Dataset Information

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.

SUBMITTER: Wilson LA

PROVIDER: S-EPMC10690022 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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

Wilson Lindsay A LA Macken William L WL Perry Luke D LD Record Christopher J CJ Schon Katherine R KR Frezatti Rodrigo S S RSS Raga Sharika S Naidu Kireshnee K Köken Özlem Yayıcı ÖY Polat Ipek I Kapapa Musambo M MM Dominik Natalia N Efthymiou Stephanie S Morsy Heba H Nel Melissa M Fassad Mahmoud R MR Gao Fei F Patel Krutik K Schoonen Maryke M Bisschoff Michelle M Vorster Armand A Jonvik Hallgeir H Human Ronel R Lubbe Elsa E Nonyane Malebo M Vengalil Seena S Nashi Saraswati S Srivastava Kosha K Lemmers Richard J L F RJLF Reyaz Alisha A Mishra Rinkle R Töpf Ana A Trainor Christina I CI Steyn Elizabeth C EC Mahungu Amokelani C AC van der Vliet Patrick J PJ Ceylan Ahmet Cevdet AC Hiz A Semra AS Çavdarlı Büşranur B Semerci Gündüz C Nur CN Ceylan Gülay Güleç GG Nagappa Madhu M Tallapaka Karthik B KB Govindaraj Periyasamy P van der Maarel Silvère M SM Narayanappa Gayathri G Nandeesh Bevinahalli N BN Wa Somwe Somwe S Bearden David R DR Kvalsund Michelle P MP Ramdharry Gita M GM Oktay Yavuz Y Yiş Uluç U Topaloğlu Haluk H Sarkozy Anna A Bugiardini Enrico E Henning Franclo F Wilmshurst Jo M JM Heckmann Jeannine M JM McFarland Robert R Taylor Robert W RW Smuts Izelle I van der Westhuizen Francois H FH Sobreira Claudia Ferreira da Rosa CFDR Tomaselli Pedro J PJ Marques Wilson W Bhatia Rohit R Dalal Ashwin A Srivastava M V Padma MVP Yareeda Sireesha S Nalini Atchayaram A Vishnu Venugopalan Y VY Thangaraj Kumarasamy K Straub Volker V Horvath Rita R Chinnery Patrick F PF Pitceathly Robert D S RDS Muntoni Francesco F Houlden Henry H Vandrovcova Jana J Reilly Mary M MM Hanna Michael G MG

Brain : a journal of neurology 20231201 12

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 s ...[more]

PMID: 37516995

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Project description:Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.

Dataset Information

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

Publications

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

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