TY - JOUR
T1 - Testing whether genetic variation explains correlation of quanfitative measures of gene expression, and application to genetic network analysis
AU - Yu, Zhaoxia
AU - Wang, Liewei
AU - Hildebrandt, Michelle A.T.
AU - Schaid, Daniel J.
PY - 2008/8/30
Y1 - 2008/8/30
N2 - Genetic networks for gene expression data are often built by graphical models, which in turn are built from pair-wise correlations of gene expression levels. A key feature of building graphical models is the evaluation of conditional independence of two traits, given other traits. When conditional independence can be assumed, the traits that are conditioned on are considered to 'explain' the correlation of a pair of traits, allowing efficient building and interpretation of a network. Overlaying genetic polymorphisms, such as single nucleotide polymorphisms (SNPs), on quantitative measures of gene expression provides a much richer set of data to build a genetic network, because it is possible to evaluate whether sets of SNPs 'explain' the correlation of gene expression levels. However, there is strong evidence that gene expression levels are controlled by multiple interacting genes, suggesting that it will be difficult to reduce the partial correlation completely to zero. Ignoring the fact that some sets of SNPs can explain at least part of the correlation between gene expression levels, if not all, might result in missing important clues on the genetic control of gene expression. To enrich the assessment of the causes of correlation between gene expression levels, we develop methods to evaluate whether a set of covariates (e.g. SNPs, or even a set of quantitative expression transcripts) explains at least some of the correlation of gene expression levels. These methods can be used to assist the interpretation of regulation of gene expression and the construction of gene regulatory networks.
AB - Genetic networks for gene expression data are often built by graphical models, which in turn are built from pair-wise correlations of gene expression levels. A key feature of building graphical models is the evaluation of conditional independence of two traits, given other traits. When conditional independence can be assumed, the traits that are conditioned on are considered to 'explain' the correlation of a pair of traits, allowing efficient building and interpretation of a network. Overlaying genetic polymorphisms, such as single nucleotide polymorphisms (SNPs), on quantitative measures of gene expression provides a much richer set of data to build a genetic network, because it is possible to evaluate whether sets of SNPs 'explain' the correlation of gene expression levels. However, there is strong evidence that gene expression levels are controlled by multiple interacting genes, suggesting that it will be difficult to reduce the partial correlation completely to zero. Ignoring the fact that some sets of SNPs can explain at least part of the correlation between gene expression levels, if not all, might result in missing important clues on the genetic control of gene expression. To enrich the assessment of the causes of correlation between gene expression levels, we develop methods to evaluate whether a set of covariates (e.g. SNPs, or even a set of quantitative expression transcripts) explains at least some of the correlation of gene expression levels. These methods can be used to assist the interpretation of regulation of gene expression and the construction of gene regulatory networks.
KW - Association
KW - Fisher's z-transformation
KW - Model selection
KW - Multiple regression
KW - Optimal linear composites
KW - Pathway
KW - Taylor expansion
UR - http://www.scopus.com/inward/record.url?scp=50449102408&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=50449102408&partnerID=8YFLogxK
U2 - 10.1002/sim.3274
DO - 10.1002/sim.3274
M3 - Article
C2 - 18444230
AN - SCOPUS:50449102408
SN - 0277-6715
VL - 27
SP - 3847
EP - 3867
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 19
ER -