Genome wide association study (GWAS) meta-analysis for the identification and characterisation of genetic variants associated with kidney function deterioration

慢性肾脏病（CKD）是一个主要的公共卫生问题，影响到每一个病例。一般人群中的第10位，糖尿病是一个关键的危险因素。目前针对心血管危险因素的预防和治疗策略不是肾脏特异性的，并且受到副作用的限制。全基因组关联研究（Genome wide association study，GWAS）通过系统地寻找与疾病或性状相关的基因位点，成功地揭示了肾功能损害的机制，为肾功能损害的防治提供了新的思路。GWAS荟萃分析（GWAMA）汇集了来自多个队列的数据，增加了统计功效，并且首先通过使用HapMap参考面板对遗传变异进行插补来促进。在由国际CKDGen联盟中的申请人协调的肾功能恶化表型的第一个GWAMA中，鉴定了几个相关基因座，尽管在样本量和HapMap估算的遗传数据中存在限制。（次要等位基因频率，MAF> 5%）和关于较不频繁的变体（MAF <5%）的更可靠的遗传信息，对复杂疾病的遗传学有希望的新见解。该提案的主要目的是a）在一般人群和高风险亚组中鉴定与肾功能恶化表型相关的新型遗传变异（即糖尿病和CKD患者），和B）我们将在47个CKDGen队列的96.366名第1阶段个体中进行肾功能恶化表型的GWAMA。在第2阶段分析中，对12，200名个体中的先导变体进行了发现分析和随访，从而使我们先前工作的样本量增加了一倍。我们将首次使用a）在所有队列中使用1000个基因组项目参考面板插补的遗传变异，以及B）在16个队列的45.409个个体中使用来自外显子组芯片基因分型的罕见遗传变异。因此，我们将显着扩大遗传覆盖率和统计能力相比，我们以前的工作完全与HapMap数据。因此，将用一系列生物信息学工具来表征所鉴定的基因座（利用条件关联测试和贝叶斯方法的SNP优先化进行精细定位;功能基因组注释，包括ENCODE数据的询问和药物靶标分析;途径、遗传风险评分和eQTL分析;解释了多效性和表型方差的分析）以确定潜在的因果变体，基因和相关信号的生物学途径。因此，这项工作将为肾功能恶化的生物学提供重要的新见解，这是开发新的治疗和预防策略的关键。

Chronic kidney disease (CKD) is a major public health issue affecting every ca. 10th person in the general population, with diabetes a key risk factor. Current preventive and therapeutic strategies, targeting cardiovascular risk factors, are not kidney specific and limited by side effects. To develop novel strategies, it is necessary to uncover unknown mechanisms in kidney function deterioration in the general population and in patients with risk factors such as diabetes.Genome wide association study (GWAS) is successful in unraveling mechanisms by systematic search for genetic loci associated with diseases or traits. GWAS meta-analysis (GWAMA), pooling data from several cohorts, increases statistical power, and was first facilitated by imputation of genetic variants using HapMap reference panels. In the first GWAMA of kidney function deterioration phenotypes, coordinated by the applicant in the international CKDGen consortium, several associated loci were identified, in spite of limitations in sample size and in HapMap imputed genetic data.Imputation with 1000 Genomes Project reference panels and Exome variant genotypes now provides improved density of common variants (minor allele frequency, MAF >5%) and more reliable genetic information on less frequent variants (MAF<5%), promising new insights into the genetics of complex diseases.The key aims of this proposal are a) to identify novel genetic variants associated with kidney function deterioration phenotypes in the general population and in high risk subgroups (i.e. individuals with diabetes and CKD), and b) to extend significantly the understanding of biological mechanisms related to these loci.We will perform GWAMAs of kidney function deterioration phenotypes in 47 CKDGen cohorts with 96.366 individuals in stage 1 discovery analysis and follow-up of lead variants in 12.200 individuals in stage 2 analysis, thus doubling the sample size of our previous work. We will use, for the first time, a) genetic variants imputed with 1000 Genomes Project reference panels in all cohorts and b) rare genetic variants from Exome Chip genotyping in 45.409 individuals of 16 cohorts. We will thus significantly extend genetic coverage and statistical power compared to our previous work performed exclusively with HapMap-data. Thus identified loci will be characterized with a battery of bioinformatics tools (fine-mapping with conditional association testing and SNP prioritization with Bayesian methods; functional genomic annotation including interrogation of ENCODE data and drug target analysis; pathway, genetic risk score and eQTL analyses; analysis of pleiotropy and phenotypic variance explained) to determine potentially causal variants, genes and biological pathways underlying the association signals. Thus, this work will provide important novel insights into the biology of kidney function deterioration which is key to the development of novel therapeutic and preventive strategies.