Rebecca L. Margraf

Multiple endocrine neoplasia type 2 RET protooncogene database: Repository of MEN2‐associated RET sequence variation and reference for genotype/phenotype correlations

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 RET exons 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 variations 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. Hum Mutat 0,1–9, 2009.

Germline Mutations in NFKB2 Implicate the Noncanonical NF-κB Pathway in the Pathogenesis of Common Variable Immunodeficiency

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 Combines Multiple Biomedical Ontologies for Accurate Identification of Disease-Causing Alleles in Single Individuals and Small Nuclear Families

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 Phevors 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.

A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data

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.

Closed-tube SNP genotyping without labeled probes: A comparison between unlabeled probe and amplicon melting

Two methods for closed-tube single nucleotide polymorphism (SNP) genotyping without labeled probes have become available: unlabeled probe and amplicon melting. Unlabeled probe and amplicon melting assays were compared using 5 SNPs: human platelet antigens 1, 2, 5, and 15 and a C>T variant located 13910 base pairs (bp) upstream of the lactase gene. LCGreen Plus (Idaho Technology, Salt Lake City, UT) was used as the saturating DNA dye. Unlabeled probe data were readily interpretable and accurate for all amplicon lengths tested. Five targets that ranged in size from 42 to 72 bp were well resolved by amplicon melting on the LightScanner (Idaho Technology) or LightTyper (Roche, Indianapolis, IN) with no errors in genotyping. However, when larger amplicons (206 bp) were used and analyzed on lower resolution instruments (LightTyper and I-Cycler, Bio-Rad, Hercules, CA), the accuracy of amplicon genotyping was only 73% to 77%. When 2 temperature standards were used to bracket the amplicon of interest, the accuracy of amplicon genotyping of SNPs was increased to 100% (LightTyper) and 88% (I-Cycler).

A novel germline PIGA mutation in Ferro-Cerebro-Cutaneous syndrome: A neurodegenerative X-linked epileptic encephalopathy with systemic iron-overload

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.Leu110del (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.

Predicting phenotypic severity of uncertain gene variants in the RET proto-oncogene.

Although reported gene variants in the RET oncogene have been directly associated with multiple endocrine neoplasia type 2 and hereditary medullary thyroid carcinoma, other mutations are classified as variants of uncertain significance (VUS) until the associated clinical phenotype is made clear. Currently, some 46 non-synonymous VUS entries exist in curated archives. In the absence of a gold standard method for predicting phenotype outcomes, this follow up study applies feature selected amino acid physical and chemical properties feeding a Bayes classifier to predict disease association of uncertain gene variants into categories of benign and pathogenic. Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms. Curated outcomes and unpublished RET gene variants with known disease association were used to benchmark predictor performance. Reliable classification of RET uncertain gene variants will augment current clinical information of RET mutations and assist in improving prediction algorithms as knowledge increases.

Rapid diagnosis of MEN2B using unlabeled probe melting analysis and the LightCycler 480 instrument.

Multiple endocrine neoplasia type 2B (MEN2B) is an autosomal dominant, inherited cancer syndrome. MEN2B patients have a high risk of developing medullary thyroid carcinoma, and prophylactic thyroidectomy is recommended by 6 months of age. Genetic testing can identify MEN2B patients before cancer progression. Two RET proto-oncogene mutations, in exon 15 at codon 883 (GCT>TTT) and in exon 16 at codon 918 (ATG>ACG), account for more than 98% of MEN2B cases. An assay using unlabeled probes and the LightCycler 480 instrument was developed to genotype these two common MEN2B RET mutations. Asymmetric polymerase chain reaction was used to increase ssDNA products followed by melting analysis of the unlabeled probe/ssDNA amplicon duplex. The available samples were either patient DNA of known RET genotype or artificial templates. Analysis of the codon 883 heterozygous mutation demonstrated a DeltaT(m) of 5.70 +/- 0.11 degrees C, while the codon 918 heterozygous mutation generated a DeltaT(m) of -5.72 +/- 0.11 degrees C. Samples with the targeted RET mutation genotypes were accurately detected and easily distinguishable from five other reported sequence changes using these probes. Thus, MEN2B diagnosis using unlabeled probes and the LightCycler 480 is a rapid, closed-tube method that is less time consuming and less expensive than sequencing. This assay demonstrates 100% specificity and sensitivity for the identification of RET mutations causative of MEN2B.

Genotyping Hepatitis C Virus by Heteroduplex Mobility Analysis Using Temperature Gradient Capillary Electrophoresis

ABSTRACT The genotype of the infecting hepatitis C virus (HCV) helps determine the patients prognosis and the duration of treatment. Heteroduplex mobility analysis (HMA) is a rapid, inexpensive method for genotyping of HCV that does not require sequencing. We developed an HMA that uses temperature gradient capillary electrophoresis (TGCE) to differentiate HCV genotypes. A 56-bp region of the HCV 5′ untranslated region (UTR) that was conserved within a genotype yet whose sequence differed between genotypes was amplified for HMA-TGCE analysis. HCV amplicons of types 1, 2a, 2b, 3a, 4, and 6a were hybridized in pairs and analyzed by TGCE. Amplicons hybridized to the same subtype yielded one homoduplex peak, while hybridization of different subtypes resulted in heteroduplexes and generated multiple TGCE peaks. Heteroduplexes contain thermodynamically unstable nucleotide mismatches that reduced their TGCE mobilities compared to those of homoduplexes. Three HCV subtypes (subtypes 1a, 3a, and 4) generated unique peak patterns when they were combined with each genotype analyzed and were chosen as the reference genotypes. A blinded study with 200 HCV-infected samples was 97% accurate compared to genotyping by 5′ UTR sequence analysis. The majority of discordant results were unexpected sequence variants; however, five of nine sequence variants were correctly genotyped. The assay also detected and correctly genotyped mixed HCV infections. Compared to conventional HMA, TGCE improves the resolution, with better separation of heteroduplexes and homoduplexes. All common HCV genotypes can be detected and differentiated by this HMA-TGCE assay.

Clinical analysis of genome next-generation sequencing data using the Omicia platform

Aims: Next-generation sequencing is being implemented in the clinical laboratory environment for the purposes of candidate causal variant discovery in patients affected with a variety of genetic disorders. The successful implementation of this technology for diagnosing genetic disorders requires a rapid, user-friendly method to annotate variants and generate short lists of clinically relevant variants of interest. This report describes Omicia’s Opal platform, a new software tool designed for variant discovery and interpretation in a clinical laboratory environment. The software allows clinical scientists to process, analyze, interpret and report on personal genome files. Materials & Methods: To demonstrate the software, the authors describe the interactive use of the system for the rapid discovery of disease-causing variants using three cases. Results & Conclusion: Here, the authors show the features of the Opal system and their use in uncovering variants of clinical significance.