- Open Access
Rare deleterious mutations of HNRNP genes result in shared neurodevelopmental disorders
Genome Medicine volume 13, Article number: 63 (2021)
With the increasing number of genomic sequencing studies, hundreds of genes have been implicated in neurodevelopmental disorders (NDDs). The rate of gene discovery far outpaces our understanding of genotype–phenotype correlations, with clinical characterization remaining a bottleneck for understanding NDDs. Most disease-associated Mendelian genes are members of gene families, and we hypothesize that those with related molecular function share clinical presentations.
We tested our hypothesis by considering gene families that have multiple members with an enrichment of de novo variants among NDDs, as determined by previous meta-analyses. One of these gene families is the heterogeneous nuclear ribonucleoproteins (hnRNPs), which has 33 members, five of which have been recently identified as NDD genes (HNRNPK, HNRNPU, HNRNPH1, HNRNPH2, and HNRNPR) and two of which have significant enrichment in our previous meta-analysis of probands with NDDs (HNRNPU and SYNCRIP). Utilizing protein homology, mutation analyses, gene expression analyses, and phenotypic characterization, we provide evidence for variation in 12 HNRNP genes as candidates for NDDs. Seven are potentially novel while the remaining genes in the family likely do not significantly contribute to NDD risk.
We report 119 new NDD cases (64 de novo variants) through sequencing and international collaborations and combined with published clinical case reports. We consider 235 cases with gene-disruptive single-nucleotide variants or indels and 15 cases with small copy number variants. Three hnRNP-encoding genes reach nominal or exome-wide significance for de novo variant enrichment, while nine are candidates for pathogenic mutations. Comparison of HNRNP gene expression shows a pattern consistent with a role in cerebral cortical development with enriched expression among radial glial progenitors. Clinical assessment of probands (n = 188–221) expands the phenotypes associated with HNRNP rare variants, and phenotypes associated with variation in the HNRNP genes distinguishes them as a subgroup of NDDs.
Overall, our novel approach of exploiting gene families in NDDs identifies new HNRNP-related disorders, expands the phenotypes of known HNRNP-related disorders, strongly implicates disruption of the hnRNPs as a whole in NDDs, and supports that NDD subtypes likely have shared molecular pathogenesis. To date, this is the first study to identify novel genetic disorders based on the presence of disorders in related genes. We also perform the first phenotypic analyses focusing on related genes. Finally, we show that radial glial expression of these genes is likely critical during neurodevelopment. This is important for diagnostics, as well as developing strategies to best study these genes for the development of therapeutics.
Among the hundreds of candidate genes proposed for neurodevelopmental disorders (NDDs), genes involved in RNA metabolic processing and regulation of gene expression have been shown to be enriched for de novo variants (DNVs) among probands with NDDs . RNA processing (splicing, transport, localization, translation, and degradation) is critically important for brain development and function, as neurons are post-mitotic cells dependent on RNA expression, as well as spatiotemporal isoform specificity, for individual growth and functionality . To successfully regulate RNA processing and protein synthesis, over 500 RNA-binding proteins (RBPs) in humans are abundantly and ubiquitously expressed, found primarily in the nucleus . Although ubiquitous, there are tissue-specific changes in alternative splicing from RBPs resulting in cell-specific phenotypes . As RBPs are necessary for many steps of neuronal RNA metabolism, there are multiple opportunities for dysfunction, which is highlighted by the range of neurological phenotypes resulting from variation in RBP-encoding genes, including neurodegenerative diseases, muscular atrophies, and various cancers. RBPs have also been implicated in NDDs, most notably FMR1 in fragile X syndrome.
Here, we hypothesize that variation in gene families with related structure and function in the brain will result in subtypes of NDDs with related pathology. With over 80% of Mendelian disease-associated genes being part of gene families and/or having functionally redundant paralogs, this provides an opportunity to divide many NDD candidate genes into subgroups [5, 6]. In fact, it has recently been shown that DNVs are enriched among a subset of gene families in probands with NDDs . This approach aids in understanding the biological impacts of variation in groups of genes, provides an opportunity for gene discovery, and allows for development of gene/protein family-specific therapeutics impacting a larger number of individuals than when targeting a single gene.
Focusing on RNA processing, we applied this gene family approach to identify gene families implicated in NDDs. Of the four gene families identified from previous meta-analyses of NDD exomes, the heterogeneous nuclear ribonucleoproteins (hnRNPs for proteins, HNRNPs for genes) stand out as strong NDD candidate genes that have yet to be fully investigated, as the fewest members of the family are associated with known disorders (Table 1). The hnRNPs are a large family of RBPs consisting of 33 core and minor members implicated in many steps of RNA processing. Several, primarily through changes in expression or localization, have been associated with neurodegenerative disorders, and, more recently, five (HNRNPH1, HNRNPH2, HNRNPK, HNRNPR, and HNRNPU) have been described as having DNVs among probands with NDDs (Table 2). We have also previously shown that two HNRNPs (HNRNPU and SYNCRIP) have a significant enrichment of DNVs among probands with NDDs, making them very strong candidate genes . Supporting their role in NDDs as a gene family, it is known that hnRNPs function cooperatively and compensatorily, suggesting that disruption among them may result in similar phenotypic consequences. Thus, we hypothesize that there may be additional hnRNPs with shared structure and function that impact neurodevelopment, resulting in shared phenotypes. Overall, the hnRNPs are just one example of how multiple members of a gene family can be involved in related NDDs. Recent data from large-scale sequencing efforts suggest that other gene families (e.g., chromodomain DNA-binding helicase gene family) may benefit from similar coordinated investigations.
Here, through international collaborations, protein, mutation and expression analyses, and an exhaustive literature search, we identify additional HNRNPs with pathogenic likely gene-disrupting (LGD) and severe missense single-nucleotide variants (SNVs), indels, or small copy number variants (CNVs) among probands with NDDs while utilizing novel methods of grouping genetic disorders by gene family instead of broader gene function. We compare findings from a total of 250 probands with NDDs with de novo or likely de novo SNVs/CNVs: 122 novel cases (118 SNVs or small indels and four chromosomal deletions) and 128 published probands (117 SNVs or small indels and 11 CNVs). Of these, 240 probands have variants occurring in 12 HNRNPs where three or more probands have been identified (termed NDD HNRNPs: HNRNPAB, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, HNRNPH3, HNRNPK, HNRNPR, SYNCRIP, HNRNPU, HNRNPUL1, and HNRNPUL2). We establish variation in a subset of the HNRNPs as a risk factor for NDDs and highlight the utility of a gene family-based approach to identify NDD-related disorders.
Identification of candidate NDD HNRNPs
Literature review to determine NDD candidate HNRNPs
First, HNRNPs with published cases were identified in large exome or genome sequencing studies, as identified by denovo-db v1.6 or case reports found by searching PubMed with all known gene aliases (n = 128; Fig. 1, Additional File 1: Table S1) [11,12,13,14,15,16,17,18, 20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61]. Cohorts in denovo-db v1.6 include ASD samples (n = 5886: including the Simons Simplex collection [SSC, n = 2508] and MSSNG [n = 1625]), DD/ID samples (n = 1010), and epilepsy samples (n = 532, including the Epi4K Consortium [n = 264]). Samples were also identified from the Autism Sequencing Consortium (ASC, n = 8157) and the Epilepsy Genetics Initiative (n = 166). We also identified samples in the Deciphering Developmental Disorders (DDD) cohort, described below as we recalled the variants using our own analyses [39, 49, 62, 63]. In total, 51,616 published individuals were screened. Cases had to have a variant in an HNRNP to be included (i.e., not solely changes in gene expression as seen in some neurodegenerative phenotypes). Publications had to include at least one of the disorders under the HPO terms “abnormality of the nervous system physiology” (HP:0012638) and/or “abnormality of the nervous system morphology” (HP:0012639). This includes, but is not limited to, developmental delay/intellectual disability (DD/ID), seizures, and behavioral abnormalities such as autism spectrum disorder (ASD). Note that neurodegenerative phenotypes, such as ALS, were not included as part of this analysis. Clinical information was requested from authors when possible for larger sequencing studies. Duplicates were removed based on sample ID, sharing of multiple variants, and disclosure of duplicates in publications. When possible, samples were excluded if they had a likely causative variant in an NDD candidate gene, as determined by multiple publications [1, 58].
Protein homology analysis to identify additional candidate NDD HNRNPs
To determine if there were a subset of hnRNPs with sequence similarity to known NDD hnRNPs, and thus potential candidate NDD genes, we utilized protein homology analysis (Fig. 2 and Additional File 2: Fig. S1). Protein homology was determined using Clustal Omega with canonical transcript sequences obtained from UniProt and clustering was performed in R (v.3.6.1) using the corrplot package (v.0.84) to identify shared protein homology.
In order to determine if any HNRNPs are dosage sensitive, we intersected the HNRNPs with a list of 58 genomic disorders based on previous CNV morbidity maps and the DECIPHER database (Table 3) [10, 13, 19, 20, 64,65,66,67]. From this, we identified 15 probands with CNVs spanning only an NDD HNRNP gene or neighboring genes not predicted to be haploinsufficient.
Identification of novel probands with HNRNP variants
Probands were collected from multiple cohorts (n = 32,359 probands) (Additional File 1: Table S1). We also queried ClinVar and DECIPHER databases. We identified 122 probands with SNVs, indels, or CNVs impacting 14 HNRNPs (Fig. 3, Additional File 1: Tables S7, S10 and S11).
Families where recontact was possible were invited to participate in a remote comprehensive clinical workup that included diagnostic evaluation, medical history, and neuropsychological assessment. All experiments carried out on these individuals were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national), and proper informed consent was obtained for sequencing, recontact for inheritance testing, and phenotypic workup. When physicians or families could not be contacted, deidentified clinical information was used. Probands were excluded if they reportedly had (1) a causative variant in another NDD-related gene, (2) a known inherited variant in an HNRNP (other than mosaic), (3) a variant observed in gnomAD, (4) a missense variant with a CADD (v1.6, GRCh38) score < 20, (5) consanguineous parents (which was based on clinical history), and/or were (6) born prematurely (prior to 37 weeks), or (7) too young to assess for DD (< 1 year). However, individuals excluded for our HNRNPs cohort were still considered in the total number of individuals for statistical analyses. For probands from large sequencing studies, these criteria were not always available; therefore, a small number of those not meeting our criteria may be included due to limited information.
For our Autism Spectrum/Intellectual Disability (ASID) network (n = 16,294 individuals), six HNRNPs were previously assessed using targeted sequencing with single-molecule molecular inversion probes: HNRNPF, HNRNPK, HNRNPR, SYNCRIP, HNRNPU, and HNRNPUL1, as described by Stessman et al., identifying 14 probands (seven with DNVs) included in the current study [34, 68]. All variants were validated using Sanger sequencing, although not all had parental DNA available for inheritance testing. Information for a subset of these probands are available in denovo-db v1.6.
From Baylor Genetics Laboratory (BGL; n = 9536, including 1744 trios), 34 novel probands are included in the current study (four with DNVs) and four previously published probands with DNVs (for a total of 38 probands) . These probands are all from clinical exome sequencing samples broken down into “neurologic” phenotypes (n = 2364, including 429 trios; developmental delay [DD]/intellectual disability [ID], speech delay, and autism spectrum disorder [ASD]) and “neurologic plus other organ systems” (n = 7172, including 1315 trios). Exome sequencing was performed as described previously, and DNVs were validated by Sanger sequencing . All HNRNPs were queried in the BGL data except HNRNPH2. Phenotypic information was obtained from BGL clinical indications for sequencing and/or from reporting physicians when available.
From the SPARK Consortium, with individuals ascertained for ASD diagnoses, nine probands with DNVs are included in the current study, as well as an additional published proband (for a total of 10 probands) . Probands identified by the SPARK Consortium were sequenced as previously described . SPARK Consortium probands were used in all statistical analyses, although complete phenotypic information was not always available.
Deciphering Developmental Disorders (DDD13K)
Data for 9860 trios from the DDD cohort were obtained and reanalyzed . We applied FreeBayes (version v1.1.0–3-g961e5f3) and GATK (version 3.7) for SNV/indel calling independently in 9308 DDD families where both parents were available (with 28,476 samples including 9860 probands) [70, 71]. BCFtools (version 1.3.1) was used for left-align and normalization post calling . For quality control, we filtered for read depth (DP > 9) for all family members and filtered allele balance (AB > 0.25) and PHRED genotype quality (GQ > 20) filters in probands. Candidate de novo SNVs/indels were only observed in probands but not in parents and were required to be identified by both FreeBayes and GATK. Variants were then annotated using CADD (v1.6, GRCh38).
GeneMatcher and GeneDx were utilized to identify 24 individuals, 21 of whom have DNVs . GeneMatcher requests were made for each HNRNP identified in an NDD cluster (Fig. 2). Probands were sequenced at varying locations, including several by GeneDx, whose protocol was described previously. Deidentified clinical information was obtained from physicians or families were invited for remote clinical workup. These probands were not used in de novo statistical calculations but were considered among phenotypic analyses.
Four probands with DNVs were obtained from the Radboud University Medical Center (RUMC) in the Netherlands. These probands were not used in de novo statistical calculations but were considered among phenotypic analyses.
Large databases were also used to identify novel probands. Each hnRNP-encoding gene was queried in MyGene2 (https://mygene2.org/MyGene2/), a database generated by the University of Washington Center for Mendelian Genomics, identifying two probands (one proband was identified via both MyGene2 and GeneMatcher) . MyGene2 probands were not used for de novo statistical calculations but were considered for phenotypic analyses.
DECIPHER (n > 33,000 individuals; https://decipher.sanger.ac.uk/) and ClinVar (n = 874,088; https://www-ncbi-nlm-nih-gov.offcampus.lib.washington.edu/clinvar/) were queried and only those with phenotypic information available were included in this study, adding 12 (10 with DNVs) and 19 (seven with DNVs) new probands, respectively. Probands were considered novel if they had not been previously published. DECIPHER and ClinVar probands were included in phenotypic analyses but not de novo statistics.
Functional impacts of novel variation in HNRNP genes
Pathogenicity predictions, de novo enrichment, and missense analyses
To further characterize the types of variation among NDD HNRNPs, we compared the probability of pathogenicity of LGD and missense variants using gnomAD (loss-of-function intolerance [pLI] and missense Z-scores; https://gnomad.broadinstitute.org/) and Gene Variation Intolerance Ranking (GeVIR; http://www.gevirank.org/) scores and percentiles, which include loss-of-function observed/expected upper bound fraction (LOEUF) metrics, of NDD HNRNPs to non-NDD HNRNPs using a one-way t-test in GraphPad Prism (v.8.3.0) (Fig. 3 and Additional File 2: Fig. S3, Additional File 1: Table S2) .
For de novo enrichment analyses, 33,688 probands with trio sequencing (n = 36,460 for HNRNPH2 and n = 36,814 for HNRNPU) were used (Additional File 1: Table S6). Only studies with a clear sample size were used to estimate de novo burden (i.e., n > 1, cohort total sizes included in publication). Only genes determined as NDD HNRNPs were analyzed. In total, 78 HNRNP variants were analyzed (Additional File 1: Table S7). Two models, the chimpanzee–human divergence (CH) model and denovolyzeR, were used to assess the excess of de novo LGD and missense variants as previously described (Fig. 4a) [1, 34, 76, 77]. Briefly, the CH model utilizes locus-specific transition/transversion/indel rates and chimpanzee–human coding sequence divergences to estimate the expected number of de novo mutations, while the denovolyzeR utilizes trinucleotide context, exome depth, and divergence between macaque and human over a ± 1 megabase-pair window and includes known mutational biases like CpG hotspots. Each model was corrected for the number of genes included in the model (n = 18,946 for the CH model and n = 19,618 for denovolyzeR) as well as three mutation-type tests per gene and two tests per mutation type (FWER for CH model: q < 4.4 × 10− 7, FWER for denovolyzeR q < 4.24 × 10− 7).
Missense variants were analyzed using two methods: CLUMP (CLUstering by Mutation Position) analysis and MetaDome assessment [78,79,80]. Only genes determined NDD HNRNPs were analyzed. CLUMP assesses the clustering of missense mutations and was performed as previously described, but here using the gnomAD non-neuropsychiatric group as controls (gnomAD n = 114,704; 1958 missense variants; current cohort missense n = 80 variants) (Fig. 4b, Additional File 1:Table S6) . All calculations were performed using R (v.4.0.0) or python (v.3.7.7). Missense variants were also assessed for clustering by phenotype. Mutations were queried in MetaDome to assess for protein domain disruptions (Fig. 5) . LGD variants were also assessed for susceptibility to nonsense mediated decay (NMD) using NMDEscPredictor (https://nmdprediction.shinyapps.io/nmdescpredictor/) and splicing impact using MaxEntScan (Fig. 5, Additional File 1: Table S7) [81, 82].
GTEx and single-cell expression analyses
Transcript-level expression from adult tissues obtained from GTEx (https://gtexportal.org/) for each gene was compared using t-tests between NDD and non-NDD HNRNPs as well as by two-way ANOVA for comparing between individual genes (Fig. 6a, Additional File 2: Figs. S3C,D, Additional File 1: Table S3). We used previously described single-cell RNA (scRNA) sequencing data generated from 48 individuals to assess gene expression during fetal human cortical development, which is available from the UCSC cell browser: https://cells.ucsc.edu/ . To determine cell enrichment, data were tested using a Wilcoxon ranked sum test followed by Bonferroni correction in R, as previously described . Student t tests and two-way ANOVA among all HNRNPs, between NDD and non-NDD HNRNPs, or HNRNPs and genes associated with similarly presenting disorders determined the difference in fold-enrichment using GraphPad Prism (Figs. 6b,c and 8b,c, Additional File 2: Figs. S3A,B, Additional File 1: Tables S4–5). Correlations among scRNA sequencing data were performed in R (v.3.6.1) using the corrplot package (v.0.84). One-way t-tests were performed between each NDD HNRNP group (as in Fig. 2) using GraphPad Prism. HNRNPs were also entered into Specific Expression Analyses (SEA) to determine enrichment of expression across brain regions (Fig. 6d) . Expression among these tissues was compared using Fisher’s exact tests and followed by Benjamini-Hochberg correction.
Phenotypes were defined by Human Phenotype Ontology (HPO) terms when possible. Those that occurred in ≥ 20% of probands in at least one HNRNP cohort were considered characteristic for the disorder and used for statistical analyses. Traits occurring less than that were considered not likely to be syndromic. This resulted in a total of 88 phenotypes. For quantitative measures (e.g., head circumference), qualitative terms were used if provided by a clinician (e.g., microcephalic) or determined based on top/bottom 3rd percentile.
Phenotypic information was compared across HNRNP cohorts using RVAideMemoire (version 0.9–73) pairwise Fisher’s exact tests with Bonferroni correction considering number of genes (n = 12 for all NDD HNRNPs, n = 9 for LGD NDD HNRNPs, n = 10 for missense NDD HNRNPs), number of phenotypes (n = 88), and number of mutation categories (n = 3: all mutations, LGD, and missense) tested, as well as across mutation types (LGD vs. missense) within HNRNP cohorts using Fisher’s exact tests (Fig. 7, Additional File 1: Tables S8–9). All calculations were performed using GraphPad Prism or R. Phenotypes were compared to other known disorders by characteristic HPO terms using PhenPath (http://phenpath.biocomp.unibo.it/phenpath/), which returns disorders and genes with shared HPO terms .
Twelve HNRNPs are NDD candidates based on literature search, protein homology, CNV intersection, and identification of new probands
In order to identify NDD candidate HNRNPs, we initially considered cases with DNVs in the literature. This resulted in 16 genes with at least one published case as potential NDD candidate genes: HNRNPA0, HNRNPA3, HNRNPAB, HNRNPA2B1, HNRNPC, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, PTBP2, HNRNPK, HNRNPLL, SYNCRIP, HNRNPR, HNRNPU, and HNRNPUL1 (n = 117 probands; Fig. 1, Additional File 1: Tables S1, S7, S8, and S11).
To identify other genes in the family that may not have published cases, we considered the extent of shared protein homology among hnRNPs (Fig. 2 and Additional File 2: Fig. S1). Among 23 core hnRNPs and 10 minor hnRNPs, we distinguished 13 protein groups based on protein homology clustering: (1) FUS (Fused in sarcoma, also known as hnRNPP), (2) hnRNPD-related proteins, (3) hnRNPA-related proteins, (4) hnRNPR/SYNCRIP, (5) hnRNPM, (6) RMBX (RNA-binding motif protein X-linked, also known as hnRNPG)-related proteins, (7) hnRNPH/F-related proteins, (8) hnRNPU-related proteins, (9) PTBP (also known as hnRNPI)-related proteins, (10) hnRNPL-related proteins, (11) hnRNPC-related proteins, (12) hnRNPK, and (13) PCBP (also known as hnRNPE)-related proteins. From this, we identified seven additional hnRNPs that, while they do not have published cases with NDDs, cluster with our other candidate hnRNPs: hnRNPCL1, hnRNPCL4, RALY, RALYL, hnRNPH3, hnRNPDL, and hnRNPUL2.
We determined if any HNRNPs are dosage sensitive by intersecting the HNRNPs with a list of 58 genomic disorders based on previous CNV morbidity maps and the DECIPHER database (Table 3) [10, 13, 19, 20, 64,65,66,67]. Of the 33 HNRNPs, six are in regions of known genomic CNV disorders: HNRNPD, HNRNPH1, HNRNPK, HNRNPR, SYNCRIP, and HNRNPU, further supporting their likely role in NDDs. This analysis did not identify any new candidate NDD HNRNPs but did provide additional evidence for their roles in NDD pathogenicity.
Genes were also considered as candidate NDD HNRNPs if we identified variants in our novel cohorts. This resulted in 17 genes with at least one novel case as potential NDD candidate genes: HNRNPA0, HNRNPA1, HNRNPA2B1, HNRNPAB, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, HNRNPH3, HNRNPK, HNRNPLL, PTBP2, SYNCRIP, HNRNPR, HNRNPU, HNRNPUL1, and HNRNPUL2 (n = 118 probands; Fig. 1, Additional File 1: Tables S1, S7, S8, and S11).
Finally, NDD HNRNPs were finalizing by identification of at least three probands in our combined literature and novel cohorts. This resulted in the following being considered NDD HNRNPs: HNRNPAB, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, HNRNPH3, HNRNPK, HNRNPR, SYNCRIP, HNRNPU, HNRNPUL1, and HNRNPUL2.
NDD HNRNPs are more sensitive to mutation than non-NDD HNRNPs
We considered each gene’s tolerance to mutation by assessing gnomAD metrics pLI and missense Z-scores as well as GeVIR scores (Fig. 3 and Additional File 2: Fig. S2, Additional File 1: Table S2) [75, 87]. As a group, pLI scores are significantly higher among NDD HNRNPs compared to non-NDD HNRNPs (p = 0.03, one-way Student’s t test). Similarly, NDD HNRNPs’ missense Z-scores trend higher than non-NDD HNRNPs; GeVIR scores show the same pattern. Specifically, NDD HNRNPs have significantly higher LOEUF autosomal dominant scores (p = 0.03, one-way Student’s t test) and Variation Intolerant Region Loss-of-Function (VIRLoF) scores, which takes both missense and LGD variants into account (p = 0.04, one-way Student’s t test). GeVIR autosomal recessive scores show a similar pattern for LOEUF scores (p = 0.04, one-way Student’s t test), but as all of our variants are heterozygous, this information was not utilized in our study. This suggests that LGD and severe missense variants are more likely to be damaging among NDD HNRNPs compared to the non-NDD HNRNP family members.
Enrichment of de novo HNRNP variants
In total, 225 probands with SNVs or indels in NDD HNRNPs were identified from 83,975 individuals. Inheritance was determined for 73.8% of NDD HNRNP SNV and indel variants (Table 4, Fig. 3, and Additional File 1: Table S7). Of variants with known inheritance, DNVs account for 98.8% of variants, with one mosaic variant inherited from the unaffected father, who also carries the variant mosaically (Proband 124, HNRNPU proband 1) . LGD variants represent 61.8% (n = 139/225 variants, inheritance known for 82.7%; 99.1% de novo) of the NDD HNRNP cohort, while severe missense variants (MIS20: CADD score ≥ 20, being in the top 1% of predicted pathogenic mutations and MIS30: CADD ≥ 30 top 0.1% of predicted pathogenic variants) account for 38.2% of variants (n = 86/225 variants, inheritance known for 59.3%; 98% de novo; MIS20, MIS30, and those without CADD scores available were included). Twelve of the 16 small CNVs have known inheritance status, and all are de novo (Additional File 1: Table S12).
In order to identify genes with a significant excess of DNVs, we examined 41,779 parent–child trios from 13,437 families with DD/ID, 20,542 families with ASD, 1421 families with epilepsy, and 6379 families from clinical exome testing. We applied two statistical models, denovolyzeR and the CH model, to genes that had DNVs from cohorts with known sample size (n = 78 variants, Fig. 4a, Additional File 1: Table S6) [76, 77]. We find that only HNRNPU achieves exome-wide significance (p < 4.24 × 10− 7) for de novo LGD and all protein-impacting variants by both models. SYNCRIP, for all protein-impacting variants and LGD variants, reaches exome-wide significance only by the CH model, while all protein-impacting variants of HNRNPD reach nominal significance (p < 0.05) by the CH model. Only HNRNPU reaches exome-wide significance for an excess of severe de novo missense variation after multiple testing correction (FWER, p < 4.24 × 10− 7), and only by the CH model. Based on these DNV models, only HNRNPU, SYNCRIP, and HNRNPD reach significance for increased number of variants than one would expect.
Missense mutation analyses
As pathogenic missense variants are known to cluster for some proteins, such as hnRNPH2 in the M9 nuclear targeting signal, we assessed whether there was any additional evidence of missense variant clustering across hnRNPs . We examined all MIS20 and MIS30 variants in our cohort (n = 80) and compared them to the non-neuropsychiatric subset in gnomAD using CLUMP (n = 1958) (Fig. 4b, Additional File 1: Table S6) . In order to control for platform differences, variants were only included if exon coverage was at least 20-fold in gnomAD (the first two exons of HNRNPUL1 were excluded). The CLUMP scores for NDD hnRNP missense variants are significantly lower than those of controls from gnomAD (p = 0.03, paired t-test) suggesting clustering of variants. hnRNPH2, hnRNPK, hnRNPR, and hnRNPUL1 independently have significantly more clustering of missense variants than controls (p < 0.05 for hnRNPK and hnRNPUL1, p < 0.01 for hnRNPH2 and hnRNPR). Thus, for multiple hnRNPs, clustering of missense variants likely contributes to pathogenicity, potentially impacting particular protein domains. Notably, this clustering was more significant among particular phenotypes, including ASD (p = 0.005, HNRNPR alone p = 0.007). In hnRNPH2, missense variants cluster significantly among probands with growth delay (p = 0.01), motor delay (p = 0.001), speech delay (p = 0.006), microcephaly (p = 0.01), hypotonia (0.003), seizures (p = 0.01), and cardiac abnormalities (p = 0.02) compared to controls. Cardiac abnormalities also have a clustering of missense variants among HNRNPK probands (p = 0.004). Variants cluster among probands with hypospadias, joint hypermobility, and scoliosis among HNRNPH1 probands (p = 0.04 for each). In addition to ASD, variants in probands with speech delay or microcephaly and HNRNPR variants are clustered (p = 0.005 and p = 0.001, respectively). We also applied MetaDome to identify such domains and as another predictor of pathogenicity. There was no significant difference between variants in known domains and unknown domains. Other than the M9 nuclear localization signal, there was clustering of missense variants in the RNA-binding domain of hnRNPH2. In hnRNPK, 70% of missense variants (n = 7/10) cluster in the KH domains, which are important in RNA binding and recognition. Almost half (42.9%, n = 3/7) of missense variants in SYNCRIP cluster in its RNA recognition motif while de novo missense mutations in hnRNPU cluster in the SPRY domain and AAA domain (n = 3/16 each). Twenty-five percent (2/8) of missense variants in hnRNPUL1 occur in the SAP domain, important for RNA and DNA binding. Thus, RNA recognition and binding are likely responsible for many of the missense variants observed in the hnRNPs.
HNRNP brain expression analyses
In order to understand the pathogenesis of variation in HNRNPs, we explored the expression of the genes in both adult tissues and the developing human cerebral cortex . GTEx analysis of adult tissues shows that the HNRNPs as a whole (n = 33) are expressed across all brain regions, as well as ubiquitously across tissues, with the exception of HNRNPCL1, RALYL (HNRNPCL3), HNRNPCL4, and RBMXL2, which broadly show low levels of expression (Fig. 6a and Additional File 2: Figs. S3C,D). Significant changes in expression are seen based on individual genes and by tissue type (p < 0.0001 for both by two-way ANOVA), with highest expression levels in the brain seen typically in the cerebellum. Between NDD and non-NDD HNRNPs, no significant differences are observed (Additional File 2: Fig. S3). The general high expression of these genes may contribute to phenotypes observed.
scRNA sequencing from the developing cortex confirms widespread expression across brain cells, especially neuronal and neuronal progenitor cell types, suggesting that these genes could play a role in neuronal differentiation (Fig. 5b and Additional File 2: Figs. S4C,D) . We find that HNRNP expression is enriched among radial glia, which act as neural stem cells, but decreased among immature inhibitory and excitatory neurons, although it does vary by HNRNP (p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction; Fig. 6a and Additional File 2: S3C, Additional File 1: Table S4). While significant differences are seen by two-way ANOVA between individual HNRNPs, particularly for PCBP1 (HNRNPI) and HNRNPCL3/RALYL, no significant differences are observed between all NDD and non-NDD HNRNPs (Additional File 2: Figs. S3A,B, Additional File 1: Table S5). It is plausible that there are differences between NDD and non-NDD HNRNP expression that contribute to the development of NDD phenotypes when perturbed.
Specifically, NDD HNRNPs are consistently significantly enriched among dividing radial glia in G2/M phase (all but HNRNPH1; p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction), dividing radial glial cells during S phase (HNRNPK, HNRNPUL1, HNRNPH3, HNRNPF, HNRNPH2, HNRNPR, HNRNPAB, and HNRNPD; p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction), and in the medial ganglionic eminences (MGE), which give rise to major populations of inhibitory neurons of the cortex (HNRNPUL2, HNRNPUL1, HNRNPU, HNRNPH3, HNRNPF, HNRNPH1, HNRNPR, and HNRNPD, p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction), while depleted in newborn neurons in the MGE (HNRNPUL1, HNRNPH3, HNRNPF, HNRNPH2, HNRNPH1, HNRNPR, SYNCRIP, and HNRNPD; p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction) and newborn excitatory neurons (HNRNPK, HNRNPUL1, HNRNPF, HNRNPH1, HNRNPR, HNRNPAB, and HNRNPD; p < 0.05 to p < 0.0001, Wilcoxon ranked sum test, Bonferroni correction). Among individual NDD HNRNPs, no significant differences are observed by two-way ANOVA for the contribution of each gene, highlighting their shared expression.
Hierarchical clustering shows that gene expression in the developing cortex is highly correlated among specific HNRNPs. Strong positive correlations are seen between HNRNPD and HNRNPH3 (Pearson’s R = 0.87, p = 1.9e−15), HNRNPU (Pearson’s R = 0.86, p = 3e−15), HNRNPR (Pearson’s R = 0.83, p = 1.4e−13), and HNRNPK expression patterns (Pearson’s R = 0.8, p = 6.1e−12), HNRNPR and HNRNPK expression patterns (Pearson’s R = 0.83, p = 2.7e−08), HNRNPUL1 and HNRNPH3 expression patterns (Pearson’s R = 0.82, p = 9.5e−13), HNRNPU and HNRNPH3 expression patterns (Pearson’s R = 0.83, p = 4e−13), and HNRNPH3 and HNRNPR (Pearson’s R = 0.83, p = 4.1e−13) and SYNCRIP expression patterns (Pearson’s R = 0.83, p = 1.8e−13). While there are positive correlations between almost all NDD HNRNP gene expression patterns, the differences in expression along with protein homology likely play a role in the phenotypes observed.
Finally, we assessed the NDD HNRNP expression among specific brain regions during development (Fig. 5d). Expression is enriched in the striatum in early fetal development (p = 0.002, Fisher’s exact test, Benjamini-Hochberg correction) and the amygdala (p = 2.6e−4, Fisher’s exact test, Benjamini-Hochberg correction) during early-mid fetal development. HNRNPK, HNRNPR, SYNCRIP, HNRNPF, and HNRNPU are expressed in the striatum while HNRNPH1, HNRNPAB, HNRNPUL1, HNRNPH3, and HNRNPU are expressed in the amygdala. Expression among the striatum and amygdala is likely impacted by variation in these genes, contributing to probands’ phenotypes.
Phenotypic assessment of probands with HNRNP variation
The pathogenesis of variation in the HNRNPs, and most genes in NDDs, has previously been discussed as independent syndromes, as opposed to a spectrum of related syndromes. While there are distinctions between the 12 HNRNPs and their related phenotypes, there is also considerable overlap, which is expected due to the known shared targets and functionality of hnRNPs . Here, we examine the phenotypic similarities and differences among probands with variation in NDD HNRNPs (Fig. 7 and Additional File 2: Figs. S4 and S5, Additional File 1: Table S7).
Our findings support the five reported disorders associated with HNRNPs—Au-Kline syndrome (AKS, HNRNPK), Bain-type ID (HNRNPH2), HNRNPH1-related syndromic ID, HNRNPR-related syndrome, and HNRNPU-related disorder—and we propose seven new disorders [12,13,14,15,16,17,18, 21,22,23, 25,26,27, 30].
Phenotypic comparisons and description
Overall, the phenotypes across the HNRNP-related disorders are significantly highly correlated (p > 0.05 for all pairs of HNRNPs except for HNRNPF likely due to small sample size; Fig. 7a). These correlations are observed when considering mutation type as well (Additional File 2: Fig. S4), which is more critical for missense variation as phenotypes could be due to a variety of molecular changes. Furthermore, this correlation is in line with the degree of protein homology, with those being more similar at a protein level also being more alike at the phenotypic level (Fig. 7b). We caution that ascertainment bias likely contributes to some of this, as the majority of our cohorts have DD/ID and/or ASD, and the availability of phenotypic information varies by gene and clinical referring center.
Neurobehavioral phenotypes have the most overlap among HNRNP genetic disorders (Fig. 7c, d, Additional File 1: Table S7). As expected, the most common phenotype among probands in our cohort is DD/ID (88.9%, n = 192/216, 12/12 disorders). DD/ID varies among disorders, with diagnoses ranging from 44.4 to 100% of probands but occurs at similar rates among LGD and missense probands (Additional File 2: Fig. S5). Delayed speech and language development (57.9%, n = 114/197, 11/12 disorders) is common among all HNRNP genetic disorders except HNRNPF-related disorder, again with similar frequency among both mutation types. Probands with Bain-type ID (HNRNPH2) tend to have regression more often than other HNRNP-related disorders (34.8%, n = 8/23). Seizures are reported in 45.6% of probands (n = 98/215, 10/12 disorders) with variation in HNRNPs, primarily driven by probands with HNRNPU-related disorder (83.1%, n = 69/83), who also have a significantly higher prevalence of seizures compared to other HNRNP-related disorders (p = 0.009 compared to HNRNPD probands, p = 0.009 compared to HNRNPH2 probands, p = 1.89e−10 compared to HNRNPK probands, p = 0.002 compared to SYNCRIP probands; Fig. 7d, Additional File 1: Table S8). Seizures overall are more common among LGD probands, but only with the inclusion of HNRNPU and HNRNPR probands (OR = 2.7, 95% CI 1.5–5.2, Fisher’s exact test).
A range of brain imaging abnormalities are observed among 37% (n = 70/189) of probands, with abnormalities of the corpus callosum (particularly for HNRNPR probands) and cerebellar vermis hypoplasia (particularly among HNRNPH1, HNRNPH2, and HNRNPR probands, although not significantly) being most common. Behavioral diagnoses, including ASD (35.6%, n = 73/205, 10/12 disorders), ADHD (8.7%, n = 17/195, 6/12 disorders), among others, are shared across disorders. While not significantly different, ASD is more common among probands with variation in HNRNPD (46.2%, n = 6/13), HNRNPF (100%, n = 3), HNRNPR (44.4%, n = 4/9), SYNCRIP (57.7%, n = 15/26), and HNRNPUL2 (66.7%, n = 4/6), while ADHD is prevalent among HNRNPR probands (44.4%, n = 4/9) and aggression is common among HNRNPD probands (33.3%, n = 4/12). HNRNPU-related disorder probands with LGD variants also exhibit stereotypy (21.7%, n = 15/69, mostly recurrent hand flapping), as do HNRNPR-related disorder probands (33.3%, n = 3/9). Abnormal movements (22.2%, n = 44/198) are reported in a subset of HNRNP genetic disorders, including abnormal gait among probands with SYNCRIP-related disorder (20%, n = 5/25), dystonia for probands with HNRNPD-related disorder (25%, n = 3/12), and poor gross motor coordination among probands with HNRNPUL2-related disorder (33.3%, n = 2/6).
Physical abnormalities appear to be specific to certain disorders. The exception to this are abnormalities of the hands and feet (36.7%, n = 73/199, 9/12 disorders), including small hands and feet, abnormalities of palmar creases, clinodactyly and brachydactyly (the latter two prevalent among probands with HNRNPR-related syndrome, 77.8%, n = 7/9 and 44.4%, n = 4/9, respectively, clinodactyly significantly more prevalent among HNRNPR probands compared to SYNCRIP [p = 0.02] and HNRNPU probands [p = 0.0004]), among others with less consistency. A subset of probands have growth delay (30.5%, n = 61/200), particularly probands with variation in HNRNPH1 (62.5%, n = 5/8), HNRNPH2 (47.8%, n = 11/23), HNRNPR (90%, n = 9/10, significantly more so than HNRNPK probands [p = 0.03]), and HNRNPUL2 (50%, n = 3/6). Muscular abnormalities are common (46.1%, n = 89/193), with hypotonia (44%) occurring more often than hypertonia (6.2%). Hypertonia is relatively specific to HNRNPUL1 variation (22.2%, n = 2/9). Of eye abnormalities (41.2%, n = 82/199), strabismus is the most common, observed frequently in HNRNPAB-related disorder (66.7%, n = 2/3), HNRNPH1-related syndromic ID (62.5%, n = 5/8), and HNRNPR-related syndrome (55.6%, n = 5/9). Hearing impairment is observed primarily among probands with HNRNPH3 variation (66.7%, n = 2/3), although with only three probands its’ unclear if this is characteristic of a majority of HNRNPH3 variants. Skeletal morphological abnormalities (29.9%, n = 59/197) are primarily observed among probands with AKS (HNRNPK, 63%, n = 17/27 and HNRNPR-related syndrome (70%, n = 7/10). The most common among these probands are abnormalities of curvature of the vertebral column, including scoliosis and kyphosis (HNRNPK: 39.1%, n = 9/23; HNRNPR: 33.3%, n = 3/9). Skeletal physiological abnormalities, including joint hyper-extensibility or laxity, only occur in 27.6% (n = 43/190) of probands over all, but are particularly common among probands with HNRNPD-related disorder (20%, n = 2/10), HNRNPH1-related syndromic ID (50%, n = 4/8), Bain-type ID (39.1%, n = 9/23), AKS (27.6%, n = 8/29), and SYNCRIP-related disorder (26.1%, n = 6/23). Cardiac abnormalities are another known AKS phenotype, occurring in half (50%, n = 15/30) of probands with AKS, but only 19.4% (n = 38/196) of all HNRNP genetic disorder probands . Genitourinary abnormalities (21.5%, n = 43/200) are a known AKS phenotype (63.3%, n = 19/30) and occur significantly more often among probands with AKS (p = 0.02 compared to Bain-type ID, p = 0.0001 compared to HNRNPU-related disorder). The most common genitourinary phenotypes observed in AKS are vesicoureteral reflux (26.7%, n = 8/30) and hydronephrosis (16.7%, n = 5/30). Enuresis is specific to HNRNPD-related disorder (23.1%, n = 3/13), while hypospadias is mostly specific to HNRNPH1-related syndromic ID (40% of missense variants, n = 2/5).
Dysmorphic facies (56.8%, n = 113/199) tend to not be consistent, although a few are seen among multiple disorders, including ear abnormalities (AKS, HNRNPR-related syndrome, HNRNPD-related disorder, and HNRNPU-related disorders), small nasal alae (HNRNPH1-related syndromic ID and AKS), microcephaly (AKS, HNRNPH1-related disorder, HNRNPR-related syndrome, and HNRNPU-related disorder), and round faces (HNRNPD-related disorder, Bain-type ID, and HNRNPR-related syndrome) (Additional File 1: Table S7). HNRNPD-related disorder most consistently results in a round face (23.1%, n = 3/13) and large ears (25%, n = 3/12). HNRNPH1-related syndromic ID probands tend of have hypoplastic alae (25%, n = 2/8). Bain-type ID probands tend to have abnormal palpebral fissures (22.7%, n = 5/22), consistent with the almond-shaped eyes previously reported for this syndrome. AKS dysmorphic features have been previously described and also consist of cleft palate (31%, n = 9/29), downturned corners of the mouth (20.7%, n = 6/29), an open bite (20.7%, n = 6/29), teeth abnormalities (17.2%, n = 5/29), a wide nasal bridge (31%, n = 9/29), shallow orbits/prominent eyes (20%, n = 6/30 each), long palpebral fissures (41.4%, n = 11/26), ptosis (30%, n = 9/30), abnormal eyebrows (20.7%, n = 6/29), metopic ridging (24.1%, n = 7/29), and abnormal nipples (20%, n = 6/30), all of which are more prevalent among probands with LGD variants . HNRNPR probands have a large range of facies as well, including micro/retrognathia (50%, n = 4/8), teeth abnormalities (50%, n = 4/8), anteverted nares (37.5%, n = 3/8), a low hanging columella (25%, n = 2/8), a narrow forehead (37.5%, n = 3/8), up-slanted palpebral fissures (37.5%, n = 3/8), unusual hair (37.5%, n = 3/8), a broad and short neck (22.2%, n = 2/9), and brachycephaly (22.2%, n = 2/9).
Other HNRNP genes
In addition to HNRNPs with multiple cases of disruptive DNVs, we identified seven with less than three cases: HNRNPA0 (n = 2, missense), HNRNPA1 (n = 1, missense), HNRNPA3 (n = 1, missense), HNRNPA2B1 (n = 2, LGD), HNRNPC (n = 2, 1 LGD and 1 missense), HNRNPLL (n = 1, missense), and PTBP2 (n = 1, missense) (Additional File 1: Table S10). Four of these seven (HNRNPA0, HNRNPA3, HNRNPA1, and HNRNPA2B1) are in a group closely related to an NDD hnRNP group (group 2; Fig. 1), suggesting that they may be implicated in NDDs, but mutations occur much less frequently. These were not included in the de novo or phenotypic statistical analyses but may be of importance for future screening of patients for candidate NDD-related genes.
Comparison to similar disorders
It is plausible that similarities observed between HNRNP-related disorders may be due to ascertainment and that our HNRNP-related disorders may actually have more similarities to other known genetic disorders. To address this, we compared each disorder’s characteristic features to those of known NDDs (Fig. 7). HPO terms that occur in 20% or more probands were entered into the PhenPathTOOL and genes sharing the most HPO terms were compared to the expression of the HNRNP (Additional File 1: Table S12) . HPO terms are shared with SLC6A8 (cerebral creatine deficiency syndrome 1), CREBBP (Rubenstein-Taybi syndrome), MECP2 (Rett syndrome, mental retardation, X-linked syndromic, Lubs type), KANSL1 (Koolen-de Vries syndrome), RAI1 (Smith-Magenis syndrome), PTEN, SNRPN, MAP 2K2, FGFR3, KMT2A, MED12, NDN, and NAA10. Notably, AKS has been suggested to be similar to Kabuki syndrome, although they do not share the most HPO terms. Additionally, methylation analyses have shown that these disorders have distinct profiles, suggesting that their shared clinical features are likely not simply due to shared genetic causes (Au et al., ASHG poster 3117). The number of genes with similar phenotypes that overlap between the HNRNPs highlights their shared phenotypic spectra. While there are similarities with other NDDs, correlation of HPO terms present for each disorder is higher among the HNRNP-related disorders, suggesting they may have shared molecular pathogenesis (p = 0.0013, paired t-test, Fig. 8a).
We assessed if shared gene expression is seen among similarly presenting disorders in developing cortical tissues (Fig. 8b, Additional File 2: Fig. S6). While there is a positive correlation (Pearson’s R > 0.5) among almost all genes in our analyses, correlations between genes with shared HPO terms are lower than those among the HNRNPs (Fig. 7c). This suggests that, while there are some shared expression patterns that are likely involved in neurodevelopment broadly, the HNRNP-related genetic disorders are indeed a unique family based on gene expression.
The goal of this study was to investigate genotype–phenotype correlations in the context of a molecularly related gene family. We selected the HNRNPs because they have been implicated in NDDs due to rare missense and LGD variation, as well as neurodegenerative disease and cancers primarily through changes in function, expression, and/or localization (Table 1) [8, 9, 12,13,14, 18, 20,21,22,23, 25, 27, 28, 89,90,91,92,93,94,95,96,97,98,99,100,101,102,103]. In addition, HNRNPs are candidates for haploinsufficiency for multiple contiguous gene deletion syndromes resulting in NDDs (HNRNPK, SYNCRIP, and HNRNPU, and likely HNRNPD and HNRNPR). More recently, SNVs and indels in HNRNPU, HNRNPK, HNRNPH1/2, and HNRNPR have been reported in probands with NDDs, and HNRNPU and SYNCRIP show an excess of DNVs in individuals with NDDs from recent large-scale sequencing studies [11,12,13,14,15,16,17,18, 20,21,22,23, 25,26,27, 35,36,37,38,39, 41, 58, 60, 104,105,106]. Thus, we hypothesized that there are (1) similar phenotypic (clinical and molecular) spectra across these disorders due to shared structure and function and (2) additional HNRNP-related disorders yet to be observed.
From our analyses, we identified 12 HNRNPs as particularly relevant to NDDs, which we term NDD HNRNPs: HNRNPAB, HNRNPD, HNRNPF, HNRNPH1, HNRNPH2, HNRNPH3, HNRNPK, HNRNPR, SYNCRIP, HNRNPU, HNRNPUL1, and HNRNPUL2. Multiple lines of evidence, including protein homology, expression analysis, and mutation intolerance, indicate that genes/proteins within this group are more molecularly related and more sensitive to variation than the other HNRNP family members. In addition to the five HNRNPs previously implicated in NDDs, we report seven new HNRNPs as candidates for emerging disorders and have integrated and compared their phenotypic features (Fig. 7, Additional File 1: Tables S7) [19, 38, 66, 104].
From over 80,000 individuals, we identify 225 probands (115 novel) with likely pathogenic SNVs and indels among the NDD HNRNPs—almost all of which with known inheritance are de novo (Table 2, Fig. 5). This study, thus, increases the sample size for de novo analyses by almost eightfold when compared to previous surveys, identifying a third gene family member, HNRNPD, that achieves nominal significance for an excess of de novo LGD or missense mutations in comparison to chimpanzee– and macaque–human mutation models (Fig. 4a). With a larger sample size, we predict that most of these NDD HNRNPs will ultimately become significantly associated with an excess of DNVs. Due to the limitations of these models and modes of ascertainment, many variants were not considered in de novo analyses (n = 147/225 not in analyses); therefore, we are underestimating the significance of variation in these genes. Overall, in large cohorts evolutionary mutational modeling provides a valuable resource for gene discovery and statistical support for NDD genes but still may miss rare disorders that are observed in the clinic. Additionally, we identified multiple disorders (HNRNPF-, HNRNPH3-, HNRNPUL1-, and HNRNPUL2-related disorders) that were not identified in the clinic or by previous statistical analyses, but by comparison to known NDDs. This highlights the need for intersection between statistical modeling, clinical identification, and novel approaches to identifying NDD candidate genes to uncover the rarest of disorders.
Our results support emerging syndromes associated with seven genes and expand upon known HNRNP-related disorders [10, 19, 38, 66, 104]. We are able to explain some of the phenotypic similarities and differences based on protein homology, gene expression—which is highly correlated among almost all HNRNPs—and known shared function. At cell type resolution, NDD HNRNP expression is most strongly enriched among progenitor populations of the human cerebral cortex and MGE, including mitotic radial glia, and their expression is depleted in both early-born excitatory neurons and newborn MGE neurons (Fig. 6b, Additional File 2: S3A,B). Radial glia, one of the earliest cells found in brain development, act as neural stem cells of the cerebral cortex and are important in the differentiation and migration of many neuronal cell types. Enriched expression of NDD HNRNPs in actively dividing radial glia suggests a possibility that disruption of hnRNPs may lead to aberrant neuronal development. Disruption of radial glia can happen at many steps in early development, including changes in proliferation leading to differing number of neurons, changes in scaffolding leading to impaired neuronal migration, and subtler defects in neurite extension, synaptogenesis, and neuronal connectivity [107,108,109]. These types of changes have been detected in ASD, DD/ID, epilepsy, and schizophrenia. Interestingly, some NDD hnRNPs have already been implicated in cell division through cancer studies, including hnRNPD, hnRNPK, SYNCRIP, and hnRNPU [110,111,112,113,114]. Thus, it is plausible that altered cell division, differentiation, and neuronal migration may be playing a role in the pathogenesis of HNRNP variation. At a brain region level, we see enrichment in the early fetal striatum and the early-mid fetal amygdala, both of which have been implicated in NDDs [1, 115,116,117]. At the adult tissue level, it is clear that HNRNPK has the highest and most ubiquitous expression, which may account for some of the more physical phenotypes observed in probands with AKS. hnRNPR is also known to regulate HOX genes, which likely plays a role in the physical manifestations seen in HNRNPR-related disorder, and dysregulation of HOX genes may be important to other HNRNP-related disorders as well . Overall, we observe that missense variants cluster for all hnRNPs more so among probands than controls, and especially for hnRNPH2, hnRNPK, hnRNPR, and hnRNPUL1, and that particular protein domains appear to be impacted, including RNA- and DNA-binding domains.
Consistent with our ascertainment, DD/ID are the most prevalently shared features among all probands with HNRNP-related disorders, which may be due to ascertainment bias (Fig. 7). Over half have speech delay and over 40% have seizures and/or hypotonia, while over one third each have structural brain abnormalities or ASD. Structural brain abnormalities include abnormalities of the corpus callosum, cerebellar vermis hypoplasia, and dysmorphic ventricles, which may be explained by the high radial glia expression of HNRNPs. Most have some dysmorphic features, and several have a range of physical abnormalities impacting the cardiac, skeletal, and genitourinary systems, although generally these are inconsistent, except for AKS and HNRNPR-related syndrome, which have been previously described (Additional File 1: Table S7) [13, 18].
While there is subjectively a pattern of phenotypes, we also observe a strong correlation between almost all of the HNRNP-related disorders, as hypothesized given they have similar molecular structure and function and shared gene expression (Fig. 7a). Notably, we observe that phenotypic correlations follow a similar pattern to those of protein homology, suggesting that more similar proteins molecularly do have more similar consequences when disrupted (Fig. 7b). HNRNPF-related disorder has the weakest correlations, although this is likely due to (1) lower sample size (n = 3) with limited phenotypic information and (2) a potential association with autism without intellectual disability (n = 1/2 with ASD and no DD/ID) . There are similar patterns in correlations based on LGD and severe missense variation, highlighting the shared impacts of the two types of variants on each gene (Additional File 2: Fig. S4). While we have far fewer probands with missense variation, they typically have a less severe phenotype, and their pathogenic significance remains to be determined given the small sample size.
Overall, our phenotypic analyses of the previously reported HNRNPU-related disorder, AKS, Bain-type ID, HNRNPH1-related syndromic ID, and HNRNPR-related syndrome are consistent with what has been published [12, 13, 18, 20,21,22,23, 25,26,27, 60]. We generally find that the phenotypic spectrum of newly recruited patients matches what has previously been reported for LGD mutations, although the phenotypic consequences of missense variation are less well established. Only a few differences were observed. One is that our data do not support the previous suggestion that probands with HNRNPU-related disorder have renal and cardiac abnormalities, or these phenotypes have much lower penetrance. Another is that ASD has a higher prevalence than previously reported for AKS, particularly for missense variants. We also provide evidence phenotypically and based on gene expression that HNRNPH1-related syndromic ID and Bain-type ID (HNRNPH2) are indeed independent disorders, which has been under debate. Also supporting this are the variant types, which cluster in the M9 nuclear targeting region of hnRNPH2, likely impacting its localization and global translation based on in vitro work, but are seen across the entirety of hnRNPH1 [118, 119].
We report seven novel disorders: SYNCRIP-, HNRNPD-, HNRNPUL1-, HNRNPUL2-, HNRNPAB-, HNRNPF-, and HNRNPAB-related disorders. Both SYNCRIP and HNRNPD have been NDD candidates due to being in the critical region of genomic disorders (Table 3). SYNCRIP, having high protein homology to hnRNPR, has also been observed to have recurrent de novo LGD variants in large NDD sequencing studies [39, 69, 74]. Indeed, we see support of this both phenotypically and statistically, with SYNCRIP LGD variants reaching exome-wide significance by the CH model (Fig. 5). We propose that SYNCRIP-related disorder primarily manifests as DD/ID, ASD, ADHD, hypotonia, speech delay, and structural brain abnormalities (including abnormal/dysmorphic ventricles, abnormalities of the corpus callosum, and Chiari malformations), with some probands having abnormal gait or ataxia, hyper-extensible joints, and hand and feet abnormalities (Additional File 1: Table S7). Many SYNCRIP RNA and protein interactors are critical in neuronal RNA and membrane trafficking, synaptic plasticity, axonogenesis, neuronal morphology, and dendritic translation [120,121,122,123,124,125].
Probands with HNRNPD-related syndrome show a high prevalence of DD/ID, speech delay, and ASD and/or other behavioral phenotypes (Additional File 1: Table S7) . These probands also have consistent facial features of a round face, epicanthus, and large ears. Other emotional disturbances are also noted, including aggression, intermittent explosive disorder, and depression. It has been suggested that the gene may play a role in the development of enkephalinergic neurons, known to be important in neurodevelopment, particularly for emotional responses and ASD, which aligns with more emotional disturbances in HNRND probands [126, 127]. HNRNPD expression is also altered following NMDA receptor stimulation, which may impact the wide range of intracellular cascades, including those involved in cell survival, differentiation, and neuroplasticity.
hnRNPUL1 and hnRNPUL2 have high protein homology to hnRNPU, although they appear to have milder phenotypes when disrupted. HNRNPUL1-related disorder consists of DD/ID, although about half as often as with HNRNPU variation, short stature, motor and speech delay, structural brain, skeletal, and cardiac abnormalities, abnormal gait, ASD, failure to thrive, and hypertonia (Additional File 1: Table S7). Previously, it was suggested that missense variation in HNRNPUL1 contributes to “high-functioning” autism, meaning an autism diagnosis and IQ in the normal range, which our data support . Missense variants cluster significantly among probands compared to controls, although not in a clear protein domain, suggesting that disruption within that part of the protein may be responsible for some phenotypes (Fig. 4). Probands with HNRNPUL2-related disorder exhibit DD/ID, ASD, and motor and speech delay. HNRNPUL2 gene expression has also been shown to be significantly decreased in the blood of first episode psychosis patients, suggesting a neuronal functional role and that we may have under-ascertained individuals with neuropsychiatric phenotypes . Although understudied, both HNRNPUL1 and HNRNPUL2 are known to be critical in the DNA damage response, which has been associated with neurodevelopment [129,130,131,132].
Three of our novel disorders only have three probands each: HNRNPAB-, HNRNPF-, and HNRNPH3-related disorders, making it difficult to draw conclusions. For probands with HNRNPAB variants, DD/ID is common, as are delayed speech and language, ASD, hypotonia, and strabismus. Little is known about hnRNPAB outside of cancer, but it has been shown to regulate expression of neurodevelopmental genes and those involved in glutamate signaling, as well as neural cell motility [133, 134]. Probands with HNRNPH3-related disorder have a high prevalence of DD/ID, like the other HNRNPH disorders, although they manifest characteristic hearing loss as well. However, the gene expression is shared with both other HNRNPH genes, suggesting that their shared phenotypes may have similar molecular causes and that additional probands may reveal a stronger phenotypic relationship. hnRNPF is highly homologous to hnRNPH1/2/3 (Fig. 2, group 7), and like those genes, we identified only reports of severe missense variation. Thus, HNRNPF-related disorder may have more molecular similarities to HNRNPH-related disorders, although with only three probands this is difficult to conclude. All probands have ASD, and half have DD/ID, suggesting that autism with a normal IQ may be an associated phenotype as previously suggested (Table S7) . While little is known about the specific function of hnRNPF, it is has been shown to interact with other hnRNPH proteins and regulate neuronal-specific splicing, as well as myelin basic protein synthesis in oligodendrocytes [135, 136]. The lack of LGD variants in our cohort as well as gnomAD for HNRNPH2, HNRNPH3, and HNRNPF may suggest that loss-of-function mutations are embryonic lethal.
As there are some shared features among NDDs overall, we wanted to ensure that the common phenotypes and expression were specific among our HNRNP-related disorders compared to similarly presenting disorders. While several disorders share multiple HPO terms with our HNRNP-related disorders, the HNRNP-related disorders are more similar to each other than to other NDDs. It is plausible that similarly presenting disorders share disrupted gene regulation with the HNRNP-related disorders, explaining some shared phenotypes. Based on gene expression in the developing fetal cortex, the expression of the HNRNPs are more similar to each other than similarly presenting disorders (Fig. 8c,d). Thus, we conclude that, while of course there are many disorders that may share phenotypes, the HNRNP-related disorders are unique in their presentation and gene expression, as well as protein function, supporting a gene family approach.
Our study highlights the value and challenge of integrating data across multiple centers and the published literature for a group of highly heterogeneous NDDs. A strength is our large sample size of > 80,000 individuals, pulling from published work, our own unpublished resources, and international collaborations, but for rare disorders this is still insufficient. As shown by our de novo mutation analyses, the sample sizes used are not high enough to detect evolutionary evidence that variation in many of these genes is pathogenic, but based on clinical patterns, protein homology, and gene expression, there is reason to conclude pathogenicity. Multiple HNRNPs have been implicated in autism with normal IQ and/or neuropsychiatric disorders that have little effect on IQ, which are severely under-ascertained not only in this study but across the board in NDD research. Additionally, the average age of patients in our study is 10.5 years, suggesting that any phenotypes associated with adults are likely missed. Thus, while much of our data shows that there are not many significant differences among HNRNP-related disorders, this is likely not the full story of these disorders. Much work is needed to establish the molecular phenotypes to fully understand the pathophysiology of these disorders, which will determine if there are shared pathways affected by these disorders and inform potential treatment strategies.
In summary, the hnRNPs are a family of proteins that have been shown repeatedly to impact neurological disease. Here, we establish both a statistical enrichment of DNVs and phenotypic evidence that many of the HNRNPs are involved in NDDs. The conservation, shared expression, and phenotypic consequences clearly support a subset of these as high likelihood candidates of NDD when mutated. More work is necessary to understand the molecular underpinnings of NDD-related HNRNP variation, especially since compounds utilized in diseases such as addiction and cancer are available to target, both up- and downregulating, a subset of hnRNPs [83, 137]. Future work may identify hnRNP-targeting compounds and/or downstream-targeting compounds that could prove to be efficacious in modifying patient outcomes. With the shared function, localization, and binding targets of many of the NDD hnRNPs, it is likely that one compound may benefit probands with changes in related genes. Using the hnRNPs as an example, we have provided an evidence-based foundation supporting that, by identifying relevant NDD gene families and distinguishing molecularly related subgroups, characterization of de novo mutated genes among NDDs can be performed more effectively. This facilitates improved diagnosis and prospective assessment of NDDs as well as potential future development of more impactful therapeutics, while also providing information on neurodevelopment as a whole.
Availability of data and materials
All data from probands generated or analyzed during this study are included in this published article and its supplementary information files. Accession numbers can be found in Additional File 3: Table S13 . All other data is available from previously published resources.
Clustal Omega: http://clustal.org/omega
Ensembl VEP (hg38): http://uswest.ensembl.org/Homo_sapiens/Tools/VEP?db=core
SFARI Base: https://base.sfari.org
UCSC Cell Browser: https://cells.ucsc.edu
Attention deficit hyperactivity disorder
Amyotrophic lateral sclerosis
Analysis of variance
Autism spectrum disorder
Baylor Genetics Laboratory
- CH model:
Chimpanzee–human divergence model
CLUstering by Mutation Position
Copy number variant
Deciphering Developmental Disorders
De novo variant
Frontotemporal lobar degeneration
Gastroesophageal reflux disease
Gene Variation Intolerance Rank
- hnRNP/HNRNP :
Heterogeneous nuclear ribonucleoprotein (hnRNP for protein, HNRNP for gene)
Human Phenotype Ontology
Likely gene disrupting
Missense variant with CADD score ≥ 20
Missense variant with CADD score ≥ 30
Probability of loss of function
Specific Expression Analyses
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GenBank Overview. Available from: https://www.ncbi.nlm.nih.gov/genbank/
We thank all of the families participating in this study. We are grateful to all of the families at the participating SSC sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren, E. Wijsman). We appreciate obtaining access to phenotypic data on SFARI Base for both SSC and SPARK samples, as well as SPARK exome data from the SPARK Consortium. Approved researchers can obtain the SSC population dataset described in this study (https://www.sfari.org/resource/resources/simons-simplex-collection/) by applying at https://base.sfari.org. We thank the DDD study, which presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12 granted by the Republic of Ireland REC). The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. The authors would like to thank the University of Washington Center for Mendelian Genomics and all contributors to MyGene2 for use of data included in MyGene2. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We thank Emily Guilbert for aid in recontacting a family included in this study. We thank Tonia Brown for assistance in editing this manuscript and Amy Wilfert for advice on statistics.
SPARK Consortium: John Acampado 1, Andrea J. Ace 1, Alpha Amatya 1, Irina Astrovskaya 1, Asif Bashar 1, Elizabeth Brooks 1, Martin E. Butler 1, Lindsey A. Cartner 1, Wubin Chin 1, Wendy K. Chung 1,2, Amy M. Daniels 1, Pamela Feliciano 1, Chris Fleisch 1, Swami Ganesan 1, William Jensen 1, Alex E. Lash1, Richard Marini 1, Vincent J. Myers 1, Eirene O’Connor 1, Chris Rigby 1, Beverly E. Robertson1, Neelay Shah 1, Swapnil Shah 1, Emily Singer 1, LeeAnne G. Snyder 1, Alexandra N. Stephens 1, Jennifer Tjernagel 1, Brianna M. Vernoia 1, Natalia Volfovsky 1, Loran Casey White1, Alexander Hsieh 2, Yufeng Shen 2, Xueya Zhou 2, Tychele N. Turner 3, Ethan Bahl 4, Taylor R. Thomas 4, Leo Brueggeman 4, Tanner Koomar 4, Jacob J. Michaelson 4, Brian J. O’Roak 5, Rebecca A. Barnard 5, Richard A. Gibbs 6, Donna Muzny 6, Aniko Sabo 6, Kelli L. Baalman Ahmed 6, Evan E. Eichler 7, Matthew Siegel 8, Leonard Abbeduto 9, David G. Amaral 9, Brittani A. Hilscher 9, Deana Li 9, Kaitlin Smith 9, Samantha Thompson 9, Charles Albright 10, Eric M. Butter 10, Sara Eldred 10, Nathan Hanna 10, Mark Jones 10, Daniel Lee Coury 10, Jessica Scherr 10, Taylor Pifher 10, Erin Roby 10, Brandy Dennis 10, Lorrin Higgins 10, Melissa Brown 10, Michael Alessandri 11, Anibal Gutierrez 11, Melissa N. Hale 11, Lynette M. Herbert 11, Hoa Lam Schneider 11, Giancarla David 11, Robert D. Annett 12, Dustin E. Sarver 12, Ivette Arriaga 13, Alexies Camba 13, Amanda C. Gulsrud 13, Monica Haley 13, James T. McCracken 13, Sophia Sandhu 13, Maira Tafolla 13, Wha S. Yang 13, Laura A. Carpenter 14, Catherine C. Bradley 14, Frampton Gwynette 14, Patricia Manning 15, Rebecca Shaffer 15, Carrie Thomas 15, Raphael A. Bernier 16, Emily A. Fox 16, Jennifer A. Gerdts 16, Micah Pepper 16, Theodore Ho 16, Daniel Cho 16, Joseph Piven 17, Holly Lechniak 18, Latha V. Soorya 18, Rachel Gordon 18, Allison Wainer 18, Lisa Yeh 18, Cesar Ochoa-Lubinoff 19, Nicole Russo 19, Elizabeth Berry-Kravis 20, Stephanie Booker 21, Craig A. Erickson 21, Lisa M. Prock 22, Katherine G. Pawlowski 22, Emily T. Matthews 22, Stephanie J. Brewster 22, Margaret A. Hojlo 22, Evi Abada 22, Elena Lamarche 23, Tianyun Wang 24, Shwetha C. Murali 24, William T. Harvey 24, Hannah E. Kaplan 25, Karen L. Pierce 25, Lindsey DeMarco 26, Susannah Horner 26, Juhi Pandey 26, Samantha Plate 26, Mustafa Sahin 27, Katherine D. Riley 27, Erin Carmody 27, Julia Constantini 7, Amy Esler 28, Ali Fatemi 29, Hanna Hutter 29, Rebecca J. Landa 29, Alexander P. McKenzie 29, Jason Neely 29, Vini Singh 29, Bonnie Van Metre 29, Ericka L. Wodka 29, Eric J. Fombonne 30, Lark Y. Huang-Storms 30, Lillian D. Pacheco 30, Sarah A. Mastel 30, Leigh A. Coppola 30, Sunday Francis 31, Andrea Jarrett 31, Suma Jacob 31, Natasha Lillie 31, Jaclyn Gunderson 31, Dalia Istephanous 31, Laura Simon 31, Ori Wasserberg 31, Angela L. Rachubinski 32, Cordelia R. Rosenberg 32, Stephen M. Kanne 33,34, Amanda D. Shocklee 34, Nicole Takahashi 34, Shelby L. Bridwell 34, Rebecca L. Klimczac 34, Melissa A. Mahurin 34, Hannah E. Cotrell 34, Cortaiga A. Grant 34, Samantha G. Hunter 34, Christa Lese Martin 35, Cora M. Taylor 35, Lauren K. Walsh 35, Katherine A. Dent 35, Andrew Mason 36, Anthony Sziklay 36, Christopher J. Smith 36.
1 Simons Foundation, New York, USA
2 Columbia University, New York, USA
3 Washington University School of Medicine, St. Louis, USA
4 University of Iowa Carver College of Medicine, Iowa City, USA
5 Oregon Health & Science University, Portland, USA
6 Baylor College of Medicine, Houston, USA
7 University of Washington School of Medicine, Howard Hughes Medical Institute, Seattle, USA
8 Maine Medical Center Research Institute, Portland, USA
9 University of California, Davis, Sacramento, USA
10 Nationwide Children’s Hospital, Columbus, USA
11 University of Miami, Coral Gables, USA
12 University of Mississippi Medical Center, Jackson, USA
13 University of California, Los Angeles, Los Angeles, USA
14 Medical University of Southern Carolina (MUSC), Portland, USA
15 Cincinnati Children’s Hospital Medical Center – Research Foundation, Cincinnati, USA
16 Seattle Children’s Autism Center/UW, Seattle, USA
17 University of North Carolina at Chapel Hill, Chapel Hill, USA
18 Department of Child & Adolescent Psychiatry, Rush University Medical Center, Chicago, USA
19 Department of Developmental & Behavioral Pediatrics, Rush University Medical Center, Chicago, USA
20 Department of Neurological Sciences, Department of Pediatrics, Department of Biochemistry, Rush University Medical Center, Chicago, USA
21 Cincinnati Children’s Hospital Medical Center – Research Foundation, Cincinnati, USA
22 Boston Children’s Hospital (BCH), Boston, USA
23 University of North Carolina at Chapel Hill, Chapel Hill, USA
24 University of Washington School of Medicine, Seattle, USA
25 University of California, San Diego, School of Medicine, La Jolla, USA
26 Children’s Hospital of Philadelphia, Philadelphia, USA
27 Boston Children’s Hospital (BCH), Boston, USA
28 University of Minnesota, Minneapolis, USA
29 Kennedy Krieger Institute, Baltimore, USA
30 Oregon Health & Science University, Portland, USA
31 University of Minnesota, Minneapolis, USA
32 University of Colorado School of Medicine, Aurora, USA
33 Department of Health Psychology, University of Missouri, Columbia, USA
34 Thompson Center for Autism and Neurodevelopmental Disorders, University of Missouri, Columbia, USA
35 Geisinger Autism & Developmental Medicine Institute, Lewisburg, USA
36 Southwest Autism Research and Resource Center, Phoenix, USA
This work was supported, in part, by the U.S. National Institutes of Health (R01MH101221) to E.E.E. Research reported in this publication was supported, in part, by the National Institute of Neurological Disorders and Stroke (NINDS) under award number K08NS092898, Jordan’s Guardian Angels, and the Brotman Baty Institute (to G.M.M.). M.I., A.C., and A.S. were supported by the G.E.N.E. (Genomic analysis Evaluation Network) Research Project founded by Progetti di Innovazione in Ambito Sanitario e Socio Sanitario (Bando EX decreto n.2713 28.02.2018) Regione Lombardia. D. L was supported by the German Research Foundation (DFG; LE 4223/1). B.B.A.d.V. and L.E.L.M.V. were supported by grants from the Dutch Organization for Health Research and Development (ZON-MW grants 917–86–319 and 912–12–109). M.E., O.G., and C.R. received funding from the Italian Ministry of Health (Project RC n. 2757328). I.T. is supported by generous donors to the Children’s Mercy Research Institute and the Genomic Answers for Kids program. K.X. is supported by the National Natural Science Foundation of China (NSFC: 8173000779) and the Science and Technology Major Project of Hunan Provincial Science and Technology Department (2018SK1030). M.A.G. was supported by the U.S. National Institutes of Health (T32HG000035). E.E.E. is an investigator of the Howard Hughes Medical Institute.
Ethics approval and consent to participate
Probands or their families were contacted directly and consented by the University of Washington Autism Center under the TIGER study (IRB#: STUDY00000813) to participate in the study. Patient samples were obtained from the ASID network with informed consent, and all clinical recontact performed on the individuals in this study were approved by the University of Washington Institutional Review Board (IRB), in accordance with the ethical standards of the responsible local institutional and national committees.
Consent for publication
Deidentified clinical data (case histories and clinical information) and variant data (exempt from IRB review) for probands or families who could not be contacted was used, and research conformed to the Helsinki Declaration. Probands or their families were contacted directly and consented by the University of Washington Autism Center under the TIGER study (IRB#: STUDY00000813), which includes consent for age and sex to be published.
The Department of Molecular and Human Genetics at Baylor College of Medicine receives revenue from clinical genetic testing conducted at Baylor Genetics Laboratory. E.T. is an employee of GeneDx. The remaining authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Table S1. Cohorts utilized in current study. Table S2. Pathogenicity predictions by gnomAD and GeVIR for each hnRNP. Table S3. Significant p values of two-way ANOVA between cell type and transcripts per million (TPM) for each NDD HNRNP’s GTEx data. Table S4. P values and fold change for scRNA data from developing human cortex. Table S5. Significant p values of two-way ANOVA between cell type and fold expression for each HNRNP. Table S6. De novo enrichment and CLUMP analyses of current cohort. Table S7. Phenotypes among HNRNP-related disorders. Table S8. Uncorrected p values for pairwise Fisher’s exact tests for each phenotype occurring in 20% of an HNRNP group. Table S9. Uncorrected p values for phenotype correlations between each HNRNP-related disorder. Table S10. Variants and phenotypes for HNRNPs with < 3 probands. Table S11. Probands with copy number variants. Table S12. Shared HPO terms with similarly presenting genetic disorders.
Fig. S1. Dendrogram of hnRNPs based on multiple sequence alignment of canonical amino acid sequences. Colors match those seen in Fig. 2. NDD hnRNPs are shown in black boxes. Fig. S2. Pathogenicity assessment of variation in hnRNPs. A) Gene Variation Intolerance Ranking (GeVIR), loss-of-function observed/expected upper bound fraction (LOEUF), and Variation Intolerant Region Loss-of-Function (VIRLoF) percentiles. Average LOEUF percentile is significantly higher for NDD HNRNPs (n = 13) compared to other HNRNPs (n = 15). B) Average fold change for GeVIR, LEOUF, and VIRLoF for autosomal dominant (AD) and autosomal recessive (AR) variants. Average LEOUF fold change for AD mutations is significantly higher for NDD HNRNPs compared to other HNRNPs, with the AD VIRLoF fold change trending in the same direction. The AR LEOUF fold change is trending towards being significantly higher among other HNRNPs compared to NDD HNRNPs. One-way t-test. * p < 0.05. Fig. S3. Expression of HNRNPs among adult tissues and the developing human cortex. A) Heatmap of all HNRNP expression in developing cortex tissues. B) Comparison of fold expression of NDD HNRNPs to non-NDD HNRNPs. C) Heatmap of all HNRNP expression (transcript level expression) in adult brain tissues from GTEx. D) Heatmap of NDD HNRNP expression (transcript level expression) in all tissues from GTEx. Fig. S4. Phenotypic correlations for LGD and missense variant probands. A) Correlation matrix of phenotypes across HNRNP probands with LGD variation (genes with only missense variation excluded) and B) severe missense variation (genes with only LGD variation excluded. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001. Fig. S5. Phenotypic comparisons between LGD and missense variants by HNRNP. Fig. S6. GTEx expression of NDD HNRNPs and genes associated with similarly presenting disorders.
Table S13. GenBank accession numbers.
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Gillentine, M.A., Wang, T., Hoekzema, K. et al. Rare deleterious mutations of HNRNP genes result in shared neurodevelopmental disorders. Genome Med 13, 63 (2021). https://doi.org/10.1186/s13073-021-00870-6
- Neurodevelopmental disorders
- Cortex development
- Gene families