Objective: The accuracy of a musculoskeletal model relies heavily on the implementation of the underlying anatomical dataset. Linear scaling of a generic model, despite being time and cost-efficient, produces substantial errors as it does not account for gender differences and inter-individual anatomical variations. The hypothesis of this study is that linear scaling to a musculoskeletal model with gender and anthropometric similarity to the individual subject produces similar results to the ones that can be obtained from a subject-specific model. Methods: A lower limb musculoskeletal anatomical atlas was developed consisting of ten datasets derived from magnetic resonance imaging of healthy subjects and an additional generic dataset from the literature. Predicted muscle activation and joint reaction force were compared with electromyography and literature data. Regressions based on gender and anthropometry were used to identify the use of atlas. Results: Primary predictors of differences for the joint reaction force predictions were mass difference for the ankle (p<0.001) and length difference for the knee and hip (p≤0.017) . Gender difference accounted for an additional 3% of the variance (p≤0.039) . Joint reaction force differences at the ankle, knee and hip were reduced by between 50% and 67% (p=0.005) when using a musculoskeletal model with the same gender and similar anthropometry in comparison with a generic model. Conclusion: Linear scaling with gender and anthropometric similarity can improve joint reaction force predictions in comparison with a scaled generic model. Significance: The scaling approach and atlas presented can improve the fidelity and utility of musculoskeletal models for subject-specific applications.