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Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families

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Title: Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families
Language: English
Authors: LaBianca, Sonja (ORCID 0000-0001-5644-6472), LaBianca, Jette, Pagsberg, Anne Katrine, Jakobsen, Klaus Damgaard, Appadurai, Vivek, Buil, Alfonso, Werge, Thomas
Source: Journal of Autism and Developmental Disorders. Jan 2021 51(1):276-285.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 10
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Autism, Pervasive Developmental Disorders, Attention Deficit Hyperactivity Disorder, Comorbidity, Age Differences, Genetic Disorders, At Risk Persons, Health Services
DOI: 10.1007/s10803-020-04552-x
ISSN: 0162-3257
Abstract: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly heritable neurodevelopmental disorders that frequently co-occur. Both rare and common genetic variants are important for ASD and ADHD risk but their combined contribution to clinical heterogeneity is unclear. In a sample of 39 ASD and/or ADHD families we estimated the overall variance explained by known rare copy number variants (CNVs) and polygenic risk score (PRS) from common variants to be 10% in comorbid ASD/ADHD, 4% in ASD and 2% in ADHD. We show that burden of large, rare CNVs and PRS is significantly higher in adult ASD and/or ADHD patients with sustained need for specialist care compared to their unaffected relatives, while affected relatives fall in-between the two.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1280759
Database: ERIC
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  Value: <anid>AN0148114446;aut01jan.21;2021Jan18.05:59;v2.2.500</anid> <title id="AN0148114446-1">Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families </title> <p>Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly heritable neurodevelopmental disorders that frequently co-occur. Both rare and common genetic variants are important for ASD and ADHD risk but their combined contribution to clinical heterogeneity is unclear. In a sample of 39 ASD and/or ADHD families we estimated the overall variance explained by known rare copy number variants (CNVs) and polygenic risk score (PRS) from common variants to be 10% in comorbid ASD/ADHD, 4% in ASD and 2% in ADHD. We show that burden of large, rare CNVs and PRS is significantly higher in adult ASD and/or ADHD patients with sustained need for specialist care compared to their unaffected relatives, while affected relatives fall in-between the two.</p> <p>Keywords: Autism spectrum disorder; Attention deficit/hyperactivity disorder; Comorbidity; Families; Copy number variants; Polygenic risk score</p> <p>Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10803-020-04552-x) contains supplementary material, which is available to authorized users.</p> <p>Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly heritable neurodevelopmental disorders (NDD). Symptoms onset in early childhood and often persist throughout a lifetime (Association [<reflink idref="bib2" id="ref1">2</reflink>]; World Health Organization [<reflink idref="bib70" id="ref2">70</reflink>]). They frequently co-occur in the individual and co-segregate in families (Ghirardi et al. [<reflink idref="bib17" id="ref3">17</reflink>]; Rommelse et al. [<reflink idref="bib51" id="ref4">51</reflink>], [<reflink idref="bib52" id="ref5">52</reflink>]; van Steijn et al. [<reflink idref="bib64" id="ref6">64</reflink>]). However, the comorbid diagnosis as well as concept of lifetime disorders with need of specialist care is new in the diagnostic manual (Association [<reflink idref="bib2" id="ref7">2</reflink>]) and recently implemented in Danish national clinical guidelines (Sundhedsstyrrelsen [<reflink idref="bib62" id="ref8">62</reflink>]).</p> <p>In Denmark adult individuals with ASD and/or ADHD are often seen in primary health care at psychiatric specialist clinics referred by their general practitioner (GP) (Danish Ministry of Health [<reflink idref="bib10" id="ref9">10</reflink>]) for diagnostic assessment and treatment or follow up after hospitalization. It is common for relatives of ASD and/or ADHD patients to also have ASD and/or ADHD or present sub-diagnostic social, inattentive or communication deficits (Bishop et al. [<reflink idref="bib6" id="ref10">6</reflink>]) or other mental health problems such as substance abuse (Skoglund et al. [<reflink idref="bib59" id="ref11">59</reflink>]) depression, bipolar or schizophrenia (Biederman et al. [<reflink idref="bib5" id="ref12">5</reflink>]; Larsson et al. [<reflink idref="bib32" id="ref13">32</reflink>]).</p> <p>While still explaining only a minor fraction of the global heritability of ASD and ADHD, estimated in twin and family studies to 70–80% (Neale et al. [<reflink idref="bib46" id="ref14">46</reflink>]), it has been well established that genetic risk variants play a substantial role in disease etiology of both (Sullivan et al. [<reflink idref="bib61" id="ref15">61</reflink>]). In particular, large and rare copy number variants (CNVs), which either arise de novo in the individual or are inherited from parent to offspring, have been associated with ASD (Glessner et al. [<reflink idref="bib22" id="ref16">22</reflink>]; Moreno-De-Luca et al. [<reflink idref="bib44" id="ref17">44</reflink>]; Olsen et al. [<reflink idref="bib47" id="ref18">47</reflink>]; Pinto et al. [<reflink idref="bib48" id="ref19">48</reflink>]; Sanders et al. [<reflink idref="bib54" id="ref20">54</reflink>]) and ADHD (Elia et al. [<reflink idref="bib14" id="ref21">14</reflink>]; Jarick et al. [<reflink idref="bib26" id="ref22">26</reflink>]; Lesch et al. [<reflink idref="bib35" id="ref23">35</reflink>]; Olsen et al. [<reflink idref="bib47" id="ref24">47</reflink>]; Williams et al. [<reflink idref="bib68" id="ref25">68</reflink>], [<reflink idref="bib69" id="ref26">69</reflink>]). In fact, several CNVs have been reported for both disorders, consistent with the observed co-occurrence and indicating genetic pleiotropy and biological overlap (Martin et al. [<reflink idref="bib40" id="ref27">40</reflink>], [<reflink idref="bib41" id="ref28">41</reflink>]; Olsen et al. [<reflink idref="bib47" id="ref29">47</reflink>]; Rommelse et al. [<reflink idref="bib51" id="ref30">51</reflink>], [<reflink idref="bib52" id="ref31">52</reflink>]; Sullivan et al. [<reflink idref="bib61" id="ref32">61</reflink>]). More recently, large-scale genome-wide association studies (GWAS) have identified common single nucleotide polymorphisms (SNPs) conferring risk to ASD (Grove et al. [<reflink idref="bib23" id="ref33">23</reflink>]) and ADHD (Demontis et al. [<reflink idref="bib11" id="ref34">11</reflink>]) and estimated a high genetic correlation between them (Grove et al. [<reflink idref="bib23" id="ref35">23</reflink>]; Schork et al. [<reflink idref="bib58" id="ref36">58</reflink>]). Furthermore, a study found that ADHD polygenic risk score (PRS) from common SNPs influence ASD-related traits in the general population (Martin et al. [<reflink idref="bib40" id="ref37">40</reflink>], [<reflink idref="bib41" id="ref38">41</reflink>]), implying that common SNPs also contribute to the clinical and biological overlap of ADHD and ASD.</p> <p>However, the combined impact and interaction of these known genetic risk variants to risk of ASD and/or ADHD is scarcely studied, as well as their contribution to clinical heterogeneity in families. Therefore, we set out to estimate the variance explained by both rare CNVs and common SNPs to ASD and/or ADHD traits in families with multiple affected individuals. Furthermore, we tested the hypothesis that the character and burden of these known genetic risk variants segregating in a family is a determinant of the clinical manifestation and need of care to the individual over time.</p> <hd id="AN0148114446-2">Methods</hd> <p>A sample of 39 ASD and/or ADHD families with multiple individuals affected was recruited from an adult psychiatric specialist clinic (clinic) in the primary health care sector of Denmark. Probands were adult ASD and/or ADHD patients in active treatment at the clinic. They facilitated contact to their 1st and 2nd degree relatives of all ages and up to four generations, as described elsewhere (LaBianca et al. [<reflink idref="bib30" id="ref39">30</reflink>]). All participants gave written informed consent/assent prior to inclusion. The study was approved by the National Committee on Health Research Ethics in Denmark (Protocol: H_B_2009_026).</p> <hd id="AN0148114446-3">Phenotypic Data</hd> <p>Clinical diagnoses were obtained from medical journals of the patients in active treatment at the clinic and met ICD-10 research diagnostic criteria and relied on standardized and validated diagnostic instruments for ASD and ADHD (Kessler et al. [<reflink idref="bib27" id="ref40">27</reflink>], [<reflink idref="bib28" id="ref41">28</reflink>]; Lord et al. [<reflink idref="bib36" id="ref42">36</reflink>]; Rutter et al. [<reflink idref="bib53" id="ref43">53</reflink>]; Szatmari et al. [<reflink idref="bib63" id="ref44">63</reflink>]). Other comorbidities were neither a criterion of inclusion nor exclusion. In systematic interview relatives self-reported whether they suffered any mental health disorder in ongoing or previous treatment elsewhere, never sought treatment or diagnostic clarification for their self-reported mental health symptoms or reported to be mentally healthy. For all participants we compared both clinical and self-reported diagnoses and symptoms with hospital diagnoses from The Danish Psychiatric Central Register (register) (Mors et al. [<reflink idref="bib45" id="ref45">45</reflink>]) as described elsewhere (LaBianca et al. [<reflink idref="bib30" id="ref46">30</reflink>]).</p> <p>In preparation for analysis of our two research aims we stratified the phenotypic data into two levels described below and details presented in Supplementary Table 1.</p> <p></p> <ulist> <item> <emph>Diagnostics traits</emph> to estimate the variance explained by CNVs & PRS: <emph>ASD (N</emph> = <emph>31)</emph> including patients and affected relatives with a clinic or register diagnosis of ASD including comorbid ADHD. <emph>ADHD (N</emph> = <emph>80)</emph> including patients and affected relatives with a clinic or register diagnosis of ADHD including comorbid ASD. <emph>A</emph> + <emph>A (N</emph> = <emph>21)</emph> including patients and affected relatives with a clinic or register diagnosis of comorbid ASD and ADHD specifically.</item> <p></p> <item> <emph>Clinical groups</emph> to quantify the role of CNVs and PRS upon clinical manifestations and need of care: <emph>Patients (N</emph> = <emph>55)</emph> diagnosed with ASD and/or ADHD and in active specialist treatment at the clinic. <emph>Affected relatives (N</emph> = <emph>116)</emph> suffering any mental health disorder including ASD and/or ADHD in ongoing or previous treatment elsewhere, as well as relatives who have never sought treatment or diagnostic clarification for their self-reported mental health symptoms. <emph>Unaffected relatives (N</emph> = <emph>97)</emph> with no self-reported mental health disorder or symptoms and no psychiatric diagnosis recorded in the register.</item> </ulist> <hd id="AN0148114446-4">Genetic Data</hd> <p>Blood or saliva samples were collected from each participant, DNA purification was performed according to instructions provided by the suppliers which were 'DNAgenotex' for saliva and 'Promega's Maxwell 16′ for blood DNA purification. Genotyping was performed on either the 'HumanOmniExpress-12v1-H' microarray or OmniExpress-24 v1.2 BeadChip at deCODE Genetics (Reykjavik, Iceland) or on the Infinium PsychChip v1.0 array at Statens Serum Institut, Denmark, as described elsewhere (LaBianca et al. [<reflink idref="bib30" id="ref47">30</reflink>]).</p> <hd id="AN0148114446-5">Analytaical Strategy</hd> <p>To achieve our first aim, we estimated the variance explained by CNVs and PRS upon the diagnostic traits as fixed effects in a Linear Mixed Model (LMM) (Lynch and Walsh [<reflink idref="bib37" id="ref48">37</reflink>]) implemented in the R package Genesis (Conomos et al. [<reflink idref="bib7" id="ref49">7</reflink>]). The LMM (Lynch and Walsh [<reflink idref="bib37" id="ref50">37</reflink>]) takes the relatedness of the participants into account by correcting for random genetic effects related to the family structure while estimating the variance explained by the fixed genetic effect of CNVs and PRS. The analysis of family data presents statistical challenges arising from the fact that samples are not independent due to their genetic similarities. These genetic similarities can be measured as the coefficient of relatedness (K) among any pair of individuals (Lynch and Walsh [<reflink idref="bib37" id="ref51">37</reflink>]). The coefficient of relatedness between two relatives is defined as the proportion of their genome that is inherited form the same ancestor, ie. K_mz_twins = 1, K_sibs = 0.5, K_cousins = 0.125. We assume K_unrelated = 0. Linear mixed models (LMM) are an extension of regular linear models that allow the inclusion of random and fixed effects in the same model (Almasy and Blangero [<reflink idref="bib1" id="ref52">1</reflink>]; Lynch and Walsh [<reflink idref="bib37" id="ref53">37</reflink>]). They are a natural way of analyzing family data where the familial effect is considered a random effect with the covariance among the relatives determined by their coefficient of relatedness. LMMs were first developed for analysis of quantitative traits but there are several implementations for binary traits, assuming the liability model (Hill and Mackay [<reflink idref="bib24" id="ref54">24</reflink>]). The liability model can assume an underlying normal distribution of risk (Williams and Blangero [<reflink idref="bib67" id="ref55">67</reflink>]) or a logistic model as the one implemented in the R package Genesis (Conomos et al. [<reflink idref="bib7" id="ref56">7</reflink>]).</p> <p>To achieve our second aim, we determined the burden of rare CNVs and PRS from common SNPs among the clinical groups. CNVs were detected with iPsychCNV (Bertalan et al. [<reflink idref="bib4" id="ref57">4</reflink>]) at a setting of > 20 SNPs. All CNVs were visually inspected and verified. Pathogenic validation was performed by examination of overlap with CNVs from the Database of Genomic Variants (DGV) in controls (MacDonald et al. [<reflink idref="bib38" id="ref58">38</reflink>]) and the Simons Foundation Autism Research Initiative (SFARI) Gene database("Copy Number Variant (CNV) Module—SFARI Gene," [<reflink idref="bib9" id="ref59">9</reflink>]). The DGV is a comprehensive, continuously updated catalog of structural variation identified in healthy control samples of 72 peer reviewed research studies (MacDonald et al. [<reflink idref="bib38" id="ref60">38</reflink>]). SFARI Gene is an evolving online database of the ever-expanding genetic risk factors for ASD that emerge in the literature, currently including 2250 CNV loci reported in individuals with ASD ("Copy Number Variant (CNV) Module—SFARI Gene," [<reflink idref="bib9" id="ref61">9</reflink>]).</p> <p>Polygenic Risk Scores (PRS) were computed with PRSice (https://prsice.info/) (Euesden et al. [<reflink idref="bib15" id="ref62">15</reflink>]) using GWAS summary statistics from the Psychiatrics Genomics Consortium (PGC) for ASD (Grove et al. [<reflink idref="bib23" id="ref63">23</reflink>]) and ADHD (Demontis et al. [<reflink idref="bib11" id="ref64">11</reflink>]) excluding Danish samples. PRS is an additive score combining the products of effect sizes of individual genetic markers from an independent GWAS with the corresponding additive dosages of the effect alleles in individuals from our sample of multiplex ASD and/or ADHD families. The SNPs used to calculate PRS were pruned to ensure independence, while no significance threshold was set (Ware et al. [<reflink idref="bib65" id="ref65">65</reflink>]). We ran PRSice with default parameters for calculating PRS (clumping r2 = 0.1, distance = 250 kb). After excluding Danish samples from the PGC summary stats the remaining sample size for each PRS is listed in Supplementary Table 4. Since the remaining samples contributing to ASD and ADHD PRS were small we also included the more powerful schizophrenia (SCZ) PRS (Ripke et al. [<reflink idref="bib49" id="ref66">49</reflink>]), as a positive control in our analysis. We found it reasonable because of the shared genetic risk and pleiotropy among mental disorders (Lee et al. [<reflink idref="bib33" id="ref67">33</reflink>]) and more specifically the high genetic correlations between ASD, ADHD and SCZ (Schork et al. [<reflink idref="bib58" id="ref68">58</reflink>]). We also included the PRS (Yengo et al. [<reflink idref="bib71" id="ref69">71</reflink>]) for Body Mass Index (BMI) as a negative control for comparison purposes.</p> <p>Layout of tables, figures and descriptive statistics for illustrations and estimates were performed with R packages FSA, lattice and tidyverse (Derek et al. [<reflink idref="bib12" id="ref70">12</reflink>]; Sarkar [<reflink idref="bib55" id="ref71">55</reflink>]; Wickham [<reflink idref="bib66" id="ref72">66</reflink>]). P-values were calculated with either Fisher´s exact test and the threshold adjusted for multiple testing according to the Bonferroni correction (Dunn [<reflink idref="bib13" id="ref73">13</reflink>]) or Kruskal–Wallis ranked sum test as well as the Kruskal–Wallis with multiple comparison p-values adjusted in accordance with the Benjamin-Hochberg method (Benjamini and Hochberg [<reflink idref="bib3" id="ref74">3</reflink>]).</p> <hd id="AN0148114446-6">Results</hd> <p></p> <hd id="AN0148114446-7">Study Population</hd> <p>A total of 39 multiplex ASD and/or ADHD families were recruited, including 277 consenting participants. After DNA purification 268 samples were genotyped while 9 were excluded because of insufficient DNA. As described in the methods and summarized in Supplementary Table 1 the study population was stratified for analyses of our two research aims respectively. <emph>Diagnostic traits;</emph> consisting of 31 patients and affected relatives with ASD, 80 with ADHD and 21 with comorbid ASD and ADHD (A + A) in order to estimate the overall variance explained by rare CNVs and PRS from common SNPs. <emph>Clinical groups;</emph> consisting of 55 adult ASD and/or ADHD patients with sustained need for psychiatric specialist care, 116 relatives affected by any mental health disorder with variable need for psychiatric specialist care as well as 97 unaffected relatives, in order to determine the role of rare CNVs and PRS from common SNPs upon clinical manifestations and need of care to the individual and within a family over time.</p> <p>Table 1 Variance explained by the known genetic risk variants</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Model</p></th><th align="left"><p>Trait</p></th><th align="left"><p>Cov</p></th><th align="left"><p>Fixeff<sup>a</sup></p></th><th align="left"><p>p-values</p></th><th align="left"><p>varComp<sup>b</sup></p></th><th align="left"><p>kl r<sup>2c</sup></p></th></tr></thead><tbody><tr><td align="left"><p>3</p></td><td align="left"><p>ASD</p></td><td align="left"><p>Sex</p></td><td char="." align="char"><p>0.03</p></td><td align="left"><p>9.40E−01</p></td><td align="left"><p>0.8 (0.5)</p></td><td align="left"><p>0.04 (4%)</p></td></tr><tr><td align="left" /><td align="left" /><td align="left"><p>Age</p></td><td char="." align="char"><p>− 0.05</p></td><td align="left"><p>5.04E−07</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>CNV</p></td><td char="." align="char"><p>0.98</p></td><td align="left"><p>8.60E−03</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>SCZ_PRS<sup>d</sup></p></td><td char="." align="char"><p>0.07</p></td><td align="left"><p>1.10E−02</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>3</p></td><td align="left"><p>A + A</p></td><td align="left"><p>Sex</p></td><td char="." align="char"><p>− 0.32</p></td><td align="left"><p>5.40E−01</p></td><td align="left"><p>0.15 (0.7)</p></td><td align="left"><p>0.098 (10%)</p></td></tr><tr><td align="left" /><td align="left" /><td align="left"><p>Age</p></td><td char="." align="char"><p>− 0.04</p></td><td align="left"><p>2.00E−03</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>CNV</p></td><td char="." align="char"><p>1.44</p></td><td align="left"><p>1.40E−02</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>SCZ_PRS<sup>d</sup></p></td><td char="." align="char"><p>0.097</p></td><td align="left"><p>1.70E−02</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>3</p></td><td align="left"><p>ADHD</p></td><td align="left"><p>Sex</p></td><td char="." align="char"><p>− 0.3</p></td><td align="left"><p>2.50E−01</p></td><td align="left"><p>0.4 (0.18)</p></td><td align="left"><p>0.02 (2%)</p></td></tr><tr><td align="left" /><td align="left" /><td align="left"><p>Age</p></td><td char="." align="char"><p>− 0.04</p></td><td align="left"><p>4.80E−08</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>CNV</p></td><td char="." align="char"><p>0.4</p></td><td align="left"><p>2.10E−01</p></td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left" /><td align="left"><p>SCZ_PRS<sup>d</sup></p></td><td char="." align="char"><p>0.06</p></td><td align="left"><p>5.90E−03</p></td><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>The table shows the estimates from linear mixed model for all three traits. Significant findings are highlighted in bold <emph>ASD</emph> autism spectrum disorder, <emph>A</emph> + <emph>A</emph> comorbid ASD and ADHD, <emph>ADHD</emph> attention deficit/hyperactivity disorder <sups>a</sups>Beta: regression coefficient for each of the fixed effects <sups>b</sups>Heritability: the variance component estimates for the random effects <sups>c</sups>Kullback-Leibler R-squared: variance explained by the genetic fixed effect (CNV and SCZ_PRS) <sups>d</sups>Schizophrenia polygenic risk score</p> <hd id="AN0148114446-8">CNVs</hd> <p>A total of 266 CNVs in 118 independent loci were identified Supplementary Table 2, among them 107 loci overlap with rare (< 1% population frequency) CNVs in the DGV golden standard 2016-05-15 dataset (MacDonald et al. [<reflink idref="bib38" id="ref75">38</reflink>]) and the remaining 11 loci were not identified in this DGV dataset at all. All 118 loci were identified in the SFARI-Gene_cnvs_04-23-2019 dataset ("Copy Number Variant (CNV) Module—SFARI Gene," 2019). Furthermore, among them we identified 7 loci previously associated (Ingason et al. [<reflink idref="bib25" id="ref76">25</reflink>]; Kirov et al. [<reflink idref="bib29" id="ref77">29</reflink>]; Malhotra and Sebat [<reflink idref="bib39" id="ref78">39</reflink>]; Sanders et al. [<reflink idref="bib54" id="ref79">54</reflink>]; Stefansson et al. [<reflink idref="bib60" id="ref80">60</reflink>]; Williams et al. [<reflink idref="bib68" id="ref81">68</reflink>]) and 7 loci previously implicated as candidates (Cooper et al. [<reflink idref="bib8" id="ref82">8</reflink>]; Girirajan et al. [<reflink idref="bib19" id="ref83">19</reflink>], [<reflink idref="bib20" id="ref84">20</reflink>]; Glessner et al. [<reflink idref="bib22" id="ref85">22</reflink>]; Jarick et al. [<reflink idref="bib26" id="ref86">26</reflink>]; Millar et al. [<reflink idref="bib43" id="ref87">43</reflink>]; Pinto et al. [<reflink idref="bib48" id="ref88">48</reflink>]) in large case/controls studies of mental health disorders, several associated and/or implicated candidates in both ASD and ADHD Supplementary Table 3.</p> <hd id="AN0148114446-9">Variance Explained by CNVs & PRS</hd> <p>For each of the three traits ASD, ADHD and A + A we ran three models including CNVs and PRS separately and combined. Covariates sex and age were included in all models. We tested several covariates for both CNV and PRS presented in Supplementary Table 5a–c, and included the most consistent among them in the combined model. The results of the combined model, presented in Table 1, shows positive regression coefficients for CNV and SCZ PRS across all three traits and negative regression coefficients for age and sex, except ASD, where sex was slightly positive. The p-values were significant across all three traits for SCZ PRS, although CNVs were significant only for ASD and A + A. Age was also significant across all traits, whereas sex was not. Kullback–Leibler R-squared was calculated to quantify the variance explained by the fixed genetic effects, rare CNVs and PRS from common SNPs. For the combined genetic risk factors (CNV and PRS) without sex and age, A + A had the most variance explained at 10% whereas ASD had 4% and ADHD 2%. In conclusion comorbid ASD and ADHD has the most variance explained by the joint contribution of both rare CNVs and SCZ PRS from common SNPs.</p> <hd id="AN0148114446-10">Burden of CNVs & PRS</hd> <p>CNVs were stratified by size in kilo base pairs (kb) and percentage of CNVs carriers among the clinical groups was estimated for each 100 kb increase as illustrated in Fig. 1. The findings show that patients have a significantly higher burden of CNVs > 200 kb than their unaffected relatives (Fisher's exact test p < 0.01 after Bonferroni correction) (Dunn [<reflink idref="bib13" id="ref89">13</reflink>]), presented in Table 2. This was also evident for particularly large CNVs, spanning more than 500 kb (p = 0.099). Affected relatives also carry a higher burden of CNVs, though not significantly different from the unaffected relatives. In addition, analysis stratifying CNVs not only by size but also copy number (CN) was performed. It showed the same trend for both deletions and duplications, although it was not statistically significant Supplementary Fig. 1a and b.</p> <p>Graph: Fig. 1 Burden of rare copy number variants (CNVs) stratified by size in kilo base (kb) on the X-axis, among percentage of individuals with CNVs listed on the Y-axis stratified as patients, relatives affected (aff.) and unaffected (unaff.)</p> <p>Table 2 P-values for Fig. 2, those highlighted in bold are significant below the Bonferroni corrected threshold 0.01</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>Patients vs. relatives aff.</p></th><th align="left"><p>Patients vs. relatives unaff.</p></th><th align="left"><p>Relatives aff. vs. unaff.</p></th></tr></thead><tbody><tr><td align="left"><p> > 100 kb</p></td><td char="." align="char"><p>0.7421</p></td><td char="." align="char"><p>0.3993</p></td><td char="." align="char"><p>0.6782</p></td></tr><tr><td align="left"><p> > 200 kb</p></td><td char="." align="char"><p>0.4619</p></td><td char="." align="char"><p><bold>0.0009</bold></p></td><td char="." align="char"><p><bold>0.0028</bold></p></td></tr><tr><td align="left"><p> > 300 kb</p></td><td char="." align="char"><p>0.5305</p></td><td char="." align="char"><p><bold>0.0080</bold></p></td><td char="." align="char"><p><bold>0.0023</bold></p></td></tr><tr><td align="left"><p> > 400 kb</p></td><td char="." align="char"><p>0.2088</p></td><td char="." align="char"><p>0.0234</p></td><td char="." align="char"><p>0.1794</p></td></tr><tr><td align="left"><p> > 500 kb</p></td><td char="." align="char"><p>0.2875</p></td><td char="." align="char"><p><bold>0.0099</bold></p></td><td char="." align="char"><p>0.1489</p></td></tr></tbody></table> </ephtml> </p> <p>We stratified PRS on a continuum from lowest to highest score within our sample. For ASD, ADHD and SCZ PRS the trend was identical to the findings from the analysis of CNVs with patients carrying a higher PRS than affected and unaffected relatives Fig. 2. There was no difference between the three clinical groups when we tested out negative control BMI PRS. P-values were calculated using Kruskal–Wallis ranked sum test with significant associations for ADHD PRS (p = 0.03), and SCZ PRS (p = 0.04) Table 3a and b. In summary, the burden of SCZ PRS and ADHD PRS is significantly higher among patients than their unaffected relatives, while affected relatives are in between. The burden of ASD PRS shows the same albeit non-significant trend, while the BMI PRS does not distinguish between clinical manifestations and need of care among the clinical groups at all.</p> <p>Graph: Fig. 2 Box plots of Polygenic Risk Scores (PRS) among patients, relatives affected (aff.) and unaffected (unaff.). ASD autism spectrum disorder, ADHD attention deficit/hyperactivity disorder, SCZ schizophrenia, BMI Body Mass Index</p> <p>Table 3 Estimates for all PRS comparing clinical groups. (A) Kruskal-Walli ranked sum test, (B) Dunn ([<reflink idref="bib13" id="ref90">13</reflink>]) Kruskal–Wallis multiple comparison p-values adjusted with the Benjamini–Hochberg method</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="4"><p>A</p></th></tr><tr><th align="left"><p>PRS</p></th><th align="left"><p>X<sup>2</sup></p></th><th align="left"><p>df</p></th><th align="left"><p>P-value</p></th></tr></thead><tbody><tr><td align="left"><p>ASD</p></td><td char="." align="char"><p>4.08</p></td><td align="left"><p>2</p></td><td char="." align="char"><p>0.13</p></td></tr><tr><td align="left"><p>ADHD</p></td><td char="." align="char"><p>7.23</p></td><td align="left"><p>2</p></td><td char="." align="char"><p><bold>0.03</bold></p></td></tr><tr><td align="left"><p>SCZ</p></td><td char="." align="char"><p>6.46</p></td><td align="left"><p>2</p></td><td char="." align="char"><p><bold>0.04</bold></p></td></tr><tr><td align="left"><p>BMI</p></td><td char="." align="char"><p>4.35</p></td><td align="left"><p>2</p></td><td char="." align="char"><p>0.11</p></td></tr></tbody></table> </ephtml> </p> <p>Table 3 Estimates for all PRS comparing clinical groups. (A) Kruskal-Walli ranked sum test, (B) Dunn ([<reflink idref="bib13" id="ref91">13</reflink>]) Kruskal–Wallis multiple comparison p-values adjusted with the Benjamini–Hochberg method</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" colspan="5"><p>B</p></th></tr><tr><th align="left"><p>PRS</p></th><th align="left"><p>Comparison</p></th><th align="left"><p>Z</p></th><th align="left"><p>P unadj.</p></th><th align="left"><p>P adj.</p></th></tr></thead><tbody><tr><td align="left" rowspan="3"><p>ASD</p></td><td align="left"><p>Pat. > < Rel. aff.</p></td><td char="." align="char"><p>1.9835</p></td><td char="." align="char"><p><bold>0.0473</bold></p></td><td char="." align="char"><p>0.1419</p></td></tr><tr><td align="left"><p>Pat. > < Rel. unaff.</p></td><td char="." align="char"><p>1.5946</p></td><td char="." align="char"><p>0.1107</p></td><td char="." align="char"><p>0.1662</p></td></tr><tr><td align="left"><p>Rel. aff. > <Rel. unaff.</p></td><td char="." align="char"><p>− 0.4038</p></td><td char="." align="char"><p>0.6863</p></td><td char="." align="char"><p>0.6863</p></td></tr><tr><td align="left" rowspan="3"><p>ADHD</p></td><td align="left"><p>Pat. > < Rel. aff.</p></td><td char="." align="char"><p>2.5550</p></td><td char="." align="char"><p><bold>0.0106</bold></p></td><td char="." align="char"><p><bold>0.0319</bold></p></td></tr><tr><td align="left"><p>Pat. > < Rel. unaff.</p></td><td char="." align="char"><p>2.3140</p></td><td char="." align="char"><p><bold>0.0206</bold></p></td><td char="." align="char"><p><bold>0.0309</bold></p></td></tr><tr><td align="left"><p>Rel. aff. > < Rel. unaff.</p></td><td char="." align="char"><p>− 0.2014</p></td><td char="." align="char"><p>0.8404</p></td><td char="." align="char"><p>0.8404</p></td></tr><tr><td align="left" rowspan="3"><p>SCZ</p></td><td align="left"><p>Pat. > < Rel. aff.</p></td><td char="." align="char"><p>2.5389</p></td><td char="." align="char"><p><bold>0.0111</bold></p></td><td char="." align="char"><p><bold>0.0334</bold></p></td></tr><tr><td align="left"><p>Pat. > < Rel. unaff.</p></td><td char="." align="char"><p>1.7452</p></td><td char="." align="char"><p>0.0809</p></td><td char="." align="char"><p>0.1214</p></td></tr><tr><td align="left"><p>Rel. aff. > < Rel. unaff.</p></td><td char="." align="char"><p>− 0.8800</p></td><td char="." align="char"><p>0.3788</p></td><td char="." align="char"><p>0.3788</p></td></tr><tr><td align="left" rowspan="3"><p>BMI</p></td><td align="left"><p>Pat. > < Rel. aff.</p></td><td char="." align="char"><p>− 1.9243</p></td><td char="." align="char"><p>0.0543</p></td><td char="." align="char"><p>0.1629</p></td></tr><tr><td align="left"><p>Pat. > < Rel. unaff.</p></td><td char="." align="char"><p>− 1.8725</p></td><td char="." align="char"><p>0.0611</p></td><td char="." align="char"><p>0.0916</p></td></tr><tr><td align="left"><p>Rel. aff. > < Rel. unaff.</p></td><td char="." align="char"><p>− 0.0074</p></td><td char="." align="char"><p>0.9940</p></td><td char="." align="char"><p>0.9940</p></td></tr></tbody></table> </ephtml> </p> <p>Significant p-values < 0.05 are highlighted in bold <emph>Pat.</emph> patients, <emph>Rel. aff.</emph> relatives affected, <emph>Rel. unaff.</emph> unaffected</p> <hd id="AN0148114446-11">Discussion</hd> <p>In this study we estimated the overall variance explained by known, rare CNVs and SCZ PRS from common SNPs to be 10% in comorbid ASD and ADHD (A + A). We found that the burden of both rare CNVs and SCZ PRS is significantly higher in adult ASD and/or ADHD patients with sustained need for specialist care than in their unaffected relatives, while their relatives affected by a broader spectrum of mental health disorders and with variable need of care were in between the two. These findings and their implications merit further discussion.</p> <p>Compared to a previous study that estimated the relative contribution of CNVs and PRS to ADHD (Martin et al. [<reflink idref="bib42" id="ref92">42</reflink>]) and found the variance explained by ADHD PRS to be 1.5%, our estimated variance explained by a joint combination of rare CNVs and SCZ PRS was higher for all three traits, particular for, comorbid ASD and ADHD (A + A). However, considering the high heritability estimates from twin and family studies, 70–80%, for both ASD and ADHD (Rommelse et al. [<reflink idref="bib51" id="ref93">51</reflink>], [<reflink idref="bib52" id="ref94">52</reflink>]), our estimates suggest that considerable work is needed to improve discovery and understanding of additional genetic, epigenetic and environmental risk factors in order to completely explain the etiology of these disorders.</p> <p>The finding that adult ASD and/or ADHD patients with sustained need for specialist care have a higher burden of large, rare CNVs and PRS than both their unaffected relatives and relatives with any mental health disorder and variable need of care implies that burden of genetic risk factors contributes to severity and heterogeneity in clinical manifestations and need for specialist care. That said the findings were only significant for patients compared to unaffected relatives, while their affected relatives presenting a broader phenotypic spectrum were in-between.</p> <p>The majority of CNVs identified in this study were inherited, and a few were de novo, while the inheritance of others could not be assessed Supplementary Table 2. Our findings support the concept that families with multiple affected individuals are more likely to carry rare, inherited variants, whereas de novo variants more often occur in sporadic cases, as also reported by Leppa et al. ([<reflink idref="bib34" id="ref95">34</reflink>]). All CNV loci identified in this study were reported to be rare (MacDonald et al. [<reflink idref="bib38" id="ref96">38</reflink>]). Furthermore, all of them were reported in SFARI ("Copy Number Variant (CNV) Module—SFARI Gene," 2019), supporting their pathological relevance to ASD and consistent with their rarity among controls. Importantly, we did identify several loci reported in the literature associated with or implicated in both ASD and ADHD consistent with pleiotropy. Interestingly, the 6q26 deletion encompassing the <emph>PARK2</emph> gene, that has previously been implicated in both ASD (Glessner et al. [<reflink idref="bib21" id="ref97">21</reflink>]; Yin et al. [<reflink idref="bib72" id="ref98">72</reflink>]) and ADHD (Jarick et al. [<reflink idref="bib26" id="ref99">26</reflink>]) samples, segregates in an ADHD patient and two affected relatives with ADHD and comorbid ASD and ADHD in our sample, although our data was too small for formal statistical validation.</p> <p>The analyses based on three selected PRS documented that clinical manifestations in the studied family setting can in part be predicted, and thus the burden of common SNPs also correlate with severity and heterogeneity of clinical manifestation and need of care. Interestingly, the most consistent performance was obtained for the schizophrenia PRS, while those for autism and ADHD showed similar predictive power. This may be explained by two reasons; first, the samples sizes of the schizophrenia GWAS studies underlying the PRS weights are much larger than the corresponding ASD and ADHD studies, and second, the considerable genetic correlation between mental disorders, thus resulting in a PRS for schizophrenia that is superior to those of ASD and ADHD. In support of this conclusion, we find that PRS of a control trait (BMI) did not predict clinical manifestation and need of care in our sample. It might also reflect an accumulation of cross-disorders polygenic risk in these comorbid, multiply affected families that is best captured by the currently strongest PRS instrument for schizophrenia.</p> <p>Ideally, standardized assessment for ASD and/or ADHD symptomatology in all participants of our study could have been performed to estimate a symptomatology severity score, although resources did not facilitate this. Alternatively, we supplemented the self-reported diagnostic information of the relatives with data from the validated Danish National Health Registers (Mors et al. [<reflink idref="bib45" id="ref100">45</reflink>]), which was available to us. In addition, further subgrouping into attentive (ICD10:F98.8), hyperactive (ICD10:F90.1, F90.8, F90.9) and combined subtype (ICD10: F90.0) for ADHD, infantile autism (F84.0), atypical autism (F84.1), Asperger's syndrome (F84.5) and pervasive (F84.8 and F84.9) for ASD as well as subgrouping by other psychiatric co-morbidities could have been analyzed to shed light on the role of genetic variants upon disorder subtypes and presence of additional co-morbidities. However, in our study further subgrouping would reduce the size of reach group further and make impossible for comparison in statistical analyses. Although, considering the extensive clinical heterogeneity of both ASD and ADHD, future studies exploring the genetic role upon subgroups with the ASD/ADHD spectrum would be highly valuable. Another limitation of our study was the relatively small sample size, and thus our findings require replication in an independent sample to draw firm conclusions. However, we are confident, that a similar recruitment strategy could be applied in any other adult psychiatric specialist clinic in Denmark or an equivalent health care facility for adult ASD and/or ADHD patients abroad. Despite the study limitations, the results imply that a higher genetic burden of both rare CNVs and PRS from common SNP yields a more severe clinical manifestation with sustained need for psychiatric specialist care, as anticipated by previous findings (Girirajan et al. [<reflink idref="bib18" id="ref101">18</reflink>], [<reflink idref="bib19" id="ref102">19</reflink>], [<reflink idref="bib20" id="ref103">20</reflink>]; Langley et al. [<reflink idref="bib31" id="ref104">31</reflink>]; Robinson et al. [<reflink idref="bib50" id="ref105">50</reflink>]).</p> <p>Our findings may be taken to support the recommendation put forward by the American College of Medical Genetics and Genomics (ACMG) guidelines for clinical genetic evaluation of ASD (Schaefer and Mendelsohn [<reflink idref="bib56" id="ref106">56</reflink>]; Schaefer et al. [<reflink idref="bib57" id="ref107">57</reflink>]), that clinical testing for combined burden of CNVs is approaching clinical utility in families with multiple affected relatives in order to inform clinical decision making and give an etiological diagnosis. Based on our findings, we suggest that PRS can be included in this practice; at least to further explore its potential. However, before such implementations should be effectuated further replication is need, as these findings are limited by the relatively small sample size in our study. Furthermore, we are fully supportive of the conviction that important ethical issues of such implementations must not be neglected (Gershon and Alliey-Rodriguez [<reflink idref="bib16" id="ref108">16</reflink>]).</p> <p>In conclusion, this is the first study to apply an integrated analysis of both rare CNVs and PRS from common SNPs and determine their role upon clinical manifestations and need of care as well as an estimation of their overall contribution to genetic variance explained within ASD and/or ADHD families with multiple affected individuals. The results reveal an increased burden of both large, rare CNVs and SCZ PRS among diseased family members with clinical manifestation of ASD and/or ADHD in need of sustained psychiatric specialist care in adulthood. These results are limited by the relatively small sample size and will need further replication, despite this limitation the findings support the current direction in the field of precision psychiatry towards clinical implementation of testing for known genetic risk factors in order to guide early intervention, predict outcome and give an etiological diagnosis. Since we also showed that current genetic tools explain a small fraction of the heritability in ADHD and ASD, we claim that there is big room for improvement in the proposed genetic tests. Larger GWAS for ASD and ADHD are currently on their way and the results will provide us with better genetic tools. Furthermore, we also <emph>suggest</emph> that clinical implementation of genetic testing could include the joint contribution of both rare CNVs and PRS from common SNPs for improved prediction, although our findings need replication.</p> <hd id="AN0148114446-12">Author Contributions</hd> <p>SLB contributed to collecting and analysing data, manuscript writing. JLB, KDJ and AKP participated in the supervising data collection. VA conceived the polygenic risk score implementation. AB and TW contributed to supervising data analysis and manuscript writing.</p> <hd id="AN0148114446-13">Funding</hd> <p>The research leading to these results has received funding from <emph>The Lundbeck Foundation</emph> N<sups>o</sups> R208-2015-3951 and <emph>Fonden for Faglig Udvikling af Speciallægepraksis</emph> N<sups>o</sups> 38850/16.</p> <hd id="AN0148114446-14">Compliance with Ethical Standards</hd> <p></p> <hd id="AN0148114446-15">Conflict of interest</hd> <p>Thomas Werge has served as a lecturer for and consultant to H. Lundbeck A/S. Jette LaBianca has served at the advisory board for H. Lundbeck A/S and as lecturer for H. Lundbeck A/S, Shire and Servier. Klaus Damgaard Jakobsen has been a consultant for AstraZeneca, on advisory board for Bristol Myers Squibb and received speaker's honoraria from Lundbeck Pharma. Anne Katrine Pagsberg, Vivek Appadurai, Alfonso Buil and Sonja LaBianca report no conflicts of interest.</p> <hd id="AN0148114446-16">Ethical Approval</hd> <p>All procedures performed in this study involving human participants were in accordance with the ethical standards of the National Committee on Health Research Ethics in Denmark (Protocol: H_B_2009_026) and with the 1964 Helsinki declaration and its later amendments.</p> <hd id="AN0148114446-17">Electronic supplementary material</hd> <p>Below is the link to the electronic supplementary material.</p> <p>Graph: Supplementary file1 (PDF 470 kb)</p> <hd id="AN0148114446-18">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0148114446-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref52" type="bt">1</bibl> <bibtext> Almasy L, Blangero J. Variance component methods for analysis of complex phenotypes. 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  Data: Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families
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  Data: <searchLink fieldCode="AR" term="%22LaBianca%2C+Sonja%22">LaBianca, Sonja</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5644-6472">0000-0001-5644-6472</externalLink>)<br /><searchLink fieldCode="AR" term="%22LaBianca%2C+Jette%22">LaBianca, Jette</searchLink><br /><searchLink fieldCode="AR" term="%22Pagsberg%2C+Anne+Katrine%22">Pagsberg, Anne Katrine</searchLink><br /><searchLink fieldCode="AR" term="%22Jakobsen%2C+Klaus+Damgaard%22">Jakobsen, Klaus Damgaard</searchLink><br /><searchLink fieldCode="AR" term="%22Appadurai%2C+Vivek%22">Appadurai, Vivek</searchLink><br /><searchLink fieldCode="AR" term="%22Buil%2C+Alfonso%22">Buil, Alfonso</searchLink><br /><searchLink fieldCode="AR" term="%22Werge%2C+Thomas%22">Werge, Thomas</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Autism+and+Developmental+Disorders%22"><i>Journal of Autism and Developmental Disorders</i></searchLink>. Jan 2021 51(1):276-285.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br /><searchLink fieldCode="DE" term="%22Pervasive+Developmental+Disorders%22">Pervasive Developmental Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Comorbidity%22">Comorbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Age+Differences%22">Age Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+Disorders%22">Genetic Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Persons%22">At Risk Persons</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Services%22">Health Services</searchLink>
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  Data: 10.1007/s10803-020-04552-x
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  Data: 0162-3257
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  Label: Abstract
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  Data: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are highly heritable neurodevelopmental disorders that frequently co-occur. Both rare and common genetic variants are important for ASD and ADHD risk but their combined contribution to clinical heterogeneity is unclear. In a sample of 39 ASD and/or ADHD families we estimated the overall variance explained by known rare copy number variants (CNVs) and polygenic risk score (PRS) from common variants to be 10% in comorbid ASD/ADHD, 4% in ASD and 2% in ADHD. We show that burden of large, rare CNVs and PRS is significantly higher in adult ASD and/or ADHD patients with sustained need for specialist care compared to their unaffected relatives, while affected relatives fall in-between the two.
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      Pagination:
        PageCount: 10
        StartPage: 276
    Subjects:
      – SubjectFull: Autism
        Type: general
      – SubjectFull: Pervasive Developmental Disorders
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Comorbidity
        Type: general
      – SubjectFull: Age Differences
        Type: general
      – SubjectFull: Genetic Disorders
        Type: general
      – SubjectFull: At Risk Persons
        Type: general
      – SubjectFull: Health Services
        Type: general
    Titles:
      – TitleFull: Copy Number Variants and Polygenic Risk Scores Predict Need of Care in Autism and/or ADHD Families
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: LaBianca, Sonja
      – PersonEntity:
          Name:
            NameFull: LaBianca, Jette
      – PersonEntity:
          Name:
            NameFull: Pagsberg, Anne Katrine
      – PersonEntity:
          Name:
            NameFull: Jakobsen, Klaus Damgaard
      – PersonEntity:
          Name:
            NameFull: Appadurai, Vivek
      – PersonEntity:
          Name:
            NameFull: Buil, Alfonso
      – PersonEntity:
          Name:
            NameFull: Werge, Thomas
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 0162-3257
          Numbering:
            – Type: volume
              Value: 51
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Autism and Developmental Disorders
              Type: main
ResultId 1