Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Curr Opin Lipidol. 2021 Apr 1;32(2):141-146. doi: 10.1097/MOL.0000000000000742.
More than one hundred loci have been identified from human genome-wide association studies (GWAS) for blood lipids. Despite the success of GWAS in identifying loci, subsequent prioritization of causal genes related to these loci remains a challenge. To address this challenge, recent work suggests that candidate causal genes within loci can be prioritized through cross-species integration using genome-wide data from the mouse.
Mouse model systems provide unparalleled access to primary tissues, like the liver, that are not readily available for human studies. Given the key role the liver plays in controlling blood lipid levels and the wealth of liver genome-wide transcript and protein data available in the mouse, these data can be leveraged. Using coexpression network analysis approaches with mouse genome-wide data, coupled with cross-species analysis of human lipid GWAS, causal genes within lipid loci can be prioritized. Prioritization through both mouse and human along with biochemical validation provide a systematic and valuable method to discover lipid metabolism genes.
The prioritization of causal lipid genes within GWAS loci is a challenging process requiring a multidisciplinary approach. Integration of data types across species, such as the mouse, can aid in causal gene prioritization.
从全基因组关联研究（GWAS）中已经鉴定出超过一百个与血液脂质相关的基因位点。尽管 GWAS 在鉴定基因座方面取得了成功，但随后对与这些基因座相关的因果基因的优先级排序仍然是一个挑战。为了解决这个挑战，最近的研究表明，可以通过使用来自小鼠的全基因组数据进行跨物种整合，对基因座内的候选因果基因进行优先级排序。
小鼠模型系统为研究提供了无与伦比的机会，可以深入研究肝脏等主要组织，而这些组织在人类研究中不容易获得。鉴于肝脏在控制血液脂质水平方面的关键作用，以及在小鼠中可用的大量肝脏全基因组转录组和蛋白质数据，这些数据可以被利用。使用小鼠全基因组数据的共表达网络分析方法，结合人类脂质 GWAS 的跨物种分析，可以对脂质基因座内的因果基因进行优先级排序。通过小鼠和人类的优先级排序以及生化验证，为发现脂质代谢基因提供了一种系统而有价值的方法。
GWAS 基因座内因果脂质基因的优先级排序是一个具有挑战性的过程，需要多学科的方法。跨物种的数据类型整合，如小鼠，可以帮助进行因果基因优先级排序。
