RESUMO
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.
Assuntos
Envelhecimento/genética , Marcha/genética , Polimorfismo de Nucleotídeo Único , Velocidade de Caminhada/genética , Idoso , Idoso de 80 Anos ou mais , Feminino , Estudo de Associação Genômica Ampla , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Locos de Características Quantitativas , Proteínas Tirosina Fosfatases Classe 2 Semelhantes a Receptores/genética , Serina Endopeptidases/genéticaRESUMO
BACKGROUND: Inexpensive, non-invasive tools for assessing Alzheimer-type pathophysiologies are needed. Computerized cognitive assessments are prime candidates. METHODS: Cognitively normal participants, aged 51-71, with magnetic resonance imaging, fluorodeoxyglucose-positron emission tomography (FDG-PET), amyloid PET, CogState computerized cognitive assessment, and standard neuropsychological tests were included. We first examined the association between the CogState battery and neuroimaging measures. We then compared that association to the one between standard neuropsychological z-scores and neuroimaging. RESULTS: Slower reaction times for CogState Identification and One Back, and lower memory and attention z-scores, were associated (P < .05) with FDG-PET hypometabolism. Slower time on the Groton Maze Learning Task and worse One Card Learning accuracy were associated (P < .05) with smaller hippocampal volumes. There were no associations with amyloid PET. Associations of CogState and neuropsychological Z-scores with neuroimaging were small and of a similar magnitude. CONCLUSIONS: CogState subtests were cross-sectionally comparable to standard neuropsychological tests in their relatively weak associations with neurodegeneration imaging markers.