Background: Handgrip strength is a widely used proxy of muscular fitness and a marker of frailty. Low or declining handgrip strength predicts a range of morbidities and all-cause mortality. We compared different strategies to perform genome-wide association study on longitudinal handgrip measurements using sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.
Methods: A total of 12,342 participants from six cohort studies (Amish, ARIC, CHS, FHS, HyperGEN, and WHI) had between two and six handgrip measures per exam from one to nine separate exams over time, totaling 32,266 observations. We selected the maximum observation per participant at each exam and used per participant: 1) all exams (ALL), 2) one exam (ONE), or 3) the mean of all exams (MEAN). We conducted association analyses with GMMAT using linear mixed models adjusted for age, sex, height, BMI, study, age×sex, BMI×sex, study×sex, and 11 ancestry principal components, with random effects for sex, study and kinship. In addition, for the ALL analysis, we included a random effect for participant to account for correlation across exams.
Results: Leveraging multiple measures per individual resulted in a 5-10% increase in effective sample size. The -log(P) from the three analyses were highly correlated (rALLvsONE=0.85, rALLvsMEAN=0.97, rONEvsMEAN=0.88). The genomic control inflation factors for the three analyses were similar; the ALL analysis had the lowest lambda for low frequency and rare variants.
Conclusion: When available, the use of multiple observations per individual using mixed effect models can increase effective sample size and thus power while controlling type I error.