Common variable immunodeficiency (CVID) is a heterogeneous disorder characterized by antibody deficiency, poor humoral response to antigens, and recurrent infections. To investigate the molecular cause of CVID, we carried out exome sequence analysis of a family diagnosed with CVID and identified a heterozygous frameshift mutation, c.2564delA (p.Lys855Serfs(∗)7), in NFKB2 affecting the C terminus of NF-κB2 (also known as p100/p52 or p100/p49). Subsequent screening of NFKB2 in 33 unrelated CVID-affected individuals uncovered a second heterozygous nonsense mutation, c.2557C>T (p.Arg853(∗)), in one simplex case. Affected individuals in both families presented with an unusual combination of childhood-onset hypogammaglobulinemia with recurrent infections, autoimmune features, and adrenal insufficiency. NF-κB2 is the principal protein involved in the noncanonical NF-κB pathway, is evolutionarily conserved, and functions in peripheral lymphoid organ development, B cell development, and antibody production. In addition, Nfkb2 mouse models demonstrate a CVID-like phenotype with hypogammaglobulinemia and poor humoral response to antigens. Immunoblot analysis and immunofluorescence microscopy of transformed B cells from affected individuals show that the NFKB2 mutations affect phosphorylation and proteasomal processing of p100 and, ultimately, p52 nuclear translocation. These findings describe germline mutations in NFKB2 and establish the noncanonical NF-κB signaling pathway as a genetic etiology for this primary immunodeficiency syndrome.

Phevor integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. Phevor works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant-prioritization tools. It does so by using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant-prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single-exome and family-trio-based diagnostic analyses, the most commonly occurring clinical scenarios and ones for which existing personal genome diagnostic tools are most inaccurate and underpowered. Here, we present a series of benchmark analyses illustrating Phevor's performance characteristics. Also presented are three recent Utah Genome Project case studies in which Phevor was used to identify disease-causing alleles. Collectively, these results show that Phevor improves diagnostic accuracy not only for individuals presenting with established disease phenotypes but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases or known disease-causing alleles. As we demonstrate, Phevor can also use latent information in ontologies to discover genes and disease-causing alleles not previously associated with disease.

Multiple endocrine neoplasia type 2 (MEN2) is an inherited, autosomal-dominant disorder caused by deleterious mutations within the RET protooncogene. MEN2 RET mutations are mainly heterozygous, missense sequence changes found in RETexons 10, 11, and 13-16. Our group has developed the publicly available, searchable MEN2 RET database to aid in genotype/phenotype correlations, using Human Genome Variation Society recommendations for sequence variation nomenclature and database content. The MEN2 RET database catalogs all RET sequence variation relevant to the MEN2 syndromes, with associated clinical information. Each database entry lists a RET sequence variation's location within the RET gene, genotype, pathogenicity classification, MEN2 phenotype, first literature reference, and comments (which may contain information on other clinical features, complex genotypes, and additional literature references). The MEN2 phenotype definitions were derived from the International RET Mutation Consortium guidelines for classification of MEN2 disease phenotypes. Although nearly all of the 132 RET sequence variation entries initially cataloged in the database were from literature reports, novel sequence variation and updated phenotypic information for any existing database entry can be submitted electronically on the database website. The database website also contains links to selected MEN2 literature reviews, gene and protein information, and RET reference sequences. The MEN2 RET database (www.arup.utah.edu/database/MEN2/ MEN2_welcome.php) will serve as a repository for MEN2-associated RET sequence variation and reference for RET genotype/MEN2 phenotype correlations.

High-throughput sequencing of related individuals has become an important tool for studying human disease. However, owing to technical complexity and lack of available tools, most pedigree-based sequencing studies rely on an ad hoc combination of suboptimal analyses. Here we present pedigree-VAAST (pVAAST), a disease-gene identification tool designed for high-throughput sequence data in pedigrees. pVAAST uses a sequence-based model to perform variant and gene-based linkage analysis. Linkage information is then combined with functional prediction and rare variant case-control association information in a unified statistical framework. pVAAST outperformed linkage and rare-variant association tests in simulations and identified disease-causing genes from whole-genome sequence data in three human pedigrees with dominant, recessive and de novo inheritance patterns. The approach is robust to incomplete penetrance and locus heterogeneity and is applicable to a wide variety of genetic traits. pVAAST maintains high power across studies of monogenic, high-penetrance phenotypes in a single pedigree to highly polygenic, common phenotypes involving hundreds of pedigrees.

Three related males presented with a newly recognized x-linked syndrome associated with neurodegeneration, cutaneous abnormalities, and systemic iron overload. Linkage studies demonstrated that they shared a haplotype on Xp21.3– Xp22.2 and exome sequencing was used to identify candidate variants. Of the segregating variants, only a PIGA mutation segregated with disease in the family. The c.328_330delCCT PIGA variant predicts, p.Leu 110 del (or c.1030_1032delCTT, p. Leu344del depending on the reference sequence). The unaffected great-grandfather shared his X allele with the proband but he did not have the PIGA mutation, indicating that the mutation arose de novo in his daughter. A single family with a germline PIGA mutation has been reported; affected males had a phenotype characterized by multiple congenital anomalies and severe neurologic impairment resulting in infantile lethality. In contrast, affected boys in the family described here were born without anomalies and were neurologically normal prior to onset of seizures after 6 months of age, with two surviving to the second decade. PIGA encodes an enzyme in the GPI anchor biosynthesis pathway. An affected individual in the family studied here was deficient in GPI anchor proteins on granulocytes but not erythrocytes. In conclusion, the PIGA mutation in this family likely causes a reduction in GPI anchor protein cell surface expression in various cell types, resulting in the observed pleiotropic phenotype involving central nervous system, skin, and iron metabolism.