Polygenic Risk Scores for Preeclampsia Prediction Beyond Gold-Standard Clinical Models in Multiethnic Populations

Abstract

Background: Preeclampsia is a major cause of maternal and fetal mortality and morbidity. Early risk stratification enables timely preventative therapy in high-risk women. Polygenic risk scores (PGS) improve prediction in complex diseases, but their added value for preeclampsia remains unclear, particularly in comparison to gold-standard first-trimester prediction models and across non-European ancestries.

Methods: We evaluated the performance of both a preeclampsia and systolic blood pressure PGS in 2 prospective pregnancy cohorts with detailed phenotyping: the Fetal Medicine Foundation study (n=5207; 2127 cases) and the Pregnancy Outcome Prediction study (n=3659; 228 cases). Risk models included (1) clinical factors; (2) clinical factors plus PGS; (3) advanced model including first-trimester mean arterial pressure, PAPP-A (pregnancy-associated plasma protein-A), and uterine artery pulsatility index; and (4) advanced model plus PGS. Discriminative performance, measured by the area under the receiver operating characteristic curve, was assessed overall and by ancestry.

Results: The preeclampsia PGS was independently associated with preeclampsia (odds ratio per SD, 1.24 [95% CI, 1.17-1.31]; P<0.001). It modestly improved prediction over clinical models (area under the receiver operating characteristic curve 0.746 versus 0.750; P=0.017) but not over the advanced model (area under the receiver operating characteristic curve 0.817 versus 0.818; P=0.326). The systolic blood pressure PGS showed stronger performance, improving prediction over both models in women of European ancestry. No improvement was observed with either score in women of African ancestry.

Conclusions: PGSs for preeclampsia and SBP provide modest added predictive value beyond clinical risk factors in European ancestry women. Limited utility in African ancestry women reflects underrepresentation in the genome-wide association studies used to develop current scores. As cohort sizes grow and models are refined, PGSs may become important tools for equitable risk stratification in maternal health.

Keywords: ancestry; genomics; polygenic score; preeclampsia; pregnancy; risk prediction.

Figure 1. Distribution of the polygenic scores in the Fetal Medicine Foundation cohort for (A) preeclampsia, overall and by ancestry (A) and (B) systolic blood pressure, overall and by ancestry.

Figure 2. Preeclampsia incidence across incrementing preeclampsia polygenic score 5‐percentile brackets, in the Fetal Medicine Foundation cohort.

PGS indicates polygenic score.

Figure 3. Association of preeclampsia polygenic score with clinical outcomes (A) on univariable analysis, and (B) on multivariable analysis after accounting for clinical risk factors and biomarkers associated with preeclampsia, in the Fetal Medicine Foundation cohort.

Figure 4. Preeclampsia discrimination conferred by each individual clinical risk factor, biomarker, and polygenic score overall and by self‐reported ancestry, in the Fetal Medicine Foundation cohort.

PAPP‐A indicates pregnancy‐associated plasma protein‐A.

Figure 5. Area under the receiver operating characteristic curve for discrimination of preeclampsia using the basic model and advanced model before and after addition of preeclampsia polygenic score, in the Fetal Medicine Foundation cohort, in (A) the whole cohort, (B) European‐ancestry participants, and (C) African‐ancestry participants.

AUC indicates area under the receiver operating characteristic curve; and PGS, polygenic score.

Figure 6. Area under the receiver operating characteristic curve for discrimination of preeclampsia using the basic model and advanced model before and after addition of systolic blood pressure polygenic score, in the Fetal Medicine Foundation cohort, in (A) the whole cohort, (B) European‐ancestry participants, and (C) African‐ancestry participants.

AUC indicates area under the receiver operating characteristic curve; and PGS, polygenic score.

Figure 7. Distribution of the preeclampsia polygenic score in the Pregnancy Outcome Prediction study (A) and preeclampsia incidence across incrementing preeclampsia polygenic score 5‐percentile brackets, in the Pregnancy Outcome Prediction study (B).

PGS indicates polygenic score.

Figure 8. Preeclampsia discrimination conferred by each individual clinical risk factor, biomarker, and polygenic score in the Pregnancy Outcome Prediction study (A) and area under the receiver operating characteristic curve for discrimination of preeclampsia using the basic model and advanced model before and after addition of preeclampsia polygenic score in the replication analysis in the Pregnancy Outcome Prediction study (B).

AUC indicates area under the receiver operating characteristic curve; PAPP‐A, pregnancy‐associated plasma protein‐A; PGS, polygenic score; and PlGF, placental growth factor.