  Many genetic mutations adversely affect the structure and function of
load-bearing soft tissues, with clinical sequelae often responsible for
disability or death. Parallel advances in genetics and histomechanical
characterization provide significant insight into these conditions, but there
remains a pressing need to integrate such information. We present a novel
genotype-to-biomechanical-phenotype neural network (G2{\Phi}net) for
characterizing and classifying biomechanical properties of soft tissues, which
serve as important functional readouts of tissue health or disease. We
illustrate the utility of our approach by inferring the nonlinear,
genotype-dependent constitutive behavior of the aorta for four mouse models
involving defects or deficiencies in extracellular constituents. We show that
G2{\Phi}net can infer the biomechanical response while simultaneously ascribing
the associated genotype correctly by utilizing limited, noisy, and unstructured
experimental data. More broadly, G2{\Phi}net provides a powerful method and a
paradigm shift for correlating genotype and biomechanical phenotype
quantitatively, promising a better understanding of their interplay in
biological tissues.
