Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics

• Main Authors: Guangsheng Pei, Hua Sun, Yulin Dai, Xiaoming Liu, Zhongming Zhao, Peilin Jia
• Format: Article
• Language: English
• Published: BMC 2019-02-01
• Series: BMC Genomics
• Subjects: GWAS; Pathway enrichment analysis; Multi-dimensional scaling; Cross-trait association; Summary statistics; Pleiotropy abbreviations
• Online Access: http://link.springer.com/article/10.1186/s12864-018-5373-7

Abstract Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted p FET < 1 × 10− 3 (p FET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future. Conclusions Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits.