Variables　selection　and　parameter　estimation　are　of　great　significance　in　all　regression　analysis.　A　variety　of　approaches　have　been　proposed　to　tackle　this　problem.　Among　those,　the　penalty-based　shrinkage　approach　has　been　most　popular　for　the　ability　to　carry　out　the　variable　selection　and　parameter　estimation　simultaneously.　However,　not　much　work　is　available　on　the　variable　selection　for　the　generalized　partially　models　(GPLMs)　with　longitudinal　data.　In　this　paper,　we　proposed　a　variable　selection　procedure　for　GPLMs　with　longitudinal　data.　The　inference　is　based　on　the　SCAD-penalized　quadratic　inference　functions,　which　is　obtained　after　the　B-spline　approximating　to　non-parametric　function　in　the　model.　The　proposed　approach　efficiently　utilized　the　within-cluster　correlation　information,　which　can　improve　estimating　efficiency.　The　proposed　approach　also　has　the　virtue　of　low　computational　cost.　With　the　tuning　parameter　chosen　by　BIC,　the　correct　model　is　identified　with　probability　tends　to　1.　The　resulted　estimator　of　the　parametric　component　is　asymptotic　to　a　normal　distribution,　and　that　of　the　non-parametric　function　achieves　the　optimal　convergence　rate.　The　performance　of　the　proposed　methods　is　evaluated　through　extensive　simulation　studies.　A　real　data　analysis　shows　that　the　proposed　approach　succeeds　in　excluding　the　insignificant　variable.