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Limited potential of genetic predisposition scores to predict muscle mass and strength performance in Flemish Caucasians between 19 and 73 years of age
Author(s) -
Ruben Charlier,
Maarten Caspers,
Sara Knaeps,
Evelien Mertens,
Diether Lambrechts,
Johan Lefevre,
Martine Thomis
Publication year - 2016
Publication title -
physiological genomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.078
H-Index - 112
eISSN - 1531-2267
pISSN - 1094-8341
DOI - 10.1152/physiolgenomics.00085.2016
Subject(s) - flemish , global positioning system , isometric exercise , heritability , bioelectrical impedance analysis , biology , statistics , genetics , medicine , mathematics , computer science , body mass index , geography , endocrinology , telecommunications , archaeology
Since both muscle mass and strength performance are polygenic in nature, the current study compared four genetic predisposition scores (GPS) in their ability to predict these phenotypes. Data were gathered within the framework of the first-generation Flemish Policy Research Centre “Sport, Physical Activity and Health” (2002–2004). Results are based on muscle characteristics data of 565 Flemish Caucasians (19–73 yr, 365 men). Skeletal muscle mass was determined from bioelectrical impedance. The Biodex dynamometer was used to measure isometric (PT static120° ) and isokinetic strength (PT dynamic60° and PT dynamic240° ), ballistic movement speed (S 20% ), and muscular endurance (Work) of the knee extensors. Genotyping was done for 153 gene variants, selected on the basis of a literature search and the expression quantitative trait loci of selected genes. Four GPS were designed: a total GPS (based on the sum of all 153 variants, each favorable allele = score 1), a data-driven and weighted GPS [respectively, the sum of favorable alleles of those variants with significant b-coefficients in stepwise regression (GPS dd ), and the sum of these variants weighted with their respective partial r 2 (GPS w )], and an elastic net GPS (based on the variants that were selected by an elastic net regularization; GPS en ). It was found that four different models for a GPS were able to significantly predict up to ~7% of the variance in strength performance. GPS en made the best prediction of SMM and Work. However, this was not the case for the remaining strength performance parameters, where best predictions were made by GPS dd and GPS w .

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