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Nat Genet. 2016 Feb;48(2):214-20. doi: 10.1038/ng.3477. Epub 2016 Jan 4.

A spectral approach integrating functional genomic annotations for coding and noncoding variants.

Author information

1
Department of Biostatistics, Columbia University, New York, New York, USA.
2
Department of Psychiatry, Columbia University, New York, New York, USA.
3
Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
4
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
5
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
6
Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
7
Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Abstract

Over the past few years, substantial effort has been put into the functional annotation of variation in human genome sequences. Such annotations can have a critical role in identifying putatively causal variants for a disease or trait among the abundant natural variation that occurs at a locus of interest. The main challenges in using these various annotations include their large numbers and their diversity. Here we develop an unsupervised approach to integrate these different annotations into one measure of functional importance (Eigen) that, unlike most existing methods, is not based on any labeled training data. We show that the resulting meta-score has better discriminatory ability using disease-associated and putatively benign variants from published studies (in both coding and noncoding regions) than the recently proposed CADD score. Across varied scenarios, the Eigen score performs generally better than any single individual annotation, representing a powerful single functional score that can be incorporated in fine-mapping studies.

PMID:
26727659
PMCID:
PMC4731313
DOI:
10.1038/ng.3477
[Indexed for MEDLINE]
Free PMC Article

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