This talk considers estimation and prediction of high-dimensional linear regression model in the setting of transfer learning, using samples from the target model as well as auxiliary samples from different but possibly related models. When the set of ``informative" auxiliary samples is known, an estimator and a predictor are proposed and their optimality is established. The optimal rates of convergence for prediction and estimation are faster than the corresponding rates without using the auxiliary samples. This implies that knowledge from the informative auxiliary samples can be transferred to improve the learning performance of the target problem. When sample informativeness is unknown, a data-driven procedure for transfer learning, called Trans-Lasso is proposed, and its robustness to non-informative auxiliary samples and its efficiency in knowledge transfer is established. The proposed procedures are demonstrated in numerical studies and in analysis of the GTEx data sets.