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Integrated approaches to functionally characterize novel factors in lipoprotein metabolism

机译:功能性表征脂蛋白代谢新因子的综合方法

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Purpose of Review: To discuss if and how the combined analysis of large-scale datasets from multiple independent sources benefits the mapping of novel genetic elements with relevance to lipoprotein metabolism and allows for conclusions on underlying molecular mechanisms. Recent Findings: Genome-wide association studies (GWAS) have identified numerous genomic loci associated with plasma lipid levels and cardiovascular disease. Yet, despite being highly successful in mapping novel loci the GWAS approach falls short to systematically extract functional information from genomic data. With the aim to complement GWAS for a better insight into disease mechanisms and identification of the most promising targets for drug development, a number of high-throughput functional genomics strategies have now been applied. These include computational approaches, consideration of gene-gene and gene-environment interactions, as well as unbiased gene-expression analyses in relevant tissues. For a limited number of loci, mechanistic insight has been gained through in-vitro and in-vivo studies by knockdown and overexpression of candidate genes. Summary: The integration of GWAS data with existing functional genomics strategies has contributed to ascertain the relevance of a number of novel factors for lipoprotein biology and disease. However, technologies are warranted that provide a more systematic insight into the molecular function and pathogenic relevance of promising candidate genes.
机译:综述的目的:探讨来自多个独立来源的大规模数据集的组合分析是否以及如何使与脂蛋白代谢相关的新颖遗传元素的绘制受益,并得出有关潜在分子机制的结论。最新发现:全基因组关联研究(GWAS)已鉴定出许多与血浆脂质水平和心血管疾病相关的基因组位点。然而,尽管在绘制新基因座方面非常成功,但是GWAS方法仍不足以从基因组数据中系统地提取功能信息。为了补充GWAS,以便更好地了解疾病机制并确定最有希望的药物开发靶标,现已应用了许多高通量功能基因组学策略。这些方法包括计算方法,考虑基因与基因和基因与环境之间的相互作用以及相关组织中无偏见的基因表达分析。对于有限数量的基因座,通过敲除和过度表达候选基因,通过体外和体内研究获得了机理上的见识。简介:GWAS数据与现有功能基因组学策略的集成已有助于确定脂蛋白生物学和疾病的许多新因素的相关性。但是,必须保证可以对有前途的候选基因的分子功能和致病相关性提供更系统的洞察力。

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