Kiryluk, Krzysztof

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

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.

Rationale Immunoglobulin A (IgA) deficiency, a rare, highly heritable trait, is associated with frequent pulmonary infections, emphysema, airway changes and low lung function; however, it is unclear if reduced IgA levels may affect lung structure and function. Methods Serum IgA, IgA1 and galactose-deficient IgA1 (Gd-IgA1) levels were measured in the population-based Multi-Ethnic Study on Atherosclerosis (MESA). The MESA Lung Study measured percent emphysema on cardiac CT and airway dimensions on chest CT, and performed spirometry. Regression models were evaluated after adjustment for demographic and CT factors. Mendelian randomisation (MR) analyses were conducted using genetic variants from the Trans-Omics for Precision Medicine (TOPMed) programme. A replication analysis was performed in the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS). Measurements and main results Among 5497 participants, lower log-normalised serum IgA levels were associated with greater percent emphysema (β=−0.084; 95% CI −0.14 to –0.026; p=0.005), which was confirmed on MR (β=−0.79; 95% CI −1.4 to –0.18; p=0.011). Greater log-normalised serum Gd-IgA1 levels were associated with airway wall thickness (β=0.0079; 95% CI 0.0017 to 0.014; p=0.012; n=2580) and decline in the forced expiratory volume in one second (FEV1) (β=−0.012; 95% CI −0.021 to –0.0036; p=0.0055; n=2778) and FEV1/forced vital capacity (FVC) ratio (β=−0.0028; 95% CI −0.0048 to –0.00084; p=0.0054; n=2778). Conclusion Lower serum IgA levels were associated with greater percent emphysema. Additionally, higher Gd-IgA1 levels were associated with airway wall thickness and lung function decline. These findings support a protective role of IgA in emphysema pathogenesis and possible deleterious role of Gd-IgA1 in airway diseases.

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.

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.

Background The 2021 Kidney Disease Improving Global Outcomes (KDIGO) guidelines recommend following anti-phospholipase A2 receptor (PLA2R) antibody levels as a marker of treatment response in membranous nephropathy; however, the optimal timing to evaluate antibody levels and how to combine them with other clinical variables are currently unknown. Methods We used a cohort of 85 patients from the Membranous Nephropathy Trial Of Rituximab (MENTOR) with anti-PLA2R antibodies ≥14 RU/ml to identify risk factors for not experiencing proteinuria remission after 12 months of treatment with cyclosporine or rituximab. Three landmark times were considered: at baseline and after 3 and 6 months of treatment. Logistic regression model performance was evaluated using C-statistics and model fit (Akaike information criterion [AIC], R 2 ). Results The model at baseline that best predicted no remission included anti-PLA2R antibodies >323 RU/ml and creatinine clearance; the best model after 3 months included the change from baseline in both antibody and albumin levels; and the best model after 6 months included antibody levels >14 RU/ml, creatinine clearance, and the change from baseline in albumin. Compared with the model at baseline, the model at 3 months had better model fit (AIC 70.9 versus 96.4, R 2 51.8% versus 30.1%) and higher C-statistic (0.93 versus 0.83, P = 0.008). The model at 6 months had no difference in performance compared with the model at 3 months (AIC 68.6, R 2 53.0%, C-statistic 0.94, P = 0.67). Conclusions In patients with membranous nephropathy treated with cyclosporine or rituximab in the MENTOR trial, we found that the optimal method to evaluate risk factors for the probability of treatment response was to use anti-PLA2R antibody levels combined with albumin levels after 3 months of treatment, which was significantly better than using antibody levels alone or risk factor evaluation at baseline, with no added benefit of waiting until 6 months of treatment. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_10_09_CJN0000000000000237.mp3

Abstract Importance Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. The most common forms of monogenic kidney disorders include autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN), caused by mutations in COL4A3, COL4A4, or COL4A5 genes. These disorders have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Objective Our objectives were to investigate the interplay of polygenic and monogenic risk of kidney disease and to test the utility of polygenic risk scores for risk stratification in ADPKD and COL4-AN. Design We combined exome/genome sequencing, SNP micro-array, and electronic health record data from the UK Biobank (N = 469,835 participants) and the All of Us (N = 98,622 participants) datasets to test the effects of the genome-wide polygenic score (GPS) for CKD in ADPKD and COL4-AN variant carriers defined by strict variant classification criteria. We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 or kidney failure) and controls (eGFR > 90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The GPS was tested as a predictor of CKD in qualifying variant carriers and non-carriers using logistic regression after adjustment for age, sex, diabetes, and genetic ancestry. Results The GPS was predictive of CKD in ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55–3.37, P = 2.6E-05) as well as non-carriers (ORmeta=1.72 per SD, 95% CI = 1.69–1.76, P < E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN carriers (ORmeta=1.78, 95% CI = 1.22–2.58, P = 2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68–1.73 P < E-300). The carriers in the top GPS tertile had a 2.5-fold higher risk of CKD while the risk for carriers in the bottom GPS tertile was not different from the average population risk. Conclusions and Relevance Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by the differences in polygenic risk profiles. Accounting for polygenic factors improves risk stratification in monogenic kidney disease and may have clinical implications for genetic counseling.

Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a “virtual proteomic” approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08–1.58; P =0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66–0.94; P =0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.