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


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.

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Figure 1: Correlation among different functional annotations for the noncoding variants on chromosome 1 in the training data set.
Figure 2: Violin plots showing the distribution of Eigen scores for de novo mutations in intellectual disability, epileptic encephalopathies, ASD (FMRP targets), ASD, schizophrenia and controls.
Figure 3: Violin plots showing the distribution of Eigen scores for noncoding variants in the COSMIC database that reside in different functional categories.


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This research was supported by US National Institutes of Health grants R01 MH095797 and R01 MH100233 and by the Seaver Foundation. All analyses were conducted on the Minerva HPC complex at the Icahn School of Medicine at Mount Sinai.

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Authors and Affiliations



I.I.-L. designed the study and wrote the manuscript. I.I.-L. and K.M. developed the statistical methods and the software. I.I.-L. and K.M. analyzed the data. B.X. and J.D.B. provided bioinformatics support and contributed to the interpretation of the results. All authors have read and contributed to the manuscript.

Corresponding author

Correspondence to Iuliana Ionita-Laza.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Correlation among different functional annotations for the coding variants on chromosome 1 from the training data set.

Supplementary Figure 2 Violin plots for Eigen scores for GWAS SNPs and eQTLs for variants in different functional classes.

Supplementary Figure 3 ROC curves of the z values estimated from a hierarchical model with the Eigen scores.

The Eigen Scores are included as a functional predictor (solid curves) and ROC curves based on the ranking of the Eigen scores (dashed curves); associations between the Eigen score and the causal status of a variant vary, with relative risks of 1:1 (blue), 2 (green) and 4 (red); estimates are averaged across 100 simulations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3, Supplementary Tables 1–22 and Supplementary Note. (PDF 2051 kb)

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Ionita-Laza, I., McCallum, K., Xu, B. et al. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48, 214–220 (2016).

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