Jarvik, Gail P.

Abstract Nephrotic syndrome is a rare, heterogeneous kidney disorder characterized by proteinuria, hypoalbuminemia, and edema. To elucidate its genetic architecture, we conducted a large-scale, electronic health record (EHR)-linked, multi-ancestry genome-wide association study comprising 5,214 cases and 1,601,060 controls. We identified 37 distinct loci associated with disease risk, including novel associations at JAML and STPG2 , and confirmed prior signals at PLA2R1 , HMCN1 , and APOL1 . Fine-mapping of the major histocompatibility complex (MHC) revealed the strongest association at DRB103:01:01G in individuals of European ancestry (OR = 2.01, P = 1.4×10⁻²⁰), alongside nominal ancestry-specific associations in African ( DOA01:01:05 ) and Latino ( DPB1*14:01:01G ) populations. Transcriptome-wide association analysis (TWAS) identified 484 significant gene-tissue associations, including C4A in kidney cortex. Expression quantitative trait locus (eQTL) mapping revealed numerous cis-eQTLs in glomerular and tubular renal tissues, largely within the MHC region. We observed significant genetic correlation between adult and pediatric nephrotic syndrome (Rg = 0.63, P = 3.5x10 -11 ), suggesting shared genetic etiology. Pathway analyses implicated estrogen receptor signaling and histone modification. Mendelian randomization implicated APOM expression and apomorphine exposure with increased disease risk (OR = 4.93, P = 1.8x10 -19 ). These results expand the understanding of nephrotic syndrome pathogenesis and highlight ancestry-informed targets for therapeutic development.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.