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Data interpretation: deciphering the biological function of Type 2 diabetes associated risk loci
Asima Zia1, Attya Bhatti, Peter John
1Healthcare Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan.
Acta Diabetologica
|January 15, 2015
Summary
This study used RegulomeDB to analyze variants associated with Type 2 diabetes (T2D), finding some noncoding variants are true associations. RegulomeDB is useful but has limitations in identifying regulatory functions.
Area of Science:
- Genetics
- Genomics
- Bioinformatics
Background:
- Type 2 diabetes (T2D) is a complex disease with over 40 associated genetic loci.
- Many T2D susceptibility loci are in noncoding regions, requiring functional interpretation.
- Databases like RegulomeDB aid in predicting the function of genetic variants.
Purpose of the Study:
- To analyze the potential regulatory function of T2D-associated variants using RegulomeDB.
- To differentiate between true disease-associated variants and tag single nucleotide polymorphisms (SNPs).
Main Methods:
- Utilized RegulomeDB to examine 1,567 SNPs from five T2D genome-wide association studies (GWAS).
- Assessed SNPs for evidence of regulatory function based on RegulomeDB scores.
Main Results:
- Out of 989 analyzed SNPs, 64 showed potential regulatory function (RegulomeDB score <3).
- Only four GWAS significant SNPs demonstrated regulatory function.
- A limitation identified was that only 63% of annotated SNPs showed regulatory function, due to limited data in RegulomeDB.
Conclusions:
- Supports that some noncoding GWAS variants represent true associations, not just tag SNPs.
- Highlights the utility and importance of RegulomeDB and similar databases for variant interpretation.
- Acknowledges limitations of RegulomeDB, particularly its focus on a limited set of regulatory elements and pathways.
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