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Addressing the Missing Heritability Problem With the Help of Regulatory Features.
Shan-Shan Dong1, Yan Guo1, Tie-Lin Yang1
1Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, P. R. China.
Evolutionary Bioinformatics Online
|July 20, 2019
Summary
Functional disease-associated single-nucleotide polymorphisms (SNPs) prediction (FDSP) identifies novel complex disease loci by integrating regulatory elements and GWAS data. This approach helps address the missing heritability problem in genetic studies.
Area of Science:
- Genetics
- Genomics
- Bioinformatics
Background:
- Genome-wide association studies (GWASs) have identified numerous susceptibility loci for complex diseases.
- A significant portion of genetic heritability for complex diseases remains unexplained, termed 'missing heritability'.
- Many identified GWAS loci reside within regulatory elements, suggesting their importance in disease etiology.
Purpose of the Study:
- To develop and validate a machine learning pipeline, functional disease-associated SNPs prediction (FDSP), for identifying novel susceptibility loci.
- To assess the ability of FDSP-predicted loci to explain additional heritability in complex diseases.
- To investigate the biological relevance of predicted susceptibility loci by examining enriched pathways in their target genes.
Main Methods:
- Developed the functional disease-associated SNPs prediction (FDSP) pipeline.
- Integrated regulatory feature interpretation with published GWAS results.
- Applied machine learning models to predict novel disease-associated SNPs.
- Validated the predictive capability of FDSP using type 2 diabetes and hypertension datasets.
- Performed pathway enrichment analysis on target genes of predicted SNPs.
Main Results:
- FDSP successfully predicted novel susceptibility loci for type 2 diabetes and hypertension.
- The predicted loci by FDSP explained additional heritability beyond previously identified GWAS loci.
- Potential target genes of the predicted positive SNPs were significantly enriched in disease-related pathways.
- The findings support the utility of incorporating regulatory features for identifying novel genetic risk factors.
Conclusions:
- The functional disease-associated SNPs prediction (FDSP) pipeline offers a promising approach to uncover novel susceptibility loci for complex diseases.
- Integrating regulatory information with GWAS data is a valuable strategy for addressing the missing heritability problem.
- FDSP has the potential to advance the identification of genetic underpinnings of complex diseases.
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