Genome Sequencing Reveals Loci under Artificial Selection that Underlie Disease Phenotypes in the Laboratory Rat
Author(s) -
Santosh S. Atanur,
Ana Garcia Diaz,
Klio Maratou,
Allison B. Sarkis,
Maxime Rotival,
Laurence Gamé,
Michael Tschannen,
Pamela J. Kaisaki,
Georg Otto,
Man Chun John,
Thomas Keane,
Oliver Hummel,
Kathrin Saar,
Wei Chen,
Victor Guryev,
Kathirvel Gopalakrishnan,
Michael R. Garrett,
Bina Joe,
Lorena Citterio,
Giuseppe Bianchi,
Martin McBride,
Anna F. Dominiczak,
David J. Adams,
Tadao Serikawa,
Paul Flicek,
Edwin Cuppen,
Norbert Hübner,
Enrico Petretto,
Dominique Gauguier,
Anne E. Kwitek,
Howard J. Jacob,
Timothy J. Aitman
Publication year - 2013
Publication title -
cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 26.304
H-Index - 776
eISSN - 1097-4172
pISSN - 0092-8674
DOI - 10.1016/j.cell.2013.06.040
Subject(s) - biology , phenotype , genetics , indel , gene , genome , selection (genetic algorithm) , single nucleotide polymorphism , genotype , artificial intelligence , computer science
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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