High Rates of Genetic Diagnosis in Psychiatric Patients with and without Neurodevelopmental Disorders: Toward Improved Genetic Diagnosis in Psychiatric Populations

Abstract

Objective:

There is a paucity of literature on genetic diagnosis in psychiatric populations, particularly the vulnerable population of patients with concomitant neurodevelopmental disorder (NDD). In this cross-sectional study, we investigated the genetic diagnostic rate in 151 adult psychiatric patients from two centers in Ontario, Canada, including a large subset (73.5%) with concurrent NDD, and performed phenotypic analysis to determine the strongest predictors for the presence of a genetic diagnosis.

Method:

Patients 16 years of age or older and affected with a psychiatric disorder plus at least one of NDD, neurological disorder, congenital anomaly, dysmorphic features, or family history of NDD were recruited through the genetics clinics between 2012 and 2016. Patients underwent genetic assessment and testing according to clinical standards. Chi-squared test was used for phenotypic comparisons. Multivariate logistic regression analysis was performed to determine which phenotypic features were predictive of genetic diagnosis types.

Results:

Overall, 45.7% of patients in the total cohort were diagnosed with genetic disorders with the vast majority of diagnoses (89.9%) comprising single gene and chromosomal disorders. There were management and treatment implications for almost two-thirds (63.8%) of diagnosed patients. Presence of a single gene disorder or chromosomal diagnosis was predicted by differing combinations of neurological, NDD, and psychiatric phenotypes.

Conclusion:

The results of this study highlight the frequency and impact of genetic diagnosis in psychiatric populations, particularly those with concomitant NDD. Genetic assessment should be considered in psychiatric patients, particularly those with multiple brain phenotypes (psychiatric, neurodevelopmental, neurological).

Introduction

Mental illness affects 1 in 5 people in Canada¹ and costs the Canadian economy an estimated CAD$51 billion per year.² Additionally, approximately 1% to 3% of Canadians have neurodevelopmental disorders (NDD).^3,4 Individuals with NDD are at increased risk of developing mental health disorders throughout their lives; an estimated 45% of adults with NDD are dually diagnosed with a psychiatric disorder.⁴

The patient population with concurrent NDD and psychiatric disorders is underserved and poorly characterized, often leading to poor mental and physical health outcomes. Individuals with psychiatric disorders, especially those with comorbid NDD, have increased rates of emergency department visits and psychiatric hospital admissions as well as higher psychotropic medication use and overall health-care costs, suggesting that the current care models are not adequate to meet the needs of this complex patient population.^5–7

Genetic disorders are estimated to affect approximately 1% of the global population at birth.⁸ Many individuals with underlying genetic disorders may initially or primarily manifest with psychiatric or cognitive phenotypes. Notable examples include inborn errors of metabolism (IEMs), such as Wilson disease or acute intermittent porphyria, and chromosomal copy number variants (CNVs), such as 22q11.2 deletion syndrome,^9–16 which is estimated to result in a 67.7-fold increased risk of developing psychosis compared to the general population.^17,18 Studies have shown an enrichment of genetic diagnoses in certain subpopulations such as in up to 40% of individuals with NDD.¹⁹ There is, however, a paucity of literature regarding genetic diagnostic rates in psychiatric populations.

Identifying underlying genetic diagnoses in psychiatric patients allows for optimization of management and treatment, including the potential for pharmacological therapies targeted to the underlying genetic defect. In addition, precise diagnosis allows for accurate recurrent risk information to be conveyed to patients and their families. In this cross-sectional study, we investigated the genetic diagnostic rates in adult psychiatric patients, including a large subset with NDD, and performed phenotypic analysis to determine the strongest predictors for the presence of a genetic diagnosis.

Methods

Patient Recruitment

Patients were recruited through the genetics clinics at Mount Sinai Hospital (MSH, Toronto, Ontario, Canada) and Ontario Shores Centre for Mental Health Sciences (OSCMHS, Whitby, Ontario, Canada) between 2012 and 2016. Inclusion criteria were being 16 years of age or older and affected with a psychiatric disorder plus at least one of the following: NDD, neurological disorder, congenital anomaly, dysmorphic features, or family history of NDD. Patients with previously molecularly or biochemically confirmed genetic diagnosis were excluded. Informed written consent to participate in the study was obtained from the patients or their legal guardians. Research ethics board approval for this study was obtained from MSH and OSCMHS.

Phenotyping

Patients underwent genetic assessment and testing according to clinical standards. Typically, this entailed chromosomal microarray, testing for Fragile X syndrome, and biochemical screening for IEMs, often followed by single-gene or gene panel testing. In 2016, clinical whole-exome sequencing (WES) became available to select patients in the province of Ontario, Canada; thus, five patients underwent WES. Phenotypic data were collected using PhenoTips database software.²⁰ Data were collected regarding patients’ medical and psychiatric history, family history, physical examination findings, clinical investigation and test results, and genetic diagnosis. Clinical information was corroborated by review of supporting medical records including psychiatric and medical specialist assessments, investigations, and imaging reports. Phenotypic data were subsequently coded according to the International Statistical Classification of Diseases, Tenth Revision for statistical analysis.²¹

Statistical Analysis

Genetic diagnoses were subcategorized into single gene disorders, chromosomal disorders, and IEMs. Chi-squared test was used for phenotypic comparisons between the various types of diagnoses and patients without a genetic diagnosis, and all significant P values were Bonferroni corrected for the total number of tests done. A Bonferroni-corrected P value of <0.05 was considered significant. Multivariate logistic regression analysis was performed to determine which phenotypic features were predictive of diagnosis types. All phenotypic and regression analyses were performed on IBM SPSS Statistics for Windows Version 20.0.

Results

Demographics and Diagnostic Yield

A total of 151 patients met recruitment criteria and completed genetic assessment and testing. The sample demographics and genetic diagnostic yield are shown in Table 1. A large subset (73.5%) of the total cohort comprised patients with concomitant NDD. There were equal numbers of males and females, and there was a tendency for females to be of older age than males at presentation. The most common psychiatric presentations (Table 1, Supplementary Table 1) were mood disorders, particularly depression, neurotic, stress-related and somatoform disorders (NSS), particularly anxiety disorders, and behavioral disorders. Many patients presented with more than 1 psychiatric disorder; Supplementary Figure 1 shows the distribution of psychiatric diagnoses among patients in the total cohort; NDD and non-NDD subsets with any genetic diagnosis; and in the subsets with single gene disorder, chromosomal disorder, and IEM. Overall, 69 of 151 patients (45.7%) were diagnosed with genetic disorders, with the vast majority identified to have single gene (40/151; 26.5%) and chromosomal (23/151; 15.2%) disorders. One patient was diagnosed with 2 single gene disorders, while another patient was diagnosed with a single gene disorder and a chromosomal CNV. The genetic diagnoses identified in the study cohort are shown in Supplementary Table 2. Recurrent diagnoses include Ehlers–Danlos syndrome (n = 10), 22q11.2 deletion syndrome (n = 3), Phelan–McDermid syndrome (n = 3), Fragile X syndrome (n = 3), mitochondrial disease (n = 3), Pettigrew syndrome (n = 2), and Cantú syndrome (n = 2). There were implications for management and treatment for 44 (63.8%) of the diagnosed patients (Supplementary Table 2); all diagnoses have recurrence risk implications for the patient and/or their family members.

Table 1.

Sample Demographics and Genetic Diagnostic Yield.

Demographic Characteristic	Total Psychiatric Cohort	NDD Subset	Non-NDD Subset
Sample size (%)	151 (100)	111 (73.5)	40 (26.5)
Male: Female (%)	76 (50.3): 75 (49.7)	56 (50.5): 55 (49.5)	20 (50.0): 20 (50.0)
Average age in years ± SD Male: Female	31.5 ± 10.7: 39.2 ± 14.0	29.7+9.0: 36.5+13.2	36.6 ± 13.4: 46.7 ± 13.6
Any genetic diagnosis (%)	69 (45.7)a	50 (45.0)a	19 (47.5)
Single gene disorder (%)	40 (26.5)b	26 (23.4)b	14 (35.0)
Chromosomal disorder (%)	23 (15.2)b	20 (18.0)b	3 (7.5)
Inborn error of metabolism (%)	4 (2.6)	2 (1.8)	2 (5.0)
Psychiatric disorder (ICD-10 code)	N (%)	N (%)	N (%)
Mood (affective) disorders (F30-F39)	81 (53.3)	52 (46.4)	29 (72.5)
Recurrent depressive disorder (F33)	58 (38.4)	37 (33.3)	21 (52.5)
Bipolar affective disorder (F31)	19 (12.6)	10 (9.0)	9 (22.5)
NSS disorders [F40-F48]	70 (46.4)	50 (45.0)	20 (50.0)
Anxiety (includes GAD [F41.1], other specified anxiety disorders [F41.8], anxiety disorder, unspecified [F41.9])	54 (35.8)	37 (33.3)	17 (42.5)
Behavioral disorders (F50-59, F60-69, F90-98)	87 (57.2)	72 (64.3)	15 (37.5)
Disturbance of activity and attention (i.e., ADHD; F90.0)	29 (19.2)	25 (22.5)	4 (10)
SSD (F20-F29)	36 (23.8)	29 (26.1)	7 (17.5)
Schizophrenia (F20)	10 (6.6)	9 (8.1)	1 (2.5)

Note. SD = standard deviation; ADHD = attention deficit hyperactivity disorder; ICD-10 = International Statistical Classification of Diseases, Tenth Revision; GAD = generalized anxiety disorder; NDD = neurodevelopmental disorder; NSS = neurotic, stress-related, and somatoform disorders; SSD = schizophrenia, schizotypal, and delusional disorders.

^a Includes 3 patients with “Other” genetic diagnoses that have unknown or heterogeneous genetic etiology.

^b One patient has both a single gene disorder and a chromosomal disorder and is included in both categories.

Specific diagnosis types are italicized under the broader diagnostic categories.

Phenotypic Analysis

Due to the small number of patients diagnosed with IEMs (n = 4), no further statistical analysis was undertaken for this subcategory. Tables 2 and 3 highlight the phenotypic features of the entire psychiatric cohort and the NDD compared to non-NDD subsets, respectively, that were significantly associated with having any type of genetic diagnosis, single gene disorder, or chromosomal disorder. Schizophrenia was significantly associated with having a chromosomal disorder, while mood disorders, especially major depressive disorder (MDD), and NSS, especially anxiety disorders, were associated with single gene disorders. Presence of NDD and neurological findings of seizures and migraines were significantly associated with both single gene and chromosomal disorders, while family history of NDD and respiratory phenotype were only associated with single gene disorders. In the NDD subset, respiratory findings, specifically obstructive lung disease, and family history of NDD were significantly associated with having a single gene disorder, while musculoskeletal phenotypes were associated with having a chromosomal disorder.

Table 2.

Phenotypic Analysis of Total Psychiatric Cohort with Genetic Diagnoses.

Phenotypic Variable	Any Genetic Diagnosis (n = 69)	Single Gene Disorder (n = 40)	Chromosomal Disorder (n = 24)
Psychiatric
Schizophrenia	ns	ns	50.0% (.031 [.217])
Mood disorders	62.32% (.01 [.07])	72.50% (0.001 [0.007])	ns
NSS	ns	60.00% (0.022 [0.154])	ns
Anxiety	ns	25.00% (0.014 [0.098])	ns
Neurological
Seizures	18.84% (.035 [.21])	7.50% (0.001 [0.005])	ns
Migraines	ns	35.00% (0.005 [0.025])	4.17% (.036 [.144])
Cardiovascular	36.76% (.046 [.276])	ns	ns
Respiratory	23.53% (.011 [.066])	25.00% (0.048 [0.24])	ns
Obstructive lung disease	14.49% (.042 [.252])	ns	ns
BMI Class II	5.00% (.049 [.294])	ns	ns
Neurodevelopmental	ns	50.00% (2.36E-04 [1.18E-03])	83.33% (.02 [.08])
Early developmental delay	ns	ns	83.33% (.019 [.076])
Autism spectrum disorder	ns	ns	37.50% (.018 [.072])
Family history of NDD	66.15% (.025 [.15])	73.68% (0.01 [0.05])	ns
Musculoskeletal	ns	ns	ns
Gastrointestinal	ns	ns	ns
Other	ns	ns	ns

Note. The data values are presented as percentage (P value [corrected]), the percentage of patients presenting with a given phenotype within the diagnosis subset, the P values of statistically significant phenotypic variables in study patients with the various types of genetic diagnoses, and the Bonferroni-corrected P values in square brackets. Broad category phenotype (nonitalicized phenotype headings) analysis was Bonferroni-corrected for 10 tests and specific phenotypes (italicized phenotype headings) within the broad phenotype were Bonferroni-corrected respective to the number of specific phenotypes within each broad phenotype. ns = not significant; BMI = body mass index; NDD = neurodevelopmental disorder; NSS = neurotic, stress-related, and somatoform disorders.

Table 3.

Phenotypic Analysis of Patient Subsets with and Without NDD with Genetic Diagnoses.

	Any Genetic Diagnosis		Single Gene Disorder		Chromosomal Disorder
Phenotypic Variable	NDD (n = 50)	Non-NDD (n = 19)	NDD (n = 26)	Non-NDD (n = 14)	NDD (n = 20)	Non-NDD (n = 3)
Cardiovascular	34.00% (.031 [.124])	44.44% (.069)
Respiratory	22.00% (.007 [.028])	27.78% (.052)	26.92% (0.012 [0.048])	21.43% (.855)
Obstructive lung disease	14.00% (.012 [.048])	15.79% (.894)	23.08% (3.49E-4 [1.40E-3])	7.14% (.307)
Musculoskeletal					33.33% (.021 [.042])	0.00% (.211)
Neurodevelopmental			73.08% (0.02 [0.08])	7.14% (.034 [.136])
Family history of NDD	72.34% (.016 [.064])	50.00% (.630)	80.00% (0.017 [0.068])	61.54% (.161)

Note. The data values are presented as percentage (P value [corrected]), the percentage of patients presenting with a given phenotype within the diagnosis subset, and the p values of statistically significant phenotypic variables in NDD study patients with the various types of genetic diagnoses are shown, with Bonferroni-corrected P values in square brackets. P values of phenotype analysis of any genetic diagnosis, single gene disorder, and chromosomal disorder were Bonferroni corrected for 4, 4, and 2 tests, respectively. NDD = neurodevelopmental disorder. Specific phenotype is italicized under the broad phenotype category.

Regression Analysis

Regression analysis using statistically significant phenotypic associations showed that presence of a single gene disorder diagnosis was best predicted by the combination of having a neurological phenotype, specifically seizures (odds ratio [OR] = 4.49, 95% CI, 1.24 to 16.3), and migraines (OR = 3.02, 95% CI, 1.12 to 8.13), mood disorder (OR = 3.36, 95% CI, 1.39 to 8.06), and family history of NDD (OR = 2.51, 95% CI, 1.03to 6.06). This model explained 28.3% (Nagelkerke R ²) of variance in single gene disorder diagnosis and correctly identified 75.9% of cases (sensitivity = 95.1%, specificity =23.7%). Presence of a chromosomal diagnosis was best predicted by the combination of neurological phenotype, specifically migraines (OR = 5.88, 95% CI, 0.75 to 46.33), NDD (OR = 3.68, 95% CI, 1.16 to 11.63), and schizophrenia (OR = 1.98, 95% CI, 0.77 to 5.08). This model explained 14.5% (Nagelkerke R ²) of variance and correctly identified 84.1% of cases (sensitivity = 100%, specificity = 0%). In the NDD subset, presence of single gene disorder was best predicted by the combination of mood disorder (OR = 2.97, 95% CI, 1.05 to 8.33), respiratory phenotype, specifically obstructive lung disease (OR = 22.22, 95% CI, 2.05 to 250), and family history of NDD (OR = 3.52, 95% CI, 1.06 to 11.76). This model explained 27.2% (Nagelkerke R ²) of variance and correctly identified 79.8% of cases (sensitivity = 98.7%, specificity = 20%). The only significant predictor of chromosomal disorder within the NDD subset was musculoskeletal features with an OR of 3.14 (95% CI, 1.16 to 8.56; sensitivity = 100%, specificity = 0%).

Discussion

In this study of 151 psychiatric patients, 42.4% were diagnosed with an underlying genetic disorder through standard clinical genetic assessment and testing and 3.3% by WES (1 patient was diagnosed with 2 single gene disorders, 1 clinically and 1 through WES). This is comparable to the genetic diagnostic rate previously reported in NDD populations.¹⁹ Although the majority of our study cohort (73.5%) had concomitant NDD, the high diagnostic rate across the entire study cohort, including similar diagnostic rate in the non-NDD subset, supports that even “pure” psychiatric patients without NDD have a high burden of underlying genetic diagnoses.

The availability of treatment and emergence of novel targeted therapies make the identification of genetic conditions in psychiatric patients all the more relevant, as psychiatric symptoms that are otherwise treatment-resistant may respond to targeted therapy toward the underlying genetic disorder.^10,16,22 In our study cohort, there were management or treatment implications for almost two-thirds of patients in whom genetic diagnoses were identified. Targeted therapies would be available to 15.9% (11 of 69) of diagnosed patients, including the use of mammalian target of rapamycin (mTOR) pathway inhibitors (e.g., everolimus, sirolimus) in the treatment of various tumors in tuberous sclerosis, acetazolamide or 4-aminopyridine to control or reduce ataxic attacks in episodic ataxia, and hemin to stem porphyric attacks in acute intermittent porphyria.^23–25 Published surveillance and management guidelines, including age-specific recommendations for Noonan syndrome, 22q11.2 deletion syndrome, and Williams syndrome, are also available for 46.4% (32 of 69) of diagnosed patients that could aid in optimizing overall health.^26–29 For practical purposes, many therapies and guidelines for surveillance and management of common genetic diagnoses that may be encountered in general and psychiatric practice are summarized in the invaluable online resource GeneReviews.³⁰ In addition, studies of patient and family perspectives on receiving a genetic diagnosis have shown that there are benefits beyond the clinical implications, including greater insight and acceptance of the patient’s condition, relief of guilt in patients’ parents, and recurrence risk information for family planning.^31–35

The significant association in our psychiatric study cohort of NDD and family history of NDD with having a genetic diagnosis is consistent with the high rate of genetic diagnosis in NDD populations reported in the literature.³⁶ We found a specific association of early and pervasive developmental delays with the presence of chromosomal disorders in line with previous studies demonstrating that approximately 15% of NDD patients are diagnosed with pathogenic CNVs.³⁷ We also detected a trend toward enrichment of chromosomal disorders in the NDD subset of psychiatric patients (18%; 20 of 111), which is consistent with other studies of similarly dually diagnosed populations reporting that 11% to 13% of patients harbor pathogenic CNVs.^38,39 There is growing interest and evidence to suggest that NDD and neuropsychiatric disorders may form a spectrum of shared phenotypes and underlying mechanisms.^40–42 However, our results showing a trend of CNV enrichment in the psychiatric cohort with NDD compared to the non-NDD subset suggest that there remain differences in the pathomechanisms underlying NDD and psychiatric disorders. Interestingly, family history of NDD was predictive of single gene diagnosis but not chromosomal diagnosis. This potentially reflects the de novo nature, incomplete penetrance or variable expressivity of many CNVs, or the reduced reproductive rates of individuals with NDD with or without psychiatric disorder.

The presence of seizure disorders was significantly associated with having any genetic diagnosis, particularly single gene disorders, while migraines were significantly associated with both single gene and chromosomal disorders. Previous studies have demonstrated that epilepsy is often comorbid with NDD and psychiatric disorders, such as depression and psychosis,^43–48 while chronic migraines are often comorbid with psychiatric disorders, such as depression, anxiety, and post-traumatic stress disorder.⁴⁹ Indeed, our regression model for predicting the presence of a single gene disorder in psychiatric patients encompasses this constellation of brain-related phenotypes (i.e., seizures, migraines, mood disorders, family history of NDD). Recently, a large genome-wide association study evaluating the genetic overlap of 25 psychiatric and neurological disorders found significant correlation of genetic risk factors among psychiatric conditions, notably attention deficit hyperactivity disorder (ADHD), bipolar affective disorder, MDD, and schizophrenia.⁵⁰ However, a similar relationship was not found within the different neurological conditions, suggesting these groups of disorders may be more distinct in terms of their genetic etiology.⁵⁰ Interestingly, the one exception to this was a significant correlation of migraines with ADHD, MDD, and Tourette syndrome.⁵⁰ This is reflected in our study cohort in which 30% of patients with migraines (n = 30) have comorbid MDD (n = 8) or ADHD (n = 1).

Several studies have evaluated the diagnostic yield of different methods of genetic testing within epilepsy patient populations. Epilepsy gene panels have reported diagnostic rates between 10% and 50% depending on the genes included and selected patient population.⁵¹ Studies evaluating diagnostic rates with CNV analysis show approximately 5% yield in unselected epilepsy populations,^51,52 while 16% to 28% of individuals with comorbid intellectual disability were found to carry pathogenic CNVs.^53,54 This is comparable to our findings of 27% of psychiatric patients with seizure history (n = 41) being found to have an underlying genetic diagnosis with 4.9% comprising single gene disorders (n = 2) and 19.5% comprising CNVs (n = 8). Our findings support that, similar to recommendations for genetic assessment and testing in patients with comorbid NDD and seizures, psychiatric patients with comorbid seizure disorders warrant investigation for underlying genetic conditions.

Other phenotypes found in our study to be significantly associated with having a genetic diagnosis include cardiovascular and respiratory, particularly obstructive lung disease, phenotypes as well as class 2 obesity (i.e., body mass index of 35 to 39.9). It is difficult to definitively determine whether these associations are primary or secondary, given that psychiatric patients are known to have lower levels of health literacy, poorer physical health outcomes, and quality of life and that numerous psychotropic medications have significant side effects of weight gain.^5,55–57 The prevalence of smoking has been shown to be higher in psychiatric populations,⁵⁸ which may account for the higher incidence of obstructive lung disease seen in this patient population. A very recent study demonstrated that psychiatric patients, particularly men with schizophrenia spectrum disorders and obesity, are at increased risk of obstructive sleep apnea and suggested that screening for obstructive sleep apnea should be mandatory in this patient population.⁵⁹ Interestingly, the most common cardiovascular phenotypes found to be associated with having a genetic diagnosis in our study cohort were structural abnormalities, arrhythmias, and “other” (i.e., murmur, pericardial effusion, palpitations). As these types of cardiovascular findings are less likely to be related to lifestyle or environmental factors, this finding may reflect that many structural cardiac defects and arrhythmias are known to have associated genetic underpinnings.^60–63 Given the potential morbidity and therapeutic implications, it may be prudent for psychiatrists and others who care for psychiatric patients to ask specifically about these types of cardiovascular phenotypes, particularly if there are other clinical findings suggestive of an underlying genetic disorder.

In the NDD subset, musculoskeletal findings were significantly associated with having a chromosomal disorder. On closer inspection, the specific musculoskeletal findings comprised primarily congenital skeletal anomalies such as syndactyly, brachydactyly, clubfeet, kyphosis, scoliosis, hip dysplasia, and digital contractures. Similar to the cardiovascular findings, these types of skeletal anomalies are known to often be associated with genetic syndromes.^64,65 Thus, screening for these musculoskeletal findings in psychiatric patients with concomitant NDD could improve diagnosis of chromosomal disorders in these patients.

Using statistical modeling based on our study results, we have developed a draft clinical decision tool (Supplementary Figure 2; Supplementary Table 3), which may assist physicians in determining which of their psychiatric patients might benefit from genetic assessment and testing based on their clinical characteristics. Our study was limited by sample size and ascertainment bias (i.e., a large proportion of patients with concomitant NDD), limiting the predictive power of the statistical model. Future larger studies with more variable psychiatric cohorts will provide more generalizable results to better refine the clinical tool. Nevertheless, the significance of our study results across the cohort support that there is an enrichment of genetic diagnoses in a more general psychiatric population as well. Thus, the results of our and previous studies should provide a foundation for psychiatrists and other physicians involved in the care of psychiatric patients for an increased awareness of genetic diagnosis in their patients in an evidence-based manner and stimulate further investigation of the burden of genetic disorders in psychiatric populations.

Studies comparing the use of WES to standard diagnostic workup in pediatric neurology and intellectual disability patients have demonstrated diagnostic yields of almost 30% using WES with significant cost-savings compared to traditional diagnostic trajectories.^66,67 Additionally, a recent study revealed a 41% diagnostic yield using whole-genome sequencing compared to 24% with conventional genetic testing in 103 pediatric patients with clinical phenotypes suggestive of an underlying genetic disorder.⁶⁸ Although these genomic technologies are not yet economically feasible or widely available in many regions of the world, they hold promise for uncovering an even higher diagnostic rate in psychiatric patients in future. Our analysis supports further research to investigate whether there might be a similar diagnostic yield in psychiatric patients, especially those with NDD. Should our findings be replicated, and our draft tool further refined and developed, it could aid in triaging psychiatric patients who could benefit from undergoing genetic testing.

Conclusion

The personal and societal cost of mental illness in Canada is very high, particularly in patients with concurrent NDD. Awareness of and achieving a genetic diagnosis in these patients may facilitate targeted treatment and management and recurrence risk counselling. Further study, validation, and refinement of a clinical tool such as we have proposed could increase the rate of genetic diagnosis in psychiatric patients, especially those with concomitant NDD. This will lead to improved outcomes for patients and their families and savings in health-care resources.