Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pgen.1003939
Title: Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts
Authors: Silver, M.
Chen, P.
Li, R.
Cheng, C.-Y. 
Wong, T.-Y. 
Tai, E.-S.
Teo, Y.-Y. 
Montana, G.
Issue Date: Nov-2013
Source: Silver, M., Chen, P., Li, R., Cheng, C.-Y., Wong, T.-Y., Tai, E.-S., Teo, Y.-Y., Montana, G. (2013-11). Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts. PLoS Genetics 9 (11) : -. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pgen.1003939
Abstract: Standard approaches to data analysis in genome-wide association studies (GWAS) ignore any potential functional relationships between gene variants. In contrast gene pathways analysis uses prior information on functional structure within the genome to identify pathways associated with a trait of interest. In a second step, important single nucleotide polymorphisms (SNPs) or genes may be identified within associated pathways. The pathways approach is motivated by the fact that genes do not act alone, but instead have effects that are likely to be mediated through their interaction in gene pathways. Where this is the case, pathways approaches may reveal aspects of a trait's genetic architecture that would otherwise be missed when considering SNPs in isolation. Most pathways methods begin by testing SNPs one at a time, and so fail to capitalise on the potential advantages inherent in a multi-SNP, joint modelling approach. Here, we describe a dual-level, sparse regression model for the simultaneous identification of pathways and genes associated with a quantitative trait. Our method takes account of various factors specific to the joint modelling of pathways with genome-wide data, including widespread correlation between genetic predictors, and the fact that variants may overlap multiple pathways. We use a resampling strategy that exploits finite sample variability to provide robust rankings for pathways and genes. We test our method through simulation, and use it to perform pathways-driven gene selection in a search for pathways and genes associated with variation in serum high-density lipoprotein cholesterol levels in two separate GWAS cohorts of Asian adults. By comparing results from both cohorts we identify a number of candidate pathways including those associated with cardiomyopathy, and T cell receptor and PPAR signalling. Highlighted genes include those associated with the L-type calcium channel, adenylate cyclase, integrin, laminin, MAPK signalling and immune function. © 2013 Silver et al.
Source Title: PLoS Genetics
URI: http://scholarbank.nus.edu.sg/handle/10635/109012
ISSN: 15537390
DOI: 10.1371/journal.pgen.1003939
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
2013-pathways-driven_sparse_regression_identifies_pathways-published.pdf2.27 MBAdobe PDF

OPEN

PublishedView/Download

SCOPUSTM   
Citations

8
checked on Jan 10, 2018

WEB OF SCIENCETM
Citations

7
checked on Jan 10, 2018

Page view(s)

57
checked on Jan 15, 2018

Download(s)

36
checked on Jan 15, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.