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      Evaluation of mitochondrial DNA copy number estimation techniques

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          Abstract

          Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes per cell, is a minimally invasive proxy measure for mitochondrial function and has been associated with several aging-related diseases. Although quantitative real-time PCR (qPCR) is the current gold standard method for measuring mtDNA-CN, mtDNA-CN can also be measured from genotyping microarray probe intensities and DNA sequencing read counts. To conduct a comprehensive examination on the performance of these methods, we use known mtDNA-CN correlates (age, sex, white blood cell count, Duffy locus genotype, incident cardiovascular disease) to evaluate mtDNA-CN calculated from qPCR, two microarray platforms, as well as whole genome (WGS) and whole exome sequence (WES) data across 1,085 participants from the Atherosclerosis Risk in Communities (ARIC) study and 3,489 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). We observe mtDNA-CN derived from WGS data is significantly more associated with known correlates compared to all other methods ( p < 0.001). Additionally, mtDNA-CN measured from WGS is on average more significantly associated with traits by 5.6 orders of magnitude and has effect size estimates 5.8 times more extreme than the current gold standard of qPCR. We further investigated the role of DNA extraction method on mtDNA-CN estimate reproducibility and found mtDNA-CN estimated from cell lysate is significantly less variable than traditional phenol-chloroform-isoamyl alcohol ( p = 5.44x10 -4) and silica-based column selection ( p = 2.82x10 -7). In conclusion, we recommend the field moves towards more accurate methods for mtDNA-CN, as well as re-analyze trait associations as more WGS data becomes available from larger initiatives such as TOPMed.

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          Most cited references 35

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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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            Fast and accurate genotype imputation in genome-wide association studies through pre-phasing.

            The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.
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              Improved whole-chromosome phasing for disease and population genetic studies.

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 January 2020
                2020
                : 15
                : 1
                Affiliations
                [1 ] Department of Genetic Medicine, McKusick-Nathans Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
                [2 ] Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States of America
                [3 ] Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States of America
                [4 ] Department of Epidemiology and the Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
                [5 ] LABioMed and Department of Pediatrics, at Harbor-UCLA Medical Center, Institute for Translational Genomics and Population Sciences, Torrance, CA, United States of America
                [6 ] Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States of America
                Vanderbilt University Medical Center, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Article
                PONE-D-19-28415
                10.1371/journal.pone.0228166
                6994099
                32004343
                © 2020 Longchamps et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 2, Pages: 14
                Product
                Funding
                This research was supported by grant R01HL131573 from the US National Institutes of Health (Longchamps, Castellani, Guallar, and Arking) and by grant P30AG021334 from the Johns Hopkins University Claude D. Pepper Older Americans Independence Center National Institute on Aging (Dr Arking). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I). The Multi-Ethnic Study of Atherosclerosis is supported by contracts HHSN268201500003I, N01-HC 95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). Dr. Rotter and Dr. Taylor’s efforts were supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. Funding for SHARe genotyping was provided by National Heart, Lung, and Blood Institute contract N02-HL-64278. The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Categories
                Research Article
                Research and analysis methods
                Extraction techniques
                DNA extraction
                Medicine and Health Sciences
                Cardiovascular Medicine
                Cardiovascular Diseases
                Biology and Life Sciences
                Biochemistry
                Bioenergetics
                Energy-Producing Organelles
                Mitochondria
                Biology and Life Sciences
                Cell Biology
                Cellular Structures and Organelles
                Energy-Producing Organelles
                Mitochondria
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Microarrays
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Genotyping
                Research and Analysis Methods
                Molecular Biology Techniques
                Genotyping
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Biology and Life Sciences
                Genetics
                Molecular Genetics
                Biology and Life Sciences
                Molecular Biology
                Molecular Genetics
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                Sequence Analysis
                Sequence Alignment
                Custom metadata
                Data from this study are available upon request as these data contain potentially identifying and sensitive patient information. Individuals who wish to access these data are welcome to contact the ARIC coordinating center at UNC ( aricpub@ 123456unc.edu ) or the MESA coordinating center ( chsccweb@ 123456u.washington.edu ).

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