title: Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines.
abstract: Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype-phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance--i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.
authors.affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
authors.forename: Edwin
authors.initials: E
authors.lastname: Choy
authors.forename: Roman
authors.initials: R
authors.lastname: Yelensky
authors.forename: Sasha
authors.initials: S
authors.lastname: Bonakdar
authors.forename: Robert M
authors.initials: RM
authors.lastname: Plenge
authors.forename: Richa
authors.initials: R
authors.lastname: Saxena
authors.forename: Philip L
authors.initials: PL
authors.lastname: De Jager
authors.forename: Stanley Y
authors.initials: SY
authors.lastname: Shaw
authors.forename: Cara S
authors.initials: CS
authors.lastname: Wolfish
authors.forename: Jacqueline M
authors.initials: JM
authors.lastname: Slavik
authors.forename: Chris
authors.initials: C
authors.lastname: Cotsapas
authors.forename: Manuel
authors.initials: M
authors.lastname: Rivas
authors.forename: Emmanouil T
authors.initials: ET
authors.lastname: Dermitzakis
authors.forename: Ellen
authors.initials: E
authors.lastname: Cahir-McFarland
authors.forename: Elliott
authors.initials: E
authors.lastname: Kieff
authors.forename: David
authors.initials: D
authors.lastname: Hafler
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Daly
authors.forename: David
authors.initials: D
authors.lastname: Altshuler
mesh_terms: D002460:Cell Line; D004272:DNA, Mitochondrial / Q000235:genetics / Q000378:metabolism; D004351:Drug Resistance / Q000235:genetics / Q000276:immunology; D020869:Gene Expression Profiling; D014644:Genetic Variation; D004854:Herpesvirus 4, Human / Q000235:genetics / Q000276:immunology; D006801:Humans; D008214:Lymphocytes / Q000187:drug effects* / Q000378:metabolism*; D010641:Phenotype; D040641:Quantitative Trait Loci; D012333:RNA, Messenger / Q000378:metabolism*
chemical_list: D004272:DNA, Mitochondrial; D012333:RNA, Messenger