Multiple endocrine neoplasia type 2 (MEN2) and RET specific modifications of the ACMG/AMP variant classification guidelines and impact on the MEN2 RET database.

The Multiple Endocrine Neoplasia type 2 (MEN2) RET proto-oncogene database, originally published in 2008, is a comprehensive repository of all publicly available RET gene variations associated with MEN2 syndromes. The variant-specific genotype/phenotype information, age of earliest reported medullary thyroid carcinoma (MTC) onset, and relevant references with a brief summary of findings are cataloged. The ACMG/AMP 2015 consensus statement on variant classification was modified specifically for MEN2 syndromes and RET variants using ClinGen sequence variant interpretation working group recommendations and ClinGen expert panel manuscripts, as well as manuscripts from the American Thyroid Association Guidelines Task Force on Medullary Thyroid Carcinoma and other MEN2 RET literature. The classifications for the 166 single unique variants in the MEN2 RET database were reanalyzed using the MEN2 RET specifically modified ACMG/AMP classification guidelines (version 1). Applying these guidelines added two new variant classifications to the database (likely benign and likely pathogenic) and resulted in clinically significant classification changes (e.g., from pathogenic to uncertain) in 15.7% (26/166) of the original variants. Of those clinically significant changes, the highest percentage of changes, 46.2% (12/26), were changes from uncertain to benign or likely benign. The modified ACMG/AMP criteria with MEN2 RET specifications will optimize and standardize RET variant classifications.

A Comprehensive Triple-Repeat Primed PCR and a Long-Range PCR Agarose-Based Assay for Improved Genotyping of Guanine-Adenine-Adenine Repeats in Friedreich Ataxia.

Friedreich ataxia is a rare autosomal recessive, neuromuscular degenerative disease caused by an expansion of a trinucleotide [guanine-adenine-adenine (GAA)] repeat in intron 1 of the FXN gene. It is common in the White population, characterized by progressive gait and limb ataxia, lack of tendon reflexes in the legs, loss of position sense, and hypertrophic cardiomyopathy. Detection and genotyping of the trinucleotide repeat length is important for the diagnosis and prognosis of the disease. A two-tier genotyping assay with an improved triple-repeat primed PCR (TR-PCR) for alleles <200 GAA repeats (±1 to 5 repeats) and an agarose gel-based, long-range PCR (LR-PCR) assay to genotype expanded alleles >200 GAA repeats (±50 repeats) is described. Of the 1236 DNA samples tested using TR-PCR, 31 were identified to have expanded alleles >200 repeats and were reflexed to the LR-PCR procedure for confirmation and quantification. The TR-PCR assay described herein is a diagnostic genotyping assay that reduces the need for further testing. The LR-PCR component is a confirmatory test for true homozygous and heterozygous samples with normal and expanded alleles, as indicated by the TR-PCR assay. The use of this two-tier method offers a comprehensive evaluation to detect and genotype the smallest and largest number of GAA repeats, improving the classification of FXN alleles as normal, mutable normal, borderline, and expanded alleles.

NF1 Somatic Mutation in Dystrophic Scoliosis.

Scoliosis is a common manifestation of neurofibromatosis type 1, causing significant morbidity. The etiology of dystrophic scoliosis in neurofibromatosis type 1 is not fully understood and therapies are lacking. Somatic mutations in NF1 have been shown in tibial pseudarthrosis providing rationale for similar processes in neurofibromatosis type 1-associated dystrophic scoliosis. Spinal samples from surgical procedures with matched peripheral blood of two individuals with neurofibromatosis type 1 and dystrophic scoliosis were obtained and DNA extracted. Next generation sequencing of various spinal sections as well as the germline/blood sample were performed using a RASopathy gene panel (includes the NF1 gene). Variants were compared between the spinal tissue samples and the germline data. In addition, the next generation sequencing allele frequency data were used to detect somatic loss of heterozygosity. All samples had a detected potentially inactivating NF1 germline mutation. Both individuals demonstrated an allelic imbalance inclusive of NF1 in the next generation sequencing data. In addition, for the same two individuals, there was an increase in the % variant reads for the germline mutation in some of the surgical spinal samples corresponding to the allelic imbalance. Contra analysis did not show any deletion in Chromosome 17 next generation sequencing data. Microarray analysis verified somatic copy neutral loss of heterozygosity for these two individuals for the majority of the chromosome 17 q-arm, inclusive of the NF1 gene. These results suggest that the cause of dystrophic scoliosis is multifactorial and that a somatic NF1 mutation contributes to the etiology.

Novel PLP1 Mutations Identified With Next-Generation Sequencing Expand the Spectrum of PLP1-Associated Leukodystrophy Clinical Phenotypes.

Next-generation sequencing was performed for 2 families with an undiagnosed neurologic disease. Analysis revealed X-linked mutations in the proteolipid protein 1 (PLP1) gene, which is associated with X-linked Pelizaeus-Merzbacher disease and Spastic Paraplegia type 2. In family A, the novel PLP1 missense mutation c.617T>A (p.M206K) was hemizygous in the 2 affected male children and heterozygous in the mother. In family B, the novel de novoPLP1 frameshift mutation c.359_369del (p.G120fs) was hemizygous in the affected male child. Although PLP1 mutations have been reported to cause an increasingly wide range of phenotypes inclusive of the dystonia, spastic paraparesis, motor neuronopathy, and leukodystrophy observed in our patients, atypical features included the cerebrospinal fluid deficiency of neurotransmitter and pterin metabolites and the delayed appearance of myelin abnormalities on neuroimaging studies. Next-generation sequencing studies provided a diagnosis for these families with complex leukodystrophy disease phenotypes, which expanded the spectrum of PLP1-associated leukodystrophy clinical phenotypes.

Utilization of Whole-Exome Next-Generation Sequencing Variant Read Frequency for Detection of Lesion-Specific, Somatic Loss of Heterozygosity in a Neurofibromatosis Type 1 Cohort with Tibial Pseudarthrosis.

A subset of neurofibromatosis type 1 patients develop tibial dysplasia, which can lead to pseudarthrosis. The tissue from the tibial pseudarthrosis region commonly has a somatic second hit in NF1: single-nucleotide variants, small deletions, or loss of heterozygosity (LOH). We used exome next-generation sequencing (NGS) variant frequency data (allelic imbalance analysis) to detect somatic LOH in pseudarthrosis tissue from three individuals with clinically and diagnostically confirmed neurofibromatosis type 1, and verified the results with microarray. The variant files were parsed and plotted using python scripts, and the NGS variant frequencies between the affected tissue and blood sample were compared. Individuals without somatic single-nucleotide variants or small insertions/deletions were tested for somatic LOH using the NGS variant allele frequencies. One individual's NGS data indicated no LOH in chromosome 17. The other two individuals demonstrated somatic LOH inclusive of NF1: one had an LOH region of approximately one million bases and Contra (NGS copy number program) indicated a somatic deletion and the other individual had LOH for most of chromosome 17q and Contra indicated no copy number change (microarray data verified this sample as copy neutral somatic LOH). Both LOH and copy number variation detected by NGS data correlated with microarray data, demonstrating the somatic LOH second hit can be detected directly from the NGS data.

Evaluation of somatic mutations in tibial pseudarthrosis samples in neurofibromatosis type 1.

BACKGROUND: Tibial pseudarthrosis is associated with neurofibromatosis type 1 (NF1) and there is wide clinical variability of the tibial dysplasia in NF1, suggesting the possibility of genetic modifiers. Double inactivation of NF1 is postulated to be necessary for the development of tibial pseudarthrosis, but tissue or cell of origin of the 'second hit' mutation remains unclear. METHODS: Exome sequencing of different sections of surgically resected NF1 tibial pseudarthrosis tissue was performed and compared to germline (peripheral blood). RESULTS: A germline NF1 splice site mutation (c.61-2A>T, p.L21 M68del) was identified from DNA extracted from peripheral blood. Exome sequencing of DNA extracted from tissue removed during surgery of the tibial pseudarthrosis showed a somatic mutation of NF1 (c.3574G>T, p.E1192*) in the normal germline allele. Further analysis of different regions of the tibial pseudarthrosis sample showed enrichment of the somatic mutation in the soft tissue within the pseudarthrosis site and absence of the somatic mutation in cortical bone. In addition, a germline variant in PTPN11 (c.1658C>T, p.T553M), a gene involved in the RAS signal transduction pathway was identified, although the clinical significance is unknown. CONCLUSIONS: Given that the NF1 somatic mutation was primarily detected in the proliferative soft tissue at the pseudarthrosis site, it is likely that the second hit occurred in mesenchymal progenitors from the periosteum. These results are consistent with a defect of differentiation, which may explain why the mutation is found in proliferative cells and not within cortical bone tissue, as the latter by definition contains mostly mature differentiated osteoblasts and osteocytes.

Neurofibromin deficiency-associated transcriptional dysregulation suggests a novel therapy for tibial pseudoarthrosis in NF1.

Neurofibromatosis type 1 (NF1) is an autosomal dominant disease caused by mutations in NF1. Among the earliest manifestations is tibial pseudoarthrosis and persistent nonunion after fracture. To further understand the pathogenesis of pseudoarthrosis and the underlying bone remodeling defect, pseudoarthrosis tissue and cells cultured from surgically resected pseudoarthrosis tissue from NF1 individuals were analyzed using whole-exome and whole-transcriptome sequencing as well as genomewide microarray analysis. Genomewide analysis identified multiple genetic mechanisms resulting in somatic biallelic NF1 inactivation; no other genes with recurring somatic mutations were identified. Gene expression profiling identified dysregulated pathways associated with neurofibromin deficiency, including phosphoinositide 3-kinase (PI3K) and mitogen-activated protein kinase (MAPK) signaling pathways. Unlike aggressive NF1-associated malignancies, tibial pseudoarthrosis tissue does not harbor a high frequency of somatic mutations in oncogenes or other tumor-suppressor genes, such as p53. However, gene expression profiling indicates that pseudoarthrosis tissue has a tumor-promoting transcriptional pattern, despite lacking tumorigenic somatic mutations. Significant overexpression of specific cancer-associated genes in pseudoarthrosis highlights a potential for receptor tyrosine kinase inhibitors to target neurofibromin-deficient pseudoarthrosis and promote proper bone remodeling and fracture healing.

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.

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 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.

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.

VarBin, a novel method for classifying true and false positive variants in NGS data.

BACKGROUND: Variant discovery for rare genetic diseases using Illumina genome or exome sequencing involves screening of up to millions of variants to find only the one or few causative variant(s). Sequencing or alignment errors create "false positive" variants, which are often retained in the variant screening process. Methods to remove false positive variants often retain many false positive variants. This report presents VarBin, a method to prioritize variants based on a false positive variant likelihood prediction. METHODS: VarBin uses the Genome Analysis Toolkit variant calling software to calculate the variant-to-wild type genotype likelihood ratio at each variant change and position divided by read depth. The resulting Phred-scaled, likelihood-ratio by depth (PLRD) was used to segregate variants into 4 Bins with Bin 1 variants most likely true and Bin 4 most likely false positive. PLRD values were calculated for a proband of interest and 41 additional Illumina HiSeq, exome and whole genome samples (proband's family or unrelated samples). At variant sites without apparent sequencing or alignment error, wild type/non-variant calls cluster near -3 PLRD and variant calls typically cluster above 10 PLRD. Sites with systematic variant calling problems (evident by variant quality scores and biases as well as displayed on the iGV viewer) tend to have higher and more variable wild type/non-variant PLRD values. Depending on the separation of a proband's variant PLRD value from the cluster of wild type/non-variant PLRD values for background samples at the same variant change and position, the VarBin method's classification is assigned to each proband variant (Bin 1 to Bin 4). RESULTS: To assess VarBin performance, Sanger sequencing was performed on 98 variants in the proband and background samples. True variants were confirmed in 97% of Bin 1 variants, 30% of Bin 2, and 0% of Bin 3/Bin 4. CONCLUSIONS: These data indicate that VarBin correctly classifies the majority of true variants as Bin 1 and Bin 3/4 contained only false positive variants. The "uncertain" Bin 2 contained both true and false positive variants. Future work will further differentiate the variants in Bin 2.

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.

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.

Developing genome and exome sequencing for candidate gene identification in inherited disorders: an integrated technical and bioinformatics approach.

CONTEXT: Advances in sequencing technology with the commercialization of next-generation sequencing (NGS) has substantially increased the feasibility of sequencing human genomes and exomes. Next-generation sequencing has been successfully applied to the discovery of disease-causing genes in rare, inherited disorders. By necessity, the advent of NGS has fostered the concurrent development of bioinformatics approaches to expeditiously analyze the large data sets generated. Next-generation sequencing has been used for important discoveries in the research setting and is now being implemented into the clinical diagnostic arena. OBJECTIVE: To review the current literature on technical and bioinformatics approaches for exome and genome sequencing and highlight examples of successful disease gene discovery in inherited disorders. To discuss the challenges for implementing NGS in the clinical research and diagnostic arenas. DATA SOURCES: Literature review and authors' experience. CONCLUSIONS: Next-generation sequencing approaches are powerful and require an investment in infrastructure and personnel expertise for effective use; however, the potential for improvement of patient care through faster and more accurate molecular diagnoses is high.

Variant identification in multi-sample pools by illumina genome analyzer sequencing.

Multi-sample pooling and Illumina Genome Analyzer (GA) sequencing allows high throughput sequencing of multiple samples to determine population sequence variation. A preliminary experiment, using the RET proto-oncogene as a model, predicted ≤ 30 samples could be pooled to reliably detect singleton variants without requiring additional confirmation testing. This report used 30 and 50 sample pools to test the hypothesized pooling limit and also to test recent protocol improvements, Illumina GAIIx upgrades, and longer read chemistry. The SequalPrep(TM) method was used to normalize amplicons before pooling. For comparison, a single 'control' sample was run in a different flow cell lane. Data was evaluated by variant read percentages and the subtractive correction method which utilizes the control sample. In total, 59 variants were detected within the pooled samples, which included all 47 known true variants. The 15 known singleton variants due to Sanger sequencing had an average of 1.62 ± 0.26% variant reads for the 30 pool (expected 1.67% for a singleton variant [unique variant within the pool]) and 1.01 ± 0.19% for the 50 pool (expected 1%). The 76 base read lengths had higher error rates than shorter read lengths (33 and 50 base reads), which eliminated the distinction of true singleton variants from background error. This report demonstrated pooling limits from 30 up to 50 samples (depending on error rates and coverage), for reliable singleton variant detection. The presented pooling protocols and analysis methods can be used for variant discovery in other genes, facilitating molecular diagnostic test design and interpretation.

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.

Translating exome sequencing from research to clinical diagnostics.

In the relatively short time frame since the introduction of next generation sequencing, it has become a method of choice for complex genomic research studies. As a paradigm shifting technology, we are now witnessing its translation into clinical diagnostic laboratories for patient care. Multi-gene panels for a variety of disorders are now available in several clinical laboratories based on targeted gene enrichment followed by next generation sequencing. Genome wide interrogation of protein coding regions, or exome sequencing, has been successfully and increasingly applied in the research setting for the elucidation of candidate genes and causal variants in individuals and families with a diversity of rare and complex genetic disorders. Based on this progress, exome sequencing is also beginning a translational process into clinical practice. However, introducing exome sequencing as a diagnostic modality poses new technical and bioinformatics challenges for clinical laboratories. In this review, we present technical and bioinformatics aspects of exome sequencing, describe representative examples from the literature of how exome sequencing has been used for candidate gene discovery, and discuss considerations for its clinical translation.

Multi-sample pooling and illumina genome analyzer sequencing methods to determine gene sequence variation for database development.

Determination of sequence variation within a genetic locus to develop clinically relevant databases is critical for molecular assay design and clinical test interpretation, so multisample pooling for Illumina genome analyzer (GA) sequencing was investigated using the RET proto-oncogene as a model. Samples were Sanger-sequenced for RET exons 10, 11, and 13-16. Ten samples with 13 known unique variants ("singleton variants" within the pool) and seven common changes were amplified and then equimolar-pooled before sequencing on a single flow cell lane, generating 36 base reads. For comparison, a single "control" sample was run in a different lane. After alignment, a 24-base quality score-screening threshold and 3; read end trimming of three bases yielded low background error rates with a 27% decrease in aligned read coverage. Sequencing data were evaluated using an established variant detection method (percent variant reads), by the presented subtractive correction method, and with SNPSeeker software. In total, 41 variants (of which 23 were singleton variants) were detected in the 10 pool data, which included all Sanger-identified variants. The 23 singleton variants were detected near the expected 5% allele frequency (average 5.17%+/-0.90% variant reads), well above the highest background error (1.25%). Based on background error rates, read coverage, simulated 30, 40, and 50 sample pool data, expected singleton allele frequencies within pools, and variant detection methods; >or=30 samples (which demonstrated a minimum 1% variant reads for singletons) could be pooled to reliably detect singleton variants by GA sequencing.