4.8 Article

Genome Sequencing Reveals Loci under Artificial Selection that Underlie Disease Phenotypes in the Laboratory Rat

Journal

CELL
Volume 154, Issue 3, Pages 691-703

Publisher

CELL PRESS
DOI: 10.1016/j.cell.2013.06.040

Keywords

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Funding

  1. Medical Research Council
  2. British Heart Foundation Center of Research Excellence [RE/08/002]
  3. EU-funded Euratrans project [HEALTH-F4-2010-241504]
  4. National BioResource Project-Rat
  5. TOP grant from the Netherlands Research Council (NWO-CW) [700.58.303]
  6. NIH [5R01HL069321]
  7. Euratrans
  8. BMPF-funded German Center for Cardiovascular Research
  9. Wellcome Trust Senior Fellowship in Basic Biomedical Science [057733]
  10. Wellcome Trust Core Award Grant [075491/Z/04]
  11. Institute of Cardiometabolism and Nutrition [ICAN, ANR10- IAHU-05]
  12. NIH/NHLBI [HL094446]
  13. Robert M. Hearin Foundation
  14. EMBL
  15. EU-funded Euratrans project
  16. MRC [G0800024, MC_U120061454, MC_U120097112] Funding Source: UKRI
  17. Cancer Research UK [13031] Funding Source: researchfish
  18. Medical Research Council [G0800024, MC_U120097112, MC_U120061454] Funding Source: researchfish

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Large numbers of inbred laboratory rat strains have been developed for a range of complex disease phenotypes. To gain insights into the evolutionary pressures underlying selection for these phenotypes, we sequenced the genomes of 27 rat strains, including 11 models of hypertension, diabetes, and insulin resistance, along with their respective control strains. Altogether, we identified more than 13 million single-nucleotide variants, indels, and structural variants across these rat strains. Analysis of strain-specific selective sweeps and gene clusters implicated genes and pathways involved in cation transport, angiotensin production, and regulators of oxidative stress in the development of cardiovascular disease phenotypes in rats. Many of the rat loci that we identified overlap with previously mapped loci for related traits in humans, indicating the presence of shared pathways underlying these phenotypes in rats and humans. These data represent a step change in resources available for evolutionary analysis of complex traits in disease models.

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