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. We sought to investigate the ability of whole-exome screening methods to detect disease-causing variants in patients with PIDDs. 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. 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. 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.

Cornelia de Lange syndrome (CdLS) is a genetically heterogeneous disorder that presents with extensive phenotypic variability, including facial dysmorphism, developmental delay/intellectual disability (DD/ID), abnormal extremities, and hirsutism. About 65% of patients harbor mutations in genes that encode subunits or regulators of the cohesin complex, including NIPBL, SMC1A, SMC3, RAD21, and HDAC8. Wiedemann-Steiner syndrome (WDSTS), which shares CdLS phenotypic features, is caused by mutations in lysine-specific methyltransferase 2A (KMT2A). Here, we performed whole-exome sequencing (WES) of 2 male siblings clinically diagnosed with WDSTS; this revealed a hemizygous, missense mutation in SMC1A that was predicted to be deleterious. Extensive clinical evaluation and WES of 32 Turkish patients clinically diagnosed with CdLS revealed the presence of a de novo heterozygous nonsense KMT2A mutation in 1 patient without characteristic WDSTS features. We also identified de novo heterozygous mutations in SMC3 or SMC1A that affected RNA splicing in 2 independent patients with combined CdLS and WDSTS features. Furthermore, in families from 2 separate world populations segregating an autosomal-recessive disorder with CdLS-like features, we identified homozygous mutations in TAF6, which encodes a core transcriptional regulatory pathway component. Together, our data, along with recent transcriptome studies, suggest that CdLS and related phenotypes may be “transcriptomopathies” rather than cohesinopathies.

Neurodevelopment is orchestrated by a wide range of genes, and the genetic causes of neurodevelopmental disorders are thus heterogeneous. We applied whole exome sequencing (WES) for molecular diagnosis and in silico analysis to identify novel disease gene candidates in a cohort from Saudi Arabia with primarily Mendelian neurologic diseases. We performed WES in 31 mostly consanguineous Arab families and analyzed both single nucleotide and copy number variants (CNVs) from WES data. Interaction/expression network and pathway analyses, as well as paralog studies were utilized to investigate potential pathogenicity and disease association of novel candidate genes. Additional cases for candidate genes were identified through the clinical WES database at Baylor Miraca Genetics Laboratories and GeneMatcher. We found known pathogenic or novel variants in known disease genes with phenotypic expansion in 6 families, disease-associated CNVs in 2 families, and 12 novel disease gene candidates in 11 families, including KIF5B, GRM7, FOXP4, MLLT1, and KDM2B. Overall, a potential molecular diagnosis was provided by variants in known disease genes in 17 families (54.8 %) and by novel candidate disease genes in an additional 11 families, making the potential molecular diagnostic rate ~90 %. Molecular diagnostic rate from WES is improved by exome-predicted CNVs. Novel candidate disease gene discovery is facilitated by paralog studies and through the use of informatics tools and available databases to identify additional evidence for pathogenicity. Not applicable.

Alu elements, the short interspersed element numbering more than 1 million copies per human genome, can mediate the formation of copy number variants (CNVs) between substrate pairs. These Alu/Alu-mediated rearrangements (AAMRs) can result in pathogenic variants that cause diseases. To investigate the impact of AAMR on gene variation and human health, we first characterized Alus that are involved in mediating CNVs (CNV-Alus) and observed that these Alus tend to be evolutionarily younger. We then computationally generated, with the assistance of a supercomputer, a test data set consisting of 78 million Alu pairs and predicted ∼18% of them are potentially susceptible to AAMR. We further determined the relative risk of AAMR in 12,074 OMIM genes using the count of predicted CNV-Alu pairs and experimentally validated the predictions with 89 samples selected by correlating predicted hotspots with a database of CNVs identified by clinical chromosomal microarrays (CMAs) on the genomes of approximately 54,000 subjects. We fine-mapped 47 duplications, 40 deletions, and two complex rearrangements and examined a total of 52 breakpoint junctions of simple CNVs. Overall, 94% of the candidate breakpoints were at least partially Alu mediated. We successfully predicted all (100%) of Alu pairs that mediated deletions (n = 21) and achieved an 87% positive predictive value overall when including AAMR-generated deletions and duplications. We provided a tool, AluAluCNVpredictor, for assessing AAMR hotspots and their role in human disease. These results demonstrate the utility of our predictive model and provide insights into the genomic features and molecular mechanisms underlying AAMR.