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      Diversity in Genomic Studies: A Roadmap to Address the Imbalance

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          Abstract

          Two decades ago, the sequence of the first human genome was published. Since then, advances in genome technologies have resulted in whole genome sequencing and microarray-based genotyping of millions of human genomes. However, genetic and genomic studies are predominantly based on populations of European ancestry. This implies that the benefits of genomic research, including improving clinical care, understanding disease aetiology, early detection of diseases, better diagnosis, and rational drug design, may elude those underrepresented populations. Here, we describe factors that have contributed to the imbalance in representation of different populations. Leveraging our experiences in setting up genomic studies in diverse global populations, we propose a roadmap to enhancing inclusion and ensuring equal health benefits of genomics advances. This proposal highlights the importance of sincere concerted global efforts towards genomic equity to achieve the benefits of genomic medicine to all.

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          Most cited references62

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          The mutational constraint spectrum quantified from variation in 141,456 humans

          Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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            The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

            Abstract The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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              Clinical use of current polygenic risk scores may exacerbate health disparities

              Polygenic risk scores (PRS) are poised to improve biomedical outcomes via precision medicine. However, the major ethical and scientific challenge surrounding clinical implementation of PRS is that those available today are several times more accurate in individuals of European ancestry than other ancestries. This disparity is an inescapable consequence of Eurocentric biases in genome-wide association studies, thus highlighting that-unlike clinical biomarkers and prescription drugs, which may individually work better in some populations but do not ubiquitously perform far better in European populations-clinical uses of PRS today would systematically afford greater improvement for European-descent populations. Early diversifying efforts show promise in leveling this vast imbalance, even when non-European sample sizes are considerably smaller than the largest studies to date. To realize the full and equitable potential of PRS, greater diversity must be prioritized in genetic studies, and summary statistics must be publically disseminated to ensure that health disparities are not increased for those individuals already most underserved.
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                Author and article information

                Journal
                9502015
                Nat Med
                Nat Med
                Nature medicine
                1078-8956
                1546-170X
                01 February 2022
                10 February 2022
                31 July 2023
                05 August 2023
                : 28
                : 2
                : 243-250
                Affiliations
                [1 ] The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
                [2 ]The Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, UK
                [3 ]Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [4 ]MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [5 ]Division of Psychiatry, University College of London, London W1T 7NF, UK
                [6 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
                [7 ]Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
                [8 ]UCL Genetics Institute, University College London, London WC1E 6BT, UK
                Author notes
                Correspondence: Segun Fatumo: segun.fatumo@ 123456lshtm.ac.uk
                Article
                EMS182230
                10.1038/s41591-021-01672-4
                7614889
                35145307
                ef955508-5ec4-4ef5-a484-9d2c061fcc74

                This work is licensed under a BY 4.0 International license.

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