1. A dual-clustering framework for association screening with whole genome sequencing data and longitudinal traits Lui, Ying; Huang, Chien-Hsun; Hu, Inchi; Zheng, Tian; Lo, Shaw-Hwa 2014 Articles GeneticsBiometry
2. A partition-based approach to identify gene-environment interactions in genome wide association studies Fan, Ruixue; Huang, Chien-Hsun; Hu, Inchi; Wang, Haitan; Zheng, Tian; Lo, Shaw-Hwa 2014 Articles GeneticsBiometry
3. Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach Liu, Ying; Huang, Chien-Hsun; Hu, Inchi; Zheng, Tian; Lo, Shaw-Hwa 2011 Articles GeneticsBiometry
4. Considering interactive effects in the identification of influential regions with extremely rare variants via fixed bin approach Agne, Michael; Huang, Chien-Hsun; Hu, Inchi; Wang, Haitian; Zheng, Tian; Lo, Shaw-Hwa 2014 Articles GeneticsBiometry
5. Correction: A dual clustering framework for association screening with whole genome sequencing data and longitudinal traits Liu, Ying; Huang, Chien-Hsun; Hu, Inchi; Lo, Shaw-Hwa; Zheng, Tian 2014 Articles GenomicsBiometry
6. Discovering pure gene-environment interactions in blood pressure genome-wide association studies data: a two-step approach incorporating new statistics Wang, Maggie Haitan; Huang, Chien-Hsun; Zheng, Tian; Lo, Shaw-Hwa; Hu, Inchi 2014 Articles GeneticsBiometry
7. Identifying influential regions in extremely rare variants using a fixed-bin approach Agne, Michael; Huang, Chien-Hsun; Hu, Inchi; Wang, Haitian; Zheng, Tian; Lo, Shaw-Hwa 2011 Articles GeneticsBiometry
8. New insights into old methods for identifying causal rare variants Hu, Inchi; Zheng, Tian; Huang, Chien-Hsun; Lo, Shaw-Hwa; Wang, Haitian 2011 Articles Human genetics--VariationBiometry--Statistical methodsStatistics--MethodologyBiometryStatistics