Aagenaes syndrome/lymphedema cholestasis syndrome 1 is caused by a founder variant in the 5'-untranslated region of UNC45A.

BACKGROUND & AIMS: Lymphedema cholestasis syndrome 1 or Aagenaes syndrome is a condition characterized by neonatal cholestasis, lymphedema, and giant cell hepatitis. The genetic background of this autosomal recessive disease was unknown up to now. METHODS: A total of 26 patients with Aagenaes syndrome and 17 parents were investigated with whole-genome sequencing and/or Sanger sequencing. PCR and western blot analyses were used to assess levels of mRNA and protein, respectively. CRISPR/Cas9 was used to generate the variant in HEK293T cells. Light microscopy, transmission electron microscopy and immunohistochemistry for biliary transport proteins were performed in liver biopsies. RESULTS: One specific variant (c.-98G>T) in the 5'-untranslated region of Unc-45 myosin chaperone A (UNC45A) was identified in all tested patients with Aagenaes syndrome. Nineteen were homozygous for the c.-98G>T variant and seven were compound heterozygous for the variant in the 5'-untranslated region and an exonic loss-of-function variant in UNC45A. Patients with Aagenaes syndrome exhibited lower expression of UNC45A mRNA and protein than controls, and this was reproduced in a CRISPR/Cas9-created cell model. Liver biopsies from the neonatal period demonstrated cholestasis, paucity of bile ducts and pronounced formation of multinucleated giant cells. Immunohistochemistry revealed mislocalization of the hepatobiliary transport proteins BSEP (bile salt export pump) and MRP2 (multidrug resistance-associated protein 2). CONCLUSIONS: c.-98G>T in the 5'-untranslated region of UNC45A is the causative genetic variant in Aagenaes syndrome. IMPACT AND IMPLICATIONS: The genetic background of Aagenaes syndrome, a disease presenting with cholestasis and lymphedema in childhood, was unknown until now. A variant in the 5'-untranslated region of the Unc-45 myosin chaperone A (UNC45A) was identified in all tested patients with Aagenaes syndrome, providing evidence of the genetic background of the disease. Identification of the genetic background provides a tool for diagnosis of patients with Aagenaes syndrome before lymphedema is evident.

Primary immunodeficiency diseases: Genomic approaches delineate heterogeneous Mendelian disorders.

BACKGROUND: Primary immunodeficiency diseases (PIDDs) are clinically and genetically heterogeneous disorders thus far associated with mutations in more than 300 genes. The clinical phenotypes derived from distinct genotypes can overlap. Genetic etiology can be a prognostic indicator of disease severity and can influence treatment decisions. OBJECTIVE: We sought to investigate the ability of whole-exome screening methods to detect disease-causing variants in patients with PIDDs. METHODS: Patients with PIDDs from 278 families from 22 countries were investigated by using whole-exome sequencing. Computational copy number variant (CNV) prediction pipelines and an exome-tiling chromosomal microarray were also applied to identify intragenic CNVs. Analytic approaches initially focused on 475 known or candidate PIDD genes but were nonexclusive and further tailored based on clinical data, family history, and immunophenotyping. RESULTS: A likely molecular diagnosis was achieved in 110 (40%) unrelated probands. Clinical diagnosis was revised in about half (60/110) and management was directly altered in nearly a quarter (26/110) of families based on molecular findings. Twelve PIDD-causing CNVs were detected, including 7 smaller than 30 Kb that would not have been detected with conventional diagnostic CNV arrays. CONCLUSION: This high-throughput genomic approach enabled detection of disease-related variants in unexpected genes; permitted detection of low-grade constitutional, somatic, and revertant mosaicism; and provided evidence of a mutational burden in mixed PIDD immunophenotypes.

Exome Sequencing Fails to Identify the Genetic Cause of Aicardi Syndrome.

Aicardi syndrome (AS) is a well-characterized neurodevelopmental disorder with an unknown etiology. In this study, we performed whole-exome sequencing in 11 female patients with the diagnosis of AS, in order to identify the disease-causing gene. In particular, we focused on detecting variants in the X chromosome, including the analysis of variants with a low number of sequencing reads, in case of somatic mosaicism. For 2 of the patients, we also sequenced the exome of the parents to search for de novo mutations. We did not identify any genetic variants likely to be damaging. Only one single missense variant was identified by the de novo analyses of the 2 trios, and this was considered benign. The failure to identify a disease gene in this study may be due to technical limitations of our study design, including the possibility that the genetic aberration leading to AS is situated in a non-exonic region or that the mutation is somatic and not detectable by our approach. Alternatively, it is possible that AS is genetically heterogeneous and that 11 patients are not sufficient to reveal the causative genes. Future studies of AS should consider designs where also non-exonic regions are explored and apply a sequencing depth so that also low-grade somatic mosaicism can be detected.

cnvScan: a CNV screening and annotation tool to improve the clinical utility of computational CNV prediction from exome sequencing data.

BACKGROUND: With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies. RESULTS: We have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets. We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families. CONCLUSIONS: In summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data.

Identification of copy number variants from exome sequence data.

BACKGROUND: With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. RESULTS: We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate. CONCLUSIONS: In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1-4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Diagnosis by sequencing: correction of misdiagnosis from FSHD2 to LGMD2A by whole-exome analysis.

We studied and validated facioscapulohumeral muscular dystrophy (FSHD) samples from patients without a D4Z4 contraction (FSHD2 or 'phenotypic FSHD'). For this, we developed non-radioactive protocols to test D4Z4 allele constitution and DNA methylation, and applied these to samples from the Coriell Institute Cell Repository. The D4Z4 sizing showed two related subjects to have classic chromosome 4 contraction-dependent FSHD1. A third sample (GM17726) did not have a short chromosome 4 fragment, and had been assigned as non-4q FSHD (FSHD2). We tested D4Z4 haplotype and methylation for this individual but found both to be inconsistent with this diagnosis. Using exome sequencing, we identified two known pathogenic mutations in CAPN3 (Arg490Gln and Thr184Argfs(*)36), indicating a case of LGMD2A rather than FSHD. Our study shows how a wrong diagnosis can easily be corrected by whole-exome sequencing by constraining the variant analysis to candidate genes after the data have been generated. This new way of 'diagnosis by sequencing' is likely to become common place in genetic diagnostic laboratories. We also publish a digoxigenin-labeled Southern protocol to test D4Z4 methylation. Our data supports hypomethylation as a good epigenetic predictor for FSHD2. The non-radioactive protocol will help to make this assay more accessible to clinical diagnostic laboratories and the wider FSHD research community.