Assessing and evaluating polygenic scores of complex phenotypic traits in admixed populations
Approved Research ID: 74348
Approval date: August 3rd 2021
In a given population, it is challenging to predict how characteristics (from physical differences to having or not a disease) occur based on each individual genetic composition (mutations carried by each person). Most prevalent conditions, such as hypertension and dementia are considered complex, because they are caused by a combination of environmental factors and genetic components, which are hard to be detected since their individual contribution (each mutation) is very small. Large initiatives such as the UK Biobank improve this detection. However, in different populations these mutations may have different effects (and different mutations also), which are still largely unknown, since most projects enroll individuals of European descent. As a result, when scientists calculate the combined effect of mutations in one group (such as the UK Biobank participants) to predict a disease, it is likely that the same list of mutations will not have the same performance in a different population, especially when they are admixed.
In this 3-year project, we wish to compare how characteristics and diseases are found in UK Biobank and in a Brazilian group of individuals, which were whole genome sequenced and are genetically admixed. After that, we will test if the combinations of mutations that predict a disease in UK Biobank can also be used in the Brazilian group. Finally, we will test if combining other types of information such as knowledge on how genes are activated and how gene products interact within the context of the diseases can be used to improve the combined effect of mutations and recover part of the predictive qualities observed in UK Biobank to other populations (such as Brazilians). Comprehending specific features that define how to increase performance when transferring these mutation across populations may contribute to direct public health applications such as monitoring higher risk groups and to understanding novel genetic interactions that promote the manifestation of complex diseases.