Bioinformatics Advance Access published online on December 21, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti241
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1 Roslin Institute, Roslin, Midlothian EH25 9 PS, UK
* To whom correspondence should be addressed.
Motivation: Dissection of the genetics underlying gene expression utilises techniques from microarray analyses as well as QTL mapping. Available QTL mapping methods are not tailored for the highly automated analyses required to deal with the thousands of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression. Results: The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as ‘high confidence’ QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. Following extensive analyses of real data and additional simulated data (altogether over 300,000 genome scans), we make the following recommendations for detect of QTL for gene expression: 1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on the repeatability of the trait and the number of replicates. 2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. 3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. 4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localisation of gene-transcripts and QTL. 5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the type I error. 6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarised in a possible strategy for mapping of QTL in a least-squares framework. Availability: The software used for this study is available on request from the authors.
Received November 16, 2004
Accepted December 17, 2004
Article
Methodological aspects of the genetic dissection of gene expression
2 University of Tennessee Health Science Center, 855 Monroe Avenue, Memphis, Tennessee 38163, USA
C. S. Haley, E-mail: Chris.Haley{at}bbsrc.ac.uk
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