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      Contributions of rare and common variation to early-onset and atypical dementia risk

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          Abstract

          We collected and analyzed genomic sequencing data from individuals with clinician-diagnosed early-onset or atypical dementia. Thirty-two patients were previously described, with 68 newly described in this report. Of those 68, 62 patients self-reported white, non-Hispanic ethnicity and 6 reported as African–American, non-Hispanic. Fifty-three percent of patients had a returnable variant. Five patients harbored a pathogenic variant as defined by the American College of Medical Genetics criteria for pathogenicity. A polygenic risk score (PRS) was calculated for Alzheimer's patients in the total cohort and compared to the scores of a late-onset Alzheimer's cohort and a control set. Patients with early-onset Alzheimer's had higher non- APOE PRSs than patients with late-onset Alzheimer's, supporting the conclusion that both rare and common genetic variation associate with early-onset neurodegenerative disease risk.

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          Most cited references54

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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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            The variant call format and VCFtools

            Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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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

                Journal
                Cold Spring Harb Mol Case Stud
                Cold Spring Harb Mol Case Stud
                cshmcs
                cshmcs
                Cold Spring Harbor Molecular Case Studies
                Cold Spring Harbor Laboratory Press
                2373-2873
                June 2023
                : 9
                : 3
                : a006271
                Affiliations
                [1 ]HudsonAlpha Institute for Biotechnology, Huntsville, Alabama 35806, USA;
                [2 ]University of Alabama in Huntsville, Huntsville, Alabama 35899, USA;
                [3 ]Alzheimer's Disease Center, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA
                Author notes
                Author information
                http://orcid.org/0000-0001-7307-8907
                http://orcid.org/0000-0001-7700-530X
                http://orcid.org/0000-0003-1735-1766
                http://orcid.org/0000-0003-0622-3275
                http://orcid.org/0000-0001-9701-676X
                http://orcid.org/0000-0002-7537-3587
                http://orcid.org/0000-0003-2958-1876
                http://orcid.org/0000-0002-9627-0309
                http://orcid.org/0000-0002-1810-9763
                http://orcid.org/0000-0002-9852-5504
                Article
                MCS006271Wri
                10.1101/mcs.a006271
                10393188
                37308299
                297452cf-1a36-4e60-9530-e265a9dd7695
                © 2023 Wright et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits reuse and redistribution, except for commercial purposes, provided that the original author and source are credited.

                History
                : 31 January 2023
                : 7 June 2023
                Page count
                Pages: 15
                Funding
                Funded by: Daniel Foundation of Alabama
                Funded by: HudsonAlpha Foundation Memory and Mobility Program
                Funded by: NIH , doi 10.13039/100000002;
                Award ID: R00AG068271
                Award ID: 5P20AG068024
                Categories
                Follow-up Report

                alzheimer disease,frontotemporal dementia
                alzheimer disease, frontotemporal dementia

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