Transferability of genetic risk scores in African populations

Abstract

The poor transferability of genetic risk scores (GRSs) derived from European ancestry data in diverse populations is a cause of concern. We set out to evaluate whether GRSs derived from data of African American individuals and multiancestry data perform better in sub-Saharan Africa (SSA) compared to European ancestry-derived scores. Using summary statistics from the Million Veteran Program (MVP), we showed that GRSs derived from data of African American individuals enhance polygenic prediction of lipid traits in SSA compared to European and multiancestry scores. However, our GRS prediction varied greatly within SSA between the South African Zulu (low-density lipoprotein cholesterol (LDL-C), R2 = 8.14%) and Ugandan cohorts (LDL-C, R2 = 0.026%). We postulate that differences in the genetic and environmental factors between these population groups might lead to the poor transferability of GRSs within SSA. More effort is required to optimize polygenic prediction in Africa.

Genetic and environmental variation impact transferability of polygenic risk scores

“…10 Since we know some genetic variation is unique to particular ancestries at appreciable frequency, a combination of cross-ancestral and ancestral-matched fine-mapping may be necessary to optimize PRS performance. Kamiza et al 6 elegantly demonstrate the challenges of implementing precision medicine equitably, and more studies like it, where the primary authors have primary appointments in Africa, should be funded to support this goal of diversifying GWASs.…”

“…Therefore, PRS accuracy, transferability, and clinical applicability will depend on the phenotype of interest. 5 To demonstrate these challenges, Kamiza and colleagues 6 compared PRS accuracy and transferability in two sub-Saharan African populations using scores estimated from African American, European, or multiancestry (African American, European, and Hispanic) cohorts for four lipid traits: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, and total cholesterol. According to their findings, PRS performance was greater in three out of four lipid traits in the South African Zulu cohort when using scores derived from African American GWAS summary statistics, which highlights how ancestral differences can affect PRS performance.…”

“… 9 In their work, Kamiza et al. 6 postulated that, in addition to allele frequency differences, genetic interactions with living conditions (urban versus rural environments), age, and BMI also limited the PRS transferability in sub-Saharan Africa populations.

Figure 1 Factors that influence the transferability of polygenic risk scores (PRSs) between populations Genetic factors, including differences in linkage disequilibrium patterns and minor allele frequencies and their interactions with environmental factors like urban versus rural living, differences in diet and exercise, and differences in age and gender may affect PRS transferability.

…”

“…We do not expect performance of PRSs trained in African American populations to perform the same in South African Zulu and Uganda populations, given the distance between these populations in genotype principal component space. 6 African American populations often comprise individuals with varying proportions of West African and European ancestries, while West African Yoruba and Mende populations cluster closer to the South African Zulu than the Ugandan population discussed in Kamiza et al 6 Given that African populations harbor the highest levels of genetic diversity among all populations in the world, 7 it is not surprising that PRSs cannot be easily transferred between them. Indeed, variants' effect sizes obtained from one African population might not accurately work in PRS assessment for other populations of the same geographic region, given the high levels of intra-continental genetic diversity in Africa.…”

“…Environmental factors and other characteristics, such as age and gender, have been shown to affect PRS prediction accuracy even within ancestral groups, where variation due to genetic factors such as linkage disequilibrium (LD) and allele frequency has been minimized. 9 In their work, Kamiza et al 6 postulated that, in addition to allele frequency differences, genetic interactions with living conditions (urban versus rural environments), age, and BMI also limited the PRS transferability in sub-Saharan Africa populations.…”