Nature Genetics37, 225 - 232 (2005)
Published online: 13 February 2005; | doi:10.1038/ng1497
Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'
Leonid Bystrykh1, Ellen Weersing1, Bert Dontje1, Sue Sutton2, Mathew T Pletcher2, Tim Wiltshire2, Andrew I Su2, Edo Vellenga3, Jintao Wang4, 5, Kenneth F Manly4, 5, Lu Lu5, Elissa J Chesler5, Rudi Alberts6, Ritsert C Jansen6, Robert W Williams5, Michael P Cooke2
& Gerald de Haan1
1
Department of Stem Cell Biology, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, the Netherlands.
2
Genomics Institute of the Novartis Research Foundation, San Diego, California 92121, USA.
3
Department of Hematology, Academic Hospital Groningen, Groningen, the Netherlands.
4
Molecular and Cellular Biology Department, Roswell Park Cancer Institute, Buffalo, New York, USA.
5
University of Tennessee Health Science Center, Memphis, Tennessee 38163, USA.
6
Groningen Bioinformatics Centre, University of Groningen, Groningen, the Netherlands.
We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.
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