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Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease
Diabetologia ( IF 8.2 ) Pub Date : 2022-06-28 , DOI: 10.1007/s00125-022-05735-0
Niina Sandholm 1, 2, 3 , Joanne B Cole 4, 5, 6 , Viji Nair 7 , Xin Sheng 8, 9, 10 , Hongbo Liu 8, 9, 10 , Emma Ahlqvist 11 , Natalie van Zuydam 12, 13, 14 , Emma H Dahlström 1, 2, 3 , Damian Fermin 7 , Laura J Smyth 15 , Rany M Salem 16 , Carol Forsblom 1, 2, 3 , Erkka Valo 1, 2, 3 , Valma Harjutsalo 1, 2, 3, 17 , Eoin P Brennan 18 , Gareth J McKay 15 , Darrell Andrews 18 , Ross Doyle 18 , Helen C Looker 19 , Robert G Nelson 19 , Colin Palmer 12 , Amy Jayne McKnight 15 , Catherine Godson 18 , Alexander P Maxwell 15, 20 , Leif Groop 11, 21 , Mark I McCarthy 13, 14 , Matthias Kretzler 7 , Katalin Susztak 8, 9, 10 , Joel N Hirschhorn 4, 5, 22 , Jose C Florez 4, 6, 23 , Per-Henrik Groop 1, 2, 3, 24 ,
Affiliation  

Aims/hypothesis

Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.

Methods

We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.

Results

The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10−9; although not withstanding correction for multiple testing, p>9.3×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p<2.7×10−6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10−6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10−11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10−8] and negatively with tubulointerstitial fibrosis [p=2.0×10−9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10−16], and SNX30 expression correlated positively with eGFR [p=5.8×10−14] and negatively with fibrosis [p<2.0×10−16]).

Conclusions/interpretation

Altogether, the results point to novel genes contributing to the pathogenesis of DKD.

Data availability

The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html).

Graphical abstract



中文翻译:

全基因组荟萃分析和组学整合识别出与糖尿病肾病相关的新基因

目标/假设

糖尿病肾病 (DKD) 是肾衰竭的主要原因,并且具有很大的遗传因素。我们的目标是通过对先前关于 DKD 的全基因组关联研究 (GWAS) 进行荟萃分析,并将结果与​​肾脏转录组数据集相整合,以确定导致 DKD 的新遗传因素和基因。

方法

我们使用 DKD 的十种表型定义进行了 GWAS 荟萃分析,其中包括近 27,000 名糖尿病患者。将荟萃分析结果与人类肾小球 ( N = 119) 和肾小管 ( N = 121) 样本的估计数量性状位点数据相结合,以进行全转录组关联研究。我们还进行了基因聚合测试,以联合测试基因内所有可用的常见遗传标记,并将结果与​​各种肾脏组学数据集相结合。

结果

荟萃分析发现了TENM2基因中的一个新的内含子变异 (rs72831309),该变异与较低的慢性肾病 (eGFR<60 ml/min per 1.73 m 2 ) 和 DKD(微量白蛋白尿或更严重)表型风险相关 ( p = 9.8×10 -9;尽管未经多次测试校正,p >9.3×10 -9)。基因水平分析确定了与 DKD 相关的 10 个基因(COL20A1DCLK1EIF4EPTPRN-RESP18GPR158INIP-SNX30LSM14AMFFp <2.7×10 -6)。GWAS 与人肾小球和肾小管表达数据的整合表明,与未患有 DKD 的个体相比,患有 DKD 的个体具有更高的肾小管AKIRIN2基因表达( p = 1.1×10 -6)。六个位点内的先导 SNP 显着改变了肾脏中附近 CpG 位点的 DNA 甲基化 ( p <1.5×10 -11 )。肾小管或肾小球中先导基因的表达与相关病理表型相关(例如TENM2表达与eGFR呈正相关[ p =1.6×10 -8 ],与肾小管间质纤维化呈负相关[ p =2.0×10 -9 ],肾小管DCLK1表达相关与纤维化呈正相关[ p =7.4×10 -16 ],SNX30表达与eGFR 呈正相关[ p =5.8×10 -14 ],与纤维化呈负相关[ p <2.0×10 -16 ])。

结论/解释

总而言之,这些结果指出了导致 DKD 发病机制的新基因。

数据可用性

GWAS 荟萃分析结果可通过 1 型和 2 型糖尿病(分别为 T1D 和 T2D)和常见代谢疾病 (CMD) 知识门户获取,并在各自的下载页面 (https://t1d.hugeamp) 上下载。 org/downloads.html;https://t2d.hugeamp.org/downloads.html;https://hugeamp.org/downloads.html)。

图形概要

更新日期:2022-06-28
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