Integrative analysis for big data in genomics

Abstract

With the rapid development of bio-technology (e.g.,genotyping chip and sequencing),world-wide researchers have accumulated massive data sets at different levels.Integrative analysis of multi-layered genomic data can greatly contribute to the completion of causal chain from genetic variants to phenotype variations,laying a scientific foundation for personalized and precise medicine.The integrative analysis from the following three aspects mainly reviewed:identification of causal variants and their functional annotation,pleiotropy in human complex traits,Mendelian randomization forcausal inference between phenotypes,and several case studies were provided.Finally,the importance of integrative analysis in genomic data for precision medicine was highlighted.

Keywords

• GWAS;integrative analysis;polygenicity;pleiotropy;Mendelian randomization