부산대학교 도서관

The integration of multi‐omics information (e.g., epigenetics and transcriptomics) can be useful for interpreting findings from genome‐wide association studies (GWAS). It has been suggested that multi‐omics could circumvent or greatly reduce the need to increase GWAS sample sizes for novel variant discovery. We tested whether incorporating multi‐omics information in earlier and smaller‐sized GWAS boosts true‐positive discovery of genes that were later revealed by larger GWAS of the same/similar traits. We applied 10 different analytic approaches to integrating multi‐omics data from 12 sources (e.g., Genotype‐Tissue Expression project) to test whether earlier and smaller GWAS of 4 brain‐related traits (alcohol use disorder/problematic alcohol use, major depression/depression, schizophrenia, and intracranial volume/brain volume) could detect genes that were revealed by a later and larger GWAS. Multi‐omics data did not reliably identify novel genes in earlier less‐powered GWAS (PPV <0.2; 80% false‐positive associations). Machine learning predictions marginally increased the number of identified novel genes, correctly identifying 1–8 additional genes, but only for well‐powered early GWAS of highly heritable traits (i.e., intracranial volume and schizophrenia). Although multi‐omics, particularly positional mapping (i.e., fastBAT, MAGMA, and H‐MAGMA), can help to prioritize genes within genome‐wide significant loci (PPVs = 0.5–1.0) and translate them into information about disease biology, it does not reliably increase novel gene discovery in brain‐related GWAS. To increase power for discovery of novel genes and loci, increasing sample size is required. [ABSTRACT FROM AUTHOR]