No abstract

Purpose Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene–disease associations. Methods We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects, and characterized the patterns of genotypes enriched across this collection of patients. Results We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10−8). This enrichment is only partially explained by mutations found in known disease-causing genes. Conclusion This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.

Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.

Esophageal squamous cell carcinoma (ESCC) is a poor-prognosis cancer type with limited understanding of its molecular etiology. Using 508 ESCC genomes, we identified five novel significantly mutated genes and uncovered mutational signature clusters associated with metastasis and patients’ outcomes. Several functional assays implicated that NFE2L2 may act as a tumor suppressor in ESCC and that mutations in NFE2L2 probably impaired its tumor-suppressive function, or even conferred oncogenic activities. Additionally, we found that the NFE2L2 mutations were significantly associated with worse prognosis of ESCC. We also identified potential noncoding driver mutations including hotspot mutations in the promoter region of SLC35E2 that were correlated with worse survival. Approximately 5.9% and 15.2% of patients had high tumor mutation burden or actionable mutations, respectively, and may benefit from immunotherapy or targeted therapies. We found clinically relevant coding and noncoding genomic alterations and revealed three major subtypes that robustly predicted patients’ outcomes. Collectively, we report the largest dataset of genomic profiling of ESCC useful for developing ESCC-specific biomarkers for diagnosis and treatment.

Whole-exome sequencing of 13 individuals with developmental delay commonly accompanied by abnormal muscle tone and seizures identified de novo missense mutations enriched within a sub-region of GNB1, a gene encoding the guanine nucleotide-binding protein subunit beta-1, Gβ. These 13 individuals were identified among a base of 5,855 individuals recruited for various undiagnosed genetic disorders. The probability of observing 13 or more de novo mutations by chance among 5,855 individuals is very low (p = 7.1 × 10(-21)), implicating GNB1 as a genome-wide-significant disease-associated gene. The majority of these 13 mutations affect known Gβ binding sites, which suggests that a likely disease mechanism is through the disruption of the protein interface required for Gα-Gβγ interaction (resulting in a constitutively active Gβγ) or through the disruption of residues relevant for interaction between Gβγ and certain downstream effectors (resulting in reduced interaction with the effectors). Strikingly, 8 of the 13 individuals recruited here for a neurodevelopmental disorder have a germline de novo GNB1 mutation that overlaps a set of five recurrent somatic tumor mutations for which recent functional studies demonstrated a gain-of-function effect due to constitutive activation of G protein downstream signaling cascades for some of the affected residues.