Wenyu Wang1, Jingcan Hao1, Shuyu Zheng2, Qianrui Fan1, Awen He1, Yan Wen1, Xiong Guo1, Cuiyan Wu1, Sen Wang1, Tielin Yang3, Hui Shen4,5, Xiangding Chen6, Qing Tian4,5, Lijun Tan6, Hong-Wen Deng4,5, Feng Zhang1. 1. Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center. 2. Department of Radiation Oncology, First Affiliated Hospital, Health Science Center. 3. Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P.R. China. 4. Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine. 5. Center for Bioinformatics and Genomics, Tulane University, New Orleans, LA, USA. 6. Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, P. R. China.
Abstract
MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: fzhxjtu@mail.xjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: Pathway association analysis has made great achievements in elucidating the genetic basis of human complex diseases. However, current pathway association analysis approaches fail to consider tissue-specificity. RESULTS: We developed a tissue-specific pathway interaction enrichment analysis algorithm (TPIEA). TPIEA was applied to two large Caucasian and Chinese genome-wide association study summary datasets of bone mineral density (BMD). TPIEA identified several significant pathways for BMD [false discovery rate (FDR) < 0.05], such as KEGG FOCAL ADHESION and KEGG AXON GUIDANCE, which had been demonstrated to be involved in the development of osteoporosis. We also compared the performance of TPIEA and classical pathway enrichment analysis, and TPIEA presented improved performance in recognizing disease relevant pathways. TPIEA may help to fill the gap of classic pathway association analysis approaches by considering tissue specificity. AVAILABILITY AND IMPLEMENTATION: The online web tool of TPIEA is available at https://sourceforge.net/projects/tpieav1/files CONTACT: fzhxjtu@mail.xjtu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.