Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.
Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease.
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DOI：
10.1007/s00125-022-05735-0
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发表时间：
2022-09
期刊：
影响因子：
8.2
通讯作者：
Groop, Per-Henrik
中科院分区：
文献类型：
--
作者：
Sandholm, Niina;Cole, Joanne B.;Nair, Viji;Sheng, Xin;Liu, Hongbo;Ahlqvist, Emma;van Zuydam, Natalie;Dahlstrom, Emma H.;Fermin, Damian;Smyth, Laura J.;Salem, Rany M.;Forsblom, Carol;Valo, Erkka;Harjutsalo, Valma;Brennan, Eoin P.;McKay, Gareth J.;Andrews, Darrell;Doyle, Ross;Looker, Helen C.;Nelson, Robert G.;Palmer, Colin;McKnight, Amy Jayne;Godson, Catherine;Maxwell, Alexander P.;Groop, Leif;McCarthy, Mark I.;Kretzler, Matthias;Susztak, Katalin;Hirschhorn, Joel N.;Florez, Jose C.;Groop, Per-Henrik
关键词：
糖尿病肾病（DKD）是肾衰竭的主要原因，具有重要的遗传成分。我们的目的是通过对先前关于DKD的全基因组关联研究（GWAS）进行荟萃分析，并将结果与肾转录组学数据集整合，以确定导致DKD的新遗传因素和基因。我们使用DKD的10种表型定义进行了GWAS荟萃分析，包括近27，000名糖尿病患者。将Meta分析结果与来自人类肾小球（N=119）和肾小管（N=121）样本的估计的数量性状位点数据整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。荟萃分析确定了TENM 2基因中的一种新的内含子变体（rs72831309），与慢性肾脏疾病（eGFR<60 ml/min/1.73 m2）和DKD（微量白蛋白尿或更差）表型的风险较低相关（p=9.8×10−9;尽管无法承受多次测试的校正，p>9.3×10−9）。基因水平分析确定了10个与DKD相关的基因（COL 20 A1，DCLK 1，EIF 4 E，PTPRN-SNX 18，GPR 158，INIP-SNX 30，LSM 14 A和MFF; p<2.7×10−6）。GWAS与人类肾小球和肾小管表达数据的整合表明，与无DKD的个体相比，肾小管AKIRIN 2基因表达更高（p=1.1×10−6）。六个基因座中的前导SNP显著改变了肾脏中附近CpG位点的DNA甲基化（p<1.5×10−11）。铅基因在肾小管和肾小球中的表达与相关病理表型的关系（例如，TENM 2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DCLK 1表达与纤维化呈正相关[p=7.4×10−16]，SNX 30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]）。总而言之，这些结果指出了DKD发病机制的新基因。GWAS荟萃分析结果可通过1型和2型糖尿病（分别为T1 D和T2 D）和常见代谢疾病（CMD）知识门户访问，并可在其各自的下载页面（https：//t1d.hugeamp.org/downloads.html; https：//t2d.hugeamp.org/downloads.html; https：//hugeamp.org/downloads.html）上下载。在线版本包含同行评审但未经编辑的补充材料，可通过10.1007/s 00125 -022-05735-0获得。
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. 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. 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]). Altogether, the results point to novel genes contributing to the pathogenesis of DKD. 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). The online version contains peer-reviewed but unedited supplementary material available at 10.1007/s00125-022-05735-0.
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