Neuromuscular disease genetics in underrepresented populations: increasing data diversity.
Lindsay A WilsonWilliam L MackenLuke D PerryChristopher J RecordKatherine R SchonRodrigo Siqueira Soares FrezattiSharika V RagaKireshnee NaiduÖzlem Yayıcı KökenIpek PolatMusambo M KapapaNatalia DominikStephanie EfthymiouHeba MorsyMelissa NelMahmoud R FassadFei GaoKrutik PatelMaryke SchoonenMichelle BisschoffArmand VorsterHallgeir JonvikRonel HumanElsa LubbeMalebo NonyaneSeena VengalilSaraswati NashiKosha SrivastavaRichard J L F LemmersAlisha ReyazRinkle MishraAna TöpfChristina I TrainorElizabeth C SteynAmokelani C MahunguPatrick J van der VlietAhmet Cevdet CeylanA Semra HizBüşranur ÇavdarlıC Nur Semerci GündüzGülay Güleç CeylanMadhu NagappaKarthik B TallapakaPeriyasamy GovindarajSilvère M van der MaarelGayathri NarayanappaBevinahalli N NandeeshSomwe Wa SomweDavid R BeardenMichelle P KvalsundGita M RamdharryYavuz OktayUluç YişHaluk TopaloğluAnna SarkozyEnrico BugiardiniFranclo HenningJo M WilmshurstJeannine M HeckmannRobert McFarlandRobert W TaylorIzelle SmutsFrancois Hendrikus van der WesthuizenClaudia Ferreira Da Rosa SobreiraPedro José TomaselliWilson MarquesRohit BhatiaAshwin B DalalM V Padma SrivastavaSireesha YareedaAtchayaram NaliniVenugopalan Yamuna VishnuKumarasamy ThangarajVolker StraubRita HorvathPatrick F ChinneryRobert D S PitceathlyFrancesco MuntoniHenry HouldenJana VandrovcovaMary M ReillyMichael G HannaPublished in: Brain : a journal of neurology (2023)
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-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 characterised 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 was 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 underrepresented 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.
Keyphrases
- copy number
- genome wide
- muscular dystrophy
- electronic health record
- healthcare
- big data
- genetic diversity
- south africa
- primary care
- dna methylation
- mental health
- cell free
- machine learning
- gene expression
- systematic review
- circulating tumor
- high resolution
- single molecule
- randomized controlled trial
- physical activity
- chronic kidney disease
- ejection fraction
- newly diagnosed
- pain management
- end stage renal disease
- prognostic factors
- artificial intelligence
- deep learning
- resistance training
- transcription factor
- genome wide identification