Gene and Gene-Set Analysis for Genome-Wide Association Studies

TL;DR

This paper introduces gene and gene-set analysis methods that leverage biological knowledge to enhance interpretation and power in GWAS, especially for neuropsychiatric disorders and small sub-phenotype studies.

Contribution

It develops new gene-based statistical methods and applies them to GWAS data, integrating gene expression and network analyses to improve insights into genetic underpinnings of diseases.

Findings

Gene-based methods improve GWAS interpretation.

Network and gene-set analyses identify relevant biological pathways.

Enhanced analysis of small-sample sub-phenotypes.

Abstract

Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~10^4-10^5) are needed to find the majority of genetic variants underlying risk for most human diseases. Therefore, the field has engaged itself in a race to increase study sample sizes with some studies yielding very successful results but also studies which provide little or no new insights. This project started early on in this new wave of studies and I decided to use an alternative approach that uses prior biological knowledge to improve both interpretation and power of GWAS. The project aimed to a) implement and develop new gene-based methods to derive gene-level statistics to use GWAS in well established system biology tools; b) use of these gene-level statistics in networks and gene-set analyses of GWAS data; c) mine GWAS of neuropsychiatric disorders using gene, gene-sets and integrative biology analyses with gene-expression studies; and d) explore the ability of these methods to improve the analysis GWAS on disease sub-phenotypes which usually suffer of very small sample sizes.