Background:Mitral valve prolapse (MVP) is a common cardiac valve disease, which affects 1 in 40 in the general population. Previous genome-wide association study has identified 6 risk loci for MVP. But these loci explained only partially the genetic risk for MVP. We aim to identify additional risk loci for MVP by adding data set from the UK Biobank.Methods:We also incorporated 434 MVP cases and 4527 controls from the UK Biobank for discovery analyses. Genetic association was conducted using SNPTEST and meta-analyses using METAL. We used Functional Mapping and Annotation of Genome-Wide Association Studies for post-genome-wide association study annotations and Multi-marker Analysis of GenoMic Annotation for gene-based and gene-set analyses.Results:We found Trans-Omics for Precision Medicine imputation to perform better in terms of accuracy in the lower ranges of minor allele frequency below 0.1. Our updated meta-analysis included UK Biobank study for ≈8 million common single-nucleotide polymorphisms (minor allele frequency >0.01) and replicated the association on Chr2 as the top association signal near TNS1. We identified an additional risk locus on Chr1 (SYT2) and 2 suggestive risk loci on chr8 (MSRA) and chr19 (FBXO46), all driven by common variants. Gene-based association using Multi-marker Analysis of GenoMic Annotation revealed 6 risk genes for MVP with pronounced expression levels in cardiovascular tissues, especially the heart and globally part of enriched GO terms related to cardiac development.Conclusions:We report an updated meta-analysis genome-wide association study for MVP using dense imputation coverage and an improved case-control sample. We describe several loci and genes with MVP spanning biological mechanisms highly relevant to MVP, especially during valve and heart development.

中文翻译：

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全基因组关联荟萃分析支持参与瓣膜和心脏发育的基因与二尖瓣脱垂相关

背景：二尖瓣脱垂（MVP）是一种常见的心脏瓣膜疾病，在普通人群中，每 40 人中有 1 人患病。先前的全基因组关联研究已经确定了 MVP 的 6 个风险位点。但这些基因座仅部分解释了 MVP 的遗传风险。我们的目标是通过添加来自英国生物银行的数据集来确定 MVP 的其他风险位点。方法：我们还纳入了来自英国生物银行的 434 个 MVP 病例和 4527 个对照进行发现分析。使用 SNPTEST 进行遗传关联，并使用 METAL 进行荟萃分析。我们使用全基因组关联研究的功能映射和注释进行全基因组关联研究后注释，并使用基因组注释的多标记分析进行基于基因和基因组的分析。结果：我们发现 Trans-Omics for Precision Medicine 插补在较小等位基因频率低于 0.1 的较低范围内的准确性方面表现更好。我们更新的荟萃分析包括英国生物银行对约 800 万个常见单核苷酸多态性（次要等位基因频率 > 0.01）的研究，并将 Chr2 上的关联复制为附近的最高关联信号TNS1。我们确定了 Chr1 ( SYT2 )上的额外风险位点和 chr8 ( MSRA ) 和 chr19 ( FBXO46 )上的 2 个提示性风险位点，所有这些都由常见变体驱动。使用基因组注释的多标记分析的基于基因的关联揭示了 MVP 的 6 个风险基因，在心血管组织中具有明显的表达水平，尤其是心脏和与心脏发育相关的富集 GO 术语的全球部分。结论：我们报告了更新的荟萃分析使用密集插补覆盖和改进的病例对照样本的 MVP 的全基因组关联研究。我们描述了几个具有 MVP 的基因座和基因，跨越与 MVP 高度相关的生物学机制，尤其是在瓣膜和心脏发育期间。