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Aging (Albany NY). 2017 Jan 10;9(1):209-246. doi: 10.18632/aging.101151.

The complex genetics of gait speed: genome-wide meta-analysis approach.

Author information

1
Department of Medicine and Genetics Albert Einstein College of Medicine, Bronx, NY 10461, USA.
2
Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02131, USA.
3
Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel.
4
Integrated Divisions of Cognitive & Motor Aging (Neurology) and Geriatrics (Medicine), Montefiore-Einstein Center for the Aging Brain, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
5
The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA.
6
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA.
7
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
8
Population Sciences Branch, National Heart Lung and Blood Institute, Framingham, MA 01702, USA.
9
Psychology Department, University of Haifa, Haifa, Israel.
10
Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands.
11
Max Planck Institute for Biology of Ageing, Köln, Germany.
12
Department of Biostatistics, University of Washington, Seattle, WA 98115, USA.
13
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60614, USA.
14
Translational Gerontology Branch, National Institute on Aging, Baltimore MD 21224, USA.
15
Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI 48104, USA.
16
Bioinformatics Core Facility, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
17
The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
18
Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands.
19
Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
20
Division of Geriatric Medicine, Johns Hopkins Medical Institutes, Baltimore, MD 21224, USA.
21
Medicine, Peninsula Health, Peninsula Clinical School, Central Clinical School, Frankston, Melbourne, Victoria, Australia.
22
Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
23
Icelandic Heart Association, Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland.
24
Broad Institute of Harvard and MIT, Cambridge, Harvard Medical School, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA.
25
Rush Institute for Healthy Aging and Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA.
26
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
27
Department of Psychology, University of Edinburgh, Edinburgh, UK.
28
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD 20892, USA.
29
Mayo Clinic, Rochester, MN 55905, USA.
30
California Pacific Medical Center Research Institute, San Francisco, CA 94107, USA.
31
Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN 55905, USA.
32
Geriatric Unit, Azienda Sanitaria Firenze (ASF), Florence, Italy.
33
Department of Human Biology, Faculty of Natural Science, University of Haifa, Haifa, Israel.
34
Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherland.
35
Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
36
Genetical Statistics, Leiden University Medical Center, Leiden, Netherland. Department of Statistics, University of Leeds, Leeds, UK.
37
Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska, Academy, University of Gothenburg, Gothenburg, Sweden.
38
Department of Neuroscience, Erasmus MC, Rotterdam, Netherlands.
39
Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden.
40
Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD 20892, USA.
41
Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC 27109, USA.
42
School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, Australia.
43
Department of Internal Medicine, Erasmus MC, and Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands.
44
Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK.
45
University of Mississippi Medical Center, Jackson, MS 39216, USA.
46
Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA 15261, USA.
47
Broad Institute of Harvard and MIT, Boston, MA 02131, USA.
48
Co senior authors.
49
Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA.

Abstract

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

KEYWORDS:

GWAS; aging; gait speed; meta-analysis

PMID:
28077804
PMCID:
PMC5310665
DOI:
10.18632/aging.101151
[Indexed for MEDLINE]
Free PMC Article

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