Exome sequencing in multiplex autism families suggests a major role for heterozygous truncating mutations

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Abstract

Autism is a severe neurodevelopmental disorder, the aetiology of which remains mainly unknown. Family and twin studies provide strong evidence that genetic factors have a major role in the aetiology of this disease. Recently, whole exome sequencing (WES) efforts have focused mainly on rare de novo variants in singleton families. Although these studies have provided pioneering insights, de novo variants probably explain only a small proportion of the autism risk variance. In this study, we performed exome sequencing of 10 autism multiplex families with the aim of investigating the role of rare variants that are coinherited in the affected sibs. The pool of variants selected in our study is enriched with genes involved in neuronal functions or previously reported in psychiatric disorders, as shown by Gene Ontology analysis and by browsing the Neurocarta database. Our data suggest that rare truncating heterozygous variants have a predominant role in the aetiology of autism. Using a multiple linear regression model, we found that the burden of truncating mutations correlates with a lower non-verbal intelligence quotient (NVIQ). Also, the number of truncating mutations that were transmitted to the affected sibs was significantly higher (twofold) than those not transmitted. Protein–protein interaction analysis performed with our list of mutated genes revealed that the postsynaptic YWHAZ is the most interconnected node of the network. Among the genes found disrupted in our study, there is evidence suggesting that YWHAZ and also the X-linked DRP2 may be considered as novel autism candidate genes.

Introduction

Autism spectrum disorders (ASDs) represent a group of neurodevelopmental disorders characterized by impairments in reciprocal social interaction, verbal and non-verbal communication and stereotyped behavioural patterns.1 The prevalence of ASD is estimated to be around 0.6%, making it one of the most prevalent disorders in childhood.2 Family and twin studies have provided strong evidence that genetic factors have a major role in the aetiology of ASD: the sibling recurrence risk is 20-fold higher than in the general population,3 and the concordance rate is 80–90% in monozygotic twins versus 10–30% in dizygotic twins.4

Although in recent years several high-throughput genetic studies have identified novel ASD genes in idiopathic autism, the vast majority of the underlying genetic factors remain still unknown. In this regard, no mutation or copy number variant (CNV) found to be associated with autism accounts for more than 1% of cases, which makes the estimation of the number of independent risk loci extremely high.5 Nowadays, whole exome sequencing (WES) represents a powerful technology for identifying rare single-nucleotide variants (SNVs) that may help to depict the complex genetic architecture of autism and unravel some of the missing heritability. Recently, several studies performed WES in parent–child trios, focusing on de novo variants.6, 7, 8, 9, 10 These studies show that de novo mutations have an increased mutation rate in autism and correlate with increased paternal age.7, 9 However, even the high mutation rate observed (approximately one de novo single-nucleotide variant in the exome per autistic child) cannot fully explain the aetiology of the disorder, in which additive effects of several gene mutations are expected under a multihit model of inheritance.11, 12 The exome studies performed to date have focused mainly on de novo variants. Although this approach is useful, it is not sufficient to uncover the complete pool of aetiological variants, the majority of which would be expected to be in the inherited fraction. Several studies in singleton and multiplex families suggest a differential genetic landscape, with families with multiple affected individuals being more likely to bear inherited events than de novo variants.13, 14, 15 According to the multihit model, we would expect mutations in several genes to be at the basis of the disorder. Current data show that most aetiological variants would be heterozygous, whereas homozygous or compound heterozygous mutations would be much less frequent.16, 17, 18

The aim of this study was to uncover new ASD candidate genes by analysing the pool of inherited rare variants in multiplex families. We performed WES on 10 families, considering those rare variants not present in public or in-house databases that were predicted to be damaging and coinherited in the affected siblings.

Materials and methods

ASD patients and phenotype assessment

In our study, we considered 10 multiplex families with ASD: nine with two affected probands and one with three affected probands. Clinical data are summarized in Table 1, and more details are provided in the Supplementary Information.

Table 1 Description of the multiplex ASD families included in our study

CNVs analysis with CytoScan HD Array

Genome-wide DNA copy number analysis was performed in all ASD patients in our study (21 individuals), using the CytoScan HD Array (Affymetrix, Santa Clara, CA, USA). This study was carried out to rule out families with chromosome abnormalities previously reported in autism. Details of this study are summarized in the Supplementary Information.

Exome capture and sequencing

A total of 41 individuals including 21 probands and their parents were processed for exome enrichment using 3 μg of blood genomic DNA. The TruSeq DNA Sample Preparation kit (Illumina, San Diego, CA, USA) and the NimbleGen SeqCap EZ Human Exome Library v.1.0 kit (Roche, Basel, Switzerland), which targets approximately 20 000 genes corresponding to 34 Mb, were used according to the manufacturer’s instructions. The resulting exome libraries were applied to an Illumina flowcell for cluster generation. Paired-end sequencing was performed on a HiSeq2000 instrument (Illumina) using 76-base reads.

Exome data analysis

Reads were aligned to the reference genome (GRCh37) using Burrows–Wheeler analysis with the same option,19 and a BAM file was generated using SAMtools.20 Polymerase chain reaction duplicates were removed using SAMtools and custom scripts, and initial single-nucleotide polymorphisms calling was performed using a combination of SAMtools and Sidrón as described previously.21 Statistics for the callability (percentage of the exome covered by at least 10 reads with mapping quality 30 and base quality 20) of each sample are shown in Supplementary Table 1. On average, individuals had 83.5% of the target covered. Common variants, defined as those present in dbSNP135 with a minor allele frequency 1%, were filtered out.

Variants selection and validation

After the common single-nucleotide polymorphisms were filtered out, we selected all variants transmitted from parents to both affected probands, with the exception of family SJD_50, in which we selected variants transmitted to two out of three or to all three affected probands. All untransmitted variants or variants transmitted to only one affected sib were excluded from the study. In-house and WES false-positive databases were also used to filter the pool of variants.22 Our selection included all truncating mutations (nonsense and indels producing a frame shift), splice sites and missense variants. Missense variants were included in our selection only if they were predicted to be pathogenic by SIFT (Sorting Intolerant From Tolerant) or PolyPhen (Polymorphism Phenotyping).23, 24 Those variants that passed all filtering steps were validated by performing standard polymerase chain reactions and Sanger sequencing. On average, 90.1% of the selected variants were successfully validated. The workflow followed in this study is summarized in Supplementary Figure 1.

Statistical and bioinformatic analyses

We performed Gene Ontology enrichment analysis of our set of selected variants using the FatiGO software available at the Babelomics suite (http://babelomics.bioinfo.cipf.es).25 The database Neurocarta (http://gemma-doc.chibi.ubc.ca/neurocarta/) was used to identify potential gene–phenotype relationships in our gene list. The SFARI database (https://gene.sfari.org/autdb/Welcome.do) was used to obtain an annotated list of genes involved in autism: genes predisposing to autism in the context of syndromic disorder (category S), strong ASD candidate genes (category 2) and ASD genes with suggestive evidence (category 3).

The pool of genes selected in this study was investigated for networking and gene function relationships using Ingenuity Pathway Analysis (IPA) software (www.ingenuity.com). IPA computes a score based on the probability of finding a given pool of genes in a network from the Ingenuity’s Knowledge Database by chance (score=−log (Fisher exact test probability)). IPA was also used to assess ‘disease and disorder’ associations emerging from our pool of genes using the ‘Top Bio Functions’ category.

A multiple linear regression model was considered to assess the relation between the non-verbal intelligence quotient (NVIQ) and the different categories of genetic variants (truncating, non-synonymous damaging, non-synonymous benign, synonymous). The basic regression assumptions (errors are normally and independently distributed random variables) were checked to ensure that the model was appropriate. Following a conservative approach, to detect the regressors (the different variant categories) significantly related to the response NVIQ, the Bonferroni-adjusted procedure was considered. For all regressor variables, the sample Pearson’s correlation coefficient with the response variable and its significance was calculated. Furthermore, a simulation study was performed. By Boostrap resampling, 50 new simulated analyses with a sample size of 100 individuals each were computed and the corresponding multiple linear regression model was checked.

A multiple logistic regression model was used to establish the relation between variants status (the non-synonymous variants transmitted to both affected siblings in a family (TR) and those variants not transmitted from parents (NT)) and SIFT, PolyPhen scores.

For details about the multiple linear regression, model adequacy, Bonferroni correction, simulation study and multiple logistic regression see Supplementary Information.

Furthermore, the [0,1] × [0,1] square determined by the SIFT and PolyPhen scores of TR and NT variants was divided into four quadrants according to the pathogenicity thresholds of 0.05 and 0.5 used for SIFT and PolyPhen, respectively. We compared the proportion of TR and NT variants between the benign (values from 0.05 to 1 for SIFT and 0 to 0.5 for PolyPhen) and pathogenic (values from 0 to 0.05 for SIFT and 0.5 to 1 for PolyPhen) quadrant using the exact binomial test and Fisher’s exact test. The analyses were performed using the R package (http://CRAN.R-project.org).

Results

In this study, we sequenced the exomes of 10 autism multiplex families and validated 220 rare variants transmitted from each parent to at least two affected siblings. All selected variants were filtered for common variants and using in-house databases and were predicted to be pathogenic. The pool of validated variants is listed in Supplementary Table 2. Most (83%) are missense changes, whereas variants predicted to cause protein truncation (nonsense mutations and frameshift indels) accounted for 16% of the total. Variants altering canonical splice sites or start codons were less represented, at 0.5% each (Supplementary Figure 2). All these variants were heterozygous, none of them was homozygous or compound heterozygous, and no gene was found mutated in more than one family. Truncating mutations represent remarkable events in the exome and may pinpoint potential candidate genes for a disease. Interestingly, in our study this kind of mutation represented a substantial proportion of the whole pool of variants, with 20 indels and 16 nonsense mutations (Table 2).

Table 2 Gene-disrupting rare variants shared by the affected sibs in each multiplex family

Chromosomal rearrangements and previously described fully penetrant deletions were ruled out in the affected probands, by karyotyping and through a CNV study. This analysis, followed by experimental validation, revealed six inherited CNVs present in five sib-pairs identifying the following genes: COL4A3-MFF, FHIT, MRPL36-NDUFS6, CTNND2, GRM1 and ASAH1 (Table 3). Structural variants spanning the genes FHIT and CTNND2 had previously been described in autism.14, 26

Table 3 Validated CNVs shared by the affected sibs in each multiplex family

To establish whether the pool of variants selected in our exome study was enriched in potential ASD susceptibility genes, we conducted a Gene Ontology study. Cellular component analysis showed ‘Cell junction’ to be a significantly enriched category after applying multiple testing corrections (P=0.043) (Supplementary Table 3). Several genes in this category such as CYFIP1, SCN1A, DNM2, FLNB, GABRA4, P2RX4, PJA2 and SV2B have been described in autism or are involved in neuronal functions. Several GO biological processes were over-represented in our study and some of them may be relevant for autism, such as ‘Neuronal tube development’ (P=0.044) and ‘Regulation of action potential’ (P=0.044) (Supplementary Table 4). In addition, we used IPA to analyze the ‘Top Bio function’ category of ‘Diseases and disorders’ and found that ‘Developmental disorders’ and ‘Neurological diseases’, among others, were the most significant groups related to the genes in our study (Supplementary Table 5). The Neurocarta database was used to build a map of psychiatric phenotypes previously linked to these genes (Figure 1). The majority of genes were associated with autism, but also with epilepsy, dyslexia, intellectual disability, attention-deficit hyperactivity disorder and schizophrenia.

Figure 1
figure1

Genes identified in our study that have already been associated with autism or with other psychiatric conditions according to Neurocarta (http://gemma-doc.chibi.ubc.ca/neurocarta/). ADHD, attention-deficit hyperactivity disorder; ASD, autism spectrum disorder; EP, epilepsy; SCZ, schizophrenia; DYS, dyslexia; ID, intellectual disability.

PowerPoint slide

We also investigated the possible correlation between the severity of the mutations and intellectual disability, by using the NVIQ. NVIQ was assessed in our sample of 21 affected siblings, with scores ranging from 35 to 139 and a mean of 91 (Supplementary Figure 3). A multiple linear regression model was applied with NVIQ as a response variable and truncating, non-synonymous (damaging and benign changes according to SIFT and PolyPhen) and synonymous variants as regressor variables. The results showed that only truncating variants contribute significantly to NVIQ (P=0.007). Furthermore, truncating variants were correlated with lower NVIQ scores (correlation coefficient r=–0.517, P=0.016; Figure 2) and explained 26% of NVIQ variance in our ASD sample (r2=0.267). A simulation study including 50 new simulated analyses of 100 individuals each obtained the same results as in the original sample (for more details see Supplementary Information, Supplementary Table 7 and Supplementary Figure 8).

Figure 2
figure2

Distribution of the average number of rare variants per proband for each interval of non-verbal intelligence quotient (NVIQ) in four variant categories: (1) truncating mutations, which include nonsense and frameshift mutations; (2) non-syn damaging, non-synonymous mutations predicted to be damaging by SIFT or PolyPhen; (3) non-syn benign, non-synonymous mutations predicted to be benign by SIFT and PolyPhen; and (4) Syn, rare synonymous variants. The pool of rare variants considered are those inherited by two or three affected sibs in a family. Multiple linear regression analysis of these data showed that the number of truncating variants contributes significantly to NVIQ (P=0.007). In contrast, the contribution of the other mutation types was not significant (P>0.385). The correlation coefficients between NVIQ and each mutation type with their significances are also presented.

PowerPoint slide

We subsequently investigated whether truncating mutations might have a major role in autism aetiology by comparing the number of those that were cotransmitted (Table 1) with those that were not transmitted (Supplementary Table 6). We found a significant difference, considering the total number of rare variants, between disrupting mutations that were transmitted to affected probands (16 nonsense variants and 20 frameshift indels) and those not transmitted (9 nonsense variants and 9 frameshift indels) (Fisher’s exact test, P=0.015; Supplementary Figure 4). Subsequently, we investigated the potential contribution to ASD of rare non-synonymous variants predicted to be pathogenic, by comparing the group of transmitted (TR) single-nucleotide variants with those that were not transmitted (NT). A multiple logistic regression analysis (fitted logit model) was performed to investigate whether the scores given by SIFT and PolyPhen were able to discriminate the single-nucleotide variants in relation to their TR or NT status, but the results were not significant (SIFT P=0.122; PolyPhen P=0.811). To further test the possible relation of the non-synonymous variants with the disease, we plotted all missense changes against the PolyPhen scores (X axis) and SIFT scores (Y axis) (Supplementary Figure 5) to determine whether there was any difference in the proportion of variants predicted to be pathogenic (PolyPhen scores>0.5 and SIFT score<0.05) between the TR and NT groups. No significant differences were detected (two-sided exact binomial test, P=0.47).

The list of 220 genes harbouring rare predicted pathogenic mutations (Supplementary Table 2) and genes found in the CNV study (Table 2) were considered together and analysed by IPA to identify canonical networks that may suggest interactions of potential ASD candidate genes. The best scoring network was obtained after considering only direct interactions (score=89) (Figure 3). It is noteworthy that this network included genes previously involved in autism or other psychiatric disorders such as SCN1A, PAX3, KDM5C, TSC1, NF1, CYFIP1, KCNQ3 and LAMA5. The YWHAZ gene was the most interconnected node in this network. Furthermore, when the protein–protein interaction analysis was performed with an additional 43 ASD genes from the SFARI Gene database (Categories S, 2 and 3), the score of this network improved (score=106). YWHAZ was again the main node and interacted directly with TSC1 and CYFIP1, two ASD genes found to be mutated in our study, and also with other ASD genes from the SFARI list such as UBE3A, DISC1, MET and TSC2 (Supplementary Figure 6).

Figure 3
figure3

Protein–protein interaction analysis (Ingenuity Pathway Analysis, IPA) including all genes found to be mutated in our study. Only direct interactions among proteins were considered. Proteins in grey or colour represent genes identified in our study, with red indicating a previous association with autism, green with epilepsy and blue with schizophrenia. Proteins depicted in white are those not present in our study. Upregulatory effects are represented by outward pointing arrows, downregulatory effects are represented by outward ticks, and circular arrows indicate homotypic interactions.

PowerPoint slide

Discussion

Several exome sequencing reports of autism trios have been published in the past few years. These studies have enabled the identification of novel candidate genes for ASD by focusing on de novo variants. Despite these encouraging results, de novo variants represent probably <5% of autism risk variance,8 and hence inherited rare variants may account for a considerable proportion of the missing heritability in autism. Here we present findings from the exome sequencing of 10 multiplex families, in which only the inherited rare variants shared by the affected siblings in a family and predicted to be pathogenic were considered. The resulting list of about 220 identified genetic variants was assessed for GO enrichment analysis and networks of gene interactions (IPA). These approaches identified interesting categories related to developmental disorders or involving neuronal functions, and revealed plausible interactions with previously reported ASD genes. In our study, we identified a substantial number of genes already associated with autism or with other psychiatric conditions, suggesting that there is a common genetic background for psychiatric disorders.27, 28 The most interesting finding emerging from our study suggests a major role for truncating mutations in autism. We found that those probands with a higher number of heterozygous disrupting mutations are those with lower NVIQ scores. In addition, we found more truncating mutations that were transmitted and shared between the affected sibs (36 mutations) than those that were not transmitted (18 mutations). Interestingly, Iossifov et al.9 analysed exome data and described a twofold higher rate of disrupting de novo mutations in affected probands compared with unaffected siblings. Also, a very recent WES study on inherited homozygous or compound heterozygous loss-of-function mutations found a twofold enrichment in autism compared to a control group.18 Such data suggest a genetic model for autism based on the cumulative contribution of truncating alleles and other rare pathogenic variants, coupled with rare structural variants, in which the impact of common variants may be less important than previously thought.29 Most genes with truncating mutations found in our study have an unknown function and none have previously been described in autism. Among these, YWHAZ and DRP2 may be considered as strong novel ASD candidate genes. The NHLBI Exome Sequencing Project (ESP) database (http://evs.gs.washington.edu/EVS/) was used to verify the frequency of disrupting mutations in these genes in about 6500 individuals. No truncating mutations were listed in either YWHAZ or DRP2.

The YWHAZ gene, encoding a postsynaptic protein, is the most intriguing candidate in our study. The protein physically interacts with numerous ASD gene products such as TSC1, TSC2, DISC1, UBE3A and CYFIP1 (Supplementary Figure 6). YWHAZ belongs to the highly conserved 14-3-3 protein family that comprises seven isoforms (β, γ, ɛ, η, ζ, σ, θ) involved in signal transduction by binding specific pSer/pThr motifs. These proteins are involved in a wide range of processes including cell cycle, transcription, neuronal development, migration and neurite outgrowth. Although ubiquitously expressed, expression levels are highest in the brain.30, 31 The members of this family have been associated with several neurodevelopmental disorders, syndromes or psychiatric diseases. Deletions of the contiguous YWHAE and PAFAH1B1 genes are responsible for two distinct Mendelian disorders depending on the size of the deletion: isolated lissencephaly and Miller–Dieker syndrome.32 Duplications encompassing only the YWHAE gene are associated with a distinct phenotype involving autism and other behavioural symptoms.33, 34 This gene has also been strongly associated with schizophrenia and found to be downregulated together with other 14-3-3 isoforms in the prefrontal cortex of schizophrenic patients.35, 36 Other 14-3-3 members have also been associated with psychiatric phenotypes: heterozygous microdeletions encompassing YWHAG and HIP1 were associated with epilepsy, learning difficulties and intellectual disability,37 whereas YWHAH was associated with bipolar disorder.38, 39 Recently, Cheah et al.40 reported that YWHAZ knockout mice show neurobehavioural and cognitive deficiencies, and aberrant development of the hippocampus with migratory defects of pyramidal cells and granular neurons. Furthermore, neuroproteomic studies showed decreased expression of YWHAZ in the brain of schizophrenic patients.41 YWHAZ forms a molecular complex with DISC1 (Disrupted In Schizophrenia 1), Ndel1 and LIS1 to control the development of the hippocampus by coordinating neuronal migration, axonal pathfinding and synapse formation.40 In our study, the 1-bp insertion found in YWHAZ causes a frame shift leading to a premature stop codon after 18 amino acids (Supplementary Figure 7). If nonsense-mediated mRNA decay does not prevent the degradation of this transcript, it is possible that this abnormal protein may act in a dominant-negative manner altering the normal protein interactions or preventing homo- and heterodimerization of the protein with 14-3-3 members.

The X-linked dystrophin-related protein 2 gene (DRP2), expressed mainly in the brain and spinal cord, is also a good ASD candidate. DRP2 forms a complex with periaxin and dystroglycan, regulating the myelination of Schwann cells. Sherman et al.42 showed that loss of DRP2 affects the organization of the Schwann cell cytoplasm in the Cajal bands, although other members of the dystrophin family may partially supply the absence of DRP2. No human disease has been associated with the DRP2 gene, although deletions spanning DRP2 were described in individuals with X-linked agammaglobulinaemia and Mohr–Tranebjaerg syndrome caused by BTK and TIMM8A mutations, respectively. The four patients described with the microdeletion spanning BTK, TIMM8A, TAF7L and DRP2 presented the typical X-linked agammaglobulinaemia and Mohr–Tranebjaerg syndrome phenotypes, but also autism and language delay.43, 44, 45 Interestingly, a recent WES study reported the same mutation (E432*) that we found in DRP2 in one autistic family, which was absent from a large cohort of controls.18

This work represents one of the first comprehensive studies of multiplex families with ASD by exome sequencing. Similar to other studies using WES technologies, known limitations should be considered. First, the coverage of the exome fraction was not complete (83.5% of target sequence covered on average); thus, it is possible that we missed disease-causing mutations. Second, we used stringent criteria to filter false-positives, and as a consequence we may have missed true aetiological variants that were not considered as being pathogenic. Third, we considered only variants shared by affected probands, although aetiological variants transmitted to only one child are also likely to contribute to the disease. Fourth, repetitive elements or variants located on non-coding sequences were not explored in our study. Finally, the sample under study comprises only 10 families.

In conclusion, our data suggest that inherited disrupting mutations in multiplex families may have a major role in the aetiology of ASD. We highlight novel potential ASD candidate genes such as YWHAZ and DRP2. Further WES studies of inherited rare variants in larger ASD samples are warranted to corroborate these results and to gain more insight into the missing heritability of ASD.

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Acknowledgements

We are grateful to all families for their participation in our study. We thank Patricia Romarís (Hospital Universitari Mútua de Terrassa) for contributing to clinical delineation of patients and Lara Nonell and Eulàlia Puigdecanet (Servei d'Anàlisi de Micorarrays, IMIM-Hospital del Mar, Parc de Recerca Biomèdica de Barcelona) for their contribution to the CNV studies. Exome sequencing services were provided by the National Centre for Genomic Analysis (CNAG). CT was supported by the European Union (Marie Curie, PIEF-GA-2009-254930) and BT by AGAUR (FI grant). Financial support was received from ‘Fundació La Marató de TV3’ (092010), ‘Fundación Alicia Koplowitz’, AGAUR (2009SGR00971) and ‘Ministerio de Economía y Competitividad, Spain’ (SAF2012-33484, SAF2010-21165).

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Correspondence to B Cormand.

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Supplementary Information accompanies the paper on the Molecular Psychiatry website

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Keywords

  • autism spectrum disorder
  • exome sequencing
  • multiplex families
  • novel candidate genes
  • rare genetic variants
  • truncating mutations

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