A SPECTRAL APPROACH INTEGRATING FUNCTIONAL GENOMIC ANNOTATIONS FOR CODING AND NONCODING VARIANTS

By Iuliana Ionita-Laza, K. McCallum, Bin Xu, Joseph D. Buxbaum. 2016

572 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, background): “Non-coding variants in Figure S1A were selected from all variants in the two set that were available in the Eigen, FunSeq2 (2) and LINSIGHT wholegenome files.”
GAVIN: Gene-Aware Variant INterpretation for medical sequencing (2017, methodology): “duced metrics, including EIGEN [30], FATHMM-MKL, DeepSEA [31], and GWAVA, specialize in predicting the functional effects of non-coding sequence variation.”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, methodology): “The metapredictors used in our study are nearly all trained using a supervised learning approach, with one exception (Ionita-Laza et al , 2016).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “Eigen, by contrast, applies an unsupervised method that uses conservation scores, protein function scores, and allele frequencies from a variety of mutation databases [136].”
Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, result): “DeepSAV’s performance is similar to (Eigen [47]) or worse than several meta-predictors (MetaSVM [48], MetaLR [48], and REVEL [49]) that use prediction results of a number of other stand-alone predictors (Figure 3A).”
LIST-S2: taxonomy based sorting of deleterious missense mutations across species (2020, unknown): “0a (17) (namely: SIFT (1), SIFT4G (18), PROVEAN (2), phyloP (3) X 3, SiPhy (5), phastCons (6) X 3, GERP++ (4), Eigen (10), CADD (9), DANN (11),”
A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease. (2016, methodology): “11 None of the methods were designed specifically for the detection of NCVs associated with Mendelian disease.”
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines (2017, methodology): “For some algorithms such as Eigen [22], hEAt [23], GERP [24], etc., cut-offs defining pathogenic or benign assertion are either not recommended or inferred arbitrarily.”