title: A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae.
abstract: BACKGROUND
  Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test.
  
  
  RESULTS
  When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance.
  
  
  CONCLUSIONS
  We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. hlyuan@pku.edu.cn.
authors.forename: Huili
authors.initials: H
authors.lastname: Yuan
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. lizhenye@pku.edu.cn.
authors.forename: Zhenye
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Chemical Pathology, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong, China. nlstang2015@gmail.com.
authors.forename: Nelson L S
authors.initials: NL
authors.lastname: Tang
authors.affiliation: LMAM, School of Mathematical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. dengmh@math.pku.edu.cn.|Center for Quantitative Biology, Peking University, Yiheyuan Road, Beijing, 100871, China. dengmh@math.pku.edu.cn.|Center for Statistical Sciences, Peking University, Yiheyuan Road, Beijing, 100871, China. dengmh@math.pku.edu.cn.
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
mesh_terms: D002453:Cell Cycle / Q000235:genetics; D003198:Computer Simulation; D066264:Datasets as Topic; D055106:Genome-Wide Association Study / Q000379:methods; D019787:Linoleic Acid / Q000378:metabolism; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D012441:Saccharomyces cerevisiae / Q000235:genetics*
chemical_list: D019787:Linoleic Acid
references.citation: Nat Rev Genet. 2015 Apr;16(4):197-212
references.pmid: 25707927
references.citation: BMC Bioinformatics. 2013 Aug 12;14:245
references.pmid: 23937249
references.citation: BMC Genomics. 2008 May 23;9:242
references.pmid: 18498664
references.citation: Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
references.pmid: 16199517
references.citation: Biometrics. 2011 Mar;67(1):133-41
references.pmid: 20528865
references.citation: Mol Cell. 2011 Feb 18;41(4):480-92
references.pmid: 21329885
references.citation: Proc Natl Acad Sci U S A. 2002 Dec 24;99(26):16875-80
references.pmid: 12486219
references.citation: Nucleic Acids Res. 2000 May 1;28(9):1913-20
references.pmid: 10756191
references.citation: Nature. 1995 Feb 9;373(6514):536-9
references.pmid: 7845468
references.citation: PLoS One. 2010 May 13;5(5):e10395
references.pmid: 20485529
references.citation: PLoS Genet. 2009 Aug;5(8):e1000587
references.pmid: 19680538
references.citation: Nat Methods. 2015 Aug;12(8):755-8
references.pmid: 26076425
references.citation: Nat Genet. 2005 Apr;37(4):382-90
references.pmid: 15778709
references.citation: Mol Syst Biol. 2012 Jan 17;8:565
references.pmid: 22252388
references.citation: Mol Cell. 2009 Jan 16;33(1):53-63
references.pmid: 19150427
references.citation: Biometrics. 2015 Jun;71(2):354-63
references.pmid: 25732839
references.citation: Nature. 2003 Oct 23;425(6960):859-64
references.pmid: 14574415
references.citation: PLoS Genet. 2013 Mar;9(3):e1003414
references.pmid: 23555313
references.citation: Philos Trans R Soc Lond B Biol Sci. 2013 May 06;368(1620):20120362
references.pmid: 23650636
references.citation: PLoS Biol. 2008 Apr 15;6(4):e83
references.pmid: 18416601
references.citation: PLoS Comput Biol. 2014 Feb 27;10(2):e1003420
references.pmid: 24586125
references.citation: Biostatistics. 2009 Jul;10(3):515-34
references.pmid: 19377034
references.citation: Nat Rev Genet. 2009 Mar;10(3):184-94
references.pmid: 19223927
references.citation: Am J Hum Genet. 2012 Jan 13;90(1):7-24
references.pmid: 22243964
references.citation: Cell. 2007 Dec 28;131(7):1354-65
references.pmid: 18160043
references.citation: Biometrics. 2011 Mar;67(1):299-308
references.pmid: 20374241
references.citation: Genetics. 2011 Feb;187(2):611-21
references.pmid: 21115971
references.citation: Proteins. 2004 Jan 1;54(1):49-57
references.pmid: 14705023
references.citation: Nucleic Acids Res. 1999 Jan 1;27(1):29-34
references.pmid: 9847135