Multiple musculoskeletal traits assessed by various methods at different skeletal sites serve as surrogates for osteoporosis risk. However, it is a challenge to select the most relevant phenotypes for genetic study of fractures. Principal component analyses (PCA) were conducted in participants of the Framingham Osteoporosis Study on 17 measures including bond mineral density (BMD) (hip and spine), heel ultrasound, leg lean mass (LLM), and hip geometric indices, adjusting for covariates (age, height, body mass index [BMI]), in a combined sample of 1180 men and 1758 women, as well as in each sex. Four principal components (PCs) jointly explained ~69% of the total variability of musculoskeletal traits. PC1, explaining ~33% of the total variance, was referred to as the component of "Bone strength," because it included the hip and spine BMD as well as several hip cross-sectional properties. PC2 (20.5% variance) was labeled as "Femoral cross-sectional geometry;" PC3 (~8% variance) captured only ultrasound measures; PC4, explaining ~7% variance, was correlated with LLM and hip geometry. We then evaluated ~2.5 mil SNPs for association with PCs 1, 2, and 4. There were genome-wide significant associations (p < 5 × 10⁻⁸) between PC2 and HTR1E (that codes for one of the serotonin receptors) and PC4 with COL4A2 in women. In the sexes-combined sample, AKAP6 was associated with PC2 (p = 1.40 × 10⁻⁷). A single nucleotide polymorphism (SNP) in HTR1E was also associated with the risk of nonvertebral fractures in women (p = 0.005). Functions of top associated genes were enriched for the skeletal and muscular system development (p < 0.05). In conclusion, multivariate combination provides genetic associations not identified in the analysis of primary phenotypes. Genome-wide screening for the linear combinations of multiple osteoporosis-related phenotypes suggests that there are variants with potentially pleiotropic effects in established and novel pathways to be followed up to provide further evidence of their functions.