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Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Abstract

Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.

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Figure 1: Expression values of parental and RI strains for 12 transcripts in kidney and fat.
Figure 2: Locations of cis-acting eQTLs and previously mapped SHR pQTLs.
Figure 3: Location of trans-acting eQTLs and previously mapped SHR pQTLs.

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Acknowledgements

We thank H. Banks, N. Cooley, F. Rahman, M. Gerhardt, H. Kistel, S. Blachut and R. Sarwar for technical assistance; K. Manly for providing the eQTL Reaper software; and Affymetrix for donation of microarrays. We acknowledge funding to T.J.A. from the MRC Clinical Sciences Centre, from the British Heart Foundation and from a Wellcome Trust Cardiovascular Functional Genomics initiative; to N.H. from the German Ministry for Science and Education (National Genome Research Network); to M.P. and to V.K. from the Grant Agency of the Czech Republic; to M.P. and T.J.A. from the Wellcome Trust Collaborative Research Initiative Grant; to T.W.K. from the US National Institutes of Health; and to T.W.K. and M.P. from a Fogarty International Research Collaboration Award. M. Pravenec is an International Research Scholar of the Howard Hughes Medical Institute.

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Correspondence to Michal Pravenec or Timothy J Aitman.

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Supplementary information

Supplementary Fig. 1

Validation of microarray gene expression data by RT-PCR. (PDF 91 kb)

Supplementary Fig. 2

Removal of redundancy resulting from linkage of expression values from an individual probe set to multiple adjacent markers. (PDF 117 kb)

Supplementary Fig. 3

Variation of number of defined cis-acting eQTLs with window-size. (PDF 195 kb)

Supplementary Fig. 4

Sequence analysis of Pik3c3 5′-upstream region, exons and exon-intron boundaries. (PDF 69 kb)

Supplementary Table 1

Number of linkages detected in fat and kidney data sets and estimated false discovery rate for different significance thresholds. (PDF 42 kb)

Supplementary Table 2

Number of linkages identified in common by eQTL Reaper and Wilcoxon-Mann-Whitney test. (PDF 51 kb)

Supplementary Table 3

Comparison of linkage results in RI strains using microarray data and quantitative real-time PCR. (PDF 54 kb)

Supplementary Table 4

Physiological SHR QTLs mapped in previous genome screens. (XLS 57 kb)

Supplementary Table 5a

cis-acting eQTLs in fat and kidney tissue mapped at P<10−4. (XLS 57 kb)

Supplementary Table 5b (XLS 76 kb)

Supplementary Table 6

trans-acting eQTLs (P<10−2). (XLS 57 kb)

Supplementary Table 7

Description of genetic map of 1,011 markers generated for the RI panel. (PDF 61 kb)

Supplementary Table 8

Detailed information on the comparative analysis of rat and human blood pressure QTLs. (XLS 264 kb)

Supplementary Note (PDF 67 kb)

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Hubner, N., Wallace, C., Zimdahl, H. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37, 243–253 (2005). https://doi.org/10.1038/ng1522

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