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      Renal disease susceptibility and hypertension are under independent genetic control in the fawn-hooded rat.

      Nature genetics
      Animals, Base Sequence, Chromosome Mapping, DNA Primers, chemistry, Female, Genetic Linkage, Hypertension, genetics, Male, Molecular Sequence Data, Proteinuria, Rats, Rats, Inbred Strains, Rats, Mutant Strains, Renal Insufficiency

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          Abstract

          Hypertension, diabetes and hyperlipidemia are risk factors for life-threatening complications such as end-stage renal disease, coronary artery disease and stroke. Why some patients develop complications is unclear, but only susceptibility genes may be involved. To test this notion, we studied crosses involving the fawn-hooded rat, an animal model of hypertension that develops chronic renal failure. Here, we report the localization of two genes, Rf-1 and Rf-2, responsible for about half of the genetic variation in key indices of renal impairment. In addition, we localize a gene, Bpfh-1, responsible for about 26% of the genetic variation in blood pressure. Rf-1 strongly affects the risk of renal impairment, but has no significant effect on blood pressure. Our results show that susceptibility to a complication of hypertension is under at least partially independent genetic control from susceptibility to hypertension itself.

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          Simplified mammalian DNA isolation procedure.

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            Dietary protein intake and the progressive nature of kidney disease: the role of hemodynamically mediated glomerular injury in the pathogenesis of progressive glomerular sclerosis in aging, renal ablation, and intrinsic renal disease.

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              Systematic detection of errors in genetic linkage data.

              Construction of dense genetic linkage maps is hampered, in practice, by the occurrence of laboratory typing errors. Even relatively low error rates cause substantial map expansion and interfere with the determination of correct genetic order. Here, we describe a systematic method for overcoming these difficulties, based on incorporating the possibility of error into the usual likelihood model for linkage analysis. Using this approach, it is possible to construct genetic maps allowing for error and to identify the typings most likely to be in error. The method has been implemented for F2 intercrosses between two inbred strains, a situation relevant to the construction of genetic maps in experimental organisms. Tests involving both simulated and real data are presented, showing that the method detects the vast majority of errors.
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