0.03 or a call rate <96%.
Individual CNVs were excluded if their log R ratio (LRR) s.d. was
Underwood et al
2

>0.2. CNVs constituting <50 kb or >10 single nucleotide polymorph-
isms (SNPs) were removed, using a UNIX-based script before anno-
tation (Supplementary Material); 373 samples remained after quality
control. We annotated the CNVs called with a list of 53 CNVs
associated with neurodevelopmental disorders (Supplementary
Material).
21–23
The breakpoints of the initial call of CNVs were
inspected to confirm they met the CNV calling criteria. We required
a CNV to cover more than half the critical interval and to include key
genes in the region (if known), or in the case ofsingle-gene CNVs, we
required deletions to intersect at least one exon and duplications to
cover the whole gene.
23
Polygenic risk scores
Genotype quality control was performed separately for polygenic
risk scores (PRS), using the self-authored function genotypeqc in
Stata. Full genotype quality control, genome-wide association
study (GWAS) quality control and GWAS and genotype merging
methods can be found in the Supplementary Material. After
quality control, PRS were calculated on a subset of linkage inde-
pendent markers (r
2
< 0.2) generated using the –clump flag in
PLINK. All risk scores were calculated based on the ‘risk’allele,
with weights for each risk allele taken from the GWAS beta-
coefficient (calculated as the natural log of the GWAS odds ratio).
PRS were calculated for ASD,
24
attention-deficit hyperactivity dis-
order (ADHD),
25
major depressive disorder
26
and schizophrenia,
27
using the –score flag in PLINK. Missing genotypes were scored
using the mean imputation routine. PRS were calculated for the
linkage independent markers with associations at ten P-value
thresholds (P< 0.5, <0.1, <0.05, <10
−2
, <10
−3
, <10
−4
, <10
−5
,
<10
−6
, <10
−7
and <10
−8
). SNPs included in each model are available
on request.
Results
By definition, control participants did not have psychiatric morbid-
ity and were not using any psychotropic medication.
Psychiatric comorbidity
Comorbid psychiatric diagnosis was reported by 89.5% (n= 94) of
individuals with ASD (Table 1). The most common comorbid diag-
noses were depression (62.9%, n= 66) and anxiety (55.2%, n= 58);
44.8% of individuals with ASD reported dual depression and anxiety
diagnoses. The mean BDI score for the participants with ASD was
20.356, which is on the border of mild (14–19) and moderate
(20–28) depression severity. The mean for the control participants
was 5.226, significantly different from the participants with ASD
(U= 328.5, Z-score of −7.205, r=−0.68, P<0.001), and within the
minimal range (0–13). The significant association was seen across
all BDI subscale scores except for question 19 (weight change).
Psychotropic medication usage
The most widely prescribed medications among participants with
ASD were antidepressants and anxiolytics, and over a quarter
reported taking antipsychotics (Table 2).
Sociodemographic phenotype
Data comparing sociodemographic phenotypes, physical health and
family history for participants with ASD and controls are presented
in Table 3. The average age of the participants with ASD was 37.8
years (s.d. 12.3) and for controls it was 40.7 years (s.d. 14.1, P=
0.89) because of matching. There were 80 males and 25 females
among the participants with ASD (76.2% male), and 55 males and
21 females in the control participants (72.4% male, P= 0.56).
Adults with ASD were significantly less likely to be currently
working (odds ratio 0.174, P<0.001), to be married or cohabiting
(odds ratio 0.29, P= 0.002), to be currently off work because of sick-
ness or disablement (odds ratio 69.305, P<0.001) and to have
Table 1 Psychiatric comorbidity as defined by ICD-10
ICD-10 code
Participants with
ASD (n= 105)
All comorbid
psychiatric
diagnosis
94 (89.5%)
F30–39
Mood (affective) disorder
Any 69 (65.7%)
Depression 66 (62.9%)
Bipolar disorder 9 (8.6%)
Hypomania <3 (<2.9%)
F40–48
Neurotic, stress-related
and somatoform
Any 63 (60.0%)
Anxiety 58 (55.2%)
OCD 18 (17.1%)
Phobias 9 (8.6%)
Agoraphobia 8 (7.6%)
Panic disorder 8 (7.6%)
PTSD 6 (5.7%)
F80–89
Psychological
development
Any 30 (28.6%)
Dyslexia 23 (21.9%)
Dyspraxia 19 (18.1%)
F90–98
Disorders with onset
occurring in childhood or
adolescence
Any 22 (21.0%)
ADHD 20 (19.0%)
Oppositional defiant
disorder
<3 (<2.9%)
Tic disorders <3 (<2.9%)
F20–29
Schizophrenia,
schizotypal and
delusional disorders
Any 13 (12.4%)
Psychosis 12 (11.4%)
Schizophrenia 4 (3.8%)
Schizoaffective
disorder
3 (2.9%)
F10–19
Disorders due to
substance use
Any 13 (12.4%)
Alcohol misuse 10 (9.5%)
Other substance
misuse
9 (8.6%)
F50–59
Syndromes associated
with physiological
disturbance
Any 8 (7.6%)
Anorexia 4 (3.8%)
Postnatal depression 3 (2.9%)
Bulimia <3 (<2.9%)
F60–69
Disorders of adult
personality
Any 4 (3.8%)
Borderline personality
Disorder
3 (2.9%)
Other personality
disorder
<3 (<2.9%)
F00–09
Organic disorders
Pick’s disease
(frontotemporal
dementia)
<3 (<2.9%)
Reported psychiatric comorbidity in participants with ASD grouped by ICD-10 classifi-
cation. nindicates the number of individuals reporting the diagnosis; % indicates the
percentage of the total amount of participants that responded.
ASD, autism spectrum disorder; OCD, obsessive–compulsive disorder; PTSD, post-
traumatic stress disorder; ADHD, attention-deficit hyperactivity disorder.
Table 2 Psychotropic medication usage among participants with ASD
Reported lifetime use, n(%)
Stimulants 6 (6.3%)
Antidepressants 74 (78.7%)
Anxiolytics 29 (31.2%)
Hypnotics 17 (18.1%)
Mood stabilisers 6 (6.5%)
Antipsychotics 25 (26.3%)
Reported psychotropic medication usage in participants with ASD based on the National
Centre for Mental Health questionnaire response fields. nindicates the number of
individuals reporting usage of medication of this type; % indicates the percentage of the
total amount of participants that responded.
ASD, autism spectrum disorder.
ASD diagnosis in adults
3

alcohol-related problem (odds ratio 6.24, P= 0.001). No differences
were found in rates of having one or more biological children.
Physical health comorbidity
Adults with ASD were more likely to have neurological problems
than controls (odds ratio 2.83, P= 0.004). Post hoc analysis within
the neurological subgroup explored whether differences in the
rates between participants with ASD and controls could be
explained by comorbid epilepsy and seizure disorder, conditions
reported to co-occur at elevated rates paediatric ASD populations.
This demonstrated that the effect was predominantly because of
an increased reported rate of migraine in individuals with ASD.
Forty-one (42.7%) individuals with ASD reported lifetime history
of migraine headaches compared with 15 (20.5%) control partici-
pants (odds ratio 2.60, P= 0.012, 95% CI 1.24–5.44). Eight indivi-
duals with ASD reported a lifetime history of epilepsy and seizure
disorder compared with three control participants (odds ratio
2.15, P= 0.273, 95% CI 0.55–8.49). A further association between
migraine headaches and epilepsy and seizure disorders was con-
firmed (odds ratio 11.38, P= 0.028).
Family history of psychiatric disorder
Reported family history of autism was seen at a greater rate among
participants with ASD (OR = 2.16, P= 0.041), but no differences
were found for family history of ADHD, bipolar disorder, schizo-
phrenia, dementia or post-traumatic stress disorder.
Genetic analysis
CNV analysis
The 53 neurodevelopmental CNVs tested for were present in 3.8%
(n= 4) of participants with ASD and 1.3% (n= 1) of controls. The
CNVs in participants with ASD were 2q13 deletion (n= 2),
15q13.3 duplication (n= 1) and 16p13.11 duplication (n= 1).
A single 2p16.3 deletion was found in one control.
Analyses of PRS
PRS derived from GWAS of ASD, ADHD, major depressive dis-
order and schizophrenia were calculated for each individual. Only
the PRS derived from the ASD GWAS showed differences
between cases and controls (Fig. 1); despite the modest sample
size of this cohort, we calculate that approximately 12.86% (P<
0.00001) of variance can be explained from PRS derived from
linkage independent markers showing association at P< 0.001 in
the ASD GWAS (433 SNPs in model). No significant difference
between cases and controls was seen for PRS for ADHD, major
depressive disorder or schizophrenia.
Discussion
In this study we report on the phenotypic characteristics and genetic
profiles of a sample of individuals with ASD diagnosed in adulthood
without intellectual disability. We found high rates of psychiatric
comorbidity, problem alcohol use and medication usage in individuals
with ASD. These individuals also had higher rates of neurological
comorbidity than controls and there was an association between ASD
diagnosis and migraine. There was a significant association between
PRS for ASD and a diagnosis of ASD, but no significant increase in
rate of rare neurodevelopmental CNVs in individuals with ASD.
Comorbidity
A total of 89.5% of participants with ASD reported a further lifetime
psychiatric comorbidity, comparable with previously reported popula-
tions including those diagnosed as adults or with intellectual disability
(69–80%).
7–10
Depression was the most common lifetime comorbid
diagnosis with a rate of 62.9%, comparable with previously reported
rates (53–70%) in larger samples including those diagnosed as chil-
dren or with intellectual disability.
7,12,28,29
Lifetime anxiety diagnosis
rates of around 50% were also similar to previously reported in
those with ASD but with intellectual disability.
3,8–10,30
The implication
Table 3 Physical health comorbidity, family history, social demographics and substance use in participants with ASD and matched controls
Participants with ASD (n, %) Matched controls (n, %) Odds ratio (95% CI) Pvalue
Currently working 31, 32.0% 53, 72.6% 0.174 (0.09–0.35) <0.001
Currently not working because of sickness/disablement 48, 49.5% 1, 0.01% 69.305 (9.23–520.42) <0.001
Biological children 36, 35.6% 41, 54.0% 0.526 (0.26–1.05) 0.068
Married or cohabiting 49, 50.5% 58, 76.3% 0.29 (0.14–0.64) 0.002
Regular smoker 34, 43.0% 35, 46.1% 1.017 (0.99–1.04) 0.169
Problems due to alcohol use 21, 36.2% 5, 8.3% 6.42 (2.20–18.72) 0.001
Regular cannabinoids use 22, 27.9% 16, 21.1% 1.00 (0.97–1.03) 0.903
Problems due to cannabinoid use 9, 29.0% 4, 16.0% 1.02 (0.96–1.08) 0.590
Use of other street drugs 10, 12.5% 4, 5.3% 0.99 (0.95–1.04) 0.711
Problems due to other street drugs use 4, 33.3% 1, 7.7% 0.98 (0.89–1.08) 0.682
Physical health comorbidity
Respiratory 36, 34.3% 16, 21.1% 1.84 (0.91–3.71) 0.089
Neurological 42, 40.0% 14, 14.0% 2.83 (1.40–5.74) 0.004
Metabolic 18, 17.1% 8, 18.4% 1.66 (0.67–4.11) 0.269
Rheumatological and orthopaedic 12, 11.4% 3, 3.9% 3.06 (0.82–11.33) 0.095
Cardiological 17, 16.2% 6, 7.9% 2.09 (0.77–5.66) 0.148
Renal and gastrointestinal 5, 4.8% 4, 5.3% 0.91 (0.23–3.59) 0.896
Oncological 1, 1.0% 0, 0% –0.996
Family history
FHx ADHD 14 (14.1%) 9 (12.2%) 1.22 (0.49–3.02) 0.674
FHx bipolar disorder 9 (9.3%) 11 (15.1%) 0.58 (0.22–1.51) 0.265
FHx schizophrenia 6 (6.3%) 3 (4.1%) 1.58 (0.38–6.60) 0.529
FHx dementia 22 (22.4%) 16 (22.5%) 1.01 (0.48–2.11) 0.977
FHx PTSD 5 (5.1%) 2 (2.7%) 1.80 (0.34–9.67) 0.491
FHx autism 30 (34.1%) 16 (21.6%) 2.16 (1.03–4.53) 0.041
Reported physical health comorbidity, family health history, social demographics and substance use in participants with ASD based on the National Centre for Mental Health questionnaire
response fields. The comparison group are matched healthy controls. For normally distributed samples, analyses were binomial logistic regression, with age and gender as covariates.
Where data were non-normally distributed, the non-parametric Mann–Whitney U-test was used, with Fisher’s exact test for expected cell counts fewer than five. nindicates the number of
individuals; % indicates the percentage of the total amount of participants that responded.
ASD, autism spectrum disorder; FHx, reported family history; ADHD, attention-deficit hyperactivity disorder; PTSD, post-traumatic stress disorder.
Underwood et al
4

that a primary diagnosis of ASD is linked to high lifetime rates of
anxiety and depression even in populations without intellectual dis-
ability is important for clinical consultations. Whether the aetiology
of ASD predisposes for depression and anxiety, or life events and dif-
ficulties experienced by individuals because of their ASD precipitate
depression and anxiety, or both, is a ‘chicken and egg’conundrum
worthy of further study.
Rates of dyslexia (21.9%), dyspraxia (18.1%) and ADHD
(19.0%), although elevated compared to the general population,
were lower than in previous studies of ASD, which have mainly
been in children.
5,7,31
As our population were diagnosed as adults,
fewer of the neurodevelopmental features that prompt assessment
may be expected, and such a profile fits with genetic findings.
Shared aetiology may explain the significantly higher rates of neuro-
logical conditions seen in participants with ASD, predominantly
due to a strong prevalence of comorbid migraine. Although a patho-
physiological link has been suggested for this in the past, this is the
first clear evidence of an association, to our knowledge, and
warrants further investigation.
32,33
Lifetime psychotropic medication usage was concordant with
the diagnosis findings. Nearly 80% of the population had taken anti-
depressant medications during their lifetime, greater than the rates
predicted in the literature.
9,30
Further, 26.3% reported taking anti-
psychotic medication when only 12.4% had a diagnosis of psychosis.
This may be partly due to antipsychotic medication usage among
four of the nine individuals reporting bipolar disorder, or for off-
label use for symptomatic or behavioural management as is evi-
denced in adolescents and young adults.
9,34
Social demographics
The approximate 3:1 male:female ratio seen in our population
was consistent with reported gender differences.
2,5,31,35
Although
alcohol and other substance use and smoking rates in our control
and participants with ASD were broadly similar, ASD was asso-
ciated with higher rates of problematic alcohol use. The reasons
were unclear, but could be usage to self-medicate for the aforemen-
tioned anxiety as suggested by other authors, or to facilitate social
interactions.
5
Adults with ASD were found to be more than five times less
likely to be employed individuals, with most reporting they were
unemployed or unable to work because of illness. This suggests
supportive employment is available for too few.
5
We hypothesise
that difficulties with social communication underlie the strikingly
lower rate of marriage for those with ASD; however, interestingly,
the difference in rates of biological children was not significant.
This points to the clear impact of ASD even when diagnosed in
adulthood, and the contribution of comorbid psychiatric conditions
to the life experience of an individual with ASD.
Genetic risk
Previous studies have demonstrated the strong heritability of ASD.
5
As expected, we demonstrated that adults with ASD were significantly
more likely to have family members with ASD. There were no other
significant associations with mental health disorder family history.
This result may be of use for patients presenting to clinical practice
wanting to know associated family risks, but requires confirmation
from a larger study. Participants with ASD had a slightly higher
number of neurodevelopmental disorder–associated CNVs than con-
trols, although this was not statistically significant in this sample.
Although a statistically significant increase in burden of CNVs in
this group might be confirmed in a larger population, rates were mark-
edly lower than those reported in paediatric ASD populations, who
may have a greater overall neurodevelopmental symptom profile.
27
PRS analysis demonstrated a significant contribution of polygenic
load of ASD-associated common variants to risk in adult participants
with ASD.
16,24,36
It is noticeable that a significantly increased poly-
genic risk burden was detected even in this relatively small sample
size. Taken together, these results suggest that adults presenting
with ASD may have a lower burden of rare penetrant variants and a
higher polygenic contribution of common risk alleles than childhood
ASD populations, potentially reflecting less severe neurodevelopmen-
tal disability.
Limitations
The sample used in this study was drawn from the NCMH database,
a resource rich in phenotypic data, and participants consented for
anonymised genetic analysis. NCMH recruits through multiple
methodologies. Sixty-nine of our 105 individuals with ASD were
recruited from secondary ASD diagnostic services, a help-seeking
population, and therefore our results may overestimate comorbidity
rates reflective of recruitment bias. The benefit of the extensive
range of data is offset by clinical diagnoses being initially self-
reported, necessitating a review of the individuals’notes to
confirm specific diagnoses by specialist clinicians. All ASD diagno-
ses in this study were confirmed against ICD-10 diagnostic criteria.
This prevents analysis by symptom severity and lacks the clarity of
diagnostic scoring systems or rating scales such as the Autism
Diagnostic Interview –Revised (ADI-R) however, this reflects the
0.20
0.18
0.16
0.14
0.12
0.10
0.08
Variance explaines by PRS
0.06
0.04
0.02
0
1×10–6
not significant P<0.05 P<0.01 P<0.001 P<0.0001
0.00001 0.0001 0.001 0.01 0.05 0.1 0.5
Fig. 1 Percentage variance explained by PRS at analysed association levels for ASD. Percentage variance at eight association marker levels
derived from linkage independent markers in the ASD genome-wide association study. Significance of associations between single nucleotide
polymorphisms and ASD range from 0.5 to 1 × 10
−6
. Probability of association to be found at each individual variance level is denoted by Pvalue.
ASD, autism spectrum disorder; PRS, polygenic risk score.
ASD diagnosis in adults
5

pattern seen within mental health services, where diagnoses are
often clinical and diagnostic tool usage varies. By design our
control population did not have psychiatric comorbidities, and
this lack of control comorbidity data prevented comparative statis-
tical analysis. It is also likely that demographic data in this control
cohort are skewed by this lack of psychiatric comorbidity away
from the general population mean, which may inflate effect sizes.
All comorbid diagnoses were required to have been made by a
doctor, but were self-reported and therefore not standardised. As
a descriptive study we benefited from the large quantity and exten-
sive domains available from the NCMH data-set. Where possible,
results were elicited through subgroup analysis to reduce test
volume. Because of the number of variables tested, only the associ-
ation with ‘not currently working’and ‘off work because of sickness
or disablement’would remain significant following a conservative
Bonferroni correction. Associations here reported therefore
require further testing with a larger sample.
Implications for services
In this study we provide comorbidity rates and social demographic
information for adults presenting with ASD, with clinical utility for
consultations in adult ASD diagnostic services. Our findings suggest
that a majority of adults with ASD have psychiatric comorbidity
and should be appropriately screened and managed. Additionally,
clinicians should be aware of associated social demographic features,
including the high rates of alcohol problem use and being out of
work. Signposting toward, and integration with, third-sector organisa-
tions and services supporting adults with ASD is vital, and our results
may inform these services toward the possible difficulties some diag-
nosed with ASD in adulthood may face. The significantly increased
polygenic risk burden seen in our sample is a difficult concept to
convey in a clinical environment. It is likely that for some individuals,
genetic testing would provide an element of assurance and diagnostic
clarity, and our results may assist in genetic counselling. For many the
benefit of an ambiguous answer is questionable. This is an area where
future work with larger population sizes promises better results. Our
findings suggest that the polygenic risk burden is present in clinical
samples, and ongoing advances may allow us to explain this fully,
along with the associations and implications.
Jack F. G. Underwood, MBBS, PgCert, MRCPsych , Clinical Research Fellow,
Neuroscience and Mental Health Research Institute, Cardiff University, UK; Kimberley
M. Kendall, BSc Hons, MBBCh, MRCPsych, Wellcome Trust Clinical Research Fellow,
MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, UK;
Jennifer Berrett, BSc, MSc, Trainee Clinical Psychologist, Neuroscience and Mental
Health Research Institute, Cardiff University, UK; Catrin Lewis, BSc, PhD, Research
Associate, National Centre for Mental Health, Cardiff University, UK; Richard Anney,
BMedSc, PhD, Senior Lecturer (Bioinformatics), MRC Centre for Neuropsychiatric
Genetics and Genomics, Cardiff University, UK; Marianne B. M. van den Bree, BSc,
MSc, PhD, Professor of Psychological Medicine, MRC Centre for Neuropsychiatric
Genetics and Genomics, Cardiff University, UK; Jeremy Hall, MA, MBBChir, MPhil, PhD,
MRCPsych, Director and Research Theme Lead, Neuroscience and Mental Health
Research Institute, Cardiff University, UK
Correspondence: Dr Jack F. G. Underwood, Neuroscience & Mental Health Research
Institute, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, UK.
Email: underwoodj4@cardiff.ac.uk
First received 27 Jul 2018, final revision 19 Dec 2018, accepted 4 Jan 2019
Supplementary material
Supplementary material is available online at https://doi.org/10.1192/bjp.2019.30.
Acknowledgements
We thank Professor Ian Jones along with the rest of the team at NCMH, the Bioinformatics team
at Cardiff University and all the staff at the Medical Research Council Centre for
Neuropsychiatric Genetics and Genomics (CNGG). The NCMH is a collaboration between
Cardiff, Swansea and Bangor Universities and is funded by the Welsh Government through
Health and Care Research Wales. We thank the NCMH study participants for their invaluable
contribution to this project.
Funding
This work was supported by the Wellcome Trust through an Institutional Strategic Support Fund
Clinical Primer Grant to J.F.G.U, and the Medical Research Council through the Mental Health
Data Pathfinder (MC_PC_17212) to J.H. and Cardiff University.
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