The effectiveness of Vision Transformers (ViTs) diminishes considerably in multi-modal face anti-spoofing (FAS) under missing modality scenarios. Existing approaches rely on modality-invariant features to alleviate this issue but ignore modality-specific features. To solve this issue, we propose a Missing Modality Adapter framework for Face Anti-Spoofing (MMA-FAS), which leverages modality-disentangle adapters and LBP-guided contrastive loss for explicit combination of modality-invariant and modality-specific features. Modality-disentangle adapters disentangle features into modality-invariant and -specific features from the view of frequency decomposition. LBP-guided contrastive loss, together with batch-level and sample-level modality masking strategies, forces the model to cluster samples according to attack types and modal combinations, which further enhances modality-specific and -specific features. Moreover, we propose an adaptively modal combination sampling strategy, which dynamically adjusts the sample probability in masking strategies to balance the training process of different modal combinations. Extensive experiments demonstrate that our proposed method achieves state-of-the-art intra-dataset and cross-dataset performance in all the missing modality scenarios.