TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Diagnosis and etiology of congenital muscular dystrophy: We are halfway there.

Objective: To evaluate the diagnostic outcomes in a large cohort of congenital muscular dystrophy (CMD) patients using traditional and next generation sequencing (NGS) technologies. Methods: A total of 123 CMD patients were investigated using the traditional approaches of histology, immunohistochemical analysis of muscle biopsy, and candidate gene sequencing. Undiagnosed patients available for further testing were investigated using NGS. Results: Muscle biopsy and immunohistochemical analysis found deficiencies of laminin a2, a-dystroglycan, or collagen VI in 50% of patients. Candidate gene sequencing and chromosomal microarray established a genetic diagnosis in 32% (39 of 123). Of 85 patients presenting in the past 20 years, 28 of 51 who lacked a confirmed genetic diagnosis (55%) consented to NGS studies, leading to confirmed diagnoses in a further 11 patients. Using the combination of approaches, a confirmed genetic diagnosis was achieved in 51% (43 of 85). The diagnoses within the cohort were heterogeneous. Forty-five of 59 probands with confirmed or probable diagnoses had variants in genes known to cause CMD (76%), and 11 of 59 (19%) had variants in genes associated with congenital myopathies, reflecting overlapping features of these conditions. One patient had a congenital myasthenic syndrome, and 2 had microdeletions. Within the cohort, 5 patients had variants in novel (PIGY and GMPPB) or recently published genes (GFPT1 and MICU1), and 7 had variants in TTN or RYR1, large genes that are technically difficult to Sanger sequence. Interpretation: These data support NGS as a first-line tool for genetic evaluation of patients with a clinical phenotype suggestive of CMD, with muscle biopsy reserved as a second-tier investigation.Gina L. O, Grady, Monkol Lek, Shireen R. Lamande, Leigh Waddell, Emily C. Oates, Jaya Punetha, Roula Ghaoui, Sarah A. Sandaradura, Heather Best, Simranpreet Kaur, Mark Davis, Nigel G. Laing, Francesco Muntoni, Eric Hoffman, Daniel G. MacArthur, Nigel F. Clarke, Sandra Cooper, and Kathryn Nort