Format

Send to

Choose Destination
Hum Genet. 2018 Jan;137(1):15-30. doi: 10.1007/s00439-017-1861-0. Epub 2017 Dec 29.

Principles and methods of in-silico prioritization of non-coding regulatory variants.

Lee PH1,2, Lee C3,4, Li X5,6, Wee B3, Dwivedi T3,7, Daly M3,8.

Author information

1
Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA. PLEE0@mgh.harvard.edu.
2
Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA. PLEE0@mgh.harvard.edu.
3
Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Simches Research Building, 185 Cambridge St, Boston, MA, 02114, USA.
4
Department of Life Sciences, Harvard University, Cambridge, MA, USA.
5
Quantitative Genomics Program, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
6
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
7
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
8
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

Abstract

Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.

PMID:
29288389
PMCID:
PMC5892192
[Available on 2019-01-01]
DOI:
10.1007/s00439-017-1861-0

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center