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Integrating de novo and inherited variants in 42,607 autism cases
identifies mutations in new moderate-risk genes
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  * Article
  * Open Access
  * Published: 18 August 2022

Integrating de novo and inherited variants in 42,607 autism cases
identifies mutations in new moderate-risk genes

  * Xueya Zhou^1,2^ na1,
  * Pamela Feliciano  ORCID: orcid.org/0000-0003-4266-1301^3^ na1,
  * Chang Shu  ORCID: orcid.org/0000-0002-3730-5102^1,2^ na1,
  * Tianyun Wang  ORCID: orcid.org/0000-0002-5179-087X^4,5,6^ na1,
  * Irina Astrovskaya  ORCID: orcid.org/0000-0001-5328-4631^3^ na1,
  * Jacob B. Hall^3,
  * Joseph U. Obiajulu  ORCID: orcid.org/0000-0002-9240-789X^1,2,
  * Jessica R. Wright^3,
  * Shwetha C. Murali^4,7,
  * Simon Xuming Xu^3,
  * Leo Brueggeman^8,
  * Taylor R. Thomas^8,
  * Olena Marchenko^3,
  * Christopher Fleisch^3,
  * Sarah D. Barns^3,
  * LeeAnne Green Snyder^3,
  * Bing Han^3,
  * Timothy S. Chang  ORCID: orcid.org/0000-0002-9225-9874^9,
  * Tychele N. Turner  ORCID: orcid.org/0000-0001-8246-6477^10,
  * William T. Harvey^4,
  * Andrew Nishida^11,
  * Brian J. O'Roak^11,
  * Daniel H. Geschwind  ORCID: orcid.org/0000-0003-2896-3450^9,
  * The SPARK Consortium,
  * Jacob J. Michaelson  ORCID: orcid.org/0000-0001-9713-0992^8,
  * Natalia Volfovsky^3,
  * Evan E. Eichler^4,7,
  * Yufeng Shen  ORCID: orcid.org/0000-0002-1299-5979^2,12^ na2 &
  * Wendy K. Chung  ORCID: orcid.org/0000-0003-3438-5685^1,3,13^ na2 

Nature Genetics (2022)Cite this article

  * Metrics details

Subjects

  * Autism spectrum disorders
  * Neuroscience
  * Sequencing

Abstract

To capture the full spectrum of genetic risk for autism, we performed
a two-stage analysis of rare de novo and inherited coding variants in
42,607 autism cases, including 35,130 new cases recruited online by
SPARK. We identified 60 genes with exome-wide significance (P 
< 2.5 x 10^-6), including five new risk genes (NAV3, ITSN1, MARK2,
SCAF1 and HNRNPUL2). The association of NAV3 with autism risk is
primarily driven by rare inherited loss-of-function (LoF) variants,
with an estimated relative risk of 4, consistent with moderate
effect. Autistic individuals with LoF variants in the four
moderate-risk genes (NAV3, ITSN1, SCAF1 and HNRNPUL2; n = 95) have
less cognitive impairment than 129 autistic individuals with LoF
variants in highly penetrant genes (CHD8, SCN2A, ADNP, FOXP1 and
SHANK3) (59% vs 88%, P = 1.9 x 10^-6). Power calculations suggest
that much larger numbers of autism cases are needed to identify
additional moderate-risk genes.

Main

Many previous genetic studies in autism spectrum disorder (ASD), a
neurodevelopmental condition characterized by social communication
difficulties and repetitive behaviors^1, focused on de novo variants
(DNVs) identified from parent-offspring trios^2,3,4,5,6,7,8. Over 100
high-confidence ASD genes enriched with likely deleterious DNVs have
been identified^8, most of which are also enriched for DNVs in other
neurodevelopmental disorders (NDDs)^9,10,11. Statistical modeling
suggests that there are ~1,000 genes with DNVs in ASD^12,13. However,
despite the large effect size of individual pathogenic DNVs, all DNVs
combined explain only ~2% of variance in liability for ASD^8,14. ASD
is highly heritable^14,15,16, and previous studies estimated that
common variants explain up to half of the heritability^14, although
only five genome-wide significant loci have been identified^17. Rare
LoF variants in genes intolerant of variation^9,18 are
overtransmitted to probands compared with siblings without ASD^7,8,19
,20,21,22. However, identification of the individual risk genes
enriched by such inherited variants has remained elusive. We have
established the largest ASD cohort, Simons Foundation Powering Autism
Research for Knowledge (SPARK)^23, which currently includes over
100,000 people with ASD, to advance research on the genetic,
behavioral and clinical features associated with ASD.

Rare LoF variants are enriched in developmental disorders including
ASD^22,24, but may also result from sequencing and annotation
artifacts^25 and present technical challenges in large sequencing
studies. Methods to distinguish between high-confidence and
low-confidence LoF variants^18,26,27 have been used to quantify
gene-level LoF intolerance^18,26,28,29 and to refine the role of LoF
DNVs in NDDs^20.

Here, we present an integrated analysis of de novo and inherited
coding variants in over 42,607 ASD cases, including cases from
previously published ASD cohorts and 35,130 new cases from SPARK. In
our two-stage design, we first characterized the contribution of DNVs
and rare inherited LoF variants to ASD risk. Results from the first
stage informed the second stage meta-analysis of 404 genes. By
combining evidence from DNVs, transmission disequilibrium tests
(TDTs) and case-control comparisons, we identified 60 ASD risk genes
with exome-wide significance, including five new genes not previously
implicated in NDDs. Finally, we estimated the effect sizes of known
and newly identified genes and conducted power calculations to inform
the design of future studies.

Results

Overview of data and workflow

We aggregated exome or whole genome sequencing (WGS) data of 35,130
new cases from SPARK and 7,665 cases from published ASD studies (ASC^
3,8, MSSNG^6 and SSC^2,30) (Supplementary Table 1) and performed a
two-stage analysis (Fig. 1). In stage 1, we analyzed DNVs in 16,877
ASD trios and assessed transmission of rare LoF variants from 20,491
parents without ASD diagnoses or intellectual disability to offspring
with ASD (including 9,504 trios and 2,966 single-parent-proband
duos). For DNVs, we characterized the enrichment pattern in known and
candidate risk genes, as well as mutation intolerance (probability of
being LoF intolerant as defined by the Exome Aggregation Consortium
(ExAC pLI)^18, and Genome Aggregation Database (gnomAD) metrics^26),
and performed gene-based burden tests of LoF and missense DNVs by
DeNovoWEST^11. For rare inherited LoFs, we estimated the
overtransmission from unaffected parents to ASD offspring in all
genes and gene sets predefined by functional genomic data or results
from DNV analysis. Based on DNV enrichment and overtransmission
patterns in gene sets, we selected 404 genes for meta-analysis in
stage 2 using 22,764 new cases with exome or WGS data. In stage 2, we
applied DeNovoWEST on DNVs, conducted TDTs on inherited LoFs in trios
or duos, performed burden tests on rare LoFs in unrelated cases
compared with population controls (104,068 subjects from non-neuro
gnomAD exomes and 132,345 TOPMed subjects) and combined the P values
to estimate a final P value for each of the 404 genes. Finally, we
performed a mega-analysis of rare LoFs in all cases and controls to
estimate the effect sizes of known or new candidate ASD genes to
inform future studies.

Fig. 1: Analysis workflow.
figure 1

In the discovery stage, we identified DNVs in 16,877 ASD trios and
rare LoF variants in 20,491 parents without ASD diagnoses and
intellectual disability. We compared properties of de novo and rare
variants to identify rare LoFs that contribute to genetic risk in
individuals with ASD. We also evaluated their associations with
cognitive impairment and enriched gene sets. We performed an initial
exome-wide scan of genes enriched by DNVs or showing transmission
disequilibrium of rare LoFs to affected offspring and selected a
total of 404 genes for further replication, including 159 de novo
enriched genes and 260 prioritized transmission disequilibrium genes
from enriched gene sets (15 genes were in both). In the meta-analysis
stage, we first evaluated evidence from de novo enrichment and
transmission disequilibrium of rare inherited LoFs in an expanded set
of family-based samples including over 6,000 additional ASD trios and
around 2,000 additional duos. The DNVs in ASD were combined with
those from an additional 31,565 NDD trios to refine the filters of
high-confidence LoF variants in de novo LoF enriched genes. We also
constructed an independent dataset of LoF variants of unknown
inheritance from 15,780 cases that were not used in de novo or
transmission analysis. We compared LoF rates in cases with two
population-based sets of controls (n = ~104,000 and ~132,000,
respectively). For 367 LoF-intolerant genes on autosomes, the final
gene-level evidence was obtained by meta-analyzing P values of de
novo enrichment, transmission disequilibrium of high-confidence rare
inherited LoFs, and comparison of high-confidence LoFs from cases and
controls not used in the de novo or transmission analysis. We also
performed a mega-analysis that analyzed high-confidence LoFs
identified in all 31,976 unrelated ASD cases and compared their rates
with population-based controls. HC, high-confidence.

Full size image

Known ASD or NDD risk genes explain most de novo burden

In the first stage, we combined data from four large-scale ASD
cohorts, including 16,877 unique ASD trios and 5,764 unaffected trios
(Supplementary Table 1). The cohorts show similar exome-wide burden
of DNVs in simplex families. The burden of LoF DNVs in cases with an
ASD family history is significantly lower than those without (P =
 1.1 x 10^-4 by Poisson test), whereas the burden of predicted de
novo damaging missense (D-mis, defined by rare exome variant ensemble
learner (REVEL) score^31 >= 0.5) and synonymous variants are similar
(Extended Data Fig. 1). Compared with unaffected offspring, the
excess of damaging DNVs (de novo LoF and D-mis variants) in
individuals with ASD is concentrated in LoF-intolerant genes, defined
as genes with an ExAC pLI >= 0.5 (ref. ^18). Using LoF observed/
expected upper-bound fraction (LOEUF), a recently developed gene
constraint metric^26, the burden of damaging DNVs is highest among
genes ranked in the top 20% of LOEUF scores (Fig. 2a). Overall, the
population attributable risk (PAR) from damaging DNVs is about 10%.
We assembled 618 previously established dominant ('known') ASD or NDD
risk genes (Supplementary Table 2). These genes explained about
two-thirds of the PAR from damaging DNVs. Excluding these genes, the
fold enrichment of damaging DNVs was greatly attenuated (Fig. 2a).

Fig. 2: Comparison of burden between de novo damaging variants and
rare inherited LoFs in ASD.
figure 2

a, The burden of DNVs was evaluated by the rate ratio and rate
difference between 16,877 ASD and 5,764 unaffected trios. The
exome-wide burden of de novo LoF and D-mis (REVEL >= 0.5) variants are
concentrated in constrained genes (ExAC pLI >= 0.5) and in genes with
the highest levels of LoF intolerance in the population (defined by
the top two deciles of gnomAD LOEUF scores). Burden analysis was
repeated after removing known ASD or NDD genes. The number of genes
before and after removing known genes in each constraint bin is shown
below the axis label. Data are presented as mean values and 95%
confidence intervals. Among constrained genes (ExAC pLI >= 0.5 or the
top 20% of gnomAD LOEUF scores), close to two-thirds of case-control
rate differences of de novo LoF and D-mis variants can be explained
by known genes. Exact P values by Poisson test are listed in
Supplementary Table 19. b, The burden of inherited LoFs was evaluated
by looking at the proportion of rare LoFs in 20,491 parents without
ASD diagnoses or intellectual disability that are transmitted to
affected offspring in 9,504 trios and 2,966 duos and show evidence of
overtransmission of LoFs per ASD trio. As a comparison, we also show
the transmission disequilibrium pattern to unaffected offspring in
5,110 trios and 129 duos. Data are presented as mean
values +- standard errors as error bars. Two-sided binomial test was
used to compute the P values for overtransmission or
undertransmission. Using ultra-rare LoFs with pExt >= 0.1, exome-wide
signals of transmission disequilibrium of rare inherited LoF variants
also concentrate in constrained genes (ExAC pLI >= 0.5) and in genes
within the top two deciles of gnomAD LOEUF scores. Analysis was
restricted to autosomal genes and repeated after removing known ASD
or NDD genes (number of genes in each constrained bin before and
after removing known genes is shown below the axis label). Among all
constrained genes, only one-fifth of overtransmission of LoFs to ASD
trios can be explained by known ASD or NDD genes. Exact P values by
binominal test are listed in Supplementary Table 19.

Full size image

To assess the evidence of DNVs in individual genes, we applied
DeNovoWEST^11, which integrates DNV enrichment with clustering of
missense variants in each gene. The initial DeNovoWEST scan of DNVs
in 16,877 ASD trios identified 159 genes with P < 0.001
(Supplementary Table 3).

Rare inherited LoFs are mostly in unknown ASD risk genes

To analyze the contribution of rare inherited LoF variants to ASD
risk, we evaluated transmission disequilibrium in ultra-rare (allele
frequency < 1 x 10^-5) high-confidence (by the loss-of-function
transcript effect estimator (LOFTEE)^26 package and proportion
expression across transcripts (pExt)^27; see Methods and
Supplementary Note) LoF variants from parents without ASD diagnoses
or intellectual disability to affected offspring with ASD in 9,504
trios and 2,966 duos from the first stage (Supplementary Table 4).
For a given set of genes, we quantified transmission disequilibrium
using the number of overtransmitted (excess in transmission over
nontransmission) LoF variants per trio; parent-offspring duos were
considered half-trios. Among autosomal genes, the overall
transmission disequilibrium signal of ultra-rare LoF variants is
enriched in LoF-intolerant genes (ExAC pLI >= 0.5) and in genes within
the top 20% of LOEUF scores (Fig. 2b), similar to the burden of
damaging DNVs. We observed both overtransmission to affected and
undertransmission to unaffected offspring, especially in genes within
the top 10% of LOEUF scores. However, known ASD or NDD genes explain
only ~20% of overtransmission of LoF variants to affected offspring
(Fig. 2b). On the X chromosome, we only considered transmission from
mothers without ASD to 9,883 affected sons and 2,571 affected
daughters (Supplementary Table 4). Rare LoF variants in mothers
without ASD show significant overtransmission to affected sons but
not affected daughters and remain significant after removing known
ASD or NDD genes (Supplementary Fig. 1). Together, these results
suggest that most genes conferring inherited ASD risk are yet to be
identified. Autosomal rare D-mis variants also show evidence of
transmission disequilibrium to affected offspring, although the
signal is much weaker (Supplementary Fig. 2).

To characterize the properties of genes contributing to ASD risk
through rare inherited variants, we defined 25 gene sets from five
categories representing both functional and genetic evidence relevant
to ASD (Supplementary Table 5 and Supplementary Fig. 3). We limited
the genes to 5,754 autosomal constrained genes (ExAC pLI >= 0.5 or top
20% of LOEUF scores) and performed TDT (Supplementary Table 6). For
each gene set, we tested if ultra-rare high-confidence rare LoF
variants show a higher transmission to ASD offspring than the
remaining genes in the overall constrained gene set. As a comparison
with DNVs, we also tested if the same set of genes are more
frequently disrupted by damaging DNVs than the rest of the genes in
ASD trios using DNENRICH^32.

Using functional gene sets derived from the neuronal transcriptome,
proteome or regulome, we confirmed significant enrichment in damaging
DNVs (P < 0.005 by simulation) in the gene sets that were previously
suggested to be enriched for ASD risk genes including expression
module M2/3^33, RBFOX1/3 targets^34, FMRP targets^35 and CHD8 targets
^36. However, this enrichment can be largely explained by known ASD
or NDD genes (Extended Data Fig. 2). For ultra-rare inherited LoF
variants, we found that the proportion of transmission to ASD
individuals in most functional gene sets is close to all other genes;
only RBFOX targets show a weak enrichment but can be largely
explained by known genes (Fig. 3). We also applied two machine
learning methods to prioritize ASD risk genes: forecASD^37 and A-risk
^38. Although enrichment of DNVs in predicted genes is mainly
explained by known genes, genes prioritized by A-risk are
significantly enriched with inherited LoFs that are not explained by
known genes. Using A-risk >= 0.4, 30% of constrained genes (n = 1,464)
were prioritized and explain 64% of the overtransmission of LoF
variants to ASD offspring (P = 2.6 x 10^-5 by kh^2 test). This
enrichment is higher than genes prioritized by the LOEUF score; 33%
of genes (n = 1,777) in the top decile of LOEUF account for 55% of
the overtransmission (P = 3.5 x 10^-4 by kh^2 test) (Fig. 3).

Fig. 3: Enrichment of rare LoF variants in ASD cases across gene
sets.
figure 3

Gene sets were defined and grouped by transcriptome proteome,
neuronal regulome, ASD gene prediction scores, genetic evidence from
neuropsychiatric diseases, and gene-level constraint. Analyses were
repeated after removing known ASD or NDD genes. (Number of genes in
each set before and after removing known genes are shown in
parentheses below gene set.) Dots represent fold enrichment of DNVs
or odds ratios for overtransmission of LoF variants in each set.
Horizontal bars are presented as mean values with 95% confidence
interval as error bars. For each gene set, we show the percentage of
overtransmission of rare LoFs to cases. Enrichment of rare inherited
LoFs was evaluated by the share of overtransmission events (the
transmission and nontransmission of ultra-rare LoFs with pExt >= 0.1)
in the selected gene set vs those in all other constrained genes
using a two-by-two table. P values were determined using the kh^2
test. Exact P values are listed in Supplementary Table 19.

Full size image

We also considered gene sets that have evidence of genetic
association with DNVs. Genes nominally enriched by DNVs (P < 0.01 by
DeNovoWEST; n = 300) in ASD from the current study have a
significantly higher overtransmission rate than other constrained
genes (odds ratio = 1.39, P = 3.0 x 10^-5 by kh^2 test) (Fig. 3),
although these genes account for only 21% of the overtransmission.
Genes nominally enriched by DNVs in other NDDs^11 are also
significantly enriched by DNVs in ASD and weakly enriched by
inherited LoFs in ASD; however, both can be largely explained by
known genes (Fig. 3). This suggests that a subset of ASD genes
increase risk by both DNVs and inherited variants, and new genes can
be identified by integrating evidence from DNV enrichment and TDT.

DNVs and some rare inherited LoFs are associated with intellectual
disability

To evaluate the association of genotypes with phenotype in ASD, we
used self-reported cognitive impairment in SPARK, a Vineland score of
<70 in the SSC or the presence of intellectual disability in ASC.
Damaging DNVs in genes ranked within the top 10% of LOEUF scores show
a higher burden (P = 1.1 x 10^-24 by kh^2 test) in ASD cases with
evidence of cognitive impairment than in other cases, consistent with
previous results^2,8 (Fig. 4a). Once known ASD or NDD genes were
excluded, the residual burden of damaging DNVs in genes within the
top 10% of LOEUF scores is greatly reduced and not significantly
associated with cognitive phenotype in ASD (Fig. 4a).
Overtransmission of rare LoFs in genes within the top 10% of LOEUF
genes to ASD cases with cognitive impairment is about 2.7 times
higher than to cases without cognitive impairment (P = 4.6 x 10^-3 by
kh^2 test) and is still 2 times higher (P = 0.04 by kh^2 test) once
known ASD or NDD genes were excluded (Fig. 4b). However, rare LoFs in
genes prioritized by A-risk are not associated with cognitive
impairment (Supplementary Fig. 4). Taken together, these results
suggest that rare variants in the top 10% of LOEUF genes--most of
which are already known to be ASD or NDD risk genes--are associated
with cognitive impairment. However, a subset of rare inherited
variants, particularly those prioritized by A-risk, are not
associated with cognitive impairment.

Fig. 4: Association of rare inherited LoFs with cognitive impairment
in ASD cases.
figure 4

Ultra-rare inherited LoFs with pExt >= 0.1 in genes with the top 10%
of gnomAD LOEUF scores also show a higher proportion of transmission
and a higher overtransmission rate to ASD offspring with cognitive
impairment (CogImp) than those without (NoCogImp). Rare LoFs in other
constrained genes are not significantly associated with phenotypic
severity. The increased burden of inherited LoFs in cases with
cognitive impairment remains significant after removing known ASD or
NDD genes. Data are presented as mean values +- standard errors as
error bars. Poisson test was used to compute the P values to assess
the fold enrichment, and binominal test was used for
overtransmission. Exact P values are listed in Supplementary Table 19
.

Full size image

Meta-analysis identifies five new risk genes

Based on results from the first stage, we identified 260 genes with
evidence of TDT (TDT statistic^39 >= 1) and in gene sets enriched with
rare inherited LoFs (top 10% LOEUF or within top 20% LOEUF and
A-risk >= 0.4) (Supplementary Table 6) and 159 genes with P < 0.001
from the DeNovoWEST analysis of DNVs (with 15 genes by both)
(Supplementary Table 3). We performed a meta-analysis on the 367
autosomal genes with all data from stage 1 and stage 2, which
includes 6,174 new ASD trios, 1,942 new duos, 15,780 unrelated cases
(see Methods) and 236,000 population controls.

We used Fisher's method^40 to combine three P values that estimate
independent evidence of DNVs, TDT and case-control comparison: (1)
DeNovoWEST with DNVs from both stage 1 and stage 2 (n = 23,039 trios,
Supplementary Tables 1 and 7) using the parameters estimated in
stage 1, (2) TDT with rare LoF variants in parents without ASD
diagnoses or intellectual disability with affected offspring in
15,586 trios and 4,907 duos (Supplementary Table 4) and (3) unrelated
cases (Supplementary Table 8) compared with population controls using
a binomial test. We used two sets of controls: gnomAD exome v2.1.1
non-neuro subset (only samples from individuals who were not
ascertained for having a neurological condition in a neurological
case-control study, n = 104,068) and TOPMed WGS (freeze 8, n =
 132,345). We performed a case-control burden test using the two sets
separately and input the larger P value for the meta-analysis. This
approach avoids sample overlap and helps ensure that significant
genes are not dependent on the choice of population reference.
Although population reference data were processed by different
pipelines, the cumulative allele frequencies (CAFs) of
high-confidence LoF variants (see Methods) are similar between
internal pseudocontrols (see Methods) and the two population
references after applying the same LoF filters (Supplementary Fig. 5
). Previous population genetic simulations predict that for genes
under moderate to strong selection (selection coefficient > 0.001),
deleterious variants are expected to arise within 1,000 generations
and population demographic histories do not confound the CAFs of
deleterious alleles in these genes^41.

For 367 selected autosomal genes, the point estimates of selection
coefficient under the mutation-selection balance model^42 are all
greater than 0.01 (Supplementary Fig. 6). Most high-confidence LoF
variants in these genes are ultra-rare (Supplementary Fig. 7), and
the CAFs of high-confidence LoF variants in European and non-European
population samples are highly correlated (Supplementary Fig. 8).
Therefore, we included population samples across all ancestries as
controls. The ultra-rare synonymous variant burden is similar between
cases and controls across the selected genes (Extended Data Fig. 3).
To make use of all genetic data collected, we also included rare
variants of unknown inheritance from ASD cases that were analyzed in
the first stage. These variants come from cases that are part of
unaffected parent-ASD duos; such variants were either inherited from
the parent not participating in the study or DNVs. Therefore, these
variants are independent of TDT, even though the same cases were
included in TDT.

We identified 60 genes with exome-wide significance (P < 2.5 x 10^
-6), and 72 genes reached study-wide significance accounting for all
5,754 constrained genes (P < 8.7 x 10^-6, Supplementary Table 9).
Figure 5 summarizes the distribution of LoF variants (with different
modes of inheritance) in genes that reached study-wide significance
by DNV enrichment (Fig. 5a) and other significant genes by
meta-analysis (Fig. 5b and Supplementary Fig. 9). Genes that are
significant only in meta-analysis tend to harbor more inherited LoF
variants than DNVs, consistent with their lower penetrance for ASD or
NDD.

Fig. 5: Distribution of de novo and inherited LoF variants in known
and novel ASD genes in cases and population controls.
figure 5

From left to right: pyramid plots summarizing the number of de novo
LoFs in 15,857 ASD trios, inherited high-confidence LoFs in 18,720
unrelated offspring included in transmission analysis, and
high-confidence LoFs in 15,780 unrelated cases; bar plot of
transmission vs nontransmission for rare high-confidence LoFs
identified in parents without ASD diagnoses or intellectual
disability; three plots comparing the high-confidence LoF rate in
31,976 unrelated ASD cases with gnomAD exomes (non-neuro subset,
104,068 individuals). Horizontal bars are presented as mean
values +- standard errors as error bars. a, Twenty-eight known ASD or
NDD genes that have LOEUF scores in the top 30% of gnomAD, have a P
 value for enrichment among all DNVs (P < 9 x 10^-6) in 23,039 ASD
trios, and have more than 10 LoFs. b, Nine additional ASD risk genes
that achieved a P value of <9 x 10^-6 in stage 2 of this analysis.
The majority of genes in b harbor more inherited LoFs than DNVs. All
five novel genes (Table 1) are shown in b. Note that the x axes of
LoF rates are in the squared root scale. Poisson test was used to
compute the P values. Exact P values are listed in Supplementary
Table 6.

Full size image

Although most significant genes were previously known, we identified
five new genes that have exome-wide significance regardless of the
choice of population reference: NAV3, MARK2, ITSN1, SCAF1 and
HNRNPUL2 (Table 1). The combined P values based on ancestry-specific
case-control analyses are similar to the overall case-control
analysis for these five genes (Supplementary Table 10). As expected,
most supporting variants are ultra-rare, and results are robust to
the allele frequency filter. These five new genes together explain
0.27% of the PAR ratio (Supplementary Table 11). NAV3 has a similar
PAR to that of CHD8 and SCN2A (~0.095%). ITSN1 is similar to PTEN
(~0.065%).

Table 1 Statistical evidence for the five novel
exome-wide-significant ASD risk genes identified in this study
Full size table

The association of NAV3 with ASD risk is primarily driven by rare
inherited variants (Table 1). NAV3 has a high A-risk score,
suggesting that the expression pattern of NAV3 is highly similar to
known ASD genes (Supplementary Data 1)^7,43. NAV3 has high expression
in the inner cortical plate of the developing cortex^33, and in
pyramidal neurons (hippocampus CA1 and somatosensory cortex) and
cortical interneurons^44,45 (Supplementary Fig. 10). The association
of MARK2 with ASD risk is primarily driven by DNVs and is also
associated with other NDDs^11 (P = 2.7 x 10^-5 by DeNovoWEST)
including Tourette syndrome^46 and epilepsy^47. We find that three
out of eight autistic offspring with variants in MARK2 report
epilepsy, two out of eight report Tourette syndrome and seven out of
eight have evidence of cognitive impairment (Supplementary Table 12).

The remaining three novel genes have support from both DNVs and rare
LoFs. ITSN1 and SCAF1 show nominal significance of DNV enrichment in
31,058 NDD trios^11 (P < 0.05 by DeNovoWEST). SCAF1 was among the top
50 genes from a gene-based burden test in a recent schizophrenia
case-control study (P = 0.0027 by burden test)^48. Both ITSN1 and
NAV3 have moderate effect sizes (point estimate of relative risk 3~6;
Supplementary Table 11). ITSN1 has been highlighted in our previous
study with evidence of enriched inherited LoFs^7. We also assessed
deletions in these new genes. For both ITSN1 and NAV3, we identified
four partial or whole gene deletions in 33,083 parents without ASD
diagnoses or intellectual disability that also show transmission
disequilibrium to affected offspring (Extended Data Fig. 4).

Although both de novo and rare inherited LoFs in the most constrained
genes are strongly associated with intellectual disability in ASD
(Fig. 4), the association of such variants in individual genes is
heterogenous, as suggested by the lack of association of rare
inherited variants in genes with high A-risk scores (Extended Data
Fig. 2). We calculated the burden of cognitive impairment (see
Methods) in 87 ASD individuals with high-confidence LoF variants in
the four novel moderate-risk genes and compared it with that in 129
individuals with high-confidence LoF in the well-established ASD risk
genes CHD8, SCN2A, SHANK3, ADNP and FOXP1, as well as 8,731
individuals with ASD (Supplementary Fig. 11). Although most
individuals with variants in well-established ASD risk genes have
some evidence of cognitive impairment (88%), individuals with LoF
variants in the moderate-risk genes had significantly lower burden
(56%, P = 4.5 x 10^-7). Individuals with high-confidence LOFs in the
moderate-risk genes did not have a significantly different burden of
cognitive impairment than 8,731 individuals with ASD in SPARK (56% vs
50%, P = n.s.). Individuals with LoF variants in the moderate-risk
genes also had a similar male:female (4:1) ratio compared with the
larger cohort, whereas individuals with variants in the
well-established ASD risk genes showed significantly less male bias
(1.6:1, P = 0.009) (Supplementary Fig. 11), as previously reported^2.
We also predicted full-scale intelligence quotient (IQ) on all
participants based on parent-reported data using a machine learning
method^49. Heterozygotes for rare LoFs in three (NAV3, SCAF1 and
HNRNPUL2) of the four new genes with substantial contribution from
rare inherited variants have similar IQ distribution as the overall
SPARK cohort (Fig. 6a), which is substantially higher than
heterozygotes with rare LoFs in well-established, highly penetrant
genes that contribute to ASD primarily through DNVs ('DN genes') such
as CHD8, SHANK3 and SCN2A. In fact, both novel and established genes
with significant contribution from rare inherited LoFs are less
associated with intellectual disability than NDD genes (Fig. 6b).
Across these genes, there is a significant negative correlation (r =
 0.78, P = 0.001) of estimated relative risk of rare LoFs with
average predicted IQ of the individuals with these variants (Fig. 6c
).

Fig. 6: Predicted full-scale IQ in individuals with pathogenic
variants in inherited or de novo genes in SPARK.
figure 6

We examined the distribution of predicted IQ using a machine learning
method^49 for 95 individuals with ASD with an LoF mutation in one of
the five novel exome-wide-significant genes (MARK2, NAV3, ITSN1,
SCAF1 and HNRNPUL2) and nine known ASD genes (CHD8, SHANK3, SCN2A,
ADNP, ARID1B, FOXP1, KDM5B, GIGYF1 and KMT2C), compared with 2,545
SPARK participants with ASD and known IQ scores. The nine known ASD
genes include six genes (pink and labeled 'de novo, known') that are
well-established de novo ASD risk genes that exceed exome-wide
significance and were most frequently identified in SPARK, which
maximizes the number of samples available for genotype-phenotype
analyses. We also included three genes (light blue and labeled
'inherited, known') that have some previous evidence for inherited
ASD risk (GIGYF1^7, KDM5B^62 and KMT2C^63) and were also frequently
identified in SPARK. We denote the genes contributing to ASD
primarily through de novo LoF variants in our analysis as 'de novo'
(red), and the genes primarily through inherited LoF variants as
'inherited' (blue). a, Distribution of predicted IQ between
individuals with ASD with LoF mutations in the five novel genes, nine
known genes and all participants with ASD and known IQ scores in
SPARK (n = 2,545). We compared the mean predicted IQ between
participants with LoF mutations in ASD genes and all participants by
two-sample t-test. *, 0.01 <= P < 0.05; **, 0.001 <= P < 0.01; ***, P 
< 0.001. Exact P values are listed in Supplementary Table 19. The box
plots represent median as center, and interquartile range (IQR) as
bounds of the box; the upper whisker extends from the upper bound of
the box to 1.5 x IQR, and the lower whisker extends from the lower
bound of the box to 1.5 x IQR. Two-sided t-test was used to compute
the P values for comparing mean predicted IQ between ASD individuals
with LoF mutation in specific gene and all ASD participants.
Individuals with pathogenic variants in de novo risk genes have
significantly lower predicted IQ than overall SPARK participants with
ASD and known IQ scores, whereas individuals with LoF variants in
moderate-risk, inherited genes show similar predicted IQ as the
overall SPARK participants, with the exception of ITSN1. b,
Distribution of predicted IQ between individuals with ASD gene
grouped by both inheritance status ('de novo' or 'inherited') and
whether the ASD genes are novel ('novel' or 'known'). We compared the
mean predicted IQ between individuals with pathogenic variants in de
novo genes and inherited genes among our five novel genes and nine
known genes. Overall, people with LoF mutations in de novo genes have
an average of 13-16 points lower predicted IQ than individuals with
LoF mutations in inherited genes, regardless of whether the ASD genes
are novel or known. The box plots represent median as center, and IQR
as the bounds of the box; the upper whisker extends from the upper
bound of the box to 1.5 x IQR, and the lower whisker extends from the
lower bound of the box to 1.5 x IQR. c, Average relative risk of ASD
and average predicted IQ among different groups. Each dot shows the
average of individuals with rare LoFs of a gene selected in a. The
relative risk is estimated from mega-analysis and capped at 60.
Pearson correlation between average IQ and log relative risk is -0.78
(P = 0.001). The horizontal line represents the average IQ (IQ = 79)
of all SPARK individuals with predicted IQs. ITSN1 is an outlier at
the bottom left corner.

Full size image

Most known ASD or NDD genes that are enriched by LoF DNVs harbor more
de novo than LoF inherited variants in ~16,000 unrelated ASD trios
(Fig. 5a and Supplementary Fig. 16), consistent with their high
penetrance for ASD or NDD phenotypes and strong negative selection.
Using population exome or WGS data, we calculated a point estimate of
selection coefficient (\(\hat s\))^50 of LoFs in each gene
(Supplementary Table 11) and found that the fraction of de novo LoFs
in ASD genes is higher in genes with large \(\hat s\), and smaller in
genes with small \(\hat s\) (Supplementary Fig. 5b), consistent with
population genetic theory^51. We also estimated average effect size
of rare LoFs in ASD genes by comparing CAF in 31,976 unrelated cases
and population exome or WGS data. As expected, known and newly
significant ASD genes with higher risk for ASD are under stronger
selection (larger \(\hat s\)) (Supplementary Fig. 13).

Functional similarity of new genes and known ASD genes

To better appreciate the probable functional implications of the new
exome-wide-significant genes that confer inherited risk for ASD, we
integrated mechanistic (STRING^52) and phenotypic (Human Phenotype
Ontology (HPO)^53) data into a single embedding space (six
dimensions, one for each archetype coefficient) using a combination
of canonical correlation analysis and archetypal analysis (see
Methods). This embedding space serves as an interpretive framework
for putative ASD risk genes (n = 1,776). Six functional or phenotypic
archetypes were identified (Fig. 7 and Supplementary Tables 13-15)
that represent pathways that are well understood to play a role in
ASD: neurotransmission (archetype 1 or A1), chromatin modification
(archetype 2 or A2), RNA processing (archetype 3 or A3),
vesicle-mediated transport (archetype 4 or A4), MAPK signaling and
migration (archetype 5 or A5), and cytoskeleton and mitosis
(archetype 6 or A6), also enriched for intermediate filaments. These
archetypes organize risk genes in a way that jointly maximizes their
association with mechanisms (STRING clusters) and phenotypes (HPO
terms). For instance, A1 genes (neurotransmission) are enriched for
the STRING cluster CL:8435 (ion channel and neuronal system) and are
also associated with seizure and epileptic phenotypes. A2 genes
(chromatin modifiers) are enriched for nuclear factors and genes
linked to growth and morphological phenotypes (Supplementary Table 14
). We call genes that strongly map to an archetype (that is, >2x the
next highest-ranking archetype) 'archetypal'; if this criterion is
not met, we call the genes 'mixed'. Archetypal genes are generally
less functionally ambiguous than mixed genes. Of the five novel
inherited risk genes, two are archetypal (suggesting function within
known risk mechanisms): NAV3 (A6, cytoskeleton and mitosis) and ITSN1
(A4, vesicle-mediated transport). SCAF1, MARK2 and HNRNPUL2 are
mixtures of the identified archetypes, largely A4 and A5. That these
new genes did not resolve clearly into archetypes (that were defined
by known and suspected autism risk genes) suggests that they may
operate in potentially novel mechanisms. To elucidate these
possibilities, we constructed an ad hoc archetype, defined by the
centroid between SCAF1, MARK2 and HNRNPUL2 (see Fig. 7c). Cell-cell
junction (CL:6549) was the STRING cluster most associated with this
centroid (P = 4.12 x 10^-14 by the Kolmogorov-Smirnov test; Fig. 7d),
which fits with its location between A4 (vesicle-mediated transport)
and A5 (MAPK signaling and migration).

Fig. 7: Functional or phenotypic embedding of ASD risk genes.
figure 7

a, Using a combination of archetypal analysis and canonical
correlation analysis, putative autism risk genes were organized into
k = 6 archetypes that represent distinct mechanistic (STRING) and
phenotypic (HPO) categorizations (neurotransmission, chromatin
modification, RNA processing, vesicle-mediated transport, MAPK
signaling and migration, and cytoskeleton and mitosis). Genes
implicated by our meta-analysis are indicated by their label, with
novel genes in red. b, For each of the five novel genes, we
identified the five nearest neighbors in the embedding space among
the 62 meta-analysis genes. SCAF1, MARK2 and HNRNPUL2 were identified
as 'mixed' rather than 'archetypal' in their probable risk
mechanisms. c, To gain further insight into possible risk mechanisms,
we calculated the embedding distance to the centroid of these three
genes, which was then used as an index variable to perform gene set
enrichment analysis. d, A STRING cluster (CL:6549) containing genes
related to cell-cell junctions and the gap junction was identified as
being highly localized in this region of the embedding space (P =
 4.12 x 10^-^14 by the Kolmogorov-Smirnov test). This may suggest
that these genes confer autism risk through dysregulation of
processes related to cell adhesion and migration.

Full size image

Power analysis

The power of identifying risk genes with rare inherited variants or
DNVs monotonically increases with increasing effect size or expected
CAF under the null. New ASD genes to be discovered are likely to have
smaller effect size than known ASD genes, as suggested by our
results. Additionally, known ASD genes are biased toward longer
genes, which have a higher background mutation rate of damaging
variants ('long genes') (Extended Data Fig. 5). Even though longer
genes are more likely to be expressed in the brain and be relevant to
ASD or NDD^54, among most constrained genes, long genes (LoF mutation
rate^55,56 above 80% quantile) and short genes (below 80%) have
similar enrichment of damaging DNVs and rare inherited LoFs
(Supplementary Fig. 14). Notably, for short genes, known genes have
virtually no contribution to overtransmitted high-confidence LoFs to
affected offspring (Supplementary Fig. 14b), suggesting that many
short ASD risk genes remain to be identified.

We used a published framework^41 to analyze power based on
case-control association of rare variants. For rare variants in genes
under strong selection, CAF is largely determined by mutation rate
and selection coefficient^41. We therefore modeled power of
discovering risk genes as a function of relative risk and selection
coefficient. With about 5,500 constrained genes, the power of the
current study was calculated for 31,976 unrelated cases and an
experiment-wise error rate of 9 x 10^-6 (Extended Data Fig. 6).

We inversed the power calculation to determine the required sample
size to achieve 90% power under the same assumptions (Extended Data
Fig. 7). For genes at median LoF mutation rate across all genes, we
estimated that it requires about 96,000 cases (three times the
current sample size) to identify genes with similar effect size as
NAV3 (relative risk = 4.5) and ITSN1 (relative risk = 5), and about
64,000 cases (twice the current sample size) to find genes with
similar effect sizes as SCAF1 (relative risk = 8) and HNRNPUL2
(relative risk = 9). We note that ten and five times the current
sample size, respectively, are required to detect genes similar to
NAV3 or ITSN1 and genes similar to SCAF1 or HNRNPUL2 by DNVs alone.

Discussion

In this study, we identified five new ASD risk genes by both DNVs and
rare inherited coding variants. We identified rare LoF variants in
new ASD risk genes with modest effect size that are not strongly
associated with intellectual disability. This finding represents a
difference in phenotypic association with intellectual disability
compared with other highly penetrant ASD genes. To find new risk
genes with relative risks of 2-5 (comparable to the low relative risk
genes from this study, NAV3 and ITSN1) in the 50th percentile for
gene-wide LoF mutation rate (2 x 10^-6) and the 50th percentile for
selection among known risk genes (0.2), our power analysis suggests
that 52,000, 73,000, 116,000 or 227,000 total ASD cases are
necessary, respectively (cf. equation (1) from power calculation in
Supplementary material).

Our results suggest that the identification of new risk genes with
rare inherited variants may substantially improve genetic diagnostic
yield. We found that rare inherited LoF variants account for 6% of
PAR, similar to de novo LoF variants. Over two-thirds of the PAR from
coding DNVs is explained by known ASD or NDD genes. In contrast, less
than 20% of PAR from rare inherited LoFs variants is explained by
known genes, suggesting that most genes contributing to ASD risk
through rare inherited variants are yet to be discovered. These
unknown risk genes are still largely constrained to LoFs in the
general population and/or have similar expression profiles in
developing brains to known ASD risk genes. Combining evidence from
both DNVs and rare inherited variants, we identified 60 genes
associated with ASD with exome-wide significance, including five
novel genes. Rare LoFs in these five new genes account for a PAR of
0.27%, about half of the PAR of the five most common highly penetrant
ASD genes (KDM5B, GIGYF1, CHD8, SCN2A and SHANK3).

NAV3's association to autism is primarily driven by rare inherited
variants. Carriers of rare LoFs in NAV3 have an average predicted IQ
of 81, slightly above the SPARK cohort average (IQ, 79). The
prevalence of intellectual disability among NAV3 heterozygotes is
similar to the SPARK cohort average. This is distinctly different
from established ASD risk genes (for example, CHD8, SHANK3 and SCN2A
), nearly all identified by highly penetrant DNVs, and are associated
with intellectual disability in ASD cohorts^2. The absence of
intellectual disability is also observed in other genes (for example,
SCAF1, HNRNPUL2, GIGYF1, KDM5B and KMT2C) with substantial
contribution from rare inherited variants and modest effect size.
Nevertheless, the data show that many individuals with variants in
these new ASD genes are affected with various neuropsychiatric
conditions such as epilepsy, schizophrenia, Tourette syndrome and
attention deficit hyperactivity disorder (ADHD) (Supplementary Table
12). Detailed phenotyping of larger numbers of individuals carrying
these rare inherited variants is needed to understand the phenotype
associated with each gene. Such strategies should include a genetic
and phenotypic assessment of family members who also carry the rare
variant without ASD. Because all individuals consented in SPARK are
recontactable, such studies will enable a more complete picture of
the broad phenotypic effects of these variants without the bias of
clinical ascertainment. Overall, these risk genes with modest effect
size may represent a different class of ASD genes that are more
directly associated with core symptoms of ASD and/or neuropsychiatric
conditions rather than global brain development and intellectual
disability.

The approaches used in this study made full use of rare variation,
and this analytical method is generalizable to many conditions. In
particular, the multiple methods used to reduce noise in LoF alleles
present in control samples were particularly effective in assessing
the signal within the novel genes of moderate effect. We also
leveraged gene expression profiles informed by machine learning
methods to help prioritize genes for the meta-analysis stage of our
analysis^38. Future studies that leverage additional multiomic data
such as the Genotype-Tissue Expression (GTEx) project may further
improve the signal-to-noise ratio.

Our archetypal analysis (Supplementary Tables 13-15) provides some
clues to the potential mechanisms of the five newly identified risk
genes. ITSN1 was unambiguously mapped to A4 (vesicle-mediated
transport) and has a role in coordinating endocytic membrane traffic
with the actin cytoskeleton^57,58. NAV3 (A6, cytoskeleton and
mitosis) is associated with both axon guidance^59 and malignant
growth and invasion^60 and is thought to regulate cytoskeletal
dynamics. Indeed, A6 is enriched for processes related to
intermediate filaments (Supplementary Table 14), a known determinant
of cell motility and polarity^61. Although MARK2, SCAF1 and HNRNPUL2
were not identified as archetypal (potentially suggesting divergence
from well-known autism risk mechanisms), a search for functional
enrichment of this interstitial region between A4 and A5 found that
their roles in developmental risk may be most relevant at the
cell-cell junction, particularly as it relates to migration (see Fig.
7d).

Taken together, our results suggest that a continued focus on DNVs
for ASD gene discovery may yield diminishing returns. By contrast,
studies designed to identify genomic risk from rare and common
inherited variants will not only yield new mechanistic insight, but
also help explain the high heritability of ASD. SPARK is designed to
recruit individuals across the autism spectrum, without relying on
ascertainment at medical centers. As a result, SPARK may be better
suited to identify genes with transmitted variants that have lower
penetrance and to identify the genetic contributions to the full
spectrum of autism. The strategy used by SPARK--to recruit and assess
large numbers of individuals with autism across the spectrum and
their available family members without costly, in-depth clinical
phenotyping--is necessary to achieve the required sample size to fully
elucidate genetic contributions to ASD. The ability to recontact and
follow all SPARK participants will also be critical to deeply assess
the phenotypes associated with the newly discovered genes and to
develop and test novel treatments.

Methods

We established the SPARK cohort to facilitate genotype-driven
research of ASD at scale^23. All participants were recruited to SPARK
under a centralized institutional review board (IRB) protocol
(Western IRB Protocol no. 20151664). All participants provided
written informed consent to take part in the study. Written informed
consent was obtained from all legal guardians or parents for all
participants aged 18 and younger and for all participants aged 18 and
older who have a legal guardian. Assent was also obtained from
dependent participants aged 10 and older. Participants with autism
were compensated US$25-50 depending on other registered family
members. The mean age of the SPARK cohort in this analysis was
16.5 years (s.d., 19.2 years). Cases made up 46% of the SPARK cohort
(the remaining 54% were controls). The sex breakdown of the full
SPARK cohort was 57% male and 43% female. The sex breakdown of the
SPARK case cohort was 77% male and 23% female.

The first stage of analysis included 28,649 SPARK participants,
including 10,242 ASD cases from over 9,000 families with exome
sequencing data that passed quality control (Supplementary Data 1). A
subset of 1,379 individuals was part of the published pilot study^7.
To replicate prioritized genes from the discovery stage, we performed
a second-stage analysis that included an additional 39,926
individuals with 16,970 ASD cases from over 20,000 families with
exome or WGS data available after the analysis in the discovery
cohort was completed. New exome sequencing samples in this study were
captured using an IDT xGen research panel and sequenced on Illumina
NovaSeq. DNA samples were also genotyped for over 600,000 single
nucleotide polymorphisms (SNPs) using Infinium Global Screening
Array. Supplementary Table 16 outlines the software version and
parameter settings for each analysis below. Details on data
preprocessing, variant-level quality control procedures, variant
annotation, high-confidence LoF variant filtering, and copy number
variants, as well as descriptions of other publicly available ASD
cohorts (SSC, MSSNG and ASC), are in the Supplementary Note.

DNVs

We identified candidate de novo single nucleotide variants (SNVs) or
insertion-deletion mutations (indels) from SPARK and SSC cohorts from
per-family variant call formats files (VCFs) generated by GATK^64
(v.4.1.2.0) and freebayes^65 (v.1.1.0) and a cohort-wide population
VCF generated by weCall^66 (v.2.0.0) using a set of heuristic filters
that aim to maximize the sensitivity while minimizing false negatives
in parents^7 (Supplementary Table 16). Candidates were retained if
they were called by DeepVariant^67 (v.0.8.0) in offspring and had no
support in parents, identified in multiple offspring that passed the
DeepVariant filter in all trios, or were shared by siblings in the
same family and the de novo quality estimated by triodenovo was
higher than 8 (or 7 for SNVs in CpG context). Before creating the
final call set, we selected subsets of variants (see Supplementary
Table 16) for manual evaluation by Integrative Genomics Viewer (IGV)
to filter out candidates with failed review. Finally, we merged
nearby clustered de novo coding variants (within 2 bp for SNVs or
50 bp for indels) on the same haplotype to form multinucleotide
variants (MNVs) or complex indels. We removed variants located in
regions known to be difficult for variant calling (HLA, mucin, and
olfactory receptors). DNVs in the final call set follow a Poisson
distribution with an average of 1.4 coding DNVs per affected
offspring and 1.3 per unaffected offspring (Supplementary Fig. 15).
The proportion of different types of DNVs, the mutation spectrum of
SNVs, and indel length distributions were similar between SPARK and
SSC (Supplementary Fig. 15). A small fraction of variants in the
final call set are likely postzygotic mosaic mutations (Supplementary
Fig. 16).

Rare variants

Rare variant genotypes were filtered from cohort-wide population VCFs
with quality-control metrics collected from individual and family
VCFs (Supplementary Fig. 17a). In brief, we initially extracted
high-quality genotypes for each individual for variants that appear
in less than 1% of families in the cohort. Evidence for the variant
genotypes was re-evaluated by DeepVariant from aligned reads and
collapsed over individuals to create site-level summary statistics
including the fraction of individual genotypes that passed
DeepVariant filter and mean genotype quality over all individuals.
For variant genotypes extracted from GLnexus^68 (v1.1.3) VCFs, we
re-examined variant genotype from per-family VCFs by GATK to collect
GATK site-level metrics (including QD, MQ and SOR), then took the
read-depth weighted average over families to create cohort-wide site
metrics. For variant genotypes extracted from GATK joint genotyping
VCFs, these site metrics were directly available from INFO fields.

De novo analysis

In the discovery-stage analysis, the DNV call sets of SPARK and SSC
were merged with published DNVs from ASC^3,8 and MSSNG^6. To infer
likely sample overlaps with published trios for which we do not have
individual-level data, we tallied the proportion of shared DNVs
between all pairs of trios. For a pair of trios, let N[1] and N[2] be
the number of coding DNVs, and let O be the number of shared DNVs
between the pair. To account for mutation hot spots, if a DNV is an
SNV within CpG context or a known recurrent DNV identified in SPARK
and SSC, it contributes 0.5 to the count. Likely overlapping samples
were identified if (O/N[1]) >= 0.5 or (O/N[2]) >= 0.5 and they have
identical sex.

To determine the expected number of DNVs in the cohort, we used a
7-mer mutation rate model^55 in which the expected haploid mutation
rate of each base pair depends on the 3-bp sequence context on both
sides. The per-base mutation rates were adjusted by the fraction of
callable trios at each base pair, which was the fraction of trios
with >=10x coverage in parents and >=15x coverage in offspring. For
published trios, we used an in-house WGS dataset of 300 trios
(average, 36x coverage) to approximate the callable regions.
Gene-level haploid mutation rates for different classes of DNVs were
calculated by summing up the depth-adjusted per-base mutation rate of
all possible SNVs of the same class. The rate for frameshift variants
was presumed to be 1.3x the rate of stop-gained SNVs^56. Mutation
rates in haploid X chromosome regions were adjusted for the observed
male:female ratio (4.2), assuming that mutation rates in
spermatogenesis are 3.4 times higher than in oogenesis^9. The
exome-wide rate of synonymous DNVs closely matches the observed
number of DNVs (Extended Data Fig. 1). We also observed similar fold
enrichment of damaging DNVs (vs expected rate) in ASD cases across
four cohorts after accounting for samples with family history
(Extended Data Fig. 1).

To identify risk genes through DNVs, we applied DeNovoWEST^11. We
used the empirical burden of DNVs to derive weights for different
variant classes in constrained genes (ExAC pLI >= 0.5) and
nonconstrained genes separately based on positive predictive values
(PPVs) (Supplementary Table 18). For ASD, we defined de novo D-mis
variants by REVEL score >= 0.5. For other NDDs, we defined two classes
of de novo D-mis variants by MPC score >= 2 or MPC <= 2 and CADD
score >= 25. We first ran DeNovoWEST to test the enrichment of all
nonsynonymous DNVs (pEnrichAll). To account for risk genes that
harbor only missense variants, we ran DenovoWEST to test the
enrichment of de novo missense variants only and applied a second
test for spatial clustering of missense variants using denovonear^9,
then combined evidence of missense enrichment and clustering
(pCombMis). The minimal of pEnrichAll and pCombMis was used as the
final P value for DeNovoWEST. The exome-wide significance threshold
was set to 1.3 x 10^-6 (=0.05/(18,000 genes x 2)). The analysis on
the replication cohort used the same weights as derived from the
discovery cohort. Compared with the original publication^11, our
implementation of DeNovoWEST used different ways to stratify genes,
determine variant weights and calculate per-base mutation rates. We
applied our DeNovoWEST implementation on 31,058 NDD trios. The
results are highly concordant with published results on the same data
(Supplementary Fig. 18). We used P values from our reanalysis on
other NDD trios in comparative analysis with ASD.

Gene set enrichment analysis of DNVs was performed using the DNENRICH
framework^32. We included all de novo LoF and D-mis variants in 5,754
constrained genes from 16,877 ASD and 5,764 control trios. For each
gene set, we calculated the fraction of weighted sums of damaging
DNVs using PPV weights of constrained genes (Supplementary Table 18)
for cases and controls respectively. The test statistic for each gene
set is the ratio of such fractions in cases over controls. To
determine the distribution of the test statistic under the null
hypothesis, we randomly placed mutations onto the exome of all
constrained genes, while holding the number of mutations, their
trinucleotide context and functional impact to be the same as
observed in cases and controls separately. Note that by conditioning
on the observed number of damaging DNVs in cases and controls, we
tested enriched gene sets in cases that are not due to an increased
overall burden. At each round of simulation, the permuted test
statistic in each gene set was calculated. Finally, the P value was
calculated as the number of times that the permuted statistic was no
less than the observed statistic. Fold enrichment was calculated as
the ratio between observed and average test statistics over all
permutations. We also approximated the 95% confidence interval for
fold enrichment by assuming that log[fold enrichment] follows normal
distribution with mean 0 and s.d. determined by the P value.

In all DNV analyses above, DNVs shared by full or twin siblings
represent single mutational events and were counted only once. When
an individual carries multiple DNVs within 100 bp in the same gene,
only one variant with the most severe effects was included in the
analysis.

Transmission disequilibrium analysis

The effect of inherited LoF variants was analyzed using TDT in each
individual gene or in gene sets. Rare LoF variants were first
identified in parents without ASD diagnoses or intellectual
disability, then for each parent-offspring pair, the number of times
the LoF variant was transmitted from parents to offspring was
tallied. For variants in the (non-PAR part of the) X chromosome, we
only used rare LoF variants carried by mothers without ASD diagnoses
or intellectual disability and analyzed transmission in different
types of mother-offspring pairs. For TDT analysis of rare inherited
missense variants, different D-mis definitions and allele frequency
cutoffs were used (Supplementary Fig. 2).

The overtransmission of LoFs to affected offspring was evaluated
using a binomial test assuming that under the null hypothesis the
chance of transmission is 0.5. In the discovery stage, ultra-rare
LoFs with pExt >= 0.1 were used in exome-wide transmission
disequilibrium and gene set enrichment analysis. For the per-gene
test, all rare LoFs with pExt >= 0.1 were also used, and the TDT
statistic^39 for each gene was calculated by \(z = \frac{{T - NT}}{{\
sqrt {T + NT} }}\), where T(NT) is the number of times LoF variants
were transmitted (not transmitted) to affected offspring. When
offspring included monozygotic twin pairs, only one was kept in the
transmission analysis. We prioritized 244 autosomal genes with z > 1
in the top 10% of LOEUF or in the top 20% of LOEUF and A-risk >= 0.4.
In the second stage gene-based test, if a gene-specific pExt
threshold was available, we used high-confidence LoF variants that
passed the gene-specific pExt filter (see Supplementary Note).

In the gene set enrichment analysis of inherited LoFs, the rate of
transmission to affected offspring in each gene set was compared with
the transmission rate in the rest of the genes in the background
using the kh^2 test.

Case-control analysis

Pseudocontrols are constructed from parents without ASD diagnoses or
intellectual disability in simplex families, using alleles that were
not transmitted to affected offspring. Each parent without ASD
diagnoses or intellectual disability contributes a sample size of 0.5
to pseudocontrols. Rare LoFs in ASD cases whose parent data are not
available and from other cases that were not used in DNV enrichment
or TDT analysis were analyzed in this stage. Specifically, for each
ASD case, we found out all of their most recent unaffected ancestors
without ASD diagnoses or intellectual disability in the pedigree and
calculated the contributing sample size as 1 minus the summation of
kinship coefficients with these ancestors. If the contributing sample
size was greater than 0, then the sample was included in pseudocases
after removing alleles that were observed in any unaffected ancestors
without ASD diagnoses or intellectual disability used in TDT and
alleles included in DNV analysis if any. Examples of such rare LoFs
in cases and their contributing sample sizes are given in
Supplementary Fig. 19.

Rare LoFs in cases and controls for the X chromosome were categorized
separately for males and females. Male controls include all fathers.
In contrast, male cases include only those whose mothers do not have
ASD diagnoses or intellectual disability (thus not included in TDT
analysis). For females, because we include only mothers without ASD
diagnoses or intellectual disability and affected sons in TDT, female
pseudocases include all affected females. Female pseudocontrols were
established from unaffected mothers in simplex families using alleles
that do not transmit to affected sons. Each unaffected mother
contributes a sample size of 0.5 to pseudocontrols. In both sexes,
DNVs were removed from pseudocases.

For gene-based case-control association in stage 2, we used
population data as controls, including gnomAD exomes^26 (v2.1.1
non-neuro subset) and TOPMed genomes^69 (freeze 8). Variants in the
population controls were filtered to keep those that passed the
default quality-control filter in released data. For variants in
gnomAD data, we further removed variants located in low-complexity
regions^70 using the same procedure that was already applied to cases
and TOPMed data. In gene-level case-control comparison of LoF burden,
we used a baseline pExt >= 0.1 filter or gene-specific pExt threshold
if available to define high-confidence LoF variants (see
Supplementary Note). For LoF variants in selected genes, we also
extracted curation results by gnomAD to remove curated non-LoF
variants, and manually reviewed IGV snapshots from the gnomAD browser
if available to remove likely variant calling artifacts
(Supplementary Data 1). The number of high-confidence LoF variants
was obtained from the allele counts in site-level VCF files. The
gene-level burden of high-confidence LoF variants between cases and
population controls is tested by comparing the high-confidence LoF
variant rates between cases and controls using the Poisson test. The
gene-level burden of ultra-rare synonymous variants (allele
frequency < 1 x 10^-5) between cases and population controls is
assessed by a quantile-quantile (Q-Q) plot among all ancestry and
European ancestry (Extended Data Fig. 3). To account for difference
in depth of coverage, sample sizes are multiplied by the fraction of
callable coding regions of each gene (>=15x for autosomes or female X
chromosome, >=10x for male X chromosome) in ASD cases and in
population controls respectively.

To account for sample relatedness in case-control analysis, we
created a relationship graph in which each node represents an
individual and each edge represents a known first-degree or
second-degree relationship between two individuals. We also added
edges to pairs of individuals without known familial relationship but
have an estimated kinship coefficient >= 0.1. From the graph, we
select one individual from each connected component to create
unrelated case-control samples. For the X chromosome, father and sons
were treated as unrelated. For population controls, only gnomAD data
included sex-specific allele counts and were used in the sex-specific
analysis.

Meta-analysis was performed for prioritized autosomal genes that are
constrained (defined as ExAC pLI >= 0.5 or ranked in the top 20% of
the LOEUF scores; Supplementary Data 2). We integrated evidence from
the enrichment of all DNVs, transmission disequilibrium and increased
burden in cases compared with population controls by combining P
 values using Fisher's method^40. Study-wide significance was set at
9 x 10^-6 (Bonferroni correction with 5,754 constrained genes). In
mega-analysis, we combined all unrelated ASD cases together and
compared CAFs of high-confidence LoF variants with two population
control sets.

Power calculation

To calculate statistical power of the current study and to estimate
sample size for future studies, we adopted the statistical framework
from ref. ^41, comparing CAF of LoF variants in N unrelated cases f
[case] with CAF f in the general population. The effects of LoFs in
the same gene are assumed to be the same (relative risk = g). We
focus only on constrained genes. We assume f to be at
mutation-selection equilibrium f = m[LoF]/s, where m[LoF] is the LoF
mutation rate and s is the selection coefficient. The test statistic
asymptotically follows a noncentral kh^2 distribution with 1 d.f. and
noncentrality parameter (NCP):

$$\lambda = 4N\left[ {\gamma f\ln \gamma + (1 - \gamma ){{{\mathrm
{ln}}}}\frac{{1 - \gamma f}}{{1 - f}}} \right]$$

Given the significance threshold a, power can be calculated
analytically by

$$1 - \beta = 1 - F\left[ {F^{ - 1}\left( {1 - \alpha ,0} \right),\
lambda } \right]$$

where F(x, l) is the cumulative distribution of \(\chi _1^2\) with
NCP l.

To calculate the sample size to achieve desired power 1 - b at
significance level a, we first solve NCP l[a,b] from the above
equation, then estimate sample size by

$$n_{\alpha ,\beta } \approx \frac{{\lambda _{\alpha ,\beta }}}{{4f\
left[ {\gamma \ln \gamma - \left( {\gamma - 1} \right)} \right]}}$$

The current study has a sample size of n = 31,976 and the type I
error rate is a = 9 x 10^-6 (experimental wide significant). Given
continuing expansion of biobank-scale sequencing, treating f as known
without error is a reasonable assumption for future studies. To
calculate power for the new genes identified in this study, we used
point estimates of g and f from mega-analysis using gnomAD exomes as
population controls, and used m[LoF] computed from the 7-mer
context-dependent mutation rate model^55 to convert f to s = m[LoF]/f
. The required sample sizes were calculated to achieve 90% power.

Power and sample size are both calculated as a function of relative
risk for ASD (g) and selection coefficient (s) across different
haploid LoF mutation rates (m[LoF]). We only considered s between
0.01 and 0.5, because most prioritized genes have point estimates of
s > 0.01 (Supplementary Fig. 5) and genes with s > 0.5 are expected
to harbor more de novo than inherited LoF variants and can be
identified from the enrichment of DNVs. We limited relative risk (g)
between 1 and 20 to focus on risk genes with moderate to small
effects. The reduction in fitness s is correlated with the increases
in ASD risk g by s = gp under the assumption of no pleiotropic
effect, where p is ASD prevalence and s[D] is decreased reproductive
fitness of ASD cases. Based on epidemiological studies, the current
estimated prevalence of ASD is \(\hat \pi = 1/54\)^71, and the
estimated s[D] is for 0.75 male and for 0.52 female^72, so
sex-averaged \(\hat s_D = 0.71\) (assuming male:female ratio of 4.2).
In reality, most known ASD genes also show pleiotropic effects with
other NDDs or are associated with prenatal death; therefore, \(s \ge
\gamma \pi s_D \approx \gamma \hat \pi \hat s_D = 0.013\gamma\). So,
we considered only combinations of (s, g) that satisfy the condition
s >= 0.013g.

Gene sets

To evaluate the contribution of known ASD risk genes to the burdens
of DNVs and inherited LoF variants identified in this study, we
collected 618 known dominant ASD or NDD genes from the following
sources: (1) Known NDD genes from DDG2P^73 (2020-02) that are
dominant or X-linked and have an organ specificity list that includes
the brain or cause multisystem syndrome; (2) high-confidence ASD
genes collected by the Simons Foundation Autism Research Initiative
(SFARI)^74 (2019-08) with score of 1 or 2, excluding known recessive
genes; and (3) dominant ASD genes reported in recent literature and
included in the SPARK genes list^75 (2020-07).

To evaluate the gene sets enriched by damaging DNVs or inherited
high-confidence LoFs, we used all constrained genes by ExAC pLI >= 0.5
or in the top 20% of the LOEUF as the background. Gene sets of the
following five categories were collected for gene set enrichment
analysis.

Transcriptome and proteome

(1) Genes with brain-specific expression, defined as the genes with
average reads per kilobase of transcript per million mapped reads
(RPKM) > 1 in the brain and over four times the median RPKM of 27
tissues in processed RNA sequencing (RNA-seq) data from ref. ^76. (2)
Genes in coexpression modules M2 and M3 derived from weighted gene
correlation network analysis (WGCNA) of BrainSpan developmental
RNA-seq data, previously reported to be enriched for known ASD genes^
33. (3) Genes expressed in excitatory or inhibitory neurons. We
selected genes from ref. ^77 that have average transcripts per
million (TPM) > 100 in excitatory neurons and in inhibitory neurons.
(4) Synaptic genes collected from SynaptomeDB^78.

Neuronal regulome

(1) Putative CELF4 target genes, defined as genes with an iCLIP
occupancy > 0.2 in ref. ^79. (2) CHD8 target genes, defined as genes
with promoter or enhancer regions that overlap with CHD8 binding
peaks in human neural stem cells or midfetal brain in ref. ^36. (3)
FMRP target genes^35 with a false discovery rate (FDR) < 0.1 that
mapped them to orthologous human genes (Mouse Genome Informatics^80,
2018-07). (4) RBFOX2 target genes^34 that have Rbfox2 tag counts >= 8.
Due to high correlations between RBFOX1 and RBFOX3, targeted genes by
the two RNA binding proteins were merged in one gene set and selected
to have total Rbfox1 and Rbfox3 tag counts > 24.

Autism gene predictions

(1) ForecASD is an ensemble classifier that integrates brain gene
expression, heterogeneous network data and previous gene-level
predictors of autism association to yield a single prediction score^
37. We created two sets of genes with forecASD prediction score
greater than 0.4 or 0.5. (2) A-risk is a classifier that uses a
gradient boosting tree to predict autism candidate genes using
cell-type specific expression signatures in the fetal brain^38. We
created three sets of genes with prediction score greater than 0.4,
0.5 or 0.6.

Genetic evidence

(1) Genes enriched with DNVs in ASD, with nominal statistical
evidence (P < 0.01 or P < 0.05 by DeNovoWEST) in the SPARK discovery
cohort of 16,877 trios. (2) Genes enriched with DNVs in other NDDs
(Supplementary Data 3), with nominal statistical evidence in 31,058
NDDs^11. (3) Genes implicated in schizophrenia, with nominal
statistical evidence (P < 0.05)^48.

Genetic constraint

Four constraint gene sets are defined by genes in top 10% of the
gnomAD LOEUF, 10-20% the gnomAD LOEUF and genes with selection
coefficient for heterozygous protein-truncating variants estimated
from ExAC data (sHet) >= 0.25 and >= 0.2.

Archetypal analysis

Archetypal analysis^81 is an unsupervised learning approach that has
similarities to other dimensionality reduction and clustering
approaches. An important distinction of archetypal analysis from
comparable approaches is that it seeks a set of k archetypes, which
are points along the convex hull of the data, from which all data
points may be expressed as a mixture. The output of archetypal
analysis is an N by k matrix, a, of [0,1] coefficients that represent
the contribution of each archetype to each data point. Whereas
cluster centroids are embedded within the interior of a cluster,
archetypes are, by design, at the extremes of the data (that is,
along the convex hull). In practice, this ensures that the archetypes
are appreciably distinct from one another, which is often not the
case for cluster centroids. Here, we use archetypal analysis as a
means to organize putative ASD risk genes in a space that has meaning
from a mechanistic (STRING) and phenotypic (HPO) standpoint. Genes of
particular interest to this study can then be considered and
interpreted in the context of the archetypes that define this space.
STRING (v11)^52 clusters and HPO^53 terms were formatted as
gene-by-term binary matrices. The working gene list was taken as the
union of forecASD top decile genes and the 62 autism-associated genes
from this study (total of 1,776 genes). A total of 583 genes from
this set had annotations in both STRING and HPO, and using these
genes, a canonical correlation analysis (CCA) was carried out using
the RGCCA package for R (https://cran.r-project.org/web/packages/
RGCCA/index.html) using five components and sparsity parameter c1 set
to 0.8 for both the HPO and STRING matrices. Component scores for all
1,776 genes were calculated using the STRING cluster annotations and
the corresponding coefficients from the CCA and was used as input for
archetypal analysis^81, with the optimal k (number of archetypes)
selected using the elbow plot heuristic^82 and the residual sums of
squares (RSS) plotted as a function of k. We displayed the archetypal
embedding using the simplexplot() function of the archetypes R
package. Genes were identified as 'archetypal' if their top archetype
coefficient was >2x the next highest archetypal coefficient. Those
genes that did not fulfill this criterion were classified as 'mixed',
whereas those that did were assigned to their maximally scoring
archetype. We applied the following heuristic to guide the naming of
each archetype: each MSigDB^83 gene set (v7.4) that has two or more
autism genes (n = 62 as described above) annotated is tested for
association with the archetypal coefficients (A1-A6, simultaneously)
in a quasibinomial generalized linear model (GLM) (Supplementary
Table 15). The one-sided association P value (that is, positive
association) for each archetype is reported in Supplementary Table 12
(top 20 for each archetype). To name each archetype, the top 20 gene
sets with the strongest associations with that archetype were
considered; this leads to the following naming conventions: A1,
neurotransmission; A2, chromatin modification; A3, RNA processing;
A4, vesicle-mediated transport; A5, MAPK signaling and migration; and
A6, cytoskeleton and mitosis (see Supplementary Table 15 and Fig. 7).
Representative genes for each archetype were chosen from among the
list of 62 risk genes identified in this study, using the top six
genes for each archetype (note that these genes do not necessarily
fulfill the 'archetypal' criterion described above, but are simply
the top six of the 62 for each archetype).

Statistics and reproducibility

No statistical method was used to predetermine sample size, and no
data were excluded from the analyses.

Reporting summary

Further information on research design is available in the Nature
Research Reporting Summary linked to this article.

Data availability

In order to abide by the informed consents that individuals with
autism and their family members signed when agreeing to participate
in a SFARI cohort (SSC and SPARK), researchers must be approved by
SFARI Base (https://base.sfari.org). To access to SPARK or SFARI
data, researchers should

(1) Obtain a SFARI Base account at https://base.sfari.org, which will
require affiliating with an institution. Currently, there are 271
institutions around the world that have signed SFARI's Researcher
Distribution Agreement (RDA), and any researcher affiliated with
those institutions can apply for SFARI Base access.

(2) Review the institute's executed RDA. The standard RDA is
available at https://s3.amazonaws.com/sf-web-assets-prod/wp-content/
uploads/sites/2/2021/06/15165956/SFARI_RDA.pdf

(3) Create a SFARI Base project, which includes a title, abstract and
an IRB approval or exemption document.

(4) Create a SFARI Base request. All requests are processed in a
timely manner.

The SPARK data is accessible as follows:

SFARI_SPARK_iWES includes exome and genotyping data on 70,487
participants, including all people analyzed in this paper plus an
additional 11,282 participants.

SFARI_SPARK_WGS_1 includes whole genome data from 2,629 individuals
from 645 families with at least one person with autism.

SFARI_SPARK_WGS_2 includes whole genome data from 2,365 individuals
from 587 families with at least one person with autism.

SFARI_SPARK_WGS_3 includes whole genome data from 2,871 individuals
from 803 families with at least one person with autism.

SSC_WES_3 is whole exome data on the Simons Simplex Collection (SSC)
as analyzed and reported in ref. ^19.

SFARI_SSC_WGS_pilot contains genomes of 40 families with autism.

SFARI_SSC_WGS_1 and SFARI_SSC_ WGS_2 contain WGS of the SSC.

SSC Dataset contains phenotypic information on 2,644 simplex autism
families.

SPARK Phenotype Dataset V7 is the current phenotypic dataset on
290,502 SPARK participants, including 111,720 participants with
autism.

Code availability

All software used in this study is publicly available. The code for
major figures and analysis can be found at https://github.com/ShenLab
/SPARK_Analysis_V1.git or https://doi.org/10.5281/zenodo.6646871.

References

 1. Lord, C. et al. Autism spectrum disorder. Nat. Rev. Dis. Primers
    6, 5 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

 2. Iossifov, I. et al. The contribution of de novo coding mutations
    to autism spectrum disorder. Nature 515, 216-221 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

 3. De Rubeis, S. et al. Synaptic, transcriptional and chromatin
    genes disrupted in autism. Nature 515, 209-215 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

 4. O'Roak, B. J. et al. Recurrent de novo mutations implicate novel
    genes underlying simplex autism risk. Nat. Commun. 5, 5595
    (2014).

    PubMed  Article  CAS  Google Scholar 

 5. Yuen, R. K. C. et al. Whole-genome sequencing of quartet families
    with autism spectrum disorder. Nat. Med. 21, 185-191 (2015).

    CAS  PubMed  Article  Google Scholar 

 6. Yuen, R. K. C. et al. Whole genome sequencing resource identifies
    18 new candidate genes for autism spectrum disorder. Nat.
    Neurosci. 20, 602-611 (2017).

    CAS  PubMed Central  Article  Google Scholar 

 7. Feliciano, P. et al. Exome sequencing of 457 autism families
    recruited online provides evidence for autism risk genes. npj
    Genomic Med 4, 19 (2019).

    Article  CAS  Google Scholar 

 8. Satterstrom, F. K. et al. Large-scale exome sequencing study
    implicates both developmental and functional changes in the
    neurobiology of autism. Cell 180, 568-584.e23 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

 9. The Deciphering Developmental Disorders Study. Large-scale
    discovery of novel genetic causes of developmental disorders.
    Nature 519, 223-228 (2015).

    Article  CAS  Google Scholar 

10. The Deciphering Developmental Disorders Study. Prevalence and
    architecture of de novo mutations in developmental disorders.
    Nature 542, 433-438 (2017).

    PubMed Central  Article  CAS  Google Scholar 

11. Kaplanis, J. et al. Evidence for 28 genetic disorders discovered
    by combining healthcare and research data. Nature 586, 757-762
    (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

12. He, X. et al. Integrated model of de novo and inherited genetic
    variants yields greater power to identify risk genes. PLoS Genet.
    9, e1003671 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

13. Nguyen, H. T. et al. Integrated Bayesian analysis of rare exonic
    variants to identify risk genes for schizophrenia and
    neurodevelopmental disorders. Genome Med. 9, 114 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

14. Gaugler, T. et al. Most genetic risk for autism resides with
    common variation. Nat. Genet. 46, 881-885 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

15. Sandin, S. et al. The familial risk of autism. JAMA 311,
    1770-1777 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

16. Sandin, S. et al. The heritability of autism spectrum disorder.
    JAMA 318, 1182-1184 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

17. Grove, J. et al. Identification of common genetic risk variants
    for autism spectrum disorder. Nat. Genet. 51, 431-444 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

18. Lek, M. et al. Analysis of protein-coding genetic variation in
    60,706 humans. Nature 536, 285-291 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

19. Krumm, N. et al. Excess of rare, inherited truncating mutations
    in autism. Nat. Genet. 47, 582-588 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

20. Kosmicki, J. A. et al. Refining the role of de novo
    protein-truncating variants in neurodevelopmental disorders by
    using population reference samples. Nat. Genet. 49, 504-510
    (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

21. Ruzzo, E. K. et al. Inherited and de novo genetic risk for autism
    impacts shared networks. Cell 178, 850-866.e26 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

22. Wilfert, A. B. et al. Recent ultra-rare inherited variants
    implicate new autism candidate risk genes. Nat. Genet. 53,
    1125-1134 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

23. The SPARK Consortium. SPARK: a US cohort of 50,000 families to
    accelerate autism research. Neuron 97, 488-493 (2018).

    PubMed Central  Article  CAS  Google Scholar 

24. MacArthur, D. G. & Tyler-Smith, C. Loss-of-function variants in
    the genomes of healthy humans. Hum. Mol. Genet. 19, R125-R130
    (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

25. MacArthur, D. G. et al. A systematic survey of loss-of-function
    variants in human protein-coding genes. Science 335, 823-828
    (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

26. Karczewski, K. J. et al. The mutational constraint spectrum
    quantified from variation in 141,456 humans. Nature 581, 434-443
    (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

27. Cummings, B. B. et al. Transcript expression-aware annotation
    improves rare variant interpretation. Nature 581, 452-458 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

28. Cassa, C. A. et al. Estimating the selective effects of
    heterozygous protein-truncating variants from human exome data.
    Nat. Genet. 49, 806-810 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

29. Fuller, Z. L., Berg, J. J., Mostafavi, H., Sella, G. &
    Przeworski, M. Measuring intolerance to mutation in human
    genetics. Nat. Genet. 51, 772-776 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

30. An, J.-Y. et al. Genome-wide de novo risk score implicates
    promoter variation in autism spectrum disorder. Science 362,
    aat6576 (2018).

    CAS  Article  Google Scholar 

31. Ioannidis, N. M. et al. REVEL: an ensemble method for predicting
    the pathogenicity of rare missense variants. Am. J. Hum. Genet.
    99, 877-885 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

32. Fromer, M. et al. De novo mutations in schizophrenia implicate
    synaptic networks. Nature 506, 179-184 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

33. Parikshak, N. N. et al. Integrative functional genomic analyses
    implicate specific molecular pathways and circuits in autism.
    Cell 155, 1008-1021 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

34. Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative
    modeling define the Rbfox splicing-regulatory network linked to
    brain development and autism. Cell Rep. 6, 1139-1152 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

35. Darnell, J. C. et al. FMRP stalls ribosomal translocation on
    mRNAs linked to synaptic function and autism. Cell 146, 247-261
    (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

36. Cotney, J. et al. The autism-associated chromatin modifier CHD8
    regulates other autism risk genes during human neurodevelopment.
    Nat. Commun. 6, 6404 (2015).

    CAS  PubMed  Article  Google Scholar 

37. Brueggeman, L., Koomar, T. & Michaelson, J. J. Forecasting risk
    gene discovery in autism with machine learning and genome-scale
    data. Sci. Rep. 10, 4569 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

38. Chen, S. et al. Dissecting autism genetic risk using single-cell
    RNA-seq data. Preprint at https://www.bioRxiv.org/content/10.1101
    /2020.06.15.153031v1 (2020).

39. Ewens, W. J. & Spielman, R. S. The transmission/disequilibrium
    test: history, subdivision, and admixture. Am. J. Hum. Genet. 57,
    455-464 (1995).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

40. Fisher, R. A. Statistical Methods for Research Workers 11th edn,
    rev (Oliver and Boyd, 1925).

41. Zuk, O. et al. Searching for missing heritability: designing rare
    variant association studies. Proc. Natl Acad. Sci. USA 111,
    E455-E464 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

42. Wright, S. Evolution in Mendelian populations. Genetics 16,
    97-159 (1931).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

43. Skene, N. G. & Grant, S. G. N. Identification of vulnerable cell
    types in major brain disorders using single cell transcriptomes
    and expression weighted cell type enrichment. Front. Neurosci. 10
    , 16 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

44. Willsey, A. J. et al. Coexpression networks implicate human
    midfetal deep cortical projection neurons in the pathogenesis of
    autism. Cell 155, 997-1007 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

45. Chang, J., Gilman, S. R., Chiang, A. H., Sanders, S. J. & Vitkup,
    D. Genotype to phenotype relationships in autism spectrum
    disorders. Nat. Neurosci. 18, 191-198 (2015).

    CAS  PubMed  Article  Google Scholar 

46. Wang, S. et al. De novo sequence and copy number variants are
    strongly associated with Tourette disorder and implicate cell
    polarity in pathogenesis. Cell Rep. 24, 3441-3454.e12 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

47. Heyne, H. O. et al. De novo variants in neurodevelopmental
    disorders with epilepsy. Nat. Genet. 50, 1048-1053 (2018).

    CAS  PubMed  Article  Google Scholar 

48. Singh, T. et al. Rare coding variants in ten genes confer
    substantial risk for schizophrenia. Nature 604, 509-516 (2022).

    CAS  PubMed  Article  Google Scholar 

49. Shu, C., Snyder, L. G., Shen, Y. & Chung, W. K. The SPARK
    Consortium. Imputing cognitive impairment in SPARK, a large
    autism cohort. Autism Res. 15, 156-170 (2022).

    PubMed  Article  Google Scholar 

50. Arnheim, N. & Calabrese, P. Understanding what determines the
    frequency and pattern of human germline mutations. Nat. Rev.
    Genet. 10, 478-488 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

51. Rees, E., Moskvina, V., Owen, M. J., O'Donovan, M. C. & Kirov, G.
    De novo rates and selection of schizophrenia-associated copy
    number variants. Biol. Psychiatry 70, 1109-1114 (2011).

    PubMed  Article  Google Scholar 

52. Szklarczyk, D. et al. STRING v11: protein-protein association
    networks with increased coverage, supporting functional discovery
    in genome-wide experimental datasets. Nucleic Acids Res. 47,
    D607-D613 (2019).

    CAS  PubMed  Article  Google Scholar 

53. Kohler, S. et al. The Human Phenotype Ontology in 2021. Nucleic
    Acids Res. 49, D1207-D1217 (2021).

    PubMed  Article  CAS  Google Scholar 

54. Raychaudhuri, S. et al. Accurately assessing the risk of
    schizophrenia conferred by rare copy-number variation affecting
    genes with brain function. PLoS Genet. 6, e1001097 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

55. Carlson, J. et al. Extremely rare variants reveal patterns of
    germline mutation rate heterogeneity in humans. Nat. Commun. 9,
    3753 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

56. Samocha, K. E. et al. A framework for the interpretation of de
    novo mutation in human disease. Nat. Genet. 46, 944-950 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

57. Yamabhai, M. et al. Intersectin, a novel adaptor protein with two
    Eps15 homology and five Src homology 3 domains. J. Biol. Chem.
    273, 31401-31407 (1998).

    CAS  PubMed  Article  Google Scholar 

58. Wang, W. et al. ITSN-1 controls vesicle recycling at the
    neuromuscular junction and functions in parallel with DAB-1.
    Traffic 9, 742-754 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

59. Stringham, E. G. & Schmidt, K. L. Navigating the cell: UNC-53 and
    the navigators, a family of cytoskeletal regulators with multiple
    roles in cell migration, outgrowth and trafficking. Cell Adhes.
    Migr. 3, 342-346 (2009).

    Article  Google Scholar 

60. Karenko, L. et al. Primary cutaneous T-cell lymphomas show a
    deletion or translocation affecting NAV3, the human UNC-53
    homologue. Cancer Res. 65, 8101-8110 (2005).

    CAS  PubMed  Article  Google Scholar 

61. Sanghvi-Shah, R. & Weber, G. F. Intermediate filaments at the
    junction of mechanotransduction, migration, and development.
    Front Cell Dev. Biol. 5, 81 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

62. Martin, H. C. et al. Quantifying the contribution of recessive
    coding variation to developmental disorders. Science 362,
    1161-1164 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

63. Guo, H. et al. Inherited and multiple de novo mutations in autism
    /developmental delay risk genes suggest a multifactorial model.
    Mol. Autism 9, 64 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

64. Poplin, R. et al. Scaling accurate genetic variant discovery to
    tens of thousands of samples. Preprint at https://www.bioRxiv.org
    /content/10.1101/201178v3 (2018).

65. Garrison, E. & Marth, G. Haplotype-based variant detection from
    short-read sequencing. Preprint at https://arXiv.org/abs/
    1207.3907 (2012).

66. GENOMICS plc - weCall: fast, accurate and simple to use command
    line tool for variant detection in NGS data. https://github.com/
    Genomicsplc/wecall

67. Poplin, R. et al. A universal SNP and small-indel variant caller
    using deep neural networks. Nat. Biotechnol. 36, 983-987 (2018).

    CAS  PubMed  Article  Google Scholar 

68. Lin, M. F. et al. GLnexus: joint variant calling for large cohort
    sequencing. Preprint at https://www.bioRxiv.org/content/10.1101/
    343970v1 (2018).

69. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the
    NHLBI TOPMed Program. Nature 590, 290-299 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

70. Li, H. Toward better understanding of artifacts in variant
    calling from high-coverage samples. Bioinformatics 30, 2843-2851
    (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

71. Maenner, M. J. et al. Prevalence of autism spectrum disorder
    among children aged 8 years -- Autism and Developmental
    Disabilities Monitoring Network, 11 Sites, United States, 2016.
    MMWR Surveill. Summ. 69, 1-12 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

72. Power, R. A. et al. Fecundity of patients with schizophrenia,
    autism, bipolar disorder, depression, anorexia nervosa, or
    substance abuse vs their unaffected siblings. JAMA Psychiatry 70,
    22-30 (2013).

    PubMed  Article  Google Scholar 

73. Wright, C. F. et al. Genetic diagnosis of developmental disorders
    in the DDD study: a scalable analysis of genome-wide research
    data. Lancet 385, 1305-1314 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

74. Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven
    knowledgebase for the autism spectrum disorders (ASDs). Mol.
    Autism 4, 36 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

75. Singer, E. What makes an autism gene? https://SPARKforAutism.org/
    discover_article/what-makes-an-autism-gene/ (2017).

76. Fagerberg, L. et al. Analysis of the human tissue-specific
    expression by genome-wide integration of transcriptomics and
    antibody-based proteomics. Mol. Cell. Proteomics 13, 397-406
    (2014).

    CAS  PubMed  Article  Google Scholar 

77. Mo, A. et al. Epigenomic signatures of neuronal diversity in the
    mammalian brain. Neuron 86, 1369-1384 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

78. Pirooznia, M. et al. SynaptomeDB: an ontology-based knowledgebase
    for synaptic genes. Bioinformatics 28, 897-899 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

79. Wagnon, J. L. et al. CELF4 regulates translation and local
    abundance of a vast set of mRNAs, including genes associated with
    regulation of synaptic function. PLoS Genet. 8, e1003067 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

80. Eppig, J. T. Mouse Genome Informatics (MGI) Resource: genetic,
    genomic, and biological knowledgebase for the laboratory mouse.
    ILAR J. 58, 17-41 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

81. Cutler, A. & Breiman, L. Archetypal analysis. Technometrics 36,
    338-347 (1994).

    Article  Google Scholar 

82. Thorndike, R. L. Who belongs in the family? Psychometrika 18,
    267-276 (1953).

    Article  Google Scholar 

83. Subramanian, A. et al. Gene set enrichment analysis: a
    knowledge-based approach for interpreting genome-wide expression
    profiles. Proc. Natl Acad. Sci. 102, 15545-15550 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

84. Packer, J. S. et al. CLAMMS: a scalable algorithm for calling
    common and rare copy number variants from exome sequencing data.
    Bioinformatics 32, 133-135 (2016).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are extremely grateful to the thousands of individuals and
families who are participating in this study. We thank the sites,
staff and volunteers of the SPARK Clinical Site Network and SFARI for
their invaluable contributions. We are grateful to the many ASD
advocacy and service organizations that have helped us inform the
community about SPARK, including the Autism Society of America and
its affiliates, the Global and Regional Autism Spectrum Partnership
(GRASP), Autism Speaks, and the Autism Science Foundation. We thank
the members of SPARK's Community Advisory Council for providing
feedback and advice. We thank members of our Scientific Advisory
Board and SFARI scientists for advice on our protocol and participant
outreach and retention strategies. Our de novo analyses involved
primary data from SSC and results from published ASD studies,
including ASC and MSSNG, and our case-control analysis used published
population controls from gnomAD (exome v2.1.1, non-neuro subset) and
TOPMed (WGS, freeze 8). Approved researchers can obtain the SPARK
population dataset described in this study by applying at https://
base.sfari.org. The SPARK initiative is funded by the Simons
Foundation as part of SFARI. This research was supported, in part, by
a grant from the National Institute of Mental Health (NIMH
R01MH101221) and grants from the Simons Foundation (SFARI nos. 608045
and 810018EE) to E.E.E., a grant from the National Institutes of
Health (NIH R01GM120609) and from the Simons Foundation (SFARI
no. 606450) to Y.S., a grant from the Simons Foundation (SFARI
no. 644038) to B.J.O. and J.J.M., a grant from the National Institute
of Mental Health to T.N.T. (NIMH 1K99MH117165), and grants MH105527
and DC014489 from the National Institute of Health to J.J.M. E.E.E.
is an investigator of the Howard Hughes Medical Institute.

Author information

Author notes

 1. These authors contributed equally: Xueya Zhou, Pamela Feliciano,
    Chang Shu, Tianyun Wang, Irina Astrovskaya.

 2. These authors jointly supervised this work: Yufeng Shen, Wendy K.
    Chung.

Authors and Affiliations

 1. Department of Pediatrics, Columbia University Medical Center, New
    York, NY, USA

    Xueya Zhou, Chang Shu, Joseph U. Obiajulu & Wendy K. Chung

 2. Department of Systems Biology, Columbia University Medical
    Center, New York, NY, USA

    Xueya Zhou, Chang Shu, Joseph U. Obiajulu, Alex Kitaygorodsky &
     Yufeng Shen

 3. Simons Foundation, New York, NY, USA

    Pamela Feliciano, Irina Astrovskaya, Jacob B. Hall, Jessica R.
    Wright, Simon Xuming Xu, Olena Marchenko, Christopher
    Fleisch, Sarah D. Barns, LeeAnne Green Snyder, Bing Han, Alpha
    Amatya, Asif Bashar, Alex E. Lash, Alexandra Goler, Alison
    Holbrook, Andrea J. Ace, Amy M. Daniels, Anup Mankar, Alexandra
    N. Stephens, Ai Nhu Nguyen, Beverly E. Robertson, Bill
    Jensen, Brianna M. Vernoia, Brady Schwind, Cheryl Cohen, Chris
    Diggins, Chris Rigby, Casey White-Lehman, Dennis Vasquez
    Montes, Dave Cho, Elizabeth Brooks, Erica Jones, Eirene
    O'Connor, Esmeralda Perez, Emily Singer, Hana Zaydens, John
    Acampado, Jennylyn Brown, Jessica Hernandez, J. Kiely Law, Julie
    Manoharan, Jibrielle Polite, Jennifer Tjernagel, Jaimie
    Toroney, Katharine Diehl, Lindsey A. Cartner, Lori Mann, Luke P.
    Grosvenor, Malcolm D. Mallardi, Martin E. Butler, Misia
    Kowanda, Mahfuza Sabiha, Marina Sarris, Noah Lawson, Nathan
    Lo, Natalie Nagpal, Neelay Shah, Richard Marini, Rishiraj
    Rana, Richard Remington, Swami Ganesan, Stanley Jean, Swapnil
    Shah, Sabrina Xiao, Simon Xu, Tunisia Greene, Vincent J.
    Myers, Wubin Chin, Natalia Volfovsky & Wendy K. Chung

 4. Department of Genome Sciences, University of Washington School of
    Medicine, Seattle, WA, USA

    Tianyun Wang, Shwetha C. Murali, William T. Harvey & Evan E.
    Eichler

 5. Department of Medical Genetics, Center for Medical Genetics,
    School of Basic Medical Sciences, Peking University Health
    Science Center, Beijing, China

    Tianyun Wang

 6. Neuroscience Research Institute, Department of Neurobiology,
    School of Basic Medical Sciences, Peking University Health
    Science Center; Key Laboratory for Neuroscience, Ministry of
    Education of China & National Health Commission of China,
    Beijing, China

    Tianyun Wang

 7. Howard Hughes Medical Institute, University of Washington,
    Seattle, WA, USA

    Shwetha C. Murali & Evan E. Eichler

 8. Department of Psychiatry, University of Iowa Carver College of
    Medicine, Iowa City, IA, USA

    Leo Brueggeman, Taylor R. Thomas, Cate Buescher, Ethan
    Bahl, Lucas Casten, Sydney Kramer, Tanner Koomar & Jacob J.
    Michaelson

 9. Program in Neurogenetics, Department of Neurology, David Geffen
    School of Medicine, University of California, Los Angeles, Los
    Angeles, CA, USA

    Timothy S. Chang, Daniel H. Geschwind & Elizabeth Ruzzo

10. Department of Genetics, Washington University, St. Louis, MO, USA

    Tychele N. Turner

11. Department of Molecular & Medical Genetics, Oregon Health &
    Science University, Portland, OR, USA

    Andrew Nishida & Brian J. O'Roak

12. Department of Biomedical Informatics, Columbia University Medical
    Center, New York, NY, USA

    Yufeng Shen

13. Department of Medicine, Columbia University Medical Center, New
    York, NY, USA

    Wendy K. Chung

14. Department of Psychiatry and Behavioral Sciences, Rush University
    Medical Center, Chicago, IL, USA

    Adrienne Adams, Anna Hirshman, Allison L. Wainer, Caroline
    Leonczyk, Cynthia Pierre, Diamond Phillips, Edith Ocampo, Holly
    Lechniak, Kathryn A. Schweers, Latha V. Soorya, Lisa Yeh, Madison
    Printen, Natalie Berger, Norma Calderon, Nicole M.
    Russo-Ponsaran, Rachel A. Gordon, Soo J. Lee & Sarely Licona

15. Department of Pediatrics, University of Utah, Salt Lake City, UT,
    USA

    Alicia Andrus, Dawn Bentley, Gabriele Baraghoshi, Gregory
    Schoonover, Laurie Lesher & Paul S. Carbone

16. Vanderbilt Kennedy Center, Vanderbilt University Medical Center,
    Nashville, TN, USA

    Anna Berman, Alexandra Miceli, Amy Swanson, Ellen Grimes, Nicole
    Bardett, Nina Harris & S. Carpenter

17. Center for Autism and Related Disorders, Kennedy Krieger
    Institute, Baltimore, MD, USA

    Alison Brown, Alexander P. McKenzie, Bonnie VanMetre, Catherine
    Edmonson, Emily Dillon, Ericka L. Wodka, Hanna Hutter, Jason
    Neely, Katrina Pama, Melinda Koza, Megan McTaggart, Nicolas
    Alvarez, Rebecca J. Landa, Roman Shikov, Sarah Michaels & Vini
    Singh

18. Department of Psychiatry and Biobehavioral Sciences, University
    of California, Los Angeles, Los Angeles, CA, USA

    Alexies Camba, Amanda C. Gulsrud, Catherine Lord, Ivette
    Arriaga, James T. McCracken, Karla Murillo, Katherine
    Tsai, Monica Haley, Maira Tafolla, Sophia Sandhu & Wha S. Yang

19. PreventionGenetics, Marshfield, WI, USA

    Anthony D. Krentz, Angela J. Gruber, Gregory J. Fischer & Marc A.
    Popp

20. Thompson Center for Autism and Neurodevelopmental Disorders,
    University of Missouri, Columbia, MO, USA

    Amanda D. Shocklee, Hannah Cottrell, Jennifer Delaporte, Kathy
    Hirst, Kerri Nowell, Megan Sweeney, Nicole Takahashi, Shelby
    Birdwell, Samantha Hunter & Stephen M. Kanne

21. Department of Pediatrics, University of Minnesota, Minneapolis,
    MN, USA

    Amy Esler

22. Child Study Center, Yale School of Medicine, New Haven, CT, USA

    Anne Fanta, Abha R. Gupta, Catherine A. W. Sullivan, Hope
    Koene, Jane Jurayj, Roger Jou & Sarah Boland

23. Department of Neurogenetics, Kennedy Krieger Institute,
    Baltimore, MD, USA

    Ali Fatemi

24. Department of Psychiatry, University of Michigan, Ann Arbor, MI,
    USA

    Angela Fish, Annes Kim, Ashley Raven, Allyson Zick, Costanza
    Colombi, Fiona K. Miller, Jessyca Judge, Kate D.
    Fitzgerald, Mohammad Ghaziuddin, Sharmista Chintalapalli & Sarah
    Mohiuddin

25. Department of Psychology, University of Miami's Center for Autism
    and Related Disabilities (UM-CARD), Coral Gables, FL, USA

    Antonio Gonzalez, Anibal Gutierrez Jr., Brandon Lobisi, Cindy
    Zha, Dahriana Correa, David Giancarla, Hoa Lam Schneider, Joaquin
    Comitre, Jacob Rosewater, Lynette M. Herbert, Michael
    Alessandri, Michele Cutri, Melissa N. Hale, Madelyn Rayos &
     Nickelle Decius

26. Department of Psychiatry and Behavioral Sciences, Stanford
    University, Stanford, CA, USA

    Antonio Hardan, Daniela Rojas, Elizabeth Orrick, Jared
    Gong, Margot Frayne, Morgan Steele, Robin Libove & Sandy
    Littlefield

27. Division of Pediatric Psychology and Neuropsychology, Nationwide
    Children's Hospital (Child Development Center), Columbus, OH, USA

    Amy Hess, Charles Albright, Daniel Lee Coury, Erin
    Given, Elizabeth Kryszak, Eric M. Butter, Gabrielle
    Tiede, Jessica Scherr, Kevin Stephenson, Megan Norris, Nancy
    Long, Nicholas Russell, Sara Eldred & Teresa Ibanez

28. John P. Hussman Institute for Human Genomics, University of Miami
    Miller School of Medicine, Miami, FL, USA

    Anthony J. Griswold

29. Department of Psychiatry, University of Minnesota, Minneapolis,
    MN, USA

    Andrea Jarratt, Alissa Jorgenson, Angela Tesng, Claudia
    Campo-Soria, Dain Howes, Dalia Istephanous, Desiree
    Rambeck, Diksha Srishyla, Gregory Hooks, Jaclyn Gunderson, Katie
    Beard, Laura Simon, Lucy Wasserburg, Megan DuBois, Natasha
    Lillie, Olivia Newman, Sunday Francis, Suma Jacob & Wenteng CaI

30. Department of Psychiatry and Behavioral Neuroscience, Cincinnati
    Children's Hospital Medical Center - Research Foundation,
    Cincinnati, OH, USA

    Anna Jelinek, Craig A. Erickson, Carrie Fassler, Elizabeth
    Blank, Erica Lampert, Ernest V. Pedapati, Jose Polanco, Kaela
    O'Brien, Natalie Madi, Nicole Mccoy, Patricia Manning, Rebecca C.
    Shaffer, Stephanie Booker, Sophia Melnyk & Shelley Randall

31. Department of Pediatrics, Vanderbilt University Medical Center,
    Nashville, TN, USA

    A. Pablo Juarez, Amy Nicholson & Zachary E. Warren

32. Department of Psychiatry, Oregon Health & Science University,
    Portland, OR, USA

    Addie Luo, Desi Limpoco, Eric J. Fombonne, Faris Fazal, Hadley
    Morotti, Landon Beeson, Leigh Coppola, Lillian D. Pacheco, Lark
    Y. Huang-Storms, Shelley Aberle & Sarah Mastel

33. Department of Pediatrics, JFK Partners/University of Colorado
    School of Medicine, Aurora, CO, USA

    Angela L. Rachubinski, Amber Tallbull, Cordelia R.
    Rosenberg, Jeanette Cordova, Lisa Cordiero, Nicole
    Targalia, Susan Hepburn, Sandra L. Friedman & Tamim Shaikh

34. Department of Neurosciences, University of California, San Diego
    and SARRC Phoenix, La Jolla, CA, USA

    Andrew Mason, Anthony Sziklay, Chris Smith, Erin Bower, Eric
    Courchesne, Hannah E. Kaplan, Hongjian Qi, Haicang Zhang, Haoquan
    Zhao, Jiayao Wang, Kendra Coleman, Karen L. Pierce, Mojeeb
    Shir, Sidi Xu, Tia Chen & Tiziano Pramparo

35. Department of Pediatrics, Boston Children's Hospital, Boston, MA,
    USA

    Anna Milliken, Christopher Zaro, Emily T. Matthews, Gabriella
    Aberbach, Katherine G. Pawlowski, Lisa M. Prock, Michelle
    Coughlin & Margaret Hojlo

36. Department of Pediatrics, Montefiore Medical Center and The
    Albert Einstein College of Medicine, Bronx, NY, USA

    Amy Morales-Lara, Lisa H. Shulman, Molly O'Neil & Maria
    Valicenti-McDermott

37. Department of Psychiatry, Weill Cornell Medicine, White Plains,
    NY, USA

    Anna Marie Paolicelli, Michelle Heyman & Shanping Qiu

38. Department of Pediatrics, Medical University of South Carolina,
    Charleston, SC, USA

    Anna Rhea, Audrey Ward, Brandi Bell, Catherine C.
    Bradley, Caitlin McCarthy, Crissy Ortiz, Casey Roche, Clara
    Shrier, Candace Van Wade, Erin Doyle, Harper Richardson, Jessie
    Montezuma, Laura Carpenter, Sarah Conyers, Sophia D'Ambrosi &
     Virginia Galbraith

39. Department of Pediatrics, Texas Children's Hospital (Baylor
    College of Medicine), Houston, TX, USA

    Andrea Simon, Danica Limon, Gabrielle Duhon, Gabriela
    Marzano, Jessica Orobio, Kelli Baalman & Robin P. Goin-Kochel

40. Department of Medicine, Boston Children's Hospital, Boston, MA,
    USA

    Aubrie Soucy, Ryan N. Doan & Timothy W. Yu

41. MIND Institute and Department of Psychiatry and Behavioral
    Sciences, University of California, Davis, Sacramento, CA, USA

    Brittani A. Hilscher, Claudine Anglo, David G. Amaral, Deana
    Li, Kaitlin Smith, Leonard Abbeduto, Nicki Rodriguez & Samantha
    Thompson

42. Children's Specialized Hospital, Toms River, NJ, USA

    Barbara Enright, Jill F. Harris, Malia Beckwith, Myriam
    Casseus, Marilyn Lopez & Natalia Gonzalez

43. Department of Psychiatry, University of North Carolina (UNC,
    TEACCH, CIDD), Chapel Hill, NC, USA

    Brenda Hauf, Catherine Gray, Corrie H. Walston, Elena
    Lamarche, Gabriel S. Dichter, Joseph Piven, Kailey Murray, Kelli
    Real, Mary Currin, Michelle Jordy & Renee D. Clark

44. Department of Psychiatry and Behavioral Sciences, Emory
    University and Marcus Autism Center, Atlanta, GA, USA

    Catherine E. Rice, Joseph F. Cubells, Michael J. Morrier & Opal
    Y. Ousley

45. Department of Pediatrics, Emory University and Marcus Autism
    Center, Atlanta, GA, USA

    Chris Gunter, Cheryl Klaiman, Megan Dunlevy, Sheena Mathai &
     Stormi White

46. Geisinger Autism & Developmental Medicine Institute, Lewisburg,
    PA, USA

    Christa Lese Martin, Cora M. Taylor, Kate Dent, Kaitlyn
    Singer, Lauren Kasperson Walsh & William Curtis Weaver

47. Department of Pediatrics, Rush University Medical Center,
    Chicago, IL, USA

    Cesar Ochoa-Lubinoff, Elizabeth Berry-Kravis, Hai Li, Josh
    Beeson, Kelsey Gonring, Lorena Ferreira Corzo, Melissa
    Baer, Peter Heydemann & Thao Tran

48. Department of Pediatrics, University of Mississippi Medical
    Center, Jackson, MS, USA

    Dustin E. Sarver, Danielle Stamps, Kristen Callahan, Lacy
    Malloch, Robert D. Annett & Sabrina White

49. Department of Psychiatry and Behavioral Sciences, University of
    Washington/Seattle Children's Autism Center, Seattle, WA, USA

    Daniel Cho, Emily A. Fox, Haidar Ghina, Jennifer A. Gerdts, Katie
    Ahlers, Raphael A. Bernier, Rachel K. Earl, Theodore Ho & Tara
    Rutter

50. Department of Psychology, University of Michigan, Ann Arbor, MI,
    USA

    Ivy F. Tso

51. Center for Autism Research, Children's Hospital of Philadelphia,
    Philadelphia, PA, USA

    Juhi Pandey, Lindsey DeMarco, Robert T. Schultz, Susannah
    Horner, Samiza Palmer, Samantha Plate & Vaikunt Ranganathan

52. Department of Molecular Physiology and Biophysics, Vanderbilt
    University, Nashville, TN, USA

    James S. Sutcliffe

53. Department of Psychiatry, Columbia University Medical Center, New
    York, NY, USA

    Jeremy Veenstra-Vanderweele

54. Department of Psychiatry and Behavioral Sciences, Medical
    University of South Carolina, Charleston, SC, USA

    McLeod F. Gwynette

55. Department of Neurology, Boston Children's Hospital, Boston, MA,
    USA

    Mustafa Sahin

56. Maine Medical Center Research Institute, Scarborough, ME, USA

    Matthew Siegel, Mary Verdi & Susan Santangelo

57. Genome Center, MIND Institute, Department of Biochemistry and
    Molecular Medicine, University of California, Davis, Sacramento,
    CA, USA

    Megan Y. Dennis

58. Translational Neuroscience Center, Boston Children's Hospital,
    Boston, MA, USA

    Stephanie J. Brewster

59. Center for Autism and the Developing Brain (CADB), Weill Cornell
    Medicine, White Plains, NY, USA

    So Hyun & Y. B. Choi

60. Department of Health Psychology, University of Missouri,
    Columbia, MO, USA

    Stephen M. Kanne

61. Greenwood Genetic Center, Greenwood, SC, USA

    Steve Skinner

62. Department of Pediatrics, University of California, San Diego and
    SARRC Phoenix, La Jolla, CA, USA

    Vahid Gazestani

Authors

 1. Xueya Zhou
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 2. Pamela Feliciano
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 3. Chang Shu
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 4. Tianyun Wang
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 5. Irina Astrovskaya
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 6. Jacob B. Hall
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 7. Joseph U. Obiajulu
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 8. Jessica R. Wright
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 9. Shwetha C. Murali
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10. Simon Xuming Xu
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11. Leo Brueggeman
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12. Taylor R. Thomas
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13. Olena Marchenko
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14. Christopher Fleisch
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15. Sarah D. Barns
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16. LeeAnne Green Snyder
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17. Bing Han
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18. Timothy S. Chang
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19. Tychele N. Turner
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20. William T. Harvey
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21. Andrew Nishida
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22. Brian J. O'Roak
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23. Daniel H. Geschwind
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24. Jacob J. Michaelson
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25. Natalia Volfovsky
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26. Evan E. Eichler
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27. Yufeng Shen
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28. Wendy K. Chung
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Consortia

The SPARK Consortium

  * Adrienne Adams
  * , Alpha Amatya
  * , Alicia Andrus
  * , Asif Bashar
  * , Anna Berman
  * , Alison Brown
  * , Alexies Camba
  * , Amanda C. Gulsrud
  * , Anthony D. Krentz
  * , Amanda D. Shocklee
  * , Amy Esler
  * , Alex E. Lash
  * , Anne Fanta
  * , Ali Fatemi
  * , Angela Fish
  * , Alexandra Goler
  * , Antonio Gonzalez
  * , Anibal Gutierrez Jr.
  * , Antonio Hardan
  * , Amy Hess
  * , Anna Hirshman
  * , Alison Holbrook
  * , Andrea J. Ace
  * , Anthony J. Griswold
  * , Angela J. Gruber
  * , Andrea Jarratt
  * , Anna Jelinek
  * , Alissa Jorgenson
  * , A. Pablo Juarez
  * , Annes Kim
  * , Alex Kitaygorodsky
  * , Addie Luo
  * , Angela L. Rachubinski
  * , Allison L. Wainer
  * , Amy M. Daniels
  * , Anup Mankar
  * , Andrew Mason
  * , Alexandra Miceli
  * , Anna Milliken
  * , Amy Morales-Lara
  * , Alexandra N. Stephens
  * , Ai Nhu Nguyen
  * , Amy Nicholson
  * , Anna Marie Paolicelli
  * , Alexander P. McKenzie
  * , Abha R. Gupta
  * , Ashley Raven
  * , Anna Rhea
  * , Andrea Simon
  * , Aubrie Soucy
  * , Amy Swanson
  * , Anthony Sziklay
  * , Amber Tallbull
  * , Angela Tesng
  * , Audrey Ward
  * , Allyson Zick
  * , Brittani A. Hilscher
  * , Brandi Bell
  * , Barbara Enright
  * , Beverly E. Robertson
  * , Brenda Hauf
  * , Bill Jensen
  * , Brandon Lobisi
  * , Brianna M. Vernoia
  * , Brady Schwind
  * , Bonnie VanMetre
  * , Craig A. Erickson
  * , Catherine A. W. Sullivan
  * , Charles Albright
  * , Claudine Anglo
  * , Cate Buescher
  * , Catherine C. Bradley
  * , Claudia Campo-Soria
  * , Cheryl Cohen
  * , Costanza Colombi
  * , Chris Diggins
  * , Catherine Edmonson
  * , Catherine E. Rice
  * , Carrie Fassler
  * , Catherine Gray
  * , Chris Gunter
  * , Corrie H. Walston
  * , Cheryl Klaiman
  * , Caroline Leonczyk
  * , Christa Lese Martin
  * , Catherine Lord
  * , Cora M. Taylor
  * , Caitlin McCarthy
  * , Cesar Ochoa-Lubinoff
  * , Crissy Ortiz
  * , Cynthia Pierre
  * , Cordelia R. Rosenberg
  * , Chris Rigby
  * , Casey Roche
  * , Clara Shrier
  * , Chris Smith
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  *  & Zachary E. Warren

Contributions

I. Astrovskaya, J.B.H., J.J.M., N.V., P.F., C. Shu, T.W., W.K.C.,
X.Z. and Y.S. designed and conceived this study. A. Adams, A. Andrus,
A. Berman, A. Brown, A.C., A.C.G., A.D.S., A.E., A. Fanta, A. Fatemi,
A. Fish, A. Goler, A. Gonzalez, A. Gutierrez Jr., A. Hardan, A. Hess,
A. Hirshman, A. Holbrook, A.J.A., A.J. Griswold, A. Jarratt, A.
Jelinek, A. Jorgenson, A. Juarez, A. Kim, A. Kitaygorodsky, A.L.,
A.L.R., A.L.W., A.M.D., A. Mankar, A. Mason, A. Miceli, A. Milliken,
A.M.-L., A.N.S., A. Nguyen, A. Nicholson, A. Nishida, A.P., A.P.M.,
A.R.G., A. Raven, A. Rhea, A. Simon, A. Swanson, A. Sziklay, A.
Tallbull, A. Tesng, A.W., A.Z., B.A.H., B.B., B.E., B.E.R., B. Hauf,
B.J.O., B.L., B.M.V., B.S., B.V., C.A.E., C.A.W.S., C. Albright, C.
Anglo, C.B., C.C.B., C.C.-S., C. Cohen, C. Colombi, C.D., C.E.,
C.E.R., C. Fassler, C. Gray, C. Gunter, C.H.W., C.K., C. Leonczyk,
C.L.M., C. Lord, C.M.T., C.M., C.O.-L., C. Ortiz, C.P., C.R.R., C.
Roche, C. Shrier, C. Smith, C.V., C.W.-L., C. Zaro, C. Zha, D.B.,
Daniel Cho, D. Correa, D.E.S., D.G., D.G.A., D.H., D.I., D.L.C., D.
Li, D. Limon, D. Limpoco, D.P., D. Rambeck, D. Rojas, D. Srishyla, D.
Stamps, E.A.F., E. Bahl, E.B.-K., E. Blank, E. Bower, E. Brooks,
E.C., E. Dillon, E. Doyle, E. Given, E. Grimes, E.J., E.J.F., E.K.,
E.L.W., E. Lamarche, E. Lampert, E.M.B., E. O'Connor, E. Ocampo, E.
Orrick, E.P., E.R., E.S., E.T.M., E.V.P., F.F., F.K.M., G.A., G.B.,
G.D., G.H., G.M., G.S., G.S.D., G.T., H.C., H.E.K., H.G., H.H., H.K.,
H.L.S., H. Lechniak, H. Li, H.M., H.R., H. Zaydens, I. Arriaga,
I.F.T., J.A., J.A.G., J. Beeson, J. Brown, J. Comitre, J. Cordova,
J.D., J.F.C., J.F.H., J. Gong, J. Gunderson, J.H., J.J.M., J. Judge,
J. Jurayj, J.K.L., J. Manoharan, J. Montezuma, J.N., J.O., J. Pandey,
J. Piven, J. Polanco, J. Polite, J.R., J.R.W., J.S., J.S.S., J.T.M.,
J. Tjernagel, J. Toroney, J.V.-V., J.W., K.A., K.A.S., K. Baalman, K.
Beard, K. Callahan, K. Coleman, K.D.F., K. Dent, K. Diehl, K.G.,
K.G.P., K.H., K.L.P., K. Murillo, K. Murray, K.N., K.O., K. Pama,
K.R., K. Singer, K. Smith, K. Stephenson, K.T., L.A., L.A.C., L.
Beeson, L. Carpenter, L. Casten, L. Coppola, L. Cordiero, L.D.,
L.D.P., L.F.C., L.G.S., L.H.S., L.K.W., L.L., L.M.H., L.M.P., L.
Malloch, L. Mann, L.P.G., L.S., L.V.S., L.W., L.Y., L.Y.-H., M.A., M.
Baer, M. Beckwith, M. Casseus, M. Coughlin, M. Currin, M. Cutri, M.
DuBois, M. Dunlevy, M.F., M.F.G., M.G., M. Haley, M. Heyman, M.
Hojlo, M.J., M.J.M., M. Kowanda, M. Koza, M.L., M.M., M.N., M.N.H.,
M.O., M.P., M.R., M. Sabiha, M. Sahin, M. Sarris, M. Shir, M. Siegel,
M. Steele, M. Sweeney, M.T., M.V.-M., M. Verdi, M.Y.D., N.A., N.
Bardett, N. Berger, N.C., N.D., N.G., N.H., N. Lillie, N. Long,
N.M.R.-P., N. Madi, N. Mccoy, N.N., N. Rodriguez, N. Russell, N.S.,
N. Takahashi, N. Targalia, N.V., O.N., O.Y.O., P.F., P.H., P.M.,
P.S.C., R.A.B., R.A.G., R.C.S., R.D.A., R.D.C., R.J., R.J.L., R.K.E.,
R.L., R.P.G.-K., R. Remington, R.S., R.T.S., S.A., S. Birdwell, S.
Boland, S. Booker, S. Carpenter, S. Chintalapalli, S. Conyers, S.D.,
S.D.B., S.E., S.F., S.G., S. Hepburn, S. Horner, S. Hunter, S.J.B.,
S.J.L., S. Jacob, S. Jean, S. Kim, S. Kramer, S.L.F., S. Licona, S.
Littlefield, S.M.K., S. Mastel, S. Mathai, S. Melnyk, S. Michaels, S.
Mohiuddin, S. Palmer, S. Plate, S.Q., S.R., S. Sandhu, S. Santangelo,
S. Skinner, S.T., S. Xu, S. Xiao, Sabrina White, Stormi White, T.C.,
T.G., T.H., T.I., T.K., T.P., T.R., T.S., T.R.T., T.T., V. Galbraith,
V. Gazestani, V.J.M., V.R., V.S., W.C.W., W. Cal, W.K.C., W.S.Y.,
Y.C. and Z.E.W. recruited participants and collected clinical data
and biospecimens. A. Amatya, A. Bashar, A.E.L., A. Mankar, A. Nguyen,
B.J., C. Rigby, Dave Cho, D.V.M., E. O'Connor, J.A., M.D.M., M.E.B.,
N. Lawson, N. Lo, N.V., R.M., R. Rana, S.G., S. Jean, S. Shah and W.
Chin built and supported the SPARKforAutism.org website, software,
databases and systems, and managed SPARK data. A.D.K., A.J. Gruber,
A. Nishida, B. Han, B.J.O., C. Fleisch, C. Shu, D.V.M., E. Brooks,
G.J.F., I. Astrovskaya, J.B.H., J.J.M., J.R.W., J.U.O., L.
Brueggeman, L.G.S., M.A.P., N. Lo, N.V., O.M., P.F., S.D.B., S.C.M.,
S.X.X., T.N.T., T.S.C., T.W., W.T.H., X.Z. and Y.S. performed
analyses, processed biospecimens and sequenced DNA samples. A.
Fatemi, A. Kitaygorodsky, A. Soucy, C. Shu, D.H.G., E.B.-K., E.E.E.,
E.R., H.Q., H. Zhang, H. Zhao, I. Astrovskaya, J.B.H., J.J.M.,
J.R.W., J.U.O., L. Brueggeman, L.G.S., M.Y.D., N.V., O.M., P.F.,
R.N.D., S.D.B., S.C.M., S.X.X., T.K., T.N.T., T.P., T.S., T.R.T.,
T.W., T.W.Y., W.K.C., X.Z. and Y.S. helped with data interpretation.
A.E.L., E.E.E., J.J.M., N.V., P.F., W.K.C. and X.Z. supervised the
work. B.J.O., I. Astrovskaya, J.B.H., J.J.M., J.U.O., C. Shu, J.R.W.,
N.V., P.F., T.N.T., T.W., W.K.C., X.Z. and Y.S. wrote this paper.

Corresponding author

Correspondence to Wendy K. Chung.

Ethics declarations

Competing interests

D.H.G. has received consulting fees or equity participation for
scientific advisory board work from Ovid Therapeutics, Axial
Biotherapeutics, Acurastem, and Falcon Computing. E.E.E. serves on
the Scientific Advisory Board of Variant Bio. W.K.C. serves on
Scientific Advisory Board of the Regeneron Genetics Center and is the
Director of Clinical Research for SFARI. All other authors declare no
competing interests.

Peer review

Peer review information

Nature Genetics thanks Anders Borglum and Xin He for their
contribution to the peer review of this work. Peer reviewer reports
are available.

Additional information

Publisher's note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional
affiliations.

Extended data

Extended Data Fig. 1 Overall burden of de novo variants in four ASD
cohorts included in the discovery sample.

(A) Observed rates of de novo LoF, Dmis (REVEL > = 0.5) and silent
variants (B) are compared with expected rates. We used a 7mer
sequence context dependent mutation rate model^55 to calculate
expected rates for different classes of de novo variants after
adjusting sequencing coverage, and found a close match with observed
de novo rates in control trios. The rates of de novo LoF and Dmis
variants in ASD cases are significantly higher than baseline
expectation and are reduced in cases with known family history. Data
are presented as mean values and 95% confidence interval as error
bars.

Extended Data Fig. 2 Enrichment of de novo damaging and rare,
inherited LoF variants in ASD cases across gene sets.

Gene sets were defined and grouped by transcriptome proteome,
neuronal regulome, ASD gene prediction scores, genetic evidence from
neuropsychiatric diseases, and gene level constraint. Analyses were
repeated after removing known ASD/NDD genes. Number of genes in each
set before and after removing known genes are shown in bracket below
gene set. Dots represent fold enrichment of DNVs or odds ratio for
over-transmission of LoFs in each set. Horizontal bars indicate 95%
confidence interval. For each gene set, also shown are the percentage
of excessive DNVs in cases and percentage of over-transmission of
rare, inherited LoFs to cases. (A) De novo enrichment analysis was
performed by dnEnrich that conditional on the overall increase in
burden of de novo damaging variants in cases compared with controls (
Methods). P-values were derived from 100,000 random permutations of
de novo damaging variants among all 5,754 constrained genes and
accounts for the tri-nucleotide sequence context and gene length. (B)
Enrichment of rare, inherited LoFs was evaluated by comparing the
transmission and non-transmission of ultra-rare LoFs with pExt > =0.1
in the gene set versus those in all other constrained genes using a
2-by-2 table. P-values were given by chi-squared test.

Extended Data Fig. 3 QQ plot showing the ultra-rare synonymous burden
test among 404 selected genes between SPARK cases and gnomAD controls
for allele frequency<1e-5.

HMCN2 is excluded (not shown) since it has poor coverage in gnomAD.
Panel A shows the cross-ancestry case-control ultra-rare synonymous
burden comparison, while Panel B shows the European-only case-control
ultra-rare synonymous burden comparison. The observed P values for
each gene are sorted from largest to smallest and plotted against
expected values from a theoretical chi-square distribution.

Extended Data Fig. 4 Transmission disequilibrium of exonic or single
gene deletions of ITSN1 (A) and NAV3 (B).

The read depth signal plots show normalized read depth (NDP) of exome
targets used in CNV calling by CLAMMS^84 for ITSN1 (A) and NAV3 (B)
in Family 1-8. NDPs of -0.5, 0 and 0.5 correspond to copy number (CN)
of 1 (deletion), 2 (normal diploid) and 3 (duplication). NDPs of exon
targets within deleted regions are colored red. Dark and gray areas
correspond to 1 and 2 estimated standard deviations of NDP for each
exon target. CN deletions were initially discovered from all
unaffected parents, then subsequently genotyped on Family 1-8. Signal
plots of Family 1-8 are shown with parents appear in the top and
offspring in the bottom. The deletion regions and affected exons of
genes are shown below each plot.

Extended Data Fig. 5 Cumulative distribution of haploid mutation
rates (per generation) of LoF and D-mis (REVEL > = 0.5) variants of
all protein coding genes on autosomes.

Panel A shows the distribution of all autosomal genes vs. known ASD/
NDD genes. Panel B shows all autosomal genes vs. prioritized genes.
Baseline mutation rates were calculated using 7mer sequence context
dependent mutation rates^55. Known ASD/NDD genes tend to have higher
mutation rates than average genes.

Extended Data Fig. 6 Power of case-control association by rare LoFs
variants ('mega-analysis') with sample size equal to current study.

The mega-analysis of current study compares the rate of LoF variants
in 32,024 unrelated ASD cases with population controls with sample
sizes about 76,000~132,000. For power calculation, we assumed that
population controls are infinite so that cumulative allele frequency
are known and presumed to be at equilibrium under selection-mutation
balance for constrained genes (f = m[LoF]/s). Experiment-wide error
rate was set at 9e-6 (0.05 divided by the number of autosomal genes
at gnomAD LOEUF 30%). Power is calculated as a function of relative
risk for ASD (RR) and selection coefficient (s) across different
haploid LoF mutation rates (m[LoF]) using an analytic approximation
by Zuk et al.^41. We only considered selection coefficient between
0.01 and 0.5 and relative risk to ASD between 1 and 20, because genes
with huge effect sizes and larger selection coefficients are expected
to be identified from the enrichment of de novo variants. The
triangular region where s < 0.013RR are left blank because the
parameters in this region are not compatible with the current
estimates of prevalence of ASD (1/54)^71 and sex-averaged reduction
of reproductive fitness (0.71)^72. Five new ASD genes identified in
this study are placed onto the heatmap closest to its LoF mutation
rate. Their positions within heatmaps are taken from point estimates
of using gnomAD exomes (non-neuro subset) as population controls.

Extended Data Fig. 7 Sample sizes required for achieving 90% of
power.

Using the same assumptions and experiment-wide error rate as power
calculation, we calculated required sample size for 90% of power.
Sample size is shown as a factor relative to the current sample size
(32,024) and as a function of relative risk to ASD and selection
coefficient across with different LoF mutation rates. Contours 1, 2
and 5 times of current size are shown as dashed lines. Regions of
parameter space that require over 10 times current sample sizes are
shown in gray.

Supplementary information

Supplementary Information

Supplementary Note, Supplementary Table Legends, Supplementary Figs.
S1-S24, References.

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Tables S1-S19.

Supplementary Data 1

Annotated de novo coding variants in ASD discovery cohorts.

Supplementary Data 2

Annotated de novo coding variants identified in the SPARK replication
cohort.

Supplementary Data 3

Annotated de novo coding variants collected from other NDD studies.

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Cite this article

Zhou, X., Feliciano, P., Shu, C. et al. Integrating de novo and
inherited variants in 42,607 autism cases identifies mutations in new
moderate-risk genes. Nat Genet (2022). https://doi.org/10.1038/
s41588-022-01148-2

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  * Received: 26 September 2021

  * Accepted: 28 June 2022

  * Published: 18 August 2022

  * DOI: https://doi.org/10.1038/s41588-022-01148-2

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