15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response

      research-article
      1 , 2 , 3 , 4 , 5 , * , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 1 , 2 , 3 , 4 , 5 , 8 , 11 , 8 , 12 , 1 , 2 , 3 , 4 , 5 , 13 , 14 , 14 , 15 , 16 , 16 , 16 , 17 , 18 , 19 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 26 , 26 , 26 ,   28 , 29 , 30 , 5 , 31 , 32 , 33 , 34 , 5 , 34 , 8 , 35 , 36 , NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, 37 , 38 , 1 , 2 , 3 , 4 , 5 , 39 , *
      Nature genetics

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large ( n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in Admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study (GWAS) data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide binding groove, explaining 12.9% of trait variance.

          Related collections

          Most cited references77

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Fast model-based estimation of ancestry in unrelated individuals.

            Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              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/.
                Bookmark

                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                12 August 2021
                October 2021
                05 October 2021
                05 April 2022
                : 53
                : 10
                : 1504-1516
                Affiliations
                [1 ]Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
                [2 ]Division of Rheumatology, Immunology, and Immunity, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
                [3 ]Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
                [4 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
                [5 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA.
                [6 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
                [7 ]Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
                [8 ]Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
                [9 ]Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
                [10 ]Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
                [11 ]Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan.
                [12 ]Department of Neurology, Osaka University Graduate School of Medicine, Osaka, Japan.
                [13 ]The Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institute for Medical Research, North Short LIJ Health System, Manhasset, NY, USA.
                [14 ]Department of Dermatology, University of Michigan, Ann Arbor, MI, USA.
                [15 ]Ann Arbor Veterans Affairs Hospital, Ann Arbor, MI, USA.
                [16 ]Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria.
                [17 ]Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
                [18 ]Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
                [19 ]Institute for Biomedicine, Eurac Research, Bolzano, Italy.
                [20 ]Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
                [21 ]School of Life Sciences, EPFL, Lausanne, Switzerland.
                [22 ]Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
                [23 ]Ragon Institute of MGH, MIT and Harvard, Boston, MA, USA.
                [24 ]Vanderbilt University Medical Center, Nashville, TN, USA.
                [25 ]Meharry Medical College, Nashville, TN, USA.
                [26 ]The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
                [27 ]Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA.
                [28 ]Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA.
                [29 ]Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
                [30 ]Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA.
                [31 ]Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
                [32 ]Cardiology Division of the Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
                [33 ]Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
                [34 ]Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
                [35 ]Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
                [36 ]Department of Medical Sciences, Seoul National University College of Medicine, Seoul, South Korea.
                [37 ]J.C. Wilt Infectious Diseases Research Centre, National Microbiology Laboratories, Public Health Agency of Canada, Winnipeg, MB, Canada.
                [38 ]Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, MB, Canada.
                [39 ]Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, UK.
                Author notes

                Author Contributions

                Y.L. and S.R. conceived, designed and performed analyses, wrote the manuscript and supervised the research. M.K. implemented the omnibus test for the HIV-1 fine-mapping study. Y.L., W.C., M.K., P.E.S., J.T.E., and B.H. contributed to the development of the HLA-TAPAS pipeline. X.L. performed the selection analysis. S. Sakaue performed imputation comparison between Beagle v.4 and Minimac4. L.F., S. Schoenherr, C.F and A.V.S. hosted the HLA imputation server. J.T.E, M.G.-A. and P.K.G helped with the GaP data acquisition. K.Y., K.O., D.W.H., X.G., N.D.P., Y.-D.I.C., J.I.R., K.D.T., S.S.R., A.C., J.G.W., S.K., M.H.C., A.M., T.E., and Y.O. contributed to the WGS data acquisition. J.F., M.C. and P.J.M contributed to the HIV-1 data acquisition. All authors contributed to the writing of the manuscript.

                Article
                NIHMS1730054
                10.1038/s41588-021-00935-7
                8959399
                34611364
                09c9fe5c-6fa0-44b4-986e-13e4ab4ecbae

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms

                History
                Categories
                Article

                Genetics
                Genetics

                Comments

                Comment on this article