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      Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil

      research-article
      1 , 2 , 3 , , 1 , 1 , 4 , 5 , 6 , 1 , 7 , 7 , 2 , 1 , 1 , 1 , 1 , 1 , 8 , 8 , 8 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 11 , 12 , 11 , 12 , 11 , 14 , 14 , 15 , 1 , 2 , 14 , 16 , 17 , 16 , 16 , 16 , 18 , 19 , 20 , 21 , 22 , 23 , 14 , 24 , 25 , 26 , 2 , 11 , 7 , 8 , 9 , 27 , 28 , 1 , 2 , 1 , 2 ,
      Nature Communications
      Nature Publishing Group UK
      Mobile elements, Haplotypes, Data publication and archiving, Structural variation, Rare variants

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          Abstract

          As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS.

          Abstract

          Whole genome sequencing (WGS) data on non-European and admixed individuals remains scarce. Here, the authors analyse WGS data from 1,171 admixed elderly Brazilians from a census cohort, characterising population-specific genetic variation and exploring the clinical utility of this expanded dataset.

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          Most cited references76

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

              The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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                Author and article information

                Contributors
                mnaslavsky@usp.br
                mayazatz@usp.br
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 March 2022
                4 March 2022
                2022
                : 13
                : 1004
                Affiliations
                [1 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Human Genome and Stem Cell Research Center, , University of São Paulo, ; São Paulo, SP Brazil
                [2 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Department of Genetics and Evolutionary Biology, Biosciences Institute, , University of São Paulo, ; São Paulo, SP Brazil
                [3 ]GRID grid.413562.7, ISNI 0000 0001 0385 1941, Hospital Israelita Albert Einstein, ; São Paulo, SP Brazil
                [4 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Instituto da Criança, , Faculdade de Medicina da Universidade de São Paulo, ; São Paulo, SP Brazil
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Orthopedic Research Labs, Boston Children’s Hospital and Department of Genetics, Harvard Medical School, ; Boston, MA USA
                [6 ]Laboratório DASA, São Paulo, Brazil
                [7 ]GRID grid.4494.d, ISNI 0000 0000 9558 4598, Laboratory of Genome Structure and Ageing, European Research Institute for the Biology of Ageing, , University Medical Center Groningen, ; Groningen, Netherlands
                [8 ]GRID grid.410543.7, ISNI 0000 0001 2188 478X, São Paulo State University (UNESP), Molecular Genetics and Bioinformatics Laboratory, School of Medicine, ; Botucatu, State of São Paulo Brazil
                [9 ]GRID grid.410543.7, ISNI 0000 0001 2188 478X, São Paulo State University (UNESP), Department of Pathology, School of Medicine, ; Botucatu, State of São Paulo Brazil
                [10 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, , Universidade de São Paulo, ; Ribeirão Preto, São Paulo Brazil
                [11 ]GRID grid.413471.4, ISNI 0000 0000 9080 8521, Centro de Oncologia Molecular, Hospital Sirio-Libanes, ; São Paulo, Brazil
                [12 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Department of Biochemistry, Institute of Chemistry, , University of São Paulo São Paulo, ; São Paulo, Brazil
                [13 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Bioinformatics Graduate program, , University of São Paulo, ; São Paulo, Brazil
                [14 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, , Universidade Federal de Minas Gerais, ; Belo Horizonte, MG Brazil
                [15 ]Núcleo de Ensino e Pesquisa, Instituto Mário Penna, Belo Horizonte, MG Brazil
                [16 ]GRID grid.419228.4, ISNI 0000 0004 0636 549X, Laboratorio de Biotecnologia y Biologia Molecular, Instituto Nacional de Salud, ; Lima, Peru
                [17 ]GRID grid.441777.6, ISNI 0000 0004 6022 3214, Universidad de Huánuco, ; Huánuco, Peru
                [18 ]GRID grid.48336.3a, ISNI 0000 0004 1936 8075, Division of Cancer Epidemiology and Genetics, National Cancer Institute, ; Bethesda, MD USA
                [19 ]GRID grid.8399.b, ISNI 0000 0004 0372 8259, Instituto de Saúde Coletiva, , Universidade Federal da Bahia, ; Salvador, BA 40110-040 Brazil
                [20 ]GRID grid.418068.3, ISNI 0000 0001 0723 0931, Center for Data and Knowledge Integration for Health, Institute Gonçalo Muniz, Fundação Oswaldo Cruz, ; Salvador, BA Brazil
                [21 ]GRID grid.418068.3, ISNI 0000 0001 0723 0931, Instituto de Pesquisas René Rachou, Fundação Oswaldo Cruz, ; Belo Horizonte, MG Brazil
                [22 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Programa De Pós-Graduação em Saúde Pública, , Universidade Federal de Minas Gerais, ; Belo Horizonte, MG Brazil
                [23 ]GRID grid.411221.5, ISNI 0000 0001 2134 6519, Programa de Pós-Graduação em Epidemiologia, , Universidade Federal de Pelotas, ; Pelotas, RS Brazil
                [24 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Mosaico Translational Genomics Initiative, , Universidade Federal de Minas Gerais, ; Belo Horizonte, MG 31270-901 Brazil
                [25 ]GRID grid.11100.31, ISNI 0000 0001 0673 9488, Facultad de Salud Pública y Administración, , Universidad Peruana Cayetano Heredia, ; Lima, Peru
                [26 ]GRID grid.8430.f, ISNI 0000 0001 2181 4888, Instituto de Estudos Avançados Transdisciplinares, , Universidade Federal de Minas Gerais, ; Belo Horizonte, MG 31270-901 Brazil
                [27 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Medical-Surgical Nursing Department, School of Nursing, , University of São Paulo, ; São Paulo, SP Brazil
                [28 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Epidemiology Department, Public Health School, , University of São Paulo, ; São Paulo, SP Brazil
                Author information
                http://orcid.org/0000-0002-9068-1713
                http://orcid.org/0000-0002-3370-9484
                http://orcid.org/0000-0001-7680-7861
                http://orcid.org/0000-0001-5090-7127
                http://orcid.org/0000-0002-7337-1203
                http://orcid.org/0000-0003-1529-2475
                http://orcid.org/0000-0003-2234-0631
                http://orcid.org/0000-0001-9843-412X
                http://orcid.org/0000-0002-4820-4155
                http://orcid.org/0000-0002-5810-6022
                Article
                28648
                10.1038/s41467-022-28648-3
                8897431
                35246524
                e293b478-4713-4759-90d6-1ad244553465
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 September 2020
                : 21 January 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: R01 GM075091
                Award Recipient :
                Funded by: 1) Brazilian Ministry of Health – MoH/Brazil National Programme of Genomic and Precision Health – Genomes Brazil. 2) Rede Mineira de Genomica Populacional e Medicina de Precisão (FAPEMIG)
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                © The Author(s) 2022

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                mobile elements,haplotypes,data publication and archiving,structural variation,rare variants

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