Integrative bioinformatic analyses of genome-wide association studies for understanding the genetic bases of human height

Abstract

Height is one of the most influential traits of human beings, it has a high heritability factor but few major alleles. Hundreds of candidate genetic variants that potentially play a role in the determination of human height have been identified through dozens of genome-wide association studies (GWAS). Profiling these variants, underlying genes, and networks can help for understanding the genetic knowledge of human height. In this study, a multi-step integrative bioinformatic analysis platform was performed to identify hub genes and their interacting partners. Single nucleotide polymorphisms (SNPs) and other variant loci (n = 673) were collected from 30 data sets (n = 327,870) from GWAS Central. Next, we performed multi-step integrative bioinformatic analyses, including function prediction of SNPs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI). A total of 372 genes were identified and mapped to pathways based on the reported and prioritized height-related SNPs. The majority were significantly enriched in skeletal system development and morphogenesis; cartilage development and differentiation; and other height-related biological process (BP); as well as the pathway which relates to long-term depression. The top 10 hub genes were identified from this analysis and a corresponding PPI network were also developed. This replication study identified candidate height-related hub genes based on input from GWAS studies and pathway analyses. This multi-step integrative bioinformatic analysis with GWAS inputs is an applicable approach to investigate the genetic background of human height and other complex polygenic traits.