Environment-adjusted genetic analysis methods for cardiometabolic traits in African populations

人们对了解遗传变异对疾病或与疾病相关的测量(例如胆固醇水平)产生影响的基本生物学很感兴趣，因为有证据表明，这可能导致更好的疾病治疗和预防。在识别与许多疾病和特征相关的数百种遗传变异方面取得了巨大成功，但这些变异中很少有在疾病生物学中具有众所周知的作用。此外，检测到的变异不一定对性状的影响有贡献，因为它可能相反地与引起影响的变异具有高度相关性(即与因果变异高度相关)。这些研究的大多数是基于欧洲血统的个人，相比之下，非洲血统的人口代表不足，只占研究中个人的2%。这种对欧洲祖先的关注限制了全球效用，并限制了将基因研究转化为临床实践或公共卫生政策的不准确或错误的高风险。非洲祖先群体之间存在高度的遗传结构差异--两个不同的欧洲祖先群体彼此之间的基因相似性比两个不同的非洲祖先群体彼此之间的相似程度更高。这增加了选择适用于多个非洲祖先的遗传变异之间相关性的单一代表性衡量标准的挑战。这一措施是构建遗传变异集所必需的，这些遗传变异很可能包含潜在的遗传关联的真正因果变异。目前的战略倾向于使用基于公开可获得的参考小组(1000个基因组)中的两个非洲祖先的衡量标准。我们将使用来自东部、西部和南非祖先的基因数据构建两个替代方案。这将对我们提出的分析有用，也将免费提供给其他人，以改进他们对非洲祖先的分析。在不同非洲人群中进行的基因研究的另一个挑战是，他们之间存在环境暴露的差异，可能会对疾病相关特征产生影响。疟疾等疾病的感染标记物就是一个例子。目前识别与性状相关的遗传变异和微调潜在因果变异的方法不考虑任何环境暴露，这样做可以更好地检测关联和更高的准确性。我们提出了环境调整的方法，利用来自一个性状的信息来检测关联和选择潜在的因果变量，以及在性状之间共享信息，以进一步减少潜在的因果变量集。然后，这些已识别的潜在因果变异将被用于构建非洲祖先的遗传风险评分(GRS)。GRS有助于评估一个人患疾病的风险水平。大多数GR以欧洲血统为基础，不太可能转移到非洲血统。这导致我们根据我们的环境调整结果得出GRS，并将这些方法与基于欧洲祖先的GRS进行比较，以检验GRS在祖先之间的可转移性。所有方法都将在用户友好的软件中免费在线，供其他人在自己的分析中使用。我们还将提供非洲血统参考小组的在线数据库，用于其他非洲基因研究。预计这些对方法论和应用研究人员都有用处。

There is substantial interest in understanding the underlying biology by which genetic variants impact on disease or disease-relevant measurements (e.g. cholesterol levels), as there is evidence that this could lead to better disease treatment and prevention. There has been great success in identifying hundreds of genetic variants associated with many diseases and traits, but very few of these variants have a well-understood role in disease biology. Also, a detected variant does not necessarily contribute to effects in the trait, since it may instead have a high correlation with the variant that causes the effect (i.e. high correlation with the causal variant). The majority of these studies are based on individuals of European ancestry and, in contrast, African ancestry populations are under-represented, accounting for only 2% of individuals in studies. This focus on European ancestries limits the global utility and a high risk of inaccuracies or errors in the translation of genetic research into clinical practice or public health policy. African ancestral cohorts have a high level of genetic structural differences between them - two different European ancestral cohorts are more genetically similar to each other than two different African ancestral cohorts are to each other. This increases the challenge in selecting a single representative measure of the correlation between genetic variants that is appropriate for multiple African ancestries. This measure is needed to construct sets of genetic variants that are likely to contain the true causal variant underlying a genetic association. Current strategies tend to use a measure based on two African ancestries from a publicly available reference panel (1000 Genomes). We will construct two alternatives using genetic data from East, West, and South African ancestries. This will be of use to our proposed analyses, and will also be made freely available for others to improve their analyses of African ancestries.Another challenge in genetic studies undertaken in different African populations is that there are environmental exposure differences between them that could have an impact on disease-related traits. An example is infection markers of diseases, such as malaria. Current methods for identifying genetic variants associated with traits and the fine-tuning of potential causal variants do not account for any environmental exposures, and doing so could lead to better detection of associations and greater accuracy. We propose environment-adjusted methods for detecting associations and selecting potential causal variants using information from one trait, as well as sharing information between traits to further reduce the set of potential causal variants. These identified potential causal variants will then be used to construct genetic risk scores (GRS) for African ancestries. GRS could contribute to assessing a person's risk level for developing a disease. The majority of GRS are based on European ancestries and are unlikely to be transferable to African ancestries. This leads us to derive GRS based on our environmental-adjusted results and compare these to those based on European ancestries to examine the transferability of GRS between ancestries.All methods will be freely available on-line in user-friendly software for others to use in their own analyses. We will also provide an on-line database of African ancestry reference panels for use in other African genetic studies. These are expected to be of use to both methodological and applied researchers.