Genetics of Variation in Regional Muscle Mass
Principal Investigator: Dr Arimantas Lionikas
Approved Research ID: 26746
Approval date: October 1st 2018
Skeletal muscle mass is positively associated with metabolism and energy expenditure, with longevity in older adults, and is inversely associated with insulin resistance. Individual variability in muscle mass is extensive, with over two fold differences in adults. Heritability estimates for muscle mass indicated that 80% of its variability is determined by genetic factors. We hypothesize that genes causing these differences would also have secondary effects on health-related measures. This project will aim to identify genes underlying differences in muscle mass. Numbers of individuals affected by obesity, metabolic syndrome and conditions such as non-insulin dependent diabetes are growing at an alarming rate and present major challenges for the healthcare system. In order to improve the prevention, diagnosis and to develop effective treatment, understanding of all underlying contributors to these conditions is essential. At present, the impact of genes that might be predisposing to reduced muscle mass is unknown. The outcome of the project will help raise awareness of the role of muscular fitness in health and wellbeing, and pave the way for diagnostics of genetic predisposition to reduced muscle mass. We will carry out a genome-wide association study (GWAS) between genotypes and muscle mass in order to locate genes underlying muscle mass differences. Fat-free leg and arm mass provide a proxy for muscle mass. However, muscles are not homogeneous and we have evidence from animal models showing that the same gene can have an opposite effect on the size of different muscles in the body. To mitigate effects of such scenarios, we will analyze leg and arm data separately. We also will take into account factors such as stature, age, sex, and physical activity to mitigate confounding effects. In general, larger sample size provides greater power for detection of genetic effects in GWAS. However, a broad range of age of subjects may hamper detection power if the decline in muscle muscle mass does not progress at a constant rate due to environmental or genetic effects. Therefore we will focus on two age groups: 37-48 and >64 years of age. The 37-48yr group will be used for identification of muscle mass affecting genes via GWAS. The repeat assessment data will be analyzed in the >64yr group to assess heritability of change.