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      Applications of long-read sequencing to Mendelian genetics

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          Abstract

          Advances in clinical genetic testing, including the introduction of exome sequencing, have uncovered the molecular etiology for many rare and previously unsolved genetic disorders, yet more than half of individuals with a suspected genetic disorder remain unsolved after complete clinical evaluation. A precise genetic diagnosis may guide clinical treatment plans, allow families to make informed care decisions, and permit individuals to participate in N-of-1 trials; thus, there is high interest in developing new tools and techniques to increase the solve rate. Long-read sequencing (LRS) is a promising technology for both increasing the solve rate and decreasing the amount of time required to make a precise genetic diagnosis. Here, we summarize current LRS technologies, give examples of how they have been used to evaluate complex genetic variation and identify missing variants, and discuss future clinical applications of LRS. As costs continue to decrease, LRS will find additional utility in the clinical space fundamentally changing how pathological variants are discovered and eventually acting as a single-data source that can be interrogated multiple times for clinical service.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13073-023-01194-3.

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          Most cited references125

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          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.
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            Minimap2: pairwise alignment for nucleotide sequences

            Heng Li (2018)
            Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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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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                Author and article information

                Contributors
                eee@gs.washington.edu
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                14 June 2023
                14 June 2023
                2023
                : 15
                : 42
                Affiliations
                [1 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Genome Sciences, , University of Washington School of Medicine, ; Seattle, WA 98195 USA
                [2 ]GRID grid.34477.33, ISNI 0000000122986657, Division of Genetic Medicine, Department of Pediatrics, , University of Washington and Seattle Children’s Hospital, ; Seattle, WA 98195 USA
                [3 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Laboratory Medicine and Pathology, , University of Washington, ; Seattle, WA 98195 USA
                [4 ]GRID grid.34477.33, ISNI 0000000122986657, Brotman Baty Institute for Precision Medicine, , University of Washington, ; Seattle, WA 98195 USA
                [5 ]GRID grid.34477.33, ISNI 0000000122986657, Howard Hughes Medical Institute, University of Washington, ; Seattle, WA 98195 USA
                Author information
                http://orcid.org/0000-0002-8246-4014
                Article
                1194
                10.1186/s13073-023-01194-3
                10266321
                37316925
                f321bc50-aff7-4e2c-83cb-744c060a43d8
                © The Author(s) 2023

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 8 December 2022
                : 18 May 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000009, Foundation for the National Institutes of Health;
                Award ID: 1DP5OD033357-01
                Award ID: R01HG002385
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: R01MH101221
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100014370, Simons Foundation Autism Research Initiative;
                Award ID: 810018EE
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Categories
                Review
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Molecular medicine
                long-read sequencing,genetic variation,medical genetics,structural variation,mendelian disorders

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