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Identifying causative genetic variants linked to reduced kidney function through congenic strain analysis and whole genome sequencing
Author(s) -
Harmon Ashlyn C.,
Johnson Ashley C.,
Atanur Santosh,
Maratou Klio,
Aitman Tim,
Garrett Michael R.
Publication year - 2013
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.27.1_supplement.955.14
Subject(s) - congenic , genetics , biology , gene , snp , locus (genetics) , candidate gene , missense mutation , genome wide association study , whole genome sequencing , spontaneously hypertensive rat , kidney disease , phenotype , genome , single nucleotide polymorphism , genotype , endocrinology , blood pressure
Factors such as hypertension, diabetes, and obesity contribute to chronic kidney disease (CKD), but genetic predisposition also plays a role. To investigate genetic factors involved in CKD, a novel congenic model [S.SHR(11)] was developed by transfer of a genomic segment (Chr. 11) from the spontaneously hypertensive rat (SHR) onto the Dahl salt‐sensitive (S) genetic background. We recently reported a detailed characterization of the S.SHR(11) model which demonstrated it exhibits accelerated kidney injury compared to the already highly susceptible S rat. At an advanced age, the S.SHR(11) exhibits increased BP, suggesting that kidney injury precedes changes in BP. A panel of congenic substrains [S.SHR(11)X1–9] is being used to systematically fine‐map the causative genetic variants. Whole genome sequencing has been completed, which revealed all genetic variation between the S and SHR models. Twenty‐eight missense SNP were identified within the 95% confidence interval of the genomic locus, with 15 SNP occurring within three genes. Two of these genes, Retnlg and Trat1, are immune‐related genes and are currently being investigated through in vivo and in vitro studies for their potential role in kidney injury. In summary, the congenic strain analysis and high quality sequence information will expedite identification of the causative genetic variations. Supported by RO1‐HL094446 (MRG) and T32‐HL105324 (ACH).