Deciphering Developmental Disorders Research Articles

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10–7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10–3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases

Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3–91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences.

Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering. Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach. HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC. Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders.

Gain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes. Using the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition. We identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation. Our study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.

Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels

BackgroundThe use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied.MethodsWe created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1–5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP.ResultsOur dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81–0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools’ training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64–0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset).ConclusionsAlgorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.

Revisiting benchmark study for response to methodological critiques of 'Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases'.

Evaluation of phenotype-driven gene prioritization approaches for Mendelian diseases could facilitate the software development and method selection for the workflow configuration and clinical practice. In our original article, the performance of 10 well-recognized causal-gene prioritization methods was benchmarked using 305 cases from the deciphering developmental disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results showed that LIRICAL and AMELIE were two of the best methods in our benchmark experiments, and the possible integrative approach of these two methods may enhance the diagnostic efficiency. However, some methodological critiques were raised by the authors of Exomiser and PhenIX, so we revisited our benchmarking studies to answer their comments in this letter.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

Detecting cryptic clinically relevant structural variation in exome-sequencing data increases diagnostic yield for developmental disorders

SummaryStructural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.

CABE-RY: A PAM-flexible dual-mutation base editor for reliable modeling of multi-nucleotide variants

Multi-nucleotide variants (MNVs) represent an important type of genetic variation and have biological and clinical significance. To simulate MNVs, we designed four dual-mutation base editors combining hA3A(Y130F), TadA8e(V106W), and protospacer adjacent motif (PAM)-flexible SpRY and selected cytosine and adenine base editor-SpRY (CABE-RY), which had the best editing performance, for further study. Characterization and comparison showed that CABE-RY had a smaller DNA editing window and lower RNA off-target edits than the corresponding single base editors. Thus, we have established a versatile tool to efficiently simulate MNVs over the genome, which could be very useful for functional studies on MNVs in humans.

Systematic assessment of outcomes following a genetic diagnosis identified through a large-scale research study into developmental disorders

PurposeThe clinical and psychosocial outcomes associated with receiving a genetic diagnosis for developmental disorders are wide-ranging but under-studied. We sought to investigate outcomes from a subset of families who received a diagnosis through the Deciphering Developmental Disorders (DDD) study. MethodsIndividuals recruited through the Peninsula Clinical Genetics Service who received a confirmed genetic diagnosis through the DDD study before August 2019 (n = 112) were included in a clinical audit. Families with no identified clinical outcomes (n = 16) were invited to participate in semistructured telephone interviews. ResultsDisease-specific treatment was identified for 7 probands (6%), while 48 probands (43%) were referred for further investigations or screening and 60 probands (54%) were recruited to further research. Just 5 families (4%) opted for prenatal testing in a subsequent pregnancy, reflecting the relatively advanced maternal age in our cohort, and 42 families (38%) were given disease-specific information or signposting to patient-specific resources such as support groups. Six interviews were performed (response rate = 47%) and thematic analysis identified four major themes: reaching a diagnosis, emotional impact, family implications, and practical issues. ConclusionOur data demonstrate that receiving a genetic diagnosis has substantial positive medical and psychosocial outcomes for the majority of patients and their families.

Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

Clinical genetic testing of protein-coding regions identifies a likely causative variant in only around half of developmental disorder (DD) cases. The contribution of regulatory variation in non-coding regions to rare disease, including DD, remains very poorly understood. We screened 9,858 probands from the Deciphering Developmental Disorders (DDD) study for de novo mutations in the 5' untranslated regions (5' UTRs) of genes within which variants have previously been shown to cause DD through a dominant haploinsufficient mechanism. We identified four single-nucleotide variants and two copy-number variants upstream of MEF2C in a total of ten individual probands. We developed multiple bespoke and orthogonal experimental approaches to demonstrate that these variants cause DD through three distinct loss-of-function mechanisms, disrupting transcription, translation, and/or protein function. These non-coding region variants represent 23% of likely diagnoses identified in MEF2C in the DDD cohort, but these would all be missed in standard clinical genetics approaches. Nonetheless, these variants are readily detectable in exome sequence data, with 30.7% of 5' UTR bases across all genes well covered in the DDD dataset. Our analyses show that non-coding variants upstream of genes within which coding variants are known to cause DD are an important cause of severe disease and demonstrate that analyzing 5' UTRs can increase diagnostic yield. We also show how non-coding variants can help inform both the disease-causing mechanism underlying protein-coding variants and dosage tolerance of the gene.

Evaluating variants classified as pathogenic in ClinVar in the DDD Study

PurposeAutomated variant filtering is an essential part of diagnostic genome-wide sequencing but may generate false negative results. We sought to investigate whether some previously identified pathogenic variants may be being routinely excluded by standard variant filtering pipelines.MethodsWe evaluated variants that were previously classified as pathogenic or likely pathogenic in ClinVar in known developmental disorder genes using exome sequence data from the Deciphering Developmental Disorders (DDD) study.ResultsOf these ClinVar pathogenic variants, 3.6% were identified among 13,462 DDD probands, and 1134/1352 (83.9%) had already been independently communicated to clinicians using DDD variant filtering pipelines as plausibly pathogenic. The remaining 218 variants failed consequence, inheritance, or other automated variant filters. Following clinical review of these additional variants, we were able to identify 112 variants in 107 (0.8%) DDD probands as potential diagnoses.ConclusionLower minor allele frequency (<0.0005%) and higher gold star review status in ClinVar (>1 star) are good predictors of a previously identified variant being plausibly diagnostic for developmental disorders. However, around half of previously identified pathogenic variants excluded by automated variant filtering did not appear to be disease-causing, underlining the continued need for clinical evaluation of candidate variants as part of the diagnostic process.

Increasing the Clinical Psychiatric Knowledge Base About Pathogenic Copy Number Variation.

Specific copy number variants (CNVs) have been robustly associated with intellectual disability, autism, and schizophrenia. Most of the literature focus has been on documenting the existence of these phenomena. There are few data to guide therapeutic choices for these “orphan” diseases. We call for systematic and longitudinal case reports which, if carefully conducted, may provide crucial initial knowledge to guide therapeutics. We provide a step-by-step overview, a tailored set of consensus criteria for high-quality case reports, and a specific set of learning resources.

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a “phenotype first” approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands.

Exome sequencing in patients with antiepileptic drug exposure and complex phenotypes

IntroductionFetal anticonvulsant syndrome (FACS) describes the pattern of physical and developmental problems seen in those children exposed to certain antiepileptic drugs (AEDs) in utero. The diagnosis of FACS is a...

Aberrant Function of the C-Terminal Tail of HIST1H1E Accelerates Cellular Senescence and Causes Premature Aging

Histones mediate dynamic packaging of nuclear DNA in chromatin, a process that is precisely controlled to guarantee efficient compaction of the genome and proper chromosomal segregation during cell division and to accomplish DNA replication, transcription, and repair. Due to the important structural and regulatory roles played by histones, it is not surprising that histone functional dysregulation or aberrant levels of histones can have severe consequences for multiple cellular processes and ultimately might affect development or contribute to cell transformation. Recently, germline frameshift mutations involving the C-terminal tail of HIST1H1E, which is a widely expressed member of the linker histone family and facilitates higher-order chromatin folding, have been causally linked to an as-yet poorly defined syndrome that includes intellectual disability. We report that these mutations result in stable proteins that reside in the nucleus, bind to chromatin, disrupt proper compaction of DNA, and are associated with a specific methylation pattern. Cells expressing these mutant proteins have a dramatically reduced proliferation rate and competence, hardly enter into the S phase, and undergo accelerated senescence. Remarkably, clinical assessment of a relatively large cohort of subjects sharing these mutations revealed a premature aging phenotype as a previously unrecognized feature of the disorder. Our findings identify a direct link between aberrant chromatin remodeling, cellular senescence, and accelerated aging.

MAN1B-CDG: Novel variants with a distinct phenotype and review of literature

BackgroundCongenital disorders of glycosylation (CDG) are a group of rare metabolic diseases due to impaired lipid and protein glycosylation. It comprises a characteristic high frequency of intellectual disability (ID) and a wide range of clinical phenotypes. ObjectiveTo identify the underlying diagnosis in two families each with two siblings with variable level of ID through trio whole exome sequencing. MethodsBoth the families were recruited to the Deciphering Developmental Disorders (DDD) study to identify the aetiology for their ID. Further work-up included isoelectric focusing (IEF) of serum transferrin done to add evidence to the molecular diagnosis. ResultsThese patients were found to have three novel variants in MAN1B1 inherited from their healthy parents. Serum transferrin IEF showed a type 2 pattern. DiscussionMAN1B1 variants were initially described in association with non-syndromic ID; subsequent literature suggested that variants in MAN1B1 resulted in a CDG-type II syndrome. However, there remains a paucity of literature on detailed clinical phenotyping and it still remains a rare form of CDG. The present patients showed the phenotype previously reported in MAN1B1-CDG: a characteristic facial dysmorphism, hypotonia, truncal obesity and in some, behavioural problems. ConclusionsIn unexplained ID, serum transferrin should be included in the first-line screening. With advances in genomic medicine, it is important to diagnose CDG as this has implications for management and recurrence risk counselling.

De novo mutations in HNRNPU result in a neurodevelopmental syndrome.

Exome sequencing in the context of developmental disorders is a useful technique, but variants found need to be interpreted in the context of detailed phenotypic information. Whole gene deletions and loss-of-function-mutations in the HNRNPU gene have been associated with intellectual disability and seizures in some patients. However, a unifying syndromic phenotype has not been previously elucidated. Here, we report a total of seven patients (six patients identified through the Wellcome Trust Deciphering Developmental Disorders study, with one additional patient), who have heterozygous de novo mutations in HNRNPU. These were found via trio-based exome sequencing. All but one of the mutations is predicted to cause loss-of-function. These patients have dysmorphic features in common, including prominent eyebrows, long palpebral fissures, overhanging columella, and thin upper lip. All patients have developmental delay and intellectual disability (ID), ranging from moderate to severe. Seizures are common from early childhood. These initially occur in the context of febrile episodes. This series demonstrates common phenotypic features, including emerging dysmorphism, associated with heterozygous HNRNPU mutations. This allows us to define a novel neurodevelopmental syndrome, with a likely mechanism of haploinsufficiency.

P.294 - An unclassified congenital myopathy diagnosed as Snyder Robinson syndrome identified through the deciphering developmental disorders study

Snyder Robinson syndrome (SRS) (OMIM #309583) is a rare X-linked intellectual disability syndrome characterised by thin habitus, reduced muscle mass, osteoporosis, kyphoscoliosis, facial asymmetry and intellectual disability. The causative gene is the spermine synthase gene (SMS) located on the X chromosome. Boys with SRS have previously been shown to have a decreased spermine to spermidine ratio. Spermine is a polyamine and its reduction is thought to be responsible for the phenotypic effects associated with SRS. Polyamines have essential roles in cell physiology including gene expression, nucleic acid and protein synthesis and protection from oxidative damage. Less than twenty clinical cases of individuals with SRS have been reported previously in the literature all with novel or familial mutations identified. Boys with SRS have a spectrum of severity with inter-familial variability suggesting some genotype phenotype correlation. We report a new case of Snyder-Robinson Syndrome identified through the Deciphering Developmental Disorders (DDD) study with a missense variant (c.335C>T, P112L) in the SMS gene. This variant has been previously studied, but clinical details are not available. Our patient presented at 6 weeks of age with hypotonia, plagiocephaly and facial asymmetry. At 4 years had myopathic face, high arched palate, marked scoliosis, had an accessory nipple, wide-based gait, Gower's negative and flat feet. At 10 years of age he has a severe thoracic scoliosis, has had femoral fractures, focal seizures, and has mild developmental and speech delay. Standard neurological investigations (EEG abnormal, normal NCS, MRI brain and spine)were done. He has had two muscle biopsies with findings suggestive of congenital myopathy or mitochondrial cytopathy. SRS is likely to be underdiagnosed in the neuromuscular cohort. Next generation sequencing studies are anticipated to identify new cases.

Analysis of exome data for 4293 trios suggests GPI-anchor biogenesis defects are a rare cause of developmental disorders

Over 150 different proteins attach to the plasma membrane using glycosylphosphatidylinositol (GPI) anchors. Mutations in 18 genes that encode components of GPI-anchor biogenesis result in a phenotypic spectrum that includes learning disability, epilepsy, microcephaly, congenital malformations and mild dysmorphic features. To determine the incidence of GPI-anchor defects, we analysed the exome data from 4293 parent–child trios recruited to the Deciphering Developmental Disorders (DDD) study. All probands recruited had a neurodevelopmental disorder. We searched for variants in 31 genes linked to GPI-anchor biogenesis and detected rare biallelic variants in PGAP3, PIGN, PIGT (n=2), PIGO and PIGL, providing a likely diagnosis for six families. In five families, the variants were in a compound heterozygous configuration while in a consanguineous Afghani kindred, a homozygous c.709G>C; p.(E237Q) variant in PIGT was identified within 10–12 Mb of autozygosity. Validation and segregation analysis was performed using Sanger sequencing. Across the six families, five siblings were available for testing and in all cases variants co-segregated consistent with them being causative. In four families, abnormal alkaline phosphatase results were observed in the direction expected. FACS analysis of knockout HEK293 cells that had been transfected with wild-type or mutant cDNA constructs demonstrated that the variants in PIGN, PIGT and PIGO all led to reduced activity. Splicing assays, performed using leucocyte RNA, showed that a c.336-2A>G variant in PIGL resulted in exon skipping and p.D113fs*2. Our results strengthen recently reported disease associations, suggest that defective GPI-anchor biogenesis may explain ~0.15% of individuals with developmental disorders and highlight the benefits of data sharing.