| Title |
Genomewide meta‐analysis identifies loci associated with IGF‐I and IGFBP‐3 levels with impact on age‐related traits
|
|---|---|
| Published in |
Aging Cell, June 2016
|
| DOI | 10.1111/acel.12490 |
| Pubmed ID | |
| Authors |
Alexander Teumer, Qibin Qi, Maria Nethander, Hugues Aschard, Stefania Bandinelli, Marian Beekman, Sonja I. Berndt, Martin Bidlingmaier, Linda Broer, CHARGE Longevity Working Group, Anne Cappola, Gian Paolo Ceda, Stephen Chanock, Ming‐Huei Chen, Tai C. Chen, Yii‐Der Ida Chen, Jonathan Chung, Fabiola Del Greco Miglianico, Joel Eriksson, Luigi Ferrucci, Nele Friedrich, Carsten Gnewuch, Mark O. Goodarzi, Niels Grarup, Tingwei Guo, Elke Hammer, Richard B. Hayes, Andrew A. Hicks, Albert Hofman, Jeanine J. Houwing‐Duistermaat, Frank Hu, David J. Hunter, Lise L. Husemoen, Aaron Isaacs, Kevin B. Jacobs, Joop A. M. J. L. Janssen, John‐Olov Jansson, Nico Jehmlich, Simon Johnson, Anders Juul, Magnus Karlsson, Tuomas O. Kilpelainen, Peter Kovacs, Peter Kraft, Chao Li, Allan Linneberg, Yongmei Liu, Ruth J. F. Loos, Body Composition Genetics Consortium, Mattias Lorentzon, Yingchang Lu, Marcello Maggio, Reedik Magi, James Meigs, Dan Mellström, Matthias Nauck, Anne B. Newman, Michael N. Pollak, Peter P. Pramstaller, Inga Prokopenko, Bruce M. Psaty, Martin Reincke, Eric B. Rimm, Jerome I. Rotter, Aude Saint Pierre, Claudia Schurmann, Sudha Seshadri, Klara Sjögren, P. Eline Slagboom, Howard D. Strickler, Michael Stumvoll, Yousin Suh, Qi Sun, Cuilin Zhang, Johan Svensson, Toshiko Tanaka, Archana Tare, Anke Tönjes, Hae‐Won Uh, Cornelia M. van Duijn, Diana van Heemst, Liesbeth Vandenput, Ramachandran S. Vasan, Uwe Völker, Sara M. Willems, Claes Ohlsson, Henri Wallaschofski, Robert C. Kaplan |
| Abstract |
The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Through genomewide association study of up to 30 884 adults of European ancestry from 21 studies, we confirmed and extended the list of previously identified loci associated with circulating IGF-I and IGFBP-3 concentrations (IGF1, IGFBP3, GCKR, TNS3, GHSR, FOXO3, ASXL2, NUBP2/IGFALS, SORCS2, and CELSR2). Significant sex interactions, which were characterized by different genotype-phenotype associations between men and women, were found only for associations of IGFBP-3 concentrations with SNPs at the loci IGFBP3 and SORCS2. Analyses of SNPs, gene expression, and protein levels suggested that interplay between IGFBP3 and genes within the NUBP2 locus (IGFALS and HAGH) may affect circulating IGF-I and IGFBP-3 concentrations. The IGF-I-decreasing allele of SNP rs934073, which is an eQTL of ASXL2, was associated with lower adiposity and higher likelihood of survival beyond 90 years. The known longevity-associated variant rs2153960 (FOXO3) was observed to be a genomewide significant SNP for IGF-I concentrations. Bioinformatics analysis suggested enrichment of putative regulatory elements among these IGF-I- and IGFBP-3-associated loci, particularly of rs646776 at CELSR2. In conclusion, this study identified several loci associated with circulating IGF-I and IGFBP-3 concentrations and provides clues to the potential role of the IGF axis in mediating effects of known (FOXO3) and novel (ASXL2) longevity-associated loci. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 27% |
| Canada | 1 | 7% |
| Mexico | 1 | 7% |
| Spain | 1 | 7% |
| Unknown | 8 | 53% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 80% |
| Scientists | 3 | 20% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Russia | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 139 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 27 | 19% |
| Student > Ph. D. Student | 22 | 16% |
| Student > Bachelor | 11 | 8% |
| Professor | 10 | 7% |
| Student > Master | 10 | 7% |
| Other | 26 | 18% |
| Unknown | 35 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 33 | 23% |
| Biochemistry, Genetics and Molecular Biology | 28 | 20% |
| Agricultural and Biological Sciences | 11 | 8% |
| Nursing and Health Professions | 3 | 2% |
| Neuroscience | 3 | 2% |
| Other | 20 | 14% |
| Unknown | 43 | 30% |