Clinical exome sequencing for genetic identification of rare Mendelian disorders.

IMPORTANCE: Clinical exome sequencing (CES) is rapidly becoming a common molecular diagnostic test for individuals with rare genetic disorders. OBJECTIVE: To report on initial clinical indications for CES referrals and molecular diagnostic rates for different indications and for different test types. DESIGN, SETTING, AND PARTICIPANTS: Clinical exome sequencing was performed on 814 consecutive patients with undiagnosed, suspected genetic conditions at the University of California, Los Angeles, Clinical Genomics Center between January 2012 and August 2014. Clinical exome sequencing was conducted as trio-CES (both parents and their affected child sequenced simultaneously) to effectively detect de novo and compound heterozygous variants or as proband-CES (only the affected individual sequenced) when parental samples were not available. MAIN OUTCOMES AND MEASURES: Clinical indications for CES requests, molecular diagnostic rates of CES overall and for phenotypic subgroups, and differences in molecular diagnostic rates between trio-CES and proband-CES. RESULTS: Of the 814 cases, the overall molecular diagnosis rate was 26% (213 of 814; 95% CI, 23%-29%). The molecular diagnosis rate for trio-CES was 31% (127 of 410 cases; 95% CI, 27%-36%) and 22% (74 of 338 cases; 95% CI, 18%-27%) for proband-CES. In cases of developmental delay in children (<5 years, n = 138), the molecular diagnosis rate was 41% (45 of 109; 95% CI, 32%-51%) for trio-CES cases and 9% (2 of 23, 95% CI, 1%-28%) for proband-CES cases. The significantly higher diagnostic yield (P value = .002; odds ratio, 7.4 [95% CI, 1.6-33.1]) of trio-CES was due to the identification of de novo and compound heterozygous variants. CONCLUSIONS AND RELEVANCE: In this sample of patients with undiagnosed, suspected genetic conditions, trio-CES was associated with higher molecular diagnostic yield than proband-CES or traditional molecular diagnostic methods. Additional studies designed to validate these findings and to explore the effect of this approach on clinical and economic outcomes are warranted.

Identification of retinol binding protein 1 promoter hypermethylation in isocitrate dehydrogenase 1 and 2 mutant gliomas.

BACKGROUND: Mutations in isocitrate dehydrogenase 1 (IDH1) and associated CpG island hypermethylation represent early events in the development of low-grade gliomas and secondary glioblastomas. To identify candidate tumor suppressor genes whose promoter methylation may contribute to gliomagenesis, we compared methylation profiles of IDH1 mutant (MUT) and IDH1 wild-type (WT) tumors using massively parallel reduced representation bisulfite sequencing. METHODS: Reduced representation bisulfite sequencing was performed on ten pathologically matched WT and MUT glioma samples and compared with data from a methylation-sensitive restriction enzyme technique and data from The Cancer Genome Atlas (TCGA). Methylation in the gene retinol-binding protein 1 (RBP1) was identified in IDH1 mutant tumors and further analyzed with primer-based bisulfite sequencing. Correlation between IDH1/IDH2 mutation status and RBP1 methylation was evaluated with Spearman correlation. Survival data were collected retrospectively and analyzed with Kaplan-Meier and Cox proportional hazards analysis. All statistical tests were two-sided. RESULTS: Methylome analysis identified coordinated CpG island hypermethylation in IDH1 MUT gliomas, consistent with previous reports. RBP1, important in retinoic acid metabolism, was found to be hypermethylated in 76 of 79 IDH1 MUT, 3 of 3 IDH2 MUT, and 0 of 116 IDH1/IDH2 WT tumors. IDH1/IDH2 mutation was highly correlated with RBP1 hypermethylation (n = 198; Spearman R = 0.94, 95% confidence interval = 0.92 to 0.95, P < .001). The Cancer Genome Atlas showed IDH1 MUT tumors (n = 23) to be RBP1-hypermethylated with decreased RBP1 expression compared with WT tumors (n = 124). Among patients with primary glioblastoma, patients with RBP1-unmethylated tumors (n = 102) had decreased median overall survival compared with patients with RBP1-methylated tumors (n = 22) (20.3 months vs 36.8 months, respectively; hazard ratio of death = 2.48, 95% confidence interval = 1.30 to 4.75, P = .006). CONCLUSION: RBP1 promoter hypermethylation is found in nearly all IDH1 and IDH2 mutant gliomas and is associated with improved patient survival. Because RBP1 is involved in retinoic acid synthesis, our results suggest that dysregulation of retinoic acid metabolism may contribute to glioma formation along the IDH1/IDH2-mutant pathway.

Disease gene characterization through large-scale co-expression analysis.

BACKGROUND: In the post genome era, a major goal of biology is the identification of specific roles for individual genes. We report a new genomic tool for gene characterization, the UCLA Gene Expression Tool (UGET). RESULTS: Celsius, the largest co-normalized microarray dataset of Affymetrix based gene expression, was used to calculate the correlation between all possible gene pairs on all platforms, and generate stored indexes in a web searchable format. The size of Celsius makes UGET a powerful gene characterization tool. Using a small seed list of known cartilage-selective genes, UGET extended the list of known genes by identifying 32 new highly cartilage-selective genes. Of these, 7 of 10 tested were validated by qPCR including the novel cartilage-specific genes SDK2 and FLJ41170. In addition, we retrospectively tested UGET and other gene expression based prioritization tools to identify disease-causing genes within known linkage intervals. We first demonstrated this utility with UGET using genetically heterogeneous disorders such as Joubert syndrome, microcephaly, neuropsychiatric disorders and type 2 limb girdle muscular dystrophy (LGMD2) and then compared UGET to other gene expression based prioritization programs which use small but discrete and well annotated datasets. Finally, we observed a significantly higher gene correlation shared between genes in disease networks associated with similar complex or Mendelian disorders. DISCUSSION: UGET is an invaluable resource for a geneticist that permits the rapid inclusion of expression criteria from one to hundreds of genes in genomic intervals linked to disease. By using thousands of arrays UGET annotates and prioritizes genes better than other tools especially with rare tissue disorders or complex multi-tissue biological processes. This information can be critical in prioritization of candidate genes for sequence analysis.