Literature DB >> 26152199

Non-coding genetic variants in human disease.

Feng Zhang1, James R Lupski2.   

Abstract

Genetic variants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), in the non-coding regions of the human genome can play an important role in human traits and complex diseases. Most of the genome-wide association study (GWAS) signals map to non-coding regions and potentially point to non-coding variants, whereas their functional interpretation is challenging. In this review, we discuss the human non-coding variants and their contributions to human diseases in the following four parts. (i) Functional annotations of non-coding SNPs mapped by GWAS: we discuss recent progress revealing some of the molecular mechanisms for GWAS signals affecting gene function. (ii) Technical progress in interpretation of non-coding variants: we briefly describe some of the technologies for functional annotations of non-coding variants, including the methods for genome-wide mapping of chromatin interaction, computational tools for functional predictions and the new genome editing technologies useful for dissecting potential functional consequences of non-coding variants. (iii) Non-coding CNVs in human diseases: we review our emerging understanding the role of non-coding CNVs in human disease. (iv) Compound inheritance of large genomic deletions and non-coding variants: compound inheritance at a locus consisting of coding variants plus non-coding ones is described.
© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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Year:  2015        PMID: 26152199      PMCID: PMC4572001          DOI: 10.1093/hmg/ddv259

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  85 in total

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10.  Structural variation-associated expression changes are paralleled by chromatin architecture modifications.

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  160 in total

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5.  Computational Prediction of Position Effects of Apparently Balanced Human Chromosomal Rearrangements.

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6.  Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

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7.  Predicting the effects of SNPs on transcription factor binding affinity.

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9.  Computational Prediction of Position Effects of Human Chromosome Rearrangements.

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