Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences
Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718).
We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10−8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10−8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10−21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition.
These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.
Human Genomics launched with BioMed Central in July 2012, transferring from its previous publisher Henry Stewart Publications. All back content is now available in the archive.
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COVID-19 and impact on peer review
As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times. Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.
Aims and scope
Human Genomics is a peer-reviewed, open access journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics.
Guest Editors: Kirill A. Veselkov, Imperial College, London, UK; Takashi Gojobori, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
We solicit manuscripts for a topical collection on "AI and Genomics" in Human Genomics. As you know, human genomics has become one of the most active areas of cutting-edge life sciences and grown to be one of the largest generators of data. In addition to the value of greatly enhanced experimental examination and validation, human genomics relies on emerging, powerful computational approaches, such as Big Data analyses and artificial intelligence (AI) including network science, machine learning, deep learning, text mining, knowledge-based database construction, and even quantum computing. The collection would also be a home for articles focusing on practical applications.
We welcome original articles as well as review papers. Please indicate in your cover letter that your article is intended for the topical collection on "AI and Genomics” and select the collection upon submission. Submissions can also retrospectively be assigned to the collection. Please notify the Journal Editorial Office accordingly.
We look forward to receiving high-quality submissions of significance that can make further contributions to the field of human genomics.
Call for Papers: Genomics of COVID-19: Molecular Mechanisms Going from Susceptibility to Severity of the Disease
Guest Editors: Giuseppe Novelli, University of Rome Tor Vergata, Italy; Juergen Reichardt, James Cook University, Australia
The current COVID-19 pandemic has highlighted the importance of science and medicine, specifically public health, in our modern societies. Countries have taken different approaches to the pandemic. Science and medicine will play an important role in our way forward in tackling COVID-19. Specifically, genetics and genomics will be central in discovering variations in virus strains and their impact on patients’ outcome, the hosts’ ability to fend off the virus and the severity of disease in patients. Furthermore, the question of long-term immunity to COVID-19 may have a genetic and genomic basis which should be investigated. Some of these human genetics and genomics investigations will undoubtedly be suitable for publication in Human Genomics. We expressly welcome submissions of manuscripts on such subjects.
Guest Editors: Ying Chen, Yale University, USA; Won Yeong Kang, The Jackson Laboratory, USA; Hassane Mchaourab, Vanderbilt University, USA
In recent decades, genetically manipulated animal models have been developed and used widely in the biomedical research field. Use of animal models thus serves as an important tool to elucidate mechanisms of human disease, as well as to develop new diagnostic and therapeutic strategies for the treatment of these diseases. In this topical collection, we intend to provide up-to-date information on recent genetic animal models, and new knowledge derived from these studies on the pathophysiology, diagnosis and therapeutic drugs of human disease. We invite investigators to contribute original research and review articles that describe: (i) newly developed animal models, (ii) intervention studies using animal models, and (iii) comparisons between existing models for certain diseases.
Guest Editors: George P Patrinos, University of Patras, Greece; Hongyu Zhao, Yale University, USA
Papers are invited which address current issues in human public health genomics, such as genomic surveillance of disease, genetic risk prediction, individual genome interpretation, gene-environment interactions, genetic diversity of vector-borne disease, vaccination and vaccine-based approaches against pathogens data sharing, economic evaluation in genomic medicine, and the role of big data and artificial intelligence on the development of translational tools and services and the overall future of public health.
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