Even when polygenic risk scores (PRSs) are trained in African ancestral populations, Kamiza and colleagues showed that genetic and environmental variation within sub-Saharan African populations impacts prediction performance, highlighting the challenges of clinical implementation of PRSs for risk assessment

Araújo, Daniel S.

Wheeler, Heather E.

English

PubMed

Loyola eCommons

Loyola University Chicago Loyola eCommons Biology: Faculty Publications and Other Works Faculty Publications and Other Works by Department 7-19-2022 Genetic and environmental variation impact transferability of polygenic risk scores Daniel S. Araújo Loyola University Chicago Heather E. Wheeler Loyola University Chicago, hwheeler1@luc.edu Follow this and additional works at: https://ecommons.luc.edu/biology_facpubs  Part of the Biology Commons Recommended Citation Araújo, Daniel S. and Wheeler, Heather E.. Genetic and environmental variation impact transferability of polygenic risk scores. Cell Reports Medicine, 3, 7: 1-3, 2022. Retrieved from Loyola eCommons, Biology: Faculty Publications and Other Works, http://dx.doi.org/10.1016/j.xcrm.2022.100687 This Article is brought to you for free and open access by the Faculty Publications and Other Works by Department at Loyola eCommons. It has been accepted for inclusion in Biology: Faculty Publications and Other Works by an authorized administrator of Loyola eCommons. For more information, please contact ecommons@luc.edu. This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License. SpotlightGenetic and environmental variationimpact transferability of polygenic risk scoresDaniel S. Araújo1 and Heather E. Wheeler1,2,*1Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA2Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA*Correspondence: hwheeler1@luc.eduhttps://doi.org/10.1016/j.xcrm.2022.100687Even when polygenic risk scores (PRSs) are trained in African ancestral populations, Kamiza and colleaguesshowed that genetic and environmental variation within sub-Saharan African populations impacts predictionperformance, highlighting the challenges of clinical implementation of PRSs for risk assessment.Common DNA variation across hundredsof thousands of individuals has beenused in genome-wide association studies(GWASs) to identify thousands of associ-ations between genetic variants and com-plex traits. FromGWASs, we have learnedthat the genetic architectures for mostcommon, complex diseases are poly-genic, meaning that many DNA variantseach additively play a small role in deter-mining phenotype variability.1 Polygenicrisk scores (PRSs) are the sum of GWASseffect sizes multiplied by an individual’sallele dosages. It may be possible to pre-dict disease susceptibility with PRSs.2However, most GWASs to date havebeen performed in European ancestries,and PRSs show reduced prediction per-formance when transferred to individualsof other ancestries.3 Therefore, clinicalimplementation of PRSs could widenhealth disparities between European andnon-European populations.When using genotypes to classify andcluster individuals, human geneticistscommonly refer to groups by discretecontinental ancestry groups, even thoughgenetic ancestry is multi-dimensional andcontinuous. All individuals have multipleancestries depending on the timescale;thus, as recently recommended, weprefer to use the plural ‘‘ancestries.’’4Continental ancestries do not accuratelyrepresent human genetic diversity, andreferring to populations by continentalancestry in the singular risks supportingthe misconception of race as a biologicalattribute.4 As such, the genetic ancestriesof individuals within GWASs are an impor-tant factor when building and interpretat-ing PRSs. Furthermore, distinct complextraits have different heritability estimates,which quantify how much of the trait vari-ability is due to genetic differences.Therefore, PRS accuracy, transferability,and clinical applicability will depend onthe phenotype of interest.5 To demon-strate these challenges, Kamiza and col-leagues6 compared PRS accuracy andtransferability in two sub-Saharan Africanpopulations using scores estimated fromAfrican American, European, or multian-cestry (African American, European, andHispanic) cohorts for four lipid traits:low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycer-ides, and total cholesterol. According totheir findings, PRS performance wasgreater in three out of four lipid traits inthe South African Zulu cohort when usingscores derived from African AmericanGWAS summary statistics, which high-lights howancestral differences can affectPRS performance. However, the authorsnoted that African American-derivedPRSs did not perform as well in the Ugan-dan cohort as they had the best perfor-mance in only one out of the four lipidtraits tested. More importantly, predictionperformance in the Ugandan cohort waslower than in South African Zulu withPRS models tested from all ancestries.We do not expect performance of PRSstrained in African American populations toperform the same in South African Zuluand Uganda populations, given the dis-tance between these populations in geno-type principal component space.6 AfricanAmerican populations often comprise indi-viduals with varying proportions of WestAfrican and European ancestries, whileWest African Yoruba and Mende popula-tions cluster closer to the South AfricanZulu than the Ugandan population dis-cussed in Kamiza et al.6 Given that Africanpopulationsharbor thehighest levelsofge-netic diversity among all populations in theworld,7 it is not surprising thatPRSscannotbe easily transferred between them.Indeed, variants’ effect sizes obtainedfrom one African population might notaccurately work in PRS assessment forother populations of the same geographicregion, given the high levels of intra-conti-nental genetic diversity in Africa.8In addition to genetic factors, we mustbe mindful of other factors that can affectPRS transferability (Figure 1). Environ-mental factors and other characteristics,such as age and gender, have beenshown to affect PRS prediction accuracyeven within ancestral groups, where vari-ation due to genetic factors such as link-age disequilibrium (LD) and allele fre-quency has been minimized.9 In theirwork, Kamiza et al.6 postulated that, inaddition to allele frequency differences,genetic interactions with living conditions(urban versus rural environments), age,and BMI also limited the PRS transfer-ability in sub-Saharan Africa populations.To realize the promise of precisionmedicine, how can we work toward amore inclusive and accurate applicabilityof PRSs? Given the current samplingbias in GWASs, it is imperative to increasediverse ancestral representation in futurestudies, which will not only contribute topotentially reducing health disparities,but also more immediately improveCell Reports Medicine 3, 100687, July 19, 2022 ª 2022 The Author(s). 1This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).llOPEN ACCESSfine-mapping methods that identify whichDNA variants are likely to cause complextraits.10 Because allele frequencies andLD patterns vary between populationsthat have been geographically and cultur-ally separated, combining informationacross populations increases fine-map-ping resolution through harnessing thesedifferences in LD and increases powerthrough larger sample size. Fine-mappingacross ancestries will lead to better reso-lution when causal variants are sharedacross ancestries, which can only betrue when variants are present in all an-cestries examined.10 Since we knowsome genetic variation is unique to partic-ular ancestries at appreciable frequency,a combination of cross-ancestral andancestral-matched fine-mapping may benecessary to optimize PRS performance.Kamiza et al.6 elegantly demonstrate thechallenges of implementing precisionmedicine equitably, and more studieslike it, where the primary authors have pri-mary appointments in Africa, should befunded to support this goal of diversifyingGWASs.Addressing transferability challengesand working toward overcoming them isthe only way to ensure that PRSs can beproperly implemented for clinical usewithout increasing health disparities.While evaluating disease risk throughPRSs seems promising, especially forthe most heritable traits, clinicians shouldalso be aware of the wide range of geneticand environmental factors that can influ-ence PRS performance. PRSs should notsubstitute for conventional clinical factorsbut rather could become another tool thatclinicians use to evaluate disease risk.ACKNOWLEDGMENTSThis work is supported by the NIH National HumanGenome Research Institute Academic ResearchEnhancement Award R15HG009569.DECLARATION OF INTERESTSThe authors declare no competing interests.REFERENCES1. Boyle, E.A., Li, Y.I., and Pritchard, J.K. (2017).An expanded view of complex traits: Frompolygenic to omnigenic. Cell 169, 1177–1186.https://doi.org/10.1016/j.cell.2017.05.038.2. Khera, A.V., Chaffin, M., Aragam, K.G., Haas,M.E., Roselli, C., Choi, S.H., Natarajan, P.,Lander, E.S., Lubitz, S.A., Ellinor, P.T., and Ka-thiresan, S. (2018). Genome-wide polygenicscores for common diseases identify individ-uals with risk equivalent to monogenic muta-tions. Nat. Genet. 50, 1219–1224. https://doi.org/10.1038/s41588-018-0183-z.3. Martin, A.R., Kanai, M., Kamatani, Y., Okada,Y., Neale, B.M., and Daly, M.J. (2019). Clinicaluse of current polygenic risk scores may exac-erbate health disparities. Nat. Genet.51, 584–591. https://doi.org/10.1038/s41588-019-0379-x.4. Lewis, A.C.F., Molina, S.J., Appelbaum, P.S.,Dauda, B., Di Rienzo, A., Fuentes, A., Fullerton,S.M., Garrison, N.A., Ghosh, N., Hammonds,E.M., et al. (2022).Gettinggeneticancestry rightfor science and society. Science 376, 250–252.https://doi.org/10.1126/science.abm7530.5. Lewis, C.M., and Vassos, E. (2020). Polygenicrisk scores: from research tools to clinicalFigure 1. Factors that influence the transferability of polygenic risk scores (PRSs) between populationsGenetic factors, including differences in linkage disequilibrium patterns and minor allele frequencies and their interactions with environmental factors like urbanversus rural living, differences in diet and exercise, and differences in age and gender may affect PRS transferability. Also, how the phenotypewasmeasuredmayattenuate PRS transferability. Created with BioRender.com.2 Cell Reports Medicine 3, 100687, July 19, 2022llOPEN ACCESS Spotlightinstruments. Genome Med. 12, 44. https://doi.org/10.1186/s13073-020-00742-5.6. Kamiza, A.B., Toure, S.M., Vujkovic, M., Ma-chipisa, T., Soremekun, O.S., Kintu, C., Cor-pas, M., Pirie, F., Young, E., Gill, D., et al.(2022). Transferability of genetic risk scoresin African populations. Nat. Med. 28, 1163–1166. 4. https://doi.org/10.1038/s41591-022-01835-x.7. Gomez, F., Hirbo, J., and Tishkoff, S.A. (2014).Genetic variationandadaptation inAfrica: Impli-cations for human evolution and disease. ColdSpring Harbor Perspect. Biol. 6, a008524.https://doi.org/10.1101/cshperspect.a008524.8. Choudhury, A., Brandenburg, J.-T., Chiko-wore, T., Sengupta, D., Boua, P.R., Crowther,N.J., Agongo, G., Asiki, G., Gómez-Olivé,F.X., Kisiangani, I., et al. (2022). Meta-analysisof sub-Saharan African studies provides in-sights into genetic architecture of lipid traits.Nat. Commun. 13, 2578. https://doi.org/10.1038/s41467-022-30098-w.9. Mostafavi, H., Harpak, A., Agarwal, I., Conley,D., Pritchard, J.K., and Przeworski, M. (2020).Variable prediction accuracy of polygenicscores within an ancestry group. Elife 9,e48376. https://doi.org/10.7554/eLife.48376.10. Weissbrod, O., Kanai, M., Shi, H., Gazal, S.,Peyrot, W.J., Khera, A.V., Okada, Y., Furu-kawa, Y., Morisaki, T., Murakami, Y., et al.(2022). Leveraging fine-mapping and multipo-pulation training data to improve cross-popu-lation polygenic risk scores. Nat. Genet. 54,450–458. https://doi.org/10.1038/s41588-022-01036-9.Cell Reports Medicine 3, 100687, July 19, 2022 3llOPEN ACCESSSpotlight

Genetic and environmental variation impact transferability of polygenic risk scores

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Genetic and environmental variation impact transferability of polygenic risk scores

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