Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses.

Genetic diagnosis of rare diseases requires accurate identification and interpretation of genomic variants. Clinical and molecular scientists from 37 expert centers across Europe created the Solve-Rare Diseases Consortium (Solve-RD) resource, encompassing clinical, pedigree and genomic rare-disease data (94.5% exomes, 5.

Multi-omics analysis in inclusion body myositis identifies mir-16 responsible for HLA overexpression.

Background: Inclusion Body Myositis is an acquired muscle disease. Its pathogenesis is unclear due to the co-existence of inflammation, muscle degeneration and mitochondrial dysfunction. We aimed to provide a more advanced understanding of the disease by combining multi-omics analysis with prior knowledge.

-Related Muscular Dystrophies, LGMD, and TMD, in an Estonian Family Caused by the Finnish Founder Variant.

Background And Objectives: Tibial muscular dystrophy (TMD) is an autosomal dominant, slowly progressive late-onset distal myopathy. TMD was first described in 1991 by Udd et al. in Finnish patients, who were later found to harbor a heterozygous unique 11-bp insertion/deletion in the last exon of the gene-the Finnish founder variant (FINmaj).

Myotilin gene duplication causing late-onset myotilinopathy.

Background: myotilinopathy is a very rare inherited muscle disease that belongs to the group of myofibrillar myopathies. These diseases share a common alteration of the sarcomere organization at the level of the Z disk resulting in pathological protein aggregation, autophagic abnormalities, and ultimately muscle degeneration. Most reported cases are due to dominant missense mutations in the MYOT gene, two of which are largely recurrent.

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing datasets.

Purpose: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome or panel sequencing datasets aligned to a GRCh37, GRCh38, or T2T reference genome.

Methods: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs.