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Functional interpretation of non-coding sequence variation: concepts and challenges.


ABSTRACT: Understanding the functional mechanisms underlying genetic signals associated with complex traits and common diseases, such as cancer, diabetes and Alzheimer's disease, is a formidable challenge. Many genetic signals discovered through genome-wide association studies map to non-protein coding sequences, where their molecular consequences are difficult to evaluate. This article summarizes concepts for the systematic interpretation of non-coding genetic signals using genome annotation data sets in different cellular systems. We outline strategies for the global analysis of multiple association intervals and the in-depth molecular investigation of individual intervals. We highlight experimental techniques to validate candidate (potential causal) regulatory variants, with a focus on novel genome-editing techniques including CRISPR/Cas9. These approaches are also applicable to low-frequency and rare variants, which have become increasingly important in genomic studies of complex traits and diseases. There is a pressing need to translate genetic signals into biological mechanisms, leading to prognostic, diagnostic and therapeutic advances.

SUBMITTER: Paul DS 

PROVIDER: S-EPMC3992842 | biostudies-other | 2014 Feb

REPOSITORIES: biostudies-other

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Functional interpretation of non-coding sequence variation: concepts and challenges.

Paul Dirk S DS   Soranzo Nicole N   Beck Stephan S  

BioEssays : news and reviews in molecular, cellular and developmental biology 20131205 2


Understanding the functional mechanisms underlying genetic signals associated with complex traits and common diseases, such as cancer, diabetes and Alzheimer's disease, is a formidable challenge. Many genetic signals discovered through genome-wide association studies map to non-protein coding sequences, where their molecular consequences are difficult to evaluate. This article summarizes concepts for the systematic interpretation of non-coding genetic signals using genome annotation data sets in  ...[more]

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