Neuromuscular disease genetics in underrepresented populations : increasing data diversity
Wilson, Lindsay A.; Macken, William L.; Perry, Luke D.; Record, Christopher J.; Schon, Katherine R.; Frezatti, Rodrigo S.S.; Raga, Sharika; Naidu, Kireshnee; Köken, Ozlem Yayıcı; Kapapa, Musambo M.; Dominik, Natalia; Efthymiou, Stephanie; Morsy, Heba; Nel, Melissa; Fassad, Mahmoud R.; Gao, Fei; Patel, Krutik; Schoonen, Maryke; Bisschoff, Michelle; Vorster, Armand; Jonvik, Hallgeir; Human, Ronel; Lubbe, Elsabeth (Elsa); Nonyane, Malebo; Vengalil, Seena; Nashi, Saraswati; Srivastava, Kosha; Lemmers, Richard J.L.F.; Reyaz, Alisha; Mishra, Rinkle; Topf, Ana; Trainor, Christina I.; Steyn, Elizabeth C.; Mahungu, Amokelani C.; Van der Vliet, Patrick J.; Ceylan, Ahmet Cevdet; Hiz, A. Semra; Cavdarlı, Bussranur; Gunduz, C. Nur Semerci; Ceylan, Gulay Gulec; Nagappa, Madhu; Tallapaka, Karthik B.; Govindaraj, Periyasamy; Van der Maarel, Silvere M.; Narayanappa, Gayathri; Nandeesh, Bevinahalli N.; Somwe, Wa Somwe; Bearden, David R.; Kvalsund, Michelle P.; Ramdharry, Gita M.; Oktay, Yavuz; Yis, Uluc; Topaloglu, Haluk; Sarkozy, Anna; Bugiardini, Enrico; Henning, Franclo; Wilmshurst, Jo M.; Heckmann, Jeannine M.; McFarland, Robert; Taylor, Robert W.; Smuts, Izelle; Van der Westhuizen, Francois H.; Da Rosa Sobreira, Claudia Ferreira; Tomaselli, Pedro J.; Marques Jr, Wilson; Bhatia, Rohit; Dalal, Ashwin; Srivastava, M.V. Padma; Yareeda, Sireesha; Nalini, Atchayaram; Vishnu, Venugopalan Y.; Thangaraj, Kumarasamy; Straub, Volker; Horvath, Rita; Chinnery, Patrick F.; Pitceathly, Robert D.S.; Muntoni, Francesco; Houlden, Henry; Vandrovcova, Jana; Reilly, Mary M.; Hanna, Michael G.
Date:
2023-07
Abstract:
Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.
Description:
DATA AVAILABILITY : At the end of the study, participants de-identified exome and genome data will be archived in the European Molecular Biology Laboratory European Bioinformatics Institute’s European Genome-Phenome Archive (EMBL EBI EGA), with community access to this and selected de-identified REDCap data managed via an ICGNMD Data Access Committee.