Neuromuscular disease genetics in under-represented populations: increasing data diversity

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.

Keywords: capacity building; equality and diversity; genomic medicine; inherited neuromuscular disease; low-to-middle income country.

Figure 1

ICGNMD workflow with key nodes for international discussion at genetic analysis decision and results review. ICGNMD Fellows’ training spans this pathway. DMD = Duchenne muscular dystrophy; FSHD = facioscapulohumeral muscular dystrophy; MLPA = multiplex ligation dependent probe analysis; SMA = spinal muscular atrophy; WES = whole exome sequencing; WGS = whole genome sequencing.

Figure 2

Recruitment across clinical diagnostic categories in REDCap by country of recruitment. Blue = Brazil; grey = Turkey; light blue = Zambia; red = South Africa; yellow = India. DMD = Duchenne muscular dystrophy; FSHD = facioscapulohumeral muscular dystrophy; MD = muscular dystrophy.

Figure 3

Genetic analysis outcomes. (A) Outcome in peripheral neuropathies (PN), limb girdle muscular dystrophy (LGMD), congenital myopathy/congenital muscular dystrophy (CM/CMD) and Duchenne muscular dystrophy/Becker muscular dystrophy (DMD/BMD) cohorts. (B) Genetic composition of (i) genetic PN; (ii) LGMD; and (iii) CM/CMD ‘solved’ cohorts showing the number of probands with variants detected in each named gene. PMP22 del = PMP22 gene deletion; PMP22 dup = PMP22 duplication; PMP22 pm = PMP22 point mutation.

Figure 4

Variants detected in Duchenne Muscular Dystrophy (DMD) participants with ‘solved’ outcomes. Blue = deletion; grey = duplication; orange = splice; red = nonsense.

Figure 5

Distribution of Duchenne Muscular Dystrophy (DMD) variants. (A) Exon distribution of causative DMD genetic variants for the whole cohort. (B) Exon distribution of causative DMD genetic variants in (i) Indian and (ii) South African cohorts. Blue = deletion; grey = duplication; orange = splice; red = nonsense.