Abstract

Genome-wide association study (GWAS) has been widely used to identify common single nucleotide polymorphisms (SNPs) associated with complex human diseases. However, this single-marker association test is not powerful to detect rare variants. To increase power, gene-based tests have been developed. In addition to SNPs, other omics data, such as gene expression and DNA methylation can be obtained from the same individuals, which offers the researchers to study multi-omics data in an integrative manner. While large consortia (e.g., conducting GWAS or eQTL) usually do not share individual-level data, it is relatively easy to access summary-level data. More recently, researchers have developed methods to utilize these summarized genetic associations for different purposes, such as (1) polygenic risk score (PRS); (2) transcriptome-wide association study (TWAS); and (3) Mendelian randomization (MR) based causal inference.