Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data
Yi-Fei Huang1, Brad Gulko1,2, Adam Siepel1
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.
Nature Genetics
|March 14, 2017
Summary
LINSIGHT is a new computational method that accurately predicts harmful genetic variants in noncoding DNA. This tool improves the identification of disease-associated variants and reveals cell-specific effects in human enhancers.
Area of Science:
- Genomics
- Computational Biology
- Evolutionary Biology
Background:
- Many genetic variants influencing phenotypes are in noncoding DNA, but identifying them is challenging.
- Existing methods for predicting the functional impact of noncoding variants have limited accuracy.
Purpose of the Study:
- To develop a computational method that improves the prediction of deleterious noncoding genetic variants.
- To identify phenotypically important noncoding sites and understand their functional consequences.
Main Methods:
- Introduced LINSIGHT, a novel computational method combining a generalized linear model for functional genomic data with a probabilistic model of molecular evolution.
- Developed a fast and scalable method to analyze large genomic datasets.
Main Results:
- LINSIGHT significantly improves the prediction of noncoding variants with deleterious fitness consequences.
- Outperformed existing methods in identifying human noncoding variants linked to inherited diseases.
- Demonstrated that the fitness impact of variants in human enhancers is context-dependent (cell type, tissue, promoter constraints).
Conclusions:
- LINSIGHT is a powerful tool for predicting the functional impact of noncoding genetic variants.
- The method enhances the identification of variants associated with human diseases.
- Functional consequences of noncoding variants, particularly in enhancers, are complex and cell-type specific.
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