Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.
Institute for Quantitative Health Science and Engineering, Systems Biology, Michigan State University, East Lansing, MI 48824, USA.
G3 (Bethesda). 2022 Aug 25;12(9). doi: 10.1093/g3journal/jkac158.
Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia.
高尿酸血症（血清尿酸＞6.8mg/dl）与多种代谢和肾脏疾病有关，如痛风和慢性肾脏病。先前的研究已经使用单变量测试或估计性状之间全基因组遗传相关性，研究了人类慢性肾脏病和高尿酸血症的共同遗传基础。个体变体通常只能解释性状之间遗传相关性的一小部分，因此，针对可用样本量，能够映射多效性位点的能力不足。相反，全基因组遗传相关性估计表明这些性状之间存在中度相关性。虽然这些估计值对于解释这些性状的共病性很有用，但全基因组遗传相关性估计并不能说明哪些区域可能与性状的共同遗传基础有关。因此，为了填补这两种方法之间的空白，我们使用局部贝叶斯多性状模型来估计特定基因组区域中慢性肾脏病（估计肾小球滤过率）标志物和血清尿酸之间的遗传协方差。我们确定了 134 个具有统计学显著协方差估计值的重叠连锁不平衡窗口，其中 49 个具有正向方向，85 个具有负向方向，后者与总体遗传协方差一致。这 134 个显著窗口浓缩为 64 个具有遗传差异的共享基因座，其中 17 个具有一致方向性的先前已确定的共享基因座得到验证，并揭示了 22 个新的多效基因。最后，为了研究这些共享基因座的潜在生物学机制，我们使用共定位分析鉴定了与基因表达相关的基因组窗口的子集。我们的局部贝叶斯多性状模型方法确定的区域可能有助于解释慢性肾脏病和高尿酸血症之间的关联。
