Implications of pleiotropy: challenges and opportunities for mining Big Data in biomedicine

Abstract

Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.

Keywords: data integration; functional annotation; genome-wide association studies (GWAS); mining Big Data in biomedicine; pleiotropy.