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

慢性肾脏疾病（CKD）是一个主要的公共卫生问题，影响着大约十分之一的普通人群，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针对肾脏的，而且受到副作用的限制。为了制定新的策略，有必要揭示普通人群和糖尿病等危险因素患者肾功能恶化的未知机制。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遗传位点，成功地揭示了相关机制。GWAS荟萃分析（GWAMA）汇集了来自多个队列的数据，提高了统计能力，并首次通过使用HapMap参考面板进行遗传变异的插入来促进。在由国际CKDGen联盟的申请人协调的肾功能恶化表型的第一次GWAMA中，尽管样本量和HapMap输入的遗传数据有限，但仍确定了几个相关的位点。现在，通过1000个基因组计划参考面板和外显子组变异基因型的插入，可以提高常见变异（次要等位基因频率，MAF<5%）的密度，并提供更可靠的不常见变异（MAF<5%）的遗传信息，有望为复杂疾病的遗传学提供新的见解。该提案的主要目的是：a)在普通人群和高风险亚群（即糖尿病和CKD患者）中识别与肾功能恶化表型相关的新遗传变异；b)显著扩展对这些基因座相关的生物学机制的理解。我们将在47个CKDGen队列中进行肾功能恶化表型的GWAMAs，一期发现分析中有96.366人，二期分析中有12.200人进行铅变异体的随访，从而使我们之前工作的样本量增加一倍。我们将首次使用a)在所有队列中使用1000个基因组计划参考面板输入的遗传变异，b)在16个队列的45.409个个体中使用来自Exome Chip基因分型的罕见遗传变异。因此，与之前仅使用hapmap数据进行的工作相比，我们将显著扩大遗传覆盖范围和统计能力。因此，确定的基因座将通过一系列生物信息学工具进行表征（使用条件关联测试进行精细定位，使用贝叶斯方法进行SNP优先排序；包括ENCODE数据和药物靶标分析在内的功能基因组注释；途径、遗传风险评分和eQTL分析；解释的多效性和表型方差分析），以确定关联信号潜在的因果变异、基因和生物学途径。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.