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.
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Abbreviations
- GWAS:
-
Genome-wide association studies
- SNPs:
-
Single nucleotide polymorphisms
- GO:
-
Gene ontology
- KEGG:
-
Kyoto encyclopedia of genes and genomes
- PPI:
-
Protein-protein interaction
- MCC:
-
Maximal clique centrality
- MCODE:
-
Molecular complex detection
- TFBS:
-
Transcription factor binding sites
- ESS:
-
Exonic splicing silencers
- BP:
-
Biological processes
- CC:
-
Cellular component
- MF:
-
Molecular functions
- ECM:
-
Extracellular matrix
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Acknowledgements
First and foremost, we thank the teams of GWAS Central (https://www.gwascentral.org/index), and the many groups who have generated and provided the record content of GWAS Central.
Funding
This study was supported by grants from Subject Construction Program of Shanghai Pudong New District Health and Family Planning Commission (PWZzb2007-23), Pudong New Area Science and Technology Development Fund (PKJ2018-Y31), and Education Foundation of Shaanxi (11JK0620).
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All authors had full access to all the data and take responsibility for the integrity of the data. Collected the data: Wang T, Jiang R, and Bai J; Analyzed the data: Zhang K; Wrote the paper: Wang T and Zhang K; Designed the study: Wang T and Zhang K.
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Wang, T., Jiang, R., Bai, J. et al. Integrative bioinformatic analyses of genome-wide association studies for understanding the genetic bases of human height. Biologia 75, 2413–2420 (2020). https://doi.org/10.2478/s11756-020-00550-7
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DOI: https://doi.org/10.2478/s11756-020-00550-7