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Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging.

Daniel L McCartney | Josine L Min | Rebecca C Richmond | Ake T Lu | Maria K Sobczyk | Gail Davies | Linda Broer | Xiuqing Guo | Ayoung Jeong | Jeesun Jung | Silva Kasela | Seyma Katrinli | Pei-Lun Kuo | Pamela R Matias-Garcia | Pashupati P Mishra | Marianne Nygaard | Teemu Palviainen | Amit Patki | Laura M Raffield | Scott M Ratliff | Tom G Richardson | Oliver Robinson | Mette Soerensen | Dianjianyi Sun | Pei-Chien Tsai | Matthijs D van der Zee | Rosie M Walker | Xiaochuan Wang | Yunzhang Wang | Rui Xia | Zongli Xu | Jie Yao | Wei Zhao | Adolfo Correa | Eric Boerwinkle | Pierre-Antoine Dugué | Peter Durda | Hannah R Elliott | Christian Gieger | Genetics of DNA Methylation Consortium | Eco J C de Geus | Sarah E Harris | Gibran Hemani | Medea Imboden | Mika Kähönen | Sharon L R Kardia | Jacob K Kresovich | Shengxu Li | Kathryn L Lunetta | Massimo Mangino | Dan Mason | Andrew M McIntosh | Jonas Mengel-From | Ann Zenobia Moore | Joanne M Murabito | NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium | Miina Ollikainen | James S Pankow | Nancy L Pedersen | Annette Peters | Silvia Polidoro | David J Porteous | Olli Raitakari | Stephen S Rich | Dale P Sandler | Elina Sillanpää | Alicia K Smith | Melissa C Southey | Konstantin Strauch | Hemant Tiwari | Toshiko Tanaka | Therese Tillin | Andre G Uitterlinden | David J Van Den Berg | Jenny van Dongen | James G Wilson | John Wright | Idil Yet | Donna Arnett | Stefania Bandinelli | Jordana T Bell | Alexandra M Binder | Dorret I Boomsma | Wei Chen | Kaare Christensen | Karen N Conneely | Paul Elliott | Luigi Ferrucci | Myriam Fornage | Sara Hägg | Caroline Hayward | Marguerite Irvin | Jaakko Kaprio | Deborah A Lawlor | Terho Lehtimäki | Falk W Lohoff | Lili Milani | Roger L Milne | Nicole Probst-Hensch | Alex P Reiner | Beate Ritz | Jerome I Rotter | Jennifer A Smith | Jack A Taylor | Joyce B J van Meurs | Paolo Vineis | Melanie Waldenberger | Ian J Deary | Caroline L Relton | Steve Horvath | Riccardo E Marioni
Genome biology | 2021

Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.

Pubmed ID: 34187551 RIS Download

Research resources used in this publication

None found

Antibodies used in this publication

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Associated grants

  • Agency: NHLBI NIH HHS, United States
    Id: R01 HL141292
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_00011/6
  • Agency: NHLBI NIH HHS, United States
    Id: HHSN268201000021C
  • Agency: Medical Research Council, United Kingdom
    Id: MR/R023484/1
  • Agency: NHLBI NIH HHS, United States
    Id: U01 HL120393
  • Agency: Wellcome Trust, United Kingdom
    Id: 208806/Z/17/Z
  • Agency: NHLBI NIH HHS, United States
    Id: R01 HL117626
  • Agency: NHLBI NIH HHS, United States
    Id: HHSN268201000001I
  • Agency: NIA NIH HHS, United States
    Id: U01 AG060908
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_00011/5
  • Agency: Cancer Research UK, United Kingdom
    Id: C18281/A191169
  • Agency: NHLBI NIH HHS, United States
    Id: R01 HL120393
  • Agency: Medical Research Council, United Kingdom
    Id: MR/S03532X/1
  • Agency: Biotechnology and Biological Sciences Research Council, United Kingdom
    Id: BB/S020845/1
  • Agency: Medical Research Council, United Kingdom
    Id: MC_UU_00007/10
  • Agency: NHLBI NIH HHS, United States
    Id: R01 HL133221
  • Agency: Medical Research Council, United Kingdom
    Id: MR/R0245065/1
  • Agency: NIA NIH HHS, United States
    Id: R01 AG054628

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This is a list of tools and resources that we have found mentioned in this publication.


Functional Mapping and Annotation of Genome Wide Association Studies (tool)

RRID:SCR_017521

Platform that can be used to annotate, prioritize, visualize and interpret GWAS results. To submit your own GWAS, login is required for security reason. You can browse public results of FUMA from Browse Public Results without registration or login.

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METAL (tool)

RRID:SCR_002013

Software application designed to facilitate meta-analysis of large datasets (such as several whole genome scans) in a convenient, rapid and memory efficient manner. (entry from Genetic Analysis Software)

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1000 Genomes: A Deep Catalog of Human Genetic Variation (tool)

RRID:SCR_006828

International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

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sabre (tool)

RRID:SCR_011843

Software tool to demultiplex barcoded reads into separate files. Works on both single-end and paired-end data in fastq format. Used in next generation sequencing to analyze a broad range of data.

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