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（如胆固醇水平）的影响的潜在生物学非常感兴趣，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性状相关的数百种遗传变异方面已经取得了巨大的成功，但这些变异中很少有在疾病生物学中具有良好理解的作用。此外，检测到的变异并不一定对性状的影响有贡献，因为它可能与导致这种影响的变异有很高的相关性（即与因果变异有很高的相关性）。这些研究大多是基于欧洲血统的个体，相比之下，非洲血统的人群代表性不足，仅占研究中个体的2%。这种对欧洲祖先的关注限制了全球效用，并且在将基因研究转化为临床实践或公共卫生政策时存在不准确或错误的高风险。非洲祖先群体之间存在着高度的遗传结构差异——两个不同的欧洲祖先群体之间的基因相似性要大于两个不同的非洲祖先群体之间的基因相似性。这增加了选择适合多个非洲祖先的遗传变异之间相关性的单一代表性测量方法的挑战。这一措施需要构建一组可能包含真正的因果变异的遗传关联的遗传变异。目前的策略倾向于使用一种基于来自公开可用的参考小组（1000个基因组）的两个非洲祖先的测量方法。我们将使用来自东方、西方和南非祖先的遗传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。在非洲不同人群中进行的遗传研究面临的另一个挑战是，他们之间的环境暴露差异可能对与疾病有关的特征产生影响。疟疾等疾病的感染标志就是一个例子。目前用于识别与性状相关的遗传变异和对潜在因果变异进行微调的方法没有考虑到任何环境暴露，这样做可以更好地检测关联并提高准确性。我们提出了环境调整的方法来检测关联和选择潜在的因果变异，利用来自一个性状的信息，并在性状之间共享信息，以进一步减少潜在的因果变异集。这些确定的潜在因果变异将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某种疾病的风险水平。大多数GRS是基于欧洲祖先的，不太可能转移到非洲祖先。这导致我们根据环境调整后的结果得出GRS，并将这些结果与基于欧洲祖先的结果进行比较，以检查GRS在祖先之间的可转移性。所有的方法都将在用户友好的软件中免费提供，供其他人在他们自己的分析中使用。我们还将提供一个非洲血统参考小组的在线数据库，用于其他非洲遗传研究。预计这些将对方法论和应用研究人员都有用。

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.