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Genetic association studies for brain connectivity phenotypes have gainedprominence due to advances in non-invasive imaging techniques and quantitativegenetics. Brain connectivity traits, characterized by network configurationsand unique biological structures, present distinct challenges compared to otherquantitative phenotypes. Furthermore, the presence of sample relatedness inmost imaging genetics studies limits the feasibility of adopting existingnetwork-response modeling. In this paper, we fill this gap by proposing aBayesian network-response mixed-effect model that considers a network-variatephenotype and incorporates population structures including pedigrees andunknown sample relatedness. To accommodate the inherent topologicalarchitecture associated with the genetic contributions to the phenotype, wemodel the effect components via a set of effect subnetworks and impose aninter-network sparsity and intra-network shrinkage to dissect the phenotypicnetwork configurations affected by the risk genetic variant. To facilitateuncertainty quantification of signaling components from both genotype andphenotype sides, we develop a Markov chain Monte Carlo (MCMC) algorithm forposterior inference. We evaluate the performance and robustness of our modelthrough extensive simulations. By further applying the method to study thegenetic bases for brain structural connectivity using data from the HumanConnectome Project with excessive family structures, we obtain plausible andinterpretable results. Beyond brain connectivity genetic studies, our proposedmodel also provides a general linear mixed-effect regression framework fornetwork-variate outcomes.

Bayesian mixed model inference for genetic association under related samples with brain network phenotype