B-223 DMD Duplications Identified by Carrier Screening Should NOT Automatically Be Classified as [Likely] Pathogenic: Data from Long-Read Sequencing of 15 Cases
Abstract Background Pathogenic variants in the DMD gene are associated with Duchenne and Becker muscular dystrophy (DMD/BMD). Professional medical societies currently recommend DMD carrier screening for all patients who are pregnant or considering pregnancy. Such testing may identify intragenic duplications of DMD (i.e., involving one or more exons but fully contained with the gene) in patients without a personal or family history of DMD/BMD. These incidentally detected DMD intragenic duplications, however, pose significant challenges for variant interpretation. Pathogenicity of an intragenic duplication depends on its structural configuration. Tandem duplications may cause reading frame disruption and gene loss-of-function; conversely, non-tandem duplications may not disrupt gene function. Current carrier screening assays cannot determine the structural configuration of an intragenic duplication. The current guidelines (e.g., the ClinGen PVS1 decision tree) presume intragenic duplications are in tandem unless proven otherwise. If this presumption of tandem configuration is inaccurate, it may lead to overestimation of variant pathogenicity, potentially resulting in unnecessary invasive procedures (e.g., amniocentesis for prenatal diagnosis) and/or having other negative impacts with regards to prenatal decision-making. This study aims to evaluate the validity of this presumption for incidentally detected DMD intragenic duplications. Methods We performed high-coverage whole-genome long-read sequencing on the PromethION instrument (Oxford Nanopore Technologies) for 15 cases with previously identified DMD intragenic duplications. Among these cases, four had personal or family history of DMD/BMD, while 11 had no known history (i.e., incidentally detected). The structural configurations of the duplications were determined by an in-house bioinformatics tool and manual inspection of the alignment files. Results In all 4 cases with a known personal or family history of DMD/BMD, the DMD intragenic duplications were confirmed to be in tandem, aligning with expectations of their pathogenicity. Interestingly, among the 11 cases with incidentally detected DMD intragenic duplications, seven (64%) were found to involve interspersed duplications. In five of these seven interspersed duplication cases, the involved DMD exons were co-duplicated with distinct segments of the Xp arm located 0.5–30 Mb away, forming complex structural variants. In another case, the duplicated DMD exons have inserted into the Xq arm, about 90 Mb downstream of DMD. The final case involved the duplicated DMD exons inserted into a repetitive region most consistent with 9q12 (i.e., the 9qh region). Conclusions Our study reveals that while DMD intragenic duplications in patients with a personal or family history of DMD/BMD were consistently in tandem, a substantial 64% of incidentally detected DMD intragenic duplications were not in tandem. These findings have important implications for both clinicians and laboratorians. They challenge the presumption of tandem configuration for DMD intragenic duplications in individuals where the variants were identified incidentally, e.g., through carrier screening in those without a family history of DMD/BMD. In these cases, we recommend classifying the DMD intragenic duplications as variants of uncertain significance (VUS), unless there is evidence of tandem configuration.
Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel
Abstract Estimates of plant traits derived from hyperspectral reflectance data have the potential to efficiently substitute for traits, which are time or labor intensive to manually score. Typical workflows for estimating plant traits from hyperspectral reflectance data employ supervised classification models that can require substantial ground truth datasets for training. We explore the potential of an unsupervised approach, autoencoders, to extract meaningful traits from plant hyperspectral reflectance data using measurements of the reflectance of 2151 individual wavelengths of light from the leaves of maize ( Zea mays ) plants harvested from 1658 field plots in a replicated field trial. A subset of autoencoder‐derived variables exhibited significant repeatability, indicating that a substantial proportion of the total variance in these variables was explained by difference between maize genotypes, while other autoencoder variables appear to capture variation resulting from changes in leaf reflectance between different batches of data collection. Several of the repeatable latent variables were significantly correlated with other traits scored from the same maize field experiment, including one autoencoder‐derived latent variable (LV8) that predicted plant chlorophyll content modestly better than a supervised model trained on the same data. In at least one case, genome‐wide association study hits for variation in autoencoder‐derived variables were proximal to genes with known or plausible links to leaf phenotypes expected to alter hyperspectral reflectance. In aggregate, these results suggest that an unsupervised, autoencoder‐based approach can identify meaningful and genetically controlled variation in high‐dimensional, high‐throughput phenotyping data and link identified variables back to known plant traits of interest.
Population‐level gene expression can repeatedly link genes to functions in maize
SUMMARY Transcriptome‐wide association studies (TWAS) can provide single gene resolution for candidate genes in plants, complementing genome‐wide association studies (GWAS) but efforts in plants have been met with, at best, mixed success. We generated expression data from 693 maize genotypes, measured in a common field experiment, sampled over a 2‐h period to minimize diurnal and environmental effects, using full‐length RNA‐seq to maximize the accurate estimation of transcript abundance. TWAS could identify roughly 10 times as many genes likely to play a role in flowering time regulation as GWAS conducted data from the same experiment. TWAS using mature leaf tissue identified known true‐positive flowering time genes known to act in the shoot apical meristem, and trait data from a new environment enabled the identification of additional flowering time genes without the need for new expression data. eQTL analysis of TWAS‐tagged genes identified at least one additional known maize flowering time gene through trans ‐eQTL interactions. Collectively these results suggest the gene expression resource described here can link genes to functions across different plant phenotypes expressed in a range of tissues and scored in different experiments.
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
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