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

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.