Author:
Verdugo Ricardo A,Farber Charles R,Warden Craig H,Medrano Juan F
Abstract
Abstract
Background
It has been proposed that the use of gene expression microarrays in nonrecombinant parental or congenic strains can accelerate the process of isolating individual genes underlying quantitative trait loci (QTL). However, the effectiveness of this approach has not been assessed.
Results
Thirty-seven studies that have implemented the QTL/microarray approach in rodents were reviewed. About 30% of studies showed enrichment for QTL candidates, mostly in comparisons between congenic and background strains. Three studies led to the identification of an underlying QTL gene. To complement the literature results, a microarray experiment was performed using three mouse congenic strains isolating the effects of at least 25 biometric QTL. Results show that genes in the congenic donor regions were preferentially selected. However, within donor regions, the distribution of differentially expressed genes was homogeneous once gene density was accounted for. Genes within identical-by-descent (IBD) regions were less likely to be differentially expressed in chromosome 2, but not in chromosomes 11 and 17. Furthermore, expression of QTL regulated in cis (cis eQTL) showed higher expression in the background genotype, which was partially explained by the presence of single nucleotide polymorphisms (SNP).
Conclusions
The literature shows limited successes from the QTL/microarray approach to identify QTL genes. Our own results from microarray profiling of three congenic strains revealed a strong tendency to select cis-eQTL over trans-eQTL. IBD regions had little effect on rate of differential expression, and we provide several reasons why IBD should not be used to discard eQTL candidates. In addition, mismatch probes produced false cis-eQTL that could not be completely removed with the current strains genotypes and low probe density microarrays. The reviewed studies did not account for lack of coverage from the platforms used and therefore removed genes that were not tested. Together, our results explain the tendency to report QTL candidates as differentially expressed and indicate that the utility of the QTL/microarray as currently implemented is limited. Alternatives are proposed that make use of microarray data from multiple experiments to overcome the outlined limitations.
Publisher
Springer Science and Business Media LLC
Subject
Cell Biology,Developmental Biology,Plant Science,General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Physiology,Ecology, Evolution, Behavior and Systematics,Structural Biology,Biotechnology
Reference116 articles.
1. Mackay TFC, Stone EA, Ayroles JF: The genetics of quantitative traits: challenges and prospects. Nat Rev Genet. 2009, 10 (8): 565-577. 10.1038/nrg2612.
2. Pomp D, Allan MF, Wesolowski SR: Quantitative genomics: Exploring the genetic architecture of complex trait predisposition. J Anim Sci. 2004, 82 (13_suppl): E300-E312.
3. Farrall M: Quantitative genetic variation: a post-modern view. Hum Mol Genet. 2004, 13 (Spec No 1): R1-7. 10.1093/hmg/ddh084.
4. Jansen RC, Nap JP: Genetical genomics: the added value from segregation. Trends Genet. 2001, 17 (7): 388-391. 10.1016/S0168-9525(01)02310-1.
5. McClurg P, Janes J, Wu C, Delano DL, Walker JR, Batalov S, Takahashi JS, Shimomura K, Kohsaka A, Bass J, Wiltshire T, Su AI: Genomewide Association Analysis in Diverse Inbred Mice: Power and Population Structure. Genetics. 2007, 176 (1): 675-683. 10.1534/genetics.106.066241.
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