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一个联合的糖尿病交叉与一个计算框架相结合,以剖析人类肥胖和 2 型糖尿病的遗传学。

A collective diabetes cross in combination with a computational framework to dissect the genetics of human obesity and Type 2 diabetes.

机构信息

Department of Experimental Diabetology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal D-14558, Germany.

German Center for Diabetes Research (DZD), München-Neuherberg D-85764, Germany.

出版信息

Hum Mol Genet. 2018 Sep 1;27(17):3099-3112. doi: 10.1093/hmg/ddy217.

DOI:10.1093/hmg/ddy217
PMID:29893858
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6097155/
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.

摘要

为了探索肥胖和 2 型糖尿病(T2D)的遗传决定因素,德国糖尿病研究中心(DZD)对肥胖和易患糖尿病的新西兰肥胖小鼠品系与四种不同的瘦小鼠品系(B6、DBA、C3H、129P2)进行了杂交,这些瘦小鼠品系在易患 T2D 方面存在差异。全基因组连锁分析在外交种群中定位了超过 290 个肥胖数量性状位点(QTL)、190 个与糖尿病相关性状的 QTL 和 100 个血浆代谢物的 QTL。开发了一种计算框架,该框架允许通过整合相互回文单倍型作图和转录组数据来细化关键区域并提名少数候选基因。通过一个肥胖 QTL 证明了该复杂程序的效率。包含 502 个注释候选基因的 35 Mb 基因组间隔被缩小到 6 个候选基因。因此,由于包含这 6 个候选基因中的三个的区间,同源小鼠保留了肥胖表型。其中,磷酸酶 PLA2G4A 在肥胖人类受试者的脂肪组织中表达升高,因此是肥胖基因座的关键调节因子。总之,我们广泛而复杂的方法表明,综合和比较杂交分析表现出提高的作图分辨率,并且是鉴定疾病基因的有效工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/2c74a24c1fd1/ddy217f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/363d3634554c/ddy217f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/95309497ffeb/ddy217f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/28c113fbd0c9/ddy217f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/08d1e1a9f6c3/ddy217f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/06343d96a25e/ddy217f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/20e8dfbc238b/ddy217f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/2c74a24c1fd1/ddy217f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/363d3634554c/ddy217f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/95309497ffeb/ddy217f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/28c113fbd0c9/ddy217f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/08d1e1a9f6c3/ddy217f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/06343d96a25e/ddy217f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/20e8dfbc238b/ddy217f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a04c/6097155/2c74a24c1fd1/ddy217f7.jpg

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