A collective diabetes cross in combination with a computational framework to dissect the genetics of human obesity and Type 2 diabetes. (8th June 2018)

• Record Type: Journal Article
• Authors: Vogel, Heike; Kamitz, Anne; Hallahan, Nicole; Lebek, Sandra; Schallschmidt, Tanja; Jonas, Wenke; Jähnert, Markus; Gottmann, Pascal; Zellner, Lisa; Kanzleiter, Timo; Damen, Mareike; Altenhofen, Delsi; Burkhardt, Ralph; Renner, Simone; Dahlhoff, Maik; Wolf, Eckhard; Müller, Timo D; Blüher, Matthias; Joost, Hans-Georg; Chadt, Alexandra; Al-Hasani, Hadi; Schürmann, Annette
• Is Part Of: Human molecular genetics. Volume 27:Number 17(2018:Sep. 01)
• Page Start: 3099
• Page End: 3112
• Publication Date: 2018-06-08
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddy217
• Languages: English
• ISSNs: 0964-6906

Abstract

Abstract: To explore the genetic determinants of obesity and Type 2 diabetes (T2D), the German Center for Diabetes Research (DZD) conducted crossbreedings of the obese and diabetes-prone New Zealand Obese mouse strain with four different lean strains (B6, DBA, C3H, 129P2) that vary in their susceptibility to develop T2D. Genome-wide linkage analyses localized more than 290 quantitative trait loci (QTL) for obesity, 190 QTL for diabetes-related traits and 100 QTL for plasma metabolites in the outcross populations. A computational framework was developed that allowed to refine critical regions and to nominate a small number of candidate genes by integrating reciprocal haplotype mapping and transcriptome data. The efficiency of the complex procedure was demonstrated for one obesity QTL. The genomic interval of 35 Mb with 502 annotated candidate genes was narrowed down to six candidates. Accordingly, congenic mice retained the obesity phenotype owing to an interval that contains three of the six candidate genes. Among these the phospholipase PLA2G4A exhibited an elevated expression in adipose tissue of obese human subjects and is therefore a critical regulator of the obesity locus. Together, our broad and complex approach demonstrates that combined- and comparative-cross analysis exhibits improved mapping resolution and represents a valid tool for the identification of disease genes.