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Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

October 2021

DOI:10.1101/2021.10.20.21264950

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Authors:

Ada Chan

University of Toronto

Worrawat Engchuan

SickKids

z. Wang

SickKids

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Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorized 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We developed a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD had a significantly higher GRVS compared to those with nondysmorphic ASD (P= 0.027). Using the polygenic transmission disequilibrium test, we observed an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P= 2.9X10-3). These findings replicated using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.

Replication of common and rare genetic findings in subset of Simons Simplex Collection cohort.

…

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Genome-wide rare variant score associates with morphological subtypes

of autism spectrum disorder

Ada J.S. Chan1,2, Worrawat Engchuan1,2, Miriam S. Reuter3, Zhuozhi Wang1,2,

Bhooma Thiruvahindrapuram1,2, Brett Trost1,2, Thomas Nalpathamkalam1,2,

Carol Negrijn4, Sylvia Lamoureux1,2, Giovanna Pellecchia1,2, Rohan Patel1,2,

Wilson W.L. Sung1,2, Jeffrey R. MacDonald1,2, Jennifer L. Howe1,2, Jacob

Vorstman1,5,6, Neal Sondheimer7-9, Nicole Takahashi10, Judith H. Miles10, Evdokia

Anagnostou9,11, Kristiina Tammimies12, Mehdi Zarrei1,2, Daniele Merico1,13, Dimitri

J. Stavropoulos14,15, Ryan K.C. Yuen1,2,7, Bridget A. Fernandez4,16,17*, Stephen

W. Scherer1,2,7,18*

Affiliations

1The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital

for Sick Children, Toronto, ON, Canada;

2Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON,

Canada

3Ted Rogers Centre for Heart Research, The Hospital for Sick Children, Toronto,

ON, Canada;

4Provincial Medical Genetics Program, Eastern Health, St. John’s, NL, Canada;

5Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada;

6Department of Psychiatry, University of Toronto, Toronto, ON, Canada;

7Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada;

8Division of Clinical and Metabolic Genetics, Department of Pediatrics, The

Hospital for Sick Children, Toronto, ON, Canada;

9Department of Pediatrics, University of Toronto, Toronto, ON, Canada;

10Thompson Center for Autism and Neurodevelopmental Disorders, University of

Missouri, Columbia, MO, USA;

11Holland Bloorview Kids Rehabilitation Hospital;

12Department of Women's and Children's Health, Karolinska Institutet,

Stockholm, Sweden;

13Deep Genomics Inc., Toronto, ON, Canada;

14Department of Paediatric Laboratory Medicine, Genome Diagnostics, The

Hospital for Sick Children, Toronto, ON Canada;

15Department of Paediatric Laboratory Medicine, Genome Diagnostics, Hospital

for Sick Children, Toronto, Ontario, Canada;

16Department of Pediatrics and The Saban Research Institute, Children's

Hospital Los Angeles, Keck School of Medicine of University of Southern

California, Los Angeles CA USA

17Discipline of Genetics, Faculty of Medicine, Memorial University of

Newfoundland, St. John's NL Canada

18McLaughin Centre, University of Toronto, Toronto, ON, Canada

Co-correspondence:

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is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted October 26, 2021. ; https://doi.org/10.1101/2021.10.20.21264950doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Stephen W. Scherer (stephen.scherer@sickkids.ca, 416-813-7613, The Centre

for Applied Genomics, Peter Gilgan Centre for Research and Learning, 686 Bay

Street Room 13.9705 Toronto, Ontario, M5G 0A4, Canada)

Bridget A. Fernandez (bfernandez@chla.usc.edu, 323-361-1007, Department of

Pediatrics and The Saban Research Institute, Children's Hospital Los Angeles,

4650 Sunset Blvd MS #001, Los Angeles CA USA)

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Abstract

Defining different genetic subtypes of autism spectrum disorder (ASD) can

enable the prediction of developmental outcomes. Based on minor physical and

major congenital anomalies, we categorized 325 Canadian children with ASD

into dysmorphic and nondysmorphic subgroups. We developed a method for

calculating a patient-level, genome-wide rare variant score (GRVS) from whole-

genome sequencing (WGS) data. GRVS is a sum of the number of variants in

morphology-associated coding and non-coding regions, weighted by their effect

sizes. Probands with dysmorphic ASD had a significantly higher GRVS

compared to those with nondysmorphic ASD (P= 0.027). Using the polygenic

transmission disequilibrium test, we observed an over-transmission of ASD-

associated common variants in nondysmorphic ASD probands (P= 2.9×10-3).

These findings replicated using WGS data from 442 ASD probands with

accompanying morphology data from the Simons Simplex Collection. Our results

provide support for an alternative genomic classification of ASD subgroups using

morphology data, which may inform intervention protocols.

Main

Autism spectrum disorder (ASD), which is diagnosed on the basis of behavioral

assessments that reveal social communication deficits and repetitive behaviors,

is often associated with traits including major congenital anomalies (MCAs),

minor physical anomalies (MPAs)1,2 and intellectual disability3-5. Increasingly,

penetrant variants of diagnostic value6,7 and lesser impact common variants are

being implicated in the etiology of ASD4,8.

Autistic individuals who are more dysmorphic (complex ASD) tend to have lower

intelligence quotients (IQ) and more brain and other major congenital

anomalies9,10 compared with those who are less dysmorphic (essential ASD),

leading to poorer developmental outcomes. Individuals with complex ASD are

also less likely to have a family history of ASD, suggesting that morphological

subtypes can reveal informative genetic differences among ASD subgroups9.

Genetic liability to ASD can be quantified using a polygenic risk score (PRS),

which is a weighted sum of ASD-associated common variants, using effect sizes

drawn from genome-wide association studies11. A similar score for rare variants

remains to be established. Rare variant studies use burden analyses to compare

the frequency of rare variants, equally weighted, between cases and controls or

among ASD subtypes3,12,13. Quadratic tests have also been used in rare variant

association tests and typically weigh variants by minor allele frequency14,15.

However, effect sizes depend on the affected gene and variant type, and these

variables should be considered in rare variant analyses.

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Here, from two independent cohorts, we used whole-genome sequences (WGS)

and detailed clinical morphology data to: 1) develop a genome-wide rare variant

score (GRVS) to measure the relationship between rare variants and

morphology, and 2) examine the contribution of rare and common variants in

morphological ASD subtypes (Figure 1 and Supplementary figure 1).

For our discovery cohort, we used a population-based sample of 325 unrelated

children with Autism Diagnostic Observation Scale (ADOS)-confirmed ASD.

Following clinical examination, a total morphology score was assigned to each

case based on the number of MPAs and MCAs9,10. The cohort was then stratified

into three subtypes of increasing morphologic severity: 187 essential ASD

(57.5%), 57 equivocal ASD (17.5%) and 81 complex ASD (24.9%)

(Supplementary Table 1). We further stratified these samples into two subtypes

by combining complex and equivocal ASD into a single dysmorphic ASD

grouping and redefining essential ASD as nondysmorphic ASD.

We performed WGS on 795 genomes (325 probands and 470 parents) and

detected all classes of variation (SNV, indel, CNV and structural variants (SVs)

(Figure 1, Supplementary Table 2-4). Using the American College of Medical

Genetics and Genomics guidelines16,17, we identified a total of 46 clinically

significant variants (CSVs) in 46 of 325 probands (14.1%) (Supplementary

Tables 5-7). The proportion of dysmorphic ASD cases with a CSV (25.9%;

35/135) was significantly higher than nondysmorphic ASD (5.8%, 11/190) (P=

3.2×10-7, one-sided Fisher’s test), consistent with our previous findings10. We

also identified 29 variants of uncertain significance (VUS) in 26 probands that

were of interest, including tandem repeat expansions in previously reported ASD

candidate loci18; three probands each had two VUS (Supplementary Tables 5-7

and Supplementary Note).

To further investigate the contribution of rare variants among morphological ASD

subtypes, we first conducted a rare variant burden analysis and multiple test

correction using the Benjamini Hochberg approach (BH-FDR) (see methods). We

found a significantly higher prevalence of rare coding deletions >10kb in

probands with more dysmorphic features (P= 5.00 × 10-4 and BH-FDR= 5.00 ×

10-3, Supplementary Table 8). Rare coding duplications >10kb and ≤10kb, genic

deletions ≤10kb, loss-of-function (LoF) and missense variants were not

significantly different among subtypes (Supplementary Table 8).

We then performed enrichment and burden analyses to identify gene sets or

noncoding regions, respectively, that were differentially affected by rare or de

novo variants between the morphological ASD subtypes. The 67 gene sets and

noncoding regions studied have been previously associated with ASD13,19-23.

After multiple-testing correction (permutation-based false discovery rate (FDR)

<20%), 20 significant gene sets or noncoding regions were identified

(Supplementary Tables 9 and 10). We observed that probands with dysmorphic

features had higher burdens of deletions and missense variants impacting genes

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responsible for various neuronal functions and duplications >10kb impacting

brain-expressed genes (Figure 2 and Supplementary Table 9). Dysmorphic

probands also had a significantly higher prevalence of rare deletions ≤10kb

overlapping promoters of long noncoding genes and duplications (larger and

smaller than 10kb) overlapping active brain enhancers (Supplementary Figure 2,

Supplementary Table 10).

We then tested the collective contribution of rare variants in morphology-

associated regions, while considering the effect size of each variant, which varies

depending on the variant type and morphology-associated region. We developed

a GRVS for each proband, which is a weighted sum of the number of rare

variants in morphology-associated regions identified from gene set enrichment

and noncoding burden tests (Supplementary Table 11). We weighed the number

of rare variants in each morphology-associated region as well as the variant type

(i.e., coding or noncoding deletions and duplications >10kb or ≤10kb, loss-of-

function variants, missense variants, and noncoding SNVs and indels) using the

coefficients from logistic regression models.

To calculate GRVSs for each proband in the discovery cohort, we used a 10-fold

cross validation strategy to reduce over-fitting (Supplementary Figure 1a). We

used Nagelkerke’s R2 to determine the optimal P value threshold (P<0.1) to

identify morphology-associated regions (Supplementary Figure 3a and Online

Methods). GRVS can be calculated for probands regardless of whether their

parents have been sequenced. However, there would be a systematic difference

in GRVSs in this cohort if all probands were used because those probands

whose parents have been sequenced would include scores from de novo

variants, whereas those without sequenced parents would not have de novo

variant scores. To avoid this, GRVS was calculated only for probands with two

sequenced parents (n= 235) (Figure 3a and Supplementary Table 12).

Probands with dysmorphic ASD had significantly higher average GRVSs than

those with nondysmorphic ASD (P= 0.027, one-sided Wilcoxon rank sum test)

(Figure 3b). Most probands (95.7%, 225/235) had more than one variant

impacting morphology-associated regions (Supplementary Table 12). Rare

coding CNVs had the highest effect size; rare noncoding SNVs and indels had

the lowest (Figure 3c and Supplementary Table 11).

Using the GRVS formula, we calculated a score for CSVs that overlapped an

ASD relevant, morphology-associated region (so that effect size was available for

calculation) and that occurred in probands with sequencing data from both

parents. 17 of the 46 CSVs met these criteria. No score was calculated for the

remaining 29 variants because 15 were identified in probands where both

parents were not available for sequencing, and 14 variants were not located in or

encompassed by one of the 20 morphology-associated regions. (Online

Methods). In 47% of samples with CSV scores (8/17 probands, Supplementary

Table 13), CSVs contributed >50% of the total GRVS. When we excluded the

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probands with CSVs, those with dysmorphic ASD still had significantly higher

average GRVSs than those with nondysmorphic ASD (P= 0.048, one-sided

Wilcoxon rank sum test, Figure 3b). These findings suggest that variants in

morphology-associated regions that are not CSVs also significantly contribute to

morphological outcomes in ASD.

To explore the contribution of common (minor allele frequency >0.05) ASD-

associated variants in ASD subtypes, we calculated polygenic risk scores (PRS)

for ASD and body mass index (BMI)8 (Online Methods and Supplementary Table

12). We then compared these scores across the morphologic groups using the

polygenic transmission disequilibrium test (pTDT)8, which compares the PRS of

the proband to parents’ mean PRS. We found a significant over-transmission of

common ASD-associated variants in probands with nondysmorphic ASD (P=

2.9×10-3, one-sided t-test) and no significant over-transmission in probands with

dysmorphic ASD (P= 0.3) (Figure 4). PRS for BMI was selected as a negative

control because there is no genetic correlation between BMI and ASD24, and we

did not find over-transmission of PRS for BMI in either subtype (Figure 4).

IQ is often negatively correlated with the burden of rare variants3,4,13,25,26. We

therefore examined our probands with dysmorphic ASD and determined they had

a significantly lower mean IQ compared to nondysmorphic ASD (P= 0.013, one-

sided t-test, Figure 5a and Supplementary Table 12). Probands with a CSV had

significantly lower IQ compared to probands without a CSV (P= 2.2 ×10-4, one-

sided t-test, Figure 5b). However, IQ was not significantly correlated with GRVS

(rho= -0.042, P= 0.64, Figure 5c) or PRS (rho= -0.15, P= 0.12, Figure 5d).

We repeated our analysis on a replication cohort of relevant samples from the

Simons Simplex Collection (442 ADOS-confirmed affected probands and 355

unaffected siblings)27. The affected probands had been categorized into two

morphological subtypes (400 nondysmorphic and 42 dysmorphic cases)27 using

the Autism Dysmorphology Measure (ADM)28. In contrast to the discovery cohort,

the SSC probands were classified by targeted physical examinations performed

by individuals without expert training in dysmorphogy, and the classification did

not incorporate the presence or absence of major congenital anomalies. To

compare the two cohorts, we reclassified a subset of the original discovery cohort

based on minor anomalies alone using the ADM algorithm (203 nondysmorphic

and 73 dysmorphic cases, Online Methods). We calculated new GRVSs for the

ADM-reclassified discovery cohort using a 10-fold cross-validation approach (143

nondysmorphic and 48 dysmorphic cases met criteria for inclusion in this

analysis, Supplementary Figure 1a, Supplementary Tables 12 and 14, and

Online Methods). We used Nagelkerke’s R2 to determine the optimal P-value

threshold and identified 32 morphology-associated regions, which largely

overlapped with our original analysis (Supplementary Figure 3b). The

morphology-associated regions (P< 0.1, Supplementary Table 15) identified in

the reclassified discovery cohort were used to calculate GRVSs for the

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replication cohort (Supplementary Figure 1b, Supplementary Table 16, and

Online Methods).

In both cohorts, probands with ADM-defined dysmorphic ASD had significantly

higher GRVSs (Pdiscovery= 3.6 ×10-6 and Preplication= 2.7 ×10-4, one-sided Wilcoxon

rank sum test, Figure 6a) and yield of CSVs (Pdiscovery= 2.7 ×10-7 and Preplication=

2.1 ×10-3, one-sided Wilcoxon rank sum test, Figure 6b and Supplementary

Tables 17 and 18) compared to ADM-defined nondysmorphic ASD, consistent

with our findings using the gold-standard dysmorphology classification. In the

replication cohort, unaffected siblings had a significantly lower GRVS compared

to ADM-defined dysmorphic ASD (P= 7.7 ×10-4 one-sided Wilcoxon rank sum

test) but did not have a significantly lower GRVS compared to ADM-defined

nondysmorphic ASD (Figure 6a). Furthermore, unaffected siblings of

nondysmorphic probands did not have a significantly lower GRVS compared

unaffected siblings of dysmorphic probands (P= 0.75, one-sided Wilcoxon rank

sum test, data not shown).

In both cohorts we also found a significant over-transmission of common ASD-

associated SNPs in ADM-defined nondysmorphic ASD (Pdiscovery= 6.7×10-3 and

Preplication= 6.3 ×10-3, one-sided Wilcoxon rank sum test, Figure 6c). In results

similar to Weiner et al.8, we did not observe over-transmission in unaffected

siblings in the replication cohort (P= 0.88, one-sided Wilcoxon rank sum test).

Individuals with ADM-defined dysmorphic ASD or with CSVs had a significantly

lower IQ compared to ADM-defined nondysmorphic ASD or those without CSVs,

respectively (Supplementary Figure 4a and b, Supplementary Tables 12, 14-16).

Although there was no correlation between IQ and GRVS in the discovery cohort

when the subtype classification was done by either gold standard dysmorphology

examination (rho= -0.042, P= 0.64, Figure 5c) or using the ADM (rho= 0.12, P=

0.21, Supplementary Figure 4c), a significant negative correlation was found in

the replication cohort (rho= -0.14, P= 4.4×10-3, Supplementary Figure 4c). We did

not find significant correlations between IQ and PRS (Figure 5d and

Supplementary Figure 4d), or PRS and GRVS in either cohort (Supplementary

Figure 5 and Supplementary Tables 12 and 16).

Differences in the correlation between GRVS and IQ between the cohorts might

be attributable to differences in ascertainment. The discovery cohort was

assembled using a population-based recruitment strategy, and the average IQ of

the cohort is 105 similar to the population average of 100. In contrast, individuals

with comorbid ID or low IQ are found in SSC29, consistent with the replication

cohort having a significantly lower IQ compared to the discovery cohort (mean

IQdiscovery= 105±23, mean IQreplication= 82±27, P= 1.1 × 10-21, two-sided t-test).

Inconsistent findings between ASD cohorts have also been observed when

examining gender differences in IQ30, where findings from cohorts with specific

selection criteria (e.g., simplex families) may not be generalizable to the ASD

population.

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Our data suggest that while both dysmorphic and nondysmorphic ASD

demonstrate over-transmission of common ASD-associated variants, there is a

significantly higher burden of rare variants in dysmorphic ASD than

nondysmorphic ASD. GRVS methods may add further specificity to identifying

clinically informative endophenotypes but exquisitely phenotyped cohorts will be

required. While dysmorpholgy classification by expert clinical examination is not

highly scalable, the use of automated tools for 2 and 3-dimensional imaging31

may make it feasible to perform high throughput dysmorphology classification.

This will allow GRVSs to be more widely used, potentially in combination with

one or more early clinical biomarkers.

Online Methods

Subjects and Methods

Subject enrolment – Discovery Cohort

The cohort consists of children residing in the Canadian province of

Newfoundland and Labrador, recruited from one of three developmental team

assessment clinics between 2010 and 2018. Assessment through one of these

clinics was required for a child with ASD to qualify for provincially funded home

Applied Behavioural Analysis (ABA) therapy. Families were invited to participate

after their child received an ASD diagnosis from the multidisciplinary team which

was led by a developmental paediatrician. Probands met ASD criteria according

to the Diagnostic and Statistical Manual of Mental Disorders (Fourth or Fifth

Edition, Text Revision)32-34 and all diagnoses were confirmed by an Autism

Diagnostic Observation Schedule35 assessment. Most probands also had an

Autism Diagnostic Interview-Revised36 assessment consistent with ASD.

Children were not excluded from the study based on syndromic features or the

presence of a known syndrome. Parents or guardians of the children provided

written informed consent. The study was approved by Newfoundland’s Health

Research Ethics Boards (HREB# 2003.027) and SickKids Research Ethics

Board (REB#0019980189).

Subject enrolment – Replication Cohort

The replication cohort consisted of a subset of samples from the Simons Simplex

Collection, including 442 affected probands with dysmorphology and WGS data

along with their unaffected siblings (n= 355).

Clinical Assessment and Morphological Examination – Discovery Cohort

Clinical assessments, morphological examinations and classification were

performed as previously described9,10. In brief, the team reviewed the child’s

family history and medical records, including radiology and

electroencephalogram (EEGs). EEGs were ordered if there was a clinical

suspicion of seizures. Other screens for birth defects were arranged based on

standard physical examination of the proband, which included a cardiovascular

examination (e.g. echocardiogram for a proband with a murmur consistent with a

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ventricular septal defect). A single experienced dysmorphologist (B.A.F.)

performed a detailed morphological examination of the child and (if possible)

parents documenting minor physical anomalies (MPAs), height, weight, head

circumference and anthropometric measurements of the head, face, hands, and

feet. As described by Miles, et al.9, each proband was assigned an MPA score;

one point was given for each embryologically unrelated MPA or for each

measurement greater than two standard deviations above or below the

population mean and that was absent from the parents if they were available for

examination. Each child was also assigned a major congenital anomaly (MCA)

score (two points were given for each MCA), and a total morphology score (MPA

+ MCA scores). Using the total morphology score, we classified each child into

essential (total morphology score 0-3), equivocal (total morphology score 4-5) or

complex (total morphology score ≥6) groups. We used the final classification for

comparing the yield of CSVs and for performing the rare and common variant

analyses.

Autism Dysmorphology Measure – Discovery and Replication Cohorts

Our replication cohort consisted of a subset of samples from the Simons Simplex

Collection27. This subset of samples had already been categorized into two

morphological groups (400 nondysmorphic and 42 dysmorphic cases) by multiple

non-geneticist examiners using the Autism Dysmorphology Measure28. In brief,

the Autism Dysmorphology Measure is a decision tree-based classifier that

assigns cases into nondysmorphic and dysmorphic groups based the presence

or absence of minor physical anomalies of 12 body areas. It was designed to be

used by clinicians who do not have expert training in dysmorphology and the

assessment is limited to the craniofacies, hands and feet of the child. The ADM

decision tree was trained on expert-derived consensus classification of 222 ASD

cases who had gold standard examinations of all body areas by clinical

geneticists with expertise in dysmorphology9,37. The latter was the approach we

used for the initial morphologic classification of our discovery cohort into

essential, equivocal and complex groups9.

In contrast to the Autism Dysmorphology Measure, the morphological scores

used to classify the discovery cohort factored in major congenital anomalies as

well as MPAs, and MPAs were documented for the entire body including areas

not assessed by the ADM (for example the thorax, arms, legs and skin). In order

to align the type of morphologic data that was used to classify the discovery and

replication cohorts, we reclassified the discovery cohort using the Autism

Dysmorphology Measure, yielding 248 nondysmorphic and 77 dysmorphic cases.

Of the 248 ADM-defined nondysmorphic cases, 18 cases were clearly

dysmorphic upon further review by an experienced dysmorphologist (B.A.F). The

Autism Dysmorphology Measure is reported to have an 82% sensitivity28, and the

sensitivity for the discovery cohort is similar at 80%. Thus, we excluded the 18

individuals with a false negative dysmorphic ADM classification to make the

discovery cohort as clean as possible. We also included only samples that were

sequenced on Illumina platforms to be consistent with the replication cohort27.

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Thus, the final number of ASD cases in the discovery cohort used for analysis

was 276, of which 203 had nondysmorphic ASD and 73 had dysmorphic ASD

according to ADM.

Whole-genome sequencing and variant detection

We extracted DNA from whole blood or lymphoblast-derived cell lines and

assessed the DNA quality with PicoGreenTM and gel electrophoresis. We

sequenced 795 genomes (325 probands and 470 parents) with one of the

following WGS technologies/sites as previously described4: Complete Genomics

(Mountain View, CA, n= 33 probands, 64 parents), Illumina HiSeq2000 by The

Centre for Applied Genomics (TCAG) (Toronto, ON, n= 24 probands, 48

parents), or Illumina HiSeq X by Macrogen (Seoul, South Korea, n= 182

probands, 250 parents) or TCAG (n= 86 probands, 108 parents). We used

KING38 to confirm familial relationships and ADMIXTURE39 and EIGENSOFT40 to

confirm ancestries (Supplementary Table 12).

Alignment and variant calling for genomes sequenced by Complete Genomics

were conducted as previously described41. For samples sequenced on Illumina

platforms, each WGS site aligned WGS reads to the human reference genome

assembly hg19 (GRCh37) using Burrows-Wheeler Aligner v.0.7.1242 (TCAG) or

Isaac v.2.0.1343 (Macrogen). For each genome, we performed local realignment

and quality recalibration and detected SNVs and small indels using the Genome

Analysis Toolkit (GATK) Haplotype Caller44 v.3.4.6 without genotype refinement.

We detected CNVs using ERDS (estimation by read depth with single nucleotide

variants)45 and CNVnator46 as previously described47. We detected SVs using

Manta v.0.29.648. When supported by the variant caller (i.e. GATK and Manta),

trio-based joint variant calling was conducted for each family.

To identify uniparental isodisomies (isoUPDs), we calculated the ratio of the

number of homozygous or hemizygous SNPs to the number of SNPs per

chromosome, for each sample. Samples with a ratio greater than 0.55 had a

putative isoUPD on the corresponding chromosome. We examined CNV and

kinship data to rule out confounding factors (i.e., large CNVs or consanguinity).

For each sample with a ratio greater than 0.55, we examined plots of B-allele

frequency per chromosome; those with runs of homozygosity > 10Mb on one

chromosome were considered to have a putative isoUPD49. We examined the

inheritance of homozygous SNPs within the region of the putative isoUPD via

visual inspection of BAM files and experimentally validated one of the SNPs to

confirm the isoUPD and inheritance.

We systematically detected aneuploidies by calculating a ratio of the average

read depth per chromosome to that for the entire sample. Ratios ≤0.5 and ≥1.5

were considered a loss or gain, respectively. For Complete Genomics data, we

identified aneuploidies by looking for an excess of large CNVs for each

chromosome per sample.

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Tandem repeats were detected from samples with PCR-free DNA library

preparation and sequenced on the Illumina HiSeq X platforms using

ExpansionHunter Denovo50 with default parameters. We detected tandem repeat

expansions in the discovery cohort using ExpansionHunterDenovo size cutoffs

as previously described51. Sample quality control procedures were performed as

previously described51.

Variant Annotation

We annotated SNVs and indels with information on population allele frequency,

variant impact predictors, and putative pathogenicity and disease association,

using a custom pipeline based on ANNOVAR52 as previously described4. For

non-genic regions, we annotated whether the variant overlapped reported ASD-

associated non-coding regions19-23 (Supplementary Table 19). These included

transcription start sites, fetal brain promoters and enhancers of LoF intolerant

genes20, histone modification (H3K27ac) sites in fetal and adult brain21, splice

sites, 3’- and 5’-untranslated regions (UTRs)23, binding sites predicted by

DeepSEA22 to cause LoF, as well as conserved promoters of any genes,

developmental delay-associated genes, and long non-coding RNA genes19. We

tested three additional functional sites that have not been previously associated

with ASD. These included boundaries of topologically associating domains53,

CTCF binding sites54, and brain enhancers from Roadmap Epigenomics

chromatin states (15-states chromHMM)55.

We annotated CNVs and SVs with a custom pipeline using RefSeq gene models,

with repeat regions, gaps, centromeres, telomeres and segmental duplications

relative to University of California at Santa Cruz genome assembly hg19. Similar

to our non-genic annotations for SNVs, we annotated whether a CNV overlapped

promoters of genes19, H3K27ac sites19, 3’UTR and 5’UTR23 (Supplementary

Table 19). We retained CNVs overlapping such regions, but not exonic regions.

We also annotated the frequency of each CNV and SV from among 3,107

parents in the MSSNG database4 (fifth version) and the putative pathogenicity

and disease association [from Human and Mouse Phenotype Ontologies56,57

(HPO and MPO), ClinGen Genome Dosage Sensitivity Map58, Online Mendelian

Inheritance in Man, and Database of genomic variation and phenotype in

humans using ensemble resources (DECIPHER)59].

We annotated mitochondrial variants using Annovar-based custom scripts with

annotations from MitoMaster (April 2019) and Ensembl v96.

Detection of rare variants

We extracted high quality rare data for SNVs and indels after applying the

following filters: 1) FILTER is PASS or varQuality is VQHIGH or PASS; 2)

population allele frequencies < 1% in 1000 Genome Project60, NHLBI-ESP61,

Exome Aggregation Consortium62, The Genome Aggregation Database63, and

internal Complete Genomics control databases; 3) reference and alternative

allele frequency > 95% and < 1%, respectively, based on allele frequencies of

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2,573 parents in MSSNG (fourth version)4 to decrease batch and cross platform

effects; and 4) allele frequency < 5% from 250 parents from this study aligned

with Isaac to decrease alignment-specific artifacts. To further minimize cross

platform and batch effects, we required heterozygous SNVs and indels to have

an alternative allele fraction of 0.3-0.7 (inclusive) and homozygous/hemizygous

SNVs and indels to have an alternative allele fraction >0.7 for variants from

Complete Genomics. For Illumina variants, we also required heterozygous SNVs

and indels to have a genotype quality score of at least 99 and 90, respectively,

and homozygous SNVs and indels to have a genotype quality score of at least

25.

We retained CNVs >2kb that had <70% overlap with gaps, centromeres,

telomeres, and segmental duplications. For CNVs from Illumina platforms, we

defined stringent CNVs as those called by both ERDS and CNVnator (with 50%

reciprocal overlap). We defined CNVs as rare if the allelic frequency was < 1% in

parents from the MSSNG database4 and < 5% in parents of this cohort that were

aligned with Isaac.

We retained as rare SVs, those with an allelic frequency of < 1% in parents

analyzed with Manta from the MSSNG database and <5% in parents in this

cohort that were aligned with Isaac. Pairs of entries with identical non-zero first

numbers in the MATEID tag were retained as one inversion. Entries with identical

MATEID values were retained as complex SVs. On average per sample, we

detected ~3.7 million SNPs, 36,514 rare single nucleotide variants (SNVs), 4,113

small insertions and deletions (indels), 13 rare copy number variants (CNVs),

390 rare structural variations (Supplementary Table 2).

Detection of de novo variants

We determined de novo SNVs and indels from Complete Genomics data as

previously described41. For Illumina WGS data, we also used DenovoGear64

(version 0.5.4) to detect de novo SNVs and indels. We extracted variants

inconsistent with Mendelian inheritance (present in offspring but not in parents)

with FILTER= PASS and defined rare, as above. To identify high confidence de

novo SNVs, we applied the following quality filters: 1) pp_DNM score ≥ 0.9 from

DenovoGear64; 2) overlap GATK44 calls with genotype quality scores ≥ 99 for

heterozygous SNVs. We defined high confidence de novo indels as those called

by DenovoGear and GATK with the same start site. We retained de novo SNVs

and indels with a ratio of sequenced reads supporting the alternative call to the

total number of reads at the position of 0.3-0.7, or > 0.7 for X- and Y-linked

variants not in the pseudoautosomal regions in male subjects.

We defined putative de novo CNVs as rare stringent CNVs (see “Detection of

rare variants”) that were inconsistent with Mendelian inheritance. For CNVs that

did not have a conclusive inheritance pattern (i.e., CNV in child and parent were

not the same size), we defined putative de novo CNV as those with a CNV length

ratio between child and parent of > 2. For each putative de novo CNV from

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Illumina platforms, we calculated a read depth ratio of the CNV with the

surrounding region in each family member, as previously described47. Ratios of

0.35-0.65 were considered heterozygous deletions, <0.35 as

homozygous/hemizygous deletions, >= 1.4 as duplications and 0.9-1.1 as a

normal copy number. Putative de novo CNVs were considered de novo if the

copy number status based on ratios were inconsistent with Mendelian

inheritance. For the 40 regions with ratios that did not meet the afore-mentioned

criteria, we visualized the WGS reads to determine the inheritance status for

samples sequenced by Illumina. To determine the inheritance status for samples

sequenced by Complete Genomics, we examined the read depth coverage of the

CNV relative to that of Complete Genomics controls65 and its flanking regions in

each family member. On average per sample, we detected 73.4 de novo SNVs,

7.3 de novo indels, and 0.1 de novo CNVs (Supplementary Table 2)

Validation of variants

We randomly selected a subset of all high quality exonic de novo SNVs, all de

novo indels and all CSVs for validation in probands and available parents. We

used Primer366 to design primers to span at least 100 bp upstream and

downstream of a putative variant, avoiding regions of known SNPs, repetitive

elements, and segmental duplications. DNA from whole blood, if available, was

used to amplify candidate regions by polymerase chain reaction and to assay

with Sanger Sequencing. For CNVs, we validated all high confidence de novo

exonic and all clinically significant CNVs in whole blood DNA (if available) of

probands and available parents using TaqManTM Copy Number Assay (Applied

Biosystems), SYBR ® Green qPCR (Thermofisher) or digital droplet PCR

(BioRad). Experimental validation rates were 94.8%, 85.7%, and 87.5%,

respectively, for de novo SNVs, indels, and CNVs (Supplementary Tables 3 and

4).

Mitochondrial variant detection

For the samples sequenced by Illumina platforms, reads aligning to the

mitochondrial genome were extracted and realigned to the revised Cambridge

Reference Sequence (NC_012920) in b37 using BWA v0.7.8. Pileups were

generated with samtools mpileup v1.1 requiring the program to include duplicate

reads in the analysis and retaining all positions in the output. Custom scripts

were developed to parse the mpileup output to determine the most frequently

occurring non-reference base at each position in the mitochondrial genome. The

heteroplasmic fractions were calculated and vcf files were generated. Fasta files

with the most frequently occurring base at every position were also generated

and used as input for the program HaploGrep v2.1.1 for haplogoup prediction.

The vcf files were annotated using Annovar based custom scripts with

annotations from MitoMaster (April 2019) and Ensembl v96.

For the samples that were sequenced by Complete Genomics, the mitochondrial

variants called by the proprietary software were extracted. Fasta files were

generated using custom scripts replacing mitochondrial reference bases with

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alternative bases at heteroplasmic sites and the files were used as input for the

program HaploGrep v2.1.1 for haplogoup prediction. The vcf files were annotated

using Annovar based custom scripts with annotations from MitoMaster (April

2019) and Ensembl v96.

Positions with heteroplasmic fraction less than 5% or greater than 95% and

common in certain haplogroups (greater than 5%) were excluded from

downstream analysis. All variants were manually reviewed, and a list of artefacts

was compiled and excluded. To identify pathogenic mitochondrial variants, the

following variants were considered: any MitoMaster pathogenic variants at 5-

100% heteroplasmy, variants between 10-90% heteroplasmy, and variants

between 5-100% heteroplasmy and seen <2% of the time in the individual’s

haplogroup.

Variant detection for replication cohort

For the replication cohort, CRAM files and sequence-level variants were

downloaded from Globus (https://www.globus.org/). We detected CNVs using

ERDS 45 and CNVnator46, as previously described47. Rare variants were filtered

as described for the discovery cohort. We identified de novo SNVs and indels

using DeNovoGear64. Allele frequencies from the Simons Simplex Collection

were calculated and de novo variants with internal frequencies <1% were

excluded. De novo SNVs and indels at poorly sequenced or highly variable sites

were also excluded from further analysis. The remaining de novo variants were

filtered as described for the discovery cohort with the exception of using a

PP_DNM <0.95 threshold for de novo SNVs. Variants were annotated as

described above for the discovery cohort.

Variant prioritization and molecular diagnosis

To identify CSVs from the discovery cohort, we prioritized rare and de novo LoF

and damaging (as predicted by at least five/seven predictors23) missense

variants, and variants reported by ClinVar67 or the Human Gene Variant

Database68. We also prioritized rare and de novo CNVs and SVs, including those

overlapping syndromic regions in DECIPHER59 or ClinGen Genome Dosage

Sensitivity Map58 databases. Genes affected by such variants were compared to

ASD candidate genes3,4,13,69,70, candidate genes for neurodevelopmental

disorders69, and genes implicated in neurodevelopmental or behavioural

phenotypes according to HPO57 and MPO56. Additionally, we considered the

mode of inheritance from the Online Mendelian Inheritance in Man and Clinical

Genomics Database70, segregation and genotype-phenotype correlations. We

classified the variants as pathogenic, likely pathogenic, variants of uncertain

significance, likely benign, or benign, based on the American College of Medical

Genetics and Genomics Guidelines16,17. Variants of unknown significance in

known or candidate ASD genes with emerging evidence were further categorized

into three ASD candidate variant categories (Supplementary Note and

Supplementary Tables 5-7). Although applying quality filters for high confidence

variants is important to minimize false positives for burden analysis, this can

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increase false negatives. Therefore, we also manually inspected WGS data when

we identified CSVs that did not pass filtering criteria for high confidence variants.

Clinically significant variants classified as pathogenic or likely pathogenic or that

were considered clinically relevant (i.e., prompting further clinical management)

were reviewed by a medical geneticist in the context of the patient’s phenotype

and family history. Relevant findings were reported back to families through a

clinical geneticist. Differences in the yield of CSVs among the morphological

groups were calculated using Fisher’s exact test.

To identify CSVs from the affected probands in the replication cohort, the afore-

mentioned approach was applied to de novo LoF, damaging missense and

CNVs. CSVs from the replication cohort were confirmed by manual inspection of

WGS reads.

Rare variant burden analysis in gene sets and noncoding regions

For the discovery cohort, we performed two ASD subtype comparisons for each

rare variant burden analysis as follows: 1) comparing complex, equivocal and

essential ASD using ordinal regression tests and 2) comparing complex and

equivocal ASD (i.e. dysmorphic ASD) to essential ASD using logistic regression

tests. The test was done by regressing an event (e.g., number of genes impacted

by rare deletions per subject) capturing a particular genomic region (i.e., coding,

gene sets, or noncoding regions) on the phenotype outcome (e.g., complex vs.

essential ASD). The events tested in this study were the number of LoF,

missense, and predicted deleterious variants for sequence-level variants and the

number of genes or noncoding regions for CNVs. Tier 1 and 2 missense variants

consist of all or only predicted damaging missense variants, respectively, as

defined in Yuen, et al.23 The CNVs were grouped into two size bins, small CNVs

(2-10kb) and large CNVs (10kb to 3Mb) due to greater proportion of these CNVs

overlapping coding or noncoding regions, respectively. The number of genes

impacted by other CNVs was based on their overlap with the coding regions of

each gene. However, the number of genes impacted by small CNVs were based

on genic overlap since there were not enough small coding CNVs for the gene

set enrichment analysis. We compiled a list of 37 gene sets related to neuronal

function, brain expression, mouse phenotypes from MPO, or human phenotypes

from HPO that have been previously associated with ASD or used as negative

control gene sets when comparing ASD to control groups (Supplementary Table

20) 71-81. For non-coding regions, we compiled a list of regions reported to be

associated with ASD (Supplementary Table 19)19-21. We also included a score

that predicts the impact of a variant on transcription factor binding as one of the

non-coding regions tested22. Logistic regression and ordinal regression were

applied for two subtypes and three subtypes comparison, respectively. Sex,

genotyping platform, and three principal components from population

stratification were included in the model as covariates to correct for any biases

caused by sex difference, platforms, or ethnicity. Deviance test P value was

calculated by comparing residuals from two regression models; one with just the

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covariates and another with all both covariates and target variable as previously

described82. Global burden analysis was performed to compare the total number

of LOF variants, missense variants, predicted deleterious variants for sequence-

level variants, and genes impacted by CNVs. The coefficients reported were

obtained from the model with the covariates. Multiple test correction for global

burden tests was done using Benjamini Hochberg approach (BH-FDR). For the

gene sets and noncoding regions burden test, total variant count (for SNVs and

noncoding CNVs) or total gene count (for CNVs) was also included as one of the

covariates to get rid of a global burden bias that might inflate the test P value.

The coefficients, however, were calculated from the model with all the covariates

except the total variant count or the total gene count for the actual magnitude of

their impact. Permutation-based FDR correction (1000 permutations) corrected

for the multiple comparison. Since different gene sets and non-coding regions

consist of different number of genes or regions, we calculated the coefficients

using z-scores for the number of features in each gene set/region to compare the

coefficients across morphology-associated regions. When examining the burden

of rare variants using logistic regression models, we used all probands from the

discovery cohort (n=325). Since some probands did not have their parents

sequenced, we used a subset of the discovery cohort (n= 235) when examining

de novo variants.

Genome-wide rare variant score

In addition to identifying relevant gene sets or regions that were differentially

enriched among ASD morphologic subgroups, we developed a procedure to

calculate a genome-wide rare variant score (GRVS) for each subject. This

allowed the contribution of different variant types towards phenotype severity to

be assessed together. The procedure involved two main steps: i) identification of

relevant, differentially enriched gene sets or noncoding regions for each variant

type along with an estimation of their effect sizes in the discovery cohort, and ii)

calculation of the score for each subject in the target cohorts.

To estimate the effect sizes in the discovery cohort, we first fitted a logistic

regression model by regressing platform, sex and first three principal

components from population stratification on the dysmorphology classification

(nondysmorphic= 0 and dysmorphic= 1, or essential= 0, equivocal= 1, complex=

2). We then used the regression coefficients of these covariates and the intercept

in the second logistic regression model, where a feature representing a particular

gene set or region was tested. Therefore, regression coefficients of all the gene

sets and regions were corrected for those possible biases from the covariates

equally. The two models can be notated as below

Y= ɑ + βC

Y= ɑ + βC + βiXi

where Y is the outcome variable of dysmorphology classification, ɑ is an

intercept, β is a regression coefficients of covariates, C is a vector of covariates,

βi is the regression coefficient of a morphology-associated region, i, and Xi is the

number of features found in a morphology-associated region. A feature is defined

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as the number of rare or de novo SNVs or indels or the number of genes or

noncoding regions impacted by rare CNVs. For rare variants, we used all

probands in the discovery cohort. Since some probands did not have their

parents sequenced, we used a subset of the discovery cohort when examining

de novo variants. To determine the optimal P value threshold to identify

significant gene sets, we calculated the Nagelkerke’s R2 at different P value

thresholds (P < 0.001, 0.005, 0.01, 0.05, 0.1. 0.5, and 1) using the discovery

cohort and 10-fold cross-validation strategy. The optimal P value threshold was

at P<0.1 (Supplementary Figure 3). To minimize the redundancy in significant

gene sets and noncoding regions, we retained the most significant gene sets and

noncoding regions with a Jaccard index < 0.75. We used the regression

coefficients (βi) of significant gene sets or noncoding regions (P < 0.1) as a

weight for the number of variants in those gene sets or regions in the GRVS

calculation.

For each individual, the GRVS was calculated using the formula below

GRVS =��



=1



=1

where n is the number of significant (P < 0.1) gene sets or regions for a particular

variant type, j, k is the number of variant types (e.g., de novo missense variants),

βi is a regression coefficient of a significant gene set or region, i, and Xi is the

number of variants (for SNVs and indels) or number of genes or regions (for

CNVs) that are found in the significant gene set or region in the sample.

To examine the GRVS in the discovery cohort, we used a 10-fold cross validation

strategy to avoid over-fitting. Using this strategy, the discovery cohort was

randomly divided into 10 equally sized subsamples (stratified by subtypes). We

calculated the GRVS of each sample in each subset using the effect sizes

determined in the remaining nine subsets. To minimize stochasticity in the GRVS

calculation, we repeated this procedure 30 times and the average GRVS and

average number of variants for each sample were used for subsequent subtypes

comparisons (Supplementary Figure 1a). For the replication cohort, we

calculated GRVSs using significant gene sets and effect sizes derived from the

discovery cohort (Supplementary Figure 1b). GRVS can be calculated for

probands regardless of whether their parents have been sequenced. However,

there would be a systematic difference in GRVSs in the discovery cohort if all

probands were used because those whose parents have been sequenced

includes scores from de novo variants, whereas probands whose parents have

not been sequenced do not have scores from de novo variants. To ensure that

the same variant types (including de novo variants) were included in each score

for probands in the discovery cohort, GRVS was calculated only for probands

whose parents had also both been sequenced. GRVSs were standardized within

each cohort and subtyping method. We tested whether GRVS is higher in

dysmorphic ASD compared to nondysmorphic ASD using a one-sided Wilcoxon’s

Signed Ranked Test.

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We used our ADM-reclassified cohort as the discovery cohort for several

reasons: 1) In contrast to the MPAs (dysmorphology data) from SSC which were

identified by multiple non-geneticist examiners, MPAs in the discovery cohort

were documented by a single dysmorphologist with over 20 years of clinical

experience (B.A.F.). MPA’s for children in the discovery cohort were then put

through the ADM algorithim and the cases were classified as ADM-dysmorphic or

ADM-nondysmorphic. This strategy allowed us to use very uniformly collected

phenotypic data to derive the morphology-associated regions and effect sizes for

GRVS calculation. 2) Our discovery cohort also contains more dysmorphic

probands than SSC, which gives more power to identify morphology-associated

regions (enriched in dysmorphic ASD). 3) Lastly, the discovery cohort was

assembled using a population-based recruitment strategy so that the

morphology-associated regions identified come from a patient collection

representative of ASD as it exists at the level of primary care providers. In

contrast there are ascertainment biases in SSC (e.g., simplex families and

exclusion of severely affected/ syndromic probands) which might limit the

generalizability of effect sizes and morphology-associated regions in a

population-based cohort30.

We calculated a score for CSVs using the GRVS formula if the CSV was

identified in a proband with two sequenced parents, and if the variant occurred in

or overlapped one of the morphology-associated gene sets or noncoding regions

so that an effect size was available for that variant. 46 CSVs were identified in 46

probands and17 of these met the above criteria allowing us to calculate a score

for the variant. Of the remaining 29 CSVs, 15 were identified in probands where

sequencing data was not available from both parents and 14 variants did not

overlap a morphology-associated region.

Common variant and PRS analysis

We examined the contribution of common SNPs among ASD subtypes. We

calculated the PRS for each sample by deriving ASD summary statistics from a

population-based genome-wide association study (GWAS) of 13,076 cases and

22,664 controls from the iPSYCH project 11. We calculated the PRS for BMI,

which was a negative control due to its lack of association with ASD24, using BMI

summary statistics from a population-based GWAS of 322,154 individuals of

European descent from the GIANT Consortium83. We preprocessed the GWAS

summary tables to fix the effect allele mismatch (swapped A1 and A2 alleles and

converted its odds ratio) and to remove ambiguous SNPs (i.e., SNPs with A to T

and C to G variations) and multi-allelic SNPs.

We conducted joint genotyping of BMI- and ASD-associated SNPs only on

samples sequenced on Illumina platforms (200 probands and 400 parents). We

could not re-genotype Complete Genomics data, so the samples were excluded

from further analysis. We retained SNPs with a minor allele frequency > 0.05 and

genotyping rate > 90%, of which 349,682 SNPs and 428,364 SNPs intersected

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with iPSYCH-ASD and GIANT-BMI SNPs passing suggested a p-value threshold

(P value < 0.1 for ASD and P value < 0.2 for BMI) by Weiner et al.2, respectively.

We then calculated PRSs using PRSice84 (parameters used: clump-kb 250,

clump-p 1.000000, clump-r2 0.100000, info-base 0.9) using a p-value threshold

of 0.1 for iPSYCH and 0.2 for GIANT BMI, as suggested by Weiner et al.8. After

clumping, only 18,549 SNPs and 38,245 SNPs remained for PRS calculation for

ASD and BMI, respectively. Using standard methods as previously described11,

we calculated PRS for ASD for the SSC replication cohort using 26,067 SNPs

with P value < 0.1 after the clumping step. The PRSs in both cohorts were

standardized (with a mean of zero and standard deviation of one). We used the

pTDT method8 and one-sided t-test to examine the over-transmission of common

variants associated with ASD susceptibility among subtypes. Probands were

used in the analysis if the probands were of European ancestry and if

sequencing data was available from both parents.

Acknowledgements

We thank the families for participation and The Centre for Applied Genomics for

their analytical and technical support. We thank Lisa Strug, Andrew Paterson,

and Delnaz Roshandel for analytical assistance. This work was funded by Autism

Speaks, Autism Speaks Canada, the University of Toronto McLaughlin Centre,

the Canada Foundation for Innovation, the Canadian Institutes of Health

Research (CIHR), Genome Canada/Ontario Genomics Institute, the Government

of Ontario, Brain Canada, Ontario Brain Institute Province of Ontario

Neurodevelopmental Disorders (POND), and The Hospital for Sick Children

Foundation. A.J.S.C. was supported throughout this research by Ontario

Graduate Scholarship from the Government of Ontario, Restracomp Research

Fellowship from The Hospital of Sick Children, and Autism Research Training

Award and Frederick Banting and Charles Best Scholarship from CIHR. S.W.S

holds the Northbridge Chair in Paediatric Research at the Hospital for Sick

Children.

Author Contributions

A.J.S.C., R.K.C.Y., S.W.S., and B.A.F. conceived and designed experiments.

B.A.F, C.N., T.N.T., and J.H.M. managed, recruited, diagnosed and examined

participants. E.A. and R.P. helped with interpreting phenotype data. Z.W., B.

Thiruvahindrapuram, B.Trost, T.N., G.P., W.S., and J.M. processed whole-

genome sequencing data. A.J.S.C., W.E., R.K.C., D.M., D.R. and M.Z.

conducted or interpreted different components of whole genome sequencing

analyses. A.J.S.C., M.S.R., D.J.S., N.S. and K.T. performed variant

interpretation. A.J.S.C. and S.L. performed experiments for variant

characterization and validation. A.J.S.C., B.A.F., S.W.S., and R.K.C.Y. conceived

and coordinated the project and wrote the manuscript.

Competing Interests statement

S.W.S. is on the Scientific Advisory Committees of Deep Genomics, Population

Bio and an Academic Consultant for the King Abdulaziz University.

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Data availability

Access to FASTQ data for samples in the discovery cohort that were consented

for MSSNG can be obtained by completing the data access agreement:

https://research.mss.ng. Access to FASTQ data for samples in the discovery

cohort not consented for MSSNG, as well as VCF files for sequence-level

variants for all samples in the discovery cohort are available at European

Genome-Phenome Archive (pending EGA link and accession number.

Submission in process.). Access to data for the replication cohort can be

obtained by completing data access agreement

(https://www.sfari.org/resource/sfari-base), as was done for this study.

Code availability

Code used in this manuscript is available at GitHub

(http://github.com/naibank/GRVS_ASD).

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Figure Legends

Figure 1: Project workflow.

Summary of phenotype stratification, whole-genome sequencing workflow, and

genomic analyses performed in this study. ASD, Autism spectrum disorder; ADM,

Autism dysmorphology measure; CNVs, copy number variants; SNVs, single

nucleotide variants; indels, insertions and deletions; ERDS, estimation by read

depth with single nucleotide variants; GATK-HC, Genome Analysis Toolkit-

Haplotype Caller; SNPs, single nucleotide polymorphisms; ACMG, American

College of Medical Genetics and Genomics; IQ, Intelligence quotient.

*Unaffected siblings were used for GRVS and PRS analyses. **Excluding

samples with false negative ADM-defined nondysmorphic ASD. We also included

only samples sequenced on Illumina platforms to be consistent with replication

cohort. For variant calling, on average per sample, we detected ~3.7 million

SNPs, 36,514 rare single nucleotide variants (SNVs), 4,113 small insertions and

deletions (indels), 13 rare copy number variants (CNVs), 390 rare SVs, 73.4 de

novo SNVs, 7.3 de novo indels, and 0.1 de novo CNVs (Supplementary Table 2).

Experimental validation rates were 94.8%, 85.7%, and 87.5%, respectively, for

de novo SNVs, indels, and CNVs (Supplementary Tables 3 and 4). Using GRVS,

we were able to quantify and validate the contribution of morphology-associated,

rare sequence-level and copy number variants to morphological ASD subtypes.

While we can call other SVs from the WGS, there needs to be higher-quality data

before these can be effectively incorporated into GRVS.

Figure 2: Gene sets for which de novo and rare coding variants are

significantly more prevalent in some subtypes of ASD.

We define events as (a,b) genes impacted by CNVs or (c) as variants for SNVs

and indels. The coefficient is the relationship between the number of events in

each gene set and the ASD subtypes; it reflects the effect size of a variant type

and gene set among different ASD subtypes. Positive coefficients indicate more

events in individuals with ASD and more dysmorphic features; negative

coefficients indicate more events in individuals with ASD and fewer dysmorphic

features. We show only gene sets for which a,b) rare CNVs, or c) de novo

missense variants are significantly more prevalent in different subtypes of ASD.

Tier 1 and 2 missense variants consist of all or only predicted damaging

missense variants, respectively, as defined in Yuen, et al.23 Symbol shapes

indicate the subtype comparisons that were conducted for each combination of

gene set and variant type. Two subtype comparison= nondysmorphic vs.

dysmorphic ASD. Three subtype comparison= essential vs. equivocal vs.

complex ASD. Coloured shapes indicate significant signals after multiple test

correction by permutation-based FDR. Error bars indicate 95% confidence

intervals.

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Figure 3: Genome-wide rare variant score in ASD subtypes.

Events are comprised of variants for SNVs and indels or genes impacted by

CNVs. For each sample, the GRVS is the sum of rare and de novo events in

morphology-associated regions, weighted by effect size (estimated from the

coefficients in the regression model). GRVSs were generated 30 times for each

sample (see methods), yielding an average score and average number of

variants. CSVs, clinically significant variants. a) Distribution of standardized

GRVS for the discovery cohort (n=235). b) GRVSs for the whole cohort (left plot,

n=235) or the whole cohort excluding the 17 probands with clinically significant

variants (right plot, n=218), were ordered and ranked by percentile. Note that

while 46 probands in the discovery cohort (n=325) had CVSs, only 17 of them

had two sequenced parents meeting inclusion criterion for the GRVS group

(n=235). Violin plots show the distributions of the samples’ GRVS percentiles;

box plots contained within show the median and quartiles of the percentiles for

each subtype. P values denote the probability that the GRVS in dysmorphic ASD

is not greater than nondysmorphic ASD (one-sided, Wilcoxon rank sum test). c)

Rare variants have different effect sizes. The mean coefficient reflects the effect

size of a variant type. Coefficients of deletions and duplications of the same size

bin were averaged together. Coefficients of predicted LoF variants, missense

variants, and predicted damaging missense variants were averaged together.

Error bars indicate mean ± standard deviation. The number of morphology-

associated regions for each variant type is indicated the y-axis with “n= ”.

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Figure 4: Inheritance of polygenic risk for ASD and BMI in morphologic

subtypes.

Differences in polygenic risk score (PRS) for ASD and BMI between subjects and

their respective mid-parent score. Box plots depict the median and quartiles of

polygenic transmission disequilibrium test (pTDT) deviation. Dots represent

pTDT deviations of subjects. P values for each subgroup indicate the probability

that the mean of the pTDT deviation distribution is not greater than zero (one-

sided, t-test), as depicted by the dotted line.

Figure 5: Relationship between IQ, morphological ASD subtypes and

genetic variants.

a) Comparison of IQ among morphological ASD subtypes. b) Comparison of IQ

between probands with and without a CSV. a,b) Violin plots show the

distributions of the probands’ IQ; box plots contained within show the median and

quartiles of IQ for each subtype. P values denote the probability that the mean IQ

of nondysmorphic ASD or probands without CSVs is not greater than dysmorphic

ASD or probands with CSVs, respectively (one-sided, t-test). Correlation

between IQ and c) GRVS and d) PRS is shown. c,d) Each dot represents the IQ

and GRVS or PRS percentile of a sample. The linear regression line indicates

the linear correlation between IQ and GRVS or PRS percentiles. Correlation

coefficient is quantified by Spearman’s rho correlation. P values indicate the

probability that the correlation is occurred due to chance.

Figure 6: Replication of rare and common genetic findings in subset of

Simons Simplex Collection cohort.

a) GRVSs for each cohort were ordered and ranked by percentile. Violin plots

show the distributions of the probands’ GRVS percentiles; box plots contained

within show the median and quartiles of the percentiles for each subtype. P

values denote the probability that the GRVS in ADM-defined dysmorphic ASD is

not greater than ADM-defined nondysmorphic ASD (one-sided, Wilcoxon rank

sum test). b) Yield of CSVs between dysmorphic and nondysmorphic subtypes in

discovery and replication cohorts. P values indicate the probability that the yield

of CSVs in nondysmorphic ASD is not lower than that of dysmorphic ASD (one-

sided, t-test). c) Inheritance of polygenic risk for ASD in dysmorphic and

nondysmorphic ASD subtypes in discovery and replication cohorts. Box plots

depict the median and quartiles of pTDT deviation. Dots represent pTDT

deviations of subjects. P values for each subgroup indicate the probability that

the mean of the pTDT deviation distribution is not greater than zero (one-sided, t-

test), as depicted by the dotted line. The finding of no significant over-

transmission in dysmorphic ASD did not replicate in SSC, which might be due to

lack of statistical power (i.e., at least 100 dysmorphic samples are needed to

achieve 80% power if PRS explains 2.45% of phenotypic variance11) and/or

ascertainment differences between the discovery and replication cohorts. The

discovery cohort included data about major congenital anomalies in morphologic

classification, whereas the replication cohort did not. While our discovery cohort

was population-based, the Simons Simplex Collection excluded probands with

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medically significant perinatal diseases, severe neurological deficits, and certain

genetic syndromes27. This likely decreased the proportion of probands with

excess MPAs and birth defects, potentially leading to a lower burden of common

ASD-associated variants85.

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Figure 1: Project workflow.

Variant Calling

•SNV & indels (DenovoGear & GATK-HC)

•CNVs (CNVnator & ERDS)

•Structural variants (Manta)

•Anneuploidies (in-house script)

•Uniparental isodisomy (in-house script)

•Mitochondrial variants (in-house script)

•Tandem repeat expansions (Expansion

Hunder denovo)

PHENOTYPE

STRATIFICATION

GENOMIC

ANALYSIS

Identify clinically

significant variants using

ACMG guidelines for

variant interpretation (all

rare & de novo variants)

Gene-set based rare

variant score

(rare & de novo

SNVs,

indels, CNVs)

Polygenic risk score

and polygenic

transmission

disequilibrium test

(common SNPs)

Relationship between genetic

findings and IQ

1.Burden analysis

2.Gene-set/noncoding

region enrichment

analysis

Discovery cohort: Population-

based recruitment of 325 cases

Clinical assessment of minor physical anomalies

(MPAs) and major congenital anomalies (MCAs)

Essential ASD (n=187)

Equivocal ASD (n=57)

Complex ASD (n=81)

Whole genome sequencing (WGS)

Gold standard

dysmorphology

(MPA+MCA score)

ADM-defined non-dysmorphic

ASD (n=203)**

ADM-

defined dysmorphic ASD

(n=73)**

ADM

Alignment

Variant Calling

•SNV & indels (DenovoGear & GATK-HC)

•CNVs (CNVnator & ERDS)

ADM on affected

probands

ADM-defined

nondysmorphic ASD (n=400)

ADM-defined

dysmorphic ASD (n=42)

Unaffected siblings (n=355)

CRAM files from WGS

Replication cohort: Subset of cases

from Simons Simplex Collection

(N= 442 affected probands and 355

unaffected siblings*)

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Figure 2: Gene sets for which de novo and rare variants are significantly more prevalent in some subtypes of

ASD.

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Figure 3: Genome-wide rare variant score in ASD subtypes.

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Figure 4: Inheritance of polygenic risk for ASD and BMI in morphologic

ASD subtypes.

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Figure 5: Relationship between IQ, morphological ASD subtypes and

genetic variants.

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Figure 6: Replication of common and rare genetic findings in subset of

Simons Simplex Collection cohort.

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ResearchGate has not been able to resolve any citations for this publication.

Genome-wide detection of tandem DNA repeats that are expanded in autism

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Oct 2020
NATURE

Tandem DNA repeats vary by the size and sequence of each unit (motif). When expanded, they have been associated with more than 40 monogenic disorders1. Their involvement in complex disorders is largely unknown, as is the extent of their heterogeneity. Here, we interrogated genome-wide characteristics of tandem repeats with 2–20-bp motifs in 17,231 genomes of families with autism2,3 and population controls4. We found extensive polymorphism in motif size and sequence. Many correlated with cytogenetic fragile sites. At 2,588 loci, gene-associated tandem repeat expansions that were rare among population controls were significantly more prevalent among individuals with autism than their unaffected siblings, particularly in exons and near splice junctions and in genes related to nervous system development and cardiovascular system or muscle. Rare tandem repeat expansions had a prevalence of 23.3% in autism-affected children versus 20.7% in unaffected children, suggesting a collective contribution to autism risk of 2.6%. They included novel autism-linked tandem repeat expansions in DMPK and FXN, known for neuromuscular conditions, and in novel loci such as FGF14 and CACNB1. These were associated with lower IQ and adaptive ability. Our results revealed a strong contribution of tandem DNA repeat expansions to the genetic etiology and phenotypic complexity of autism.

The mutational constraint spectrum quantified from variation in 141,456 humans

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May 2020
NATURE

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.

ExpansionHunter Denovo: A computational method for locating known and novel repeat expansions in short-read sequencing data

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Apr 2020
GENOME BIOL

Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

Clinical versus automated assessments of morphological variants in twins with and without neurodevelopmental disorders

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Full-text available

Mar 2020

Physical examinations are recommended as part of a comprehensive evaluation for individuals with neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention–deficit/hyperactivity disorder. These examinations should include assessment for morphological variants. Previous studies have shown an increase in morphological variants in individuals with NDDs, particularly ASD, and that these variants may be present in greater amounts in individuals with genetic alterations. Unfortunately, assessment for morphological variants can be subjective and time‐consuming, and require a high degree of clinical expertise. Therefore, objective, automated methods of morphological assessment are desirable. This study compared the use of Face2Gene, an automated tool to explore facial morphological variants, to clinical consensus assessment, using a cohort of N = 290 twins enriched for NDDs (n = 135 with NDD diagnoses). Agreement between automated and clinical assessments were satisfactory to complete (78.3–100%). In our twin sample, individuals with NDDs did not have greater numbers of facial morphological variants when compared to those with typical development, nor when controlling for shared genetic and environmental factors within twin pairs. Common facial morphological variants in those with and without NDDs were similar and included thick upper lip vermilion, abnormality of the nasal tip, long face, and upslanted palpebral fissure. We conclude that although facial morphological variants can be assessed reliably in NDDs with automated tools like Face2Gene, clinical utility is limited when just exploring the facial region. Therefore, currently, automated assessments may best complement, rather than replace, in‐person clinical assessments.

Gene discoveries in autism are biased towards comorbidity with intellectual disability

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Mar 2020

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Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen)

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Nov 2019

Copy-number analysis to detect disease-causing losses and gains across the genome is recommended for the evaluation of individuals with neurodevelopmental disorders and/or multiple congenital anomalies, as well as for fetuses with ultrasound abnormalities. In the decade that this analysis has been in widespread clinical use, tremendous strides have been made in understanding the effects of copy-number variants (CNVs) in both affected individuals and the general population. However, continued broad implementation of array and next-generation sequencing–based technologies will expand the types of CNVs encountered in the clinical setting, as well as our understanding of their impact on human health. To assist clinical laboratories in the classification and reporting of CNVs, irrespective of the technology used to identify them, the American College of Medical Genetics and Genomics has developed the following professional standards in collaboration with the National Institutes of Health (NIH)–funded Clinical Genome Resource (ClinGen) project. This update introduces a quantitative, evidence-based scoring framework; encourages the implementation of the five-tier classification system widely used in sequence variant classification; and recommends “uncoupling” the evidence-based classification of a variant from its potential implications for a particular individual. These professional standards will guide the evaluation of constitutional CNVs and encourage consistency and transparency across clinical laboratories.

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Jun 2019
Nat Genet

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations—ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

Identification of common genetic risk variants for autism spectrum disorder

Article

Full-text available

Mar 2019
Nat Genet

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Effect Sizes of Deletions and Duplications on Autism Risk Across the Genome

Article

Sep 2020
AM J PSYCHIAT

Objective: Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility. Methods: The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains. Results: The "probability of being loss-of-function intolerant" (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills. Conclusions: Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.

A framework for an evidence-based gene list relevant to autism spectrum disorder

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Apr 2020

Autism spectrum disorder (ASD) is often grouped with other brain-related phenotypes into a broader category of neurodevelopmental disorders (NDDs). In clinical practice, providers need to decide which genes to test in individuals with ASD phenotypes, which requires an understanding of the level of evidence for individual NDD genes that supports an association with ASD. Consensus is currently lacking about which NDD genes have sufficient evidence to support a relationship to ASD. Estimates of the number of genes relevant to ASD differ greatly among research groups and clinical sequencing panels, varying from a few to several hundred. This Roadmap discusses important considerations necessary to provide an evidence-based framework for the curation of NDD genes based on the level of information supporting a clinically relevant relationship between a given gene and ASD.

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