HEROD: a human ethnic and regional specific omics database

The Diagnostic Yield of Next Generation Sequencing in Inherited Retinal Diseases: A Systematic Review and Meta-analysis

PURPOSE

Accurate genotyping of individuals with inherited retinal diseases (IRD) is essential for patient management and identifying suitable candidates for gene therapies. This study evaluated the diagnostic yield of next generation sequencing (NGS) in IRDs.

DESIGN

Systematic review and meta-analysis.

METHODS

This systematic review was prospectively registered (CRD42021293619). Ovid MEDLINE and Ovid Embase were searched on 6 June 2022. Clinical studies evaluating the diagnostic yield of NGS in individuals with IRDs were eligible for inclusion. Risk of bias assessment was performed. Studies were pooled using a random...effects inverse variance model. Sources of heterogeneity were explored using stratified analysis, meta-regression, and sensitivity analysis.

RESULTS

This study included 105 publications from 28 countries. Most studies (90 studies) used targeted gene panels. The diagnostic yield of NGS was 61.3% (95% confidence interval: 57.8-64.7%; 51 studies) in mixed IRD phenotypes, 58.2% (51.6-64.6%; 41 studies) in rod-cone dystrophies, 57.7% (46.8-68.3%; eight studies) in macular and cone/cone-rod dystrophies, and 47.6% (95% CI: 41.0-54.3%; four studies) in familial exudative vitreoretinopathy. For mixed IRD phenotypes, a higher diagnostic yield was achieved pooling studies published between 2018-2022 (64.2% [59.5-68.7%]), studies using exome sequencing (73.5% [58.9-86.1%]), and studies using the American College of Medical Genetics variant interpretation standards (65.6% [60.8-70.4%]).

CONCLUSION

The current diagnostic yield of NGS in IRDs is between 52-74%. The certainty of the evidence was judged as low or very low. A key limitation of the current evidence is the significant heterogeneity between studies. Adoption of standardized reporting guidelines could improve confidence in future meta-analyses.

GGDB: A Grameneae genome alignment database of homologous genes hierarchically related to evolutionary events

The genomes of Gramineae plants have been preferentially sequenced owing to their economic value. These genomes are often quite complex, for example harboring many duplicated genes, and are the main source of genetic innovation and often the result of recurrent polyploidization. Deciphering these complex genome structures and linking duplicated genes to specific polyploidization events are important for understanding the biology and evolution of plants. However, efforts have been hampered by the complexity of analyzing these genomes. Here, we analyzed 29 well-assembled and up-to-date Gramineae genome sequences by hierarchically relating duplicated genes in collinear regions to specific polyploidization or speciation events. We separated duplicated genes produced by each event, established lists of paralogous and orthologous genes, and ultimately constructed an online database, GGDB (http://www.grassgenome.com/). Homologous gene lists from each plant and between plants can be displayed, searched, and downloaded from the database. Interactive comparison tools are deployed to demonstrate homology among user-selected plants and to draw genome-scale or local alignment figures and gene-based phylogenetic trees corrected by exploiting gene collinearity. Using these tools and figures, users can easily detect structural changes in genomes and explore the effects of paleo-polyploidy on crop genome structure and function. The GGDB will provide a useful platform for improving our understanding of genome changes and functional innovation in Gramineae plants.

Transcriptome modeling and phenotypic assays for cancer precision medicine

Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery.